From 2d90bd5a52cdd58b635e95ec5d5ef3d59a239caf Mon Sep 17 00:00:00 2001 From: Takayuki Murooka Date: Fri, 25 Jan 2019 02:01:51 +0900 Subject: [PATCH 001/909] add n_joint_arm_3d & implement forward kinematics --- ArmNavigation/n_joint_arm_3d/NLinkArm.py | 94 +++++++++++++++++++ .../n_joint_arm_3d/random_joint_angles.py | 20 ++++ 2 files changed, 114 insertions(+) create mode 100644 ArmNavigation/n_joint_arm_3d/NLinkArm.py create mode 100644 ArmNavigation/n_joint_arm_3d/random_joint_angles.py diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py new file mode 100644 index 0000000000..d50f9d5c1b --- /dev/null +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py @@ -0,0 +1,94 @@ +import numpy as np +import math +from mpl_toolkits.mplot3d import Axes3D +import matplotlib.pyplot as plt + +class Link: + def __init__(self, dh_params): + self.dh_params_ = dh_params + + def calc_transformation_matrix(self): + theta = self.dh_params_[0] + alpha = self.dh_params_[1] + a = self.dh_params_[2] + d = self.dh_params_[3] + + trans = np.array( + [[math.cos(theta), -math.sin(theta), 0, a], + [math.cos(alpha) * math.sin(theta), math.cos(alpha) * math.cos(theta), -math.sin(alpha), -d * math.sin(alpha)], + [math.sin(alpha) * math.sin(theta), math.sin(alpha) * math.cos(theta), math.cos(alpha), d * math.cos(alpha)], + [0, 0, 0, 1]]) + + return trans + +class NLinkArm: + def __init__(self, dh_params_list): + self.link_list = [] + for i in range(len(dh_params_list)): + self.link_list.append(Link(dh_params_list[i])) + + self.fig = plt.figure() + self.ax = Axes3D(self.fig) + + def calc_transformation_matrix(self): + trans = np.array([[1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1]]) + for i in range(len(self.link_list)): + trans = np.dot(trans, self.link_list[i].calc_transformation_matrix()) + + return trans + + def forward_kinematics(self): + trans = self.calc_transformation_matrix() + + x = trans[0, 3] + y = trans[1, 3] + z = trans[2, 3] + alpha = math.atan2(trans[1, 2], trans[1, 3]) + beta = math.atan2(trans[0, 2] * math.cos(alpha) + trans[1, 2] * math.sin(alpha), trans[2, 2]) + gamma = math.atan2(-trans[0, 0] * math.sin(alpha) + trans[1, 0] * math.cos(alpha), -trans[0, 1] * math.sin(alpha) + trans[1, 1] * math.cos(alpha)) + + return [x, y, z, alpha, beta, gamma] + + def set_joint_angles(self, joint_angle_list): + for i in range(len(self.link_list)): + self.link_list[i].dh_params_[0] = joint_angle_list[i] + + def plot(self): + x_list = [] + y_list = [] + z_list = [] + + trans = np.identity(4) + + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + for i in range(len(self.link_list)): + trans = np.dot(trans, self.link_list[i].calc_transformation_matrix()) + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + + self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5) + self.ax.plot([0], [0], [0], "o") + + self.ax.set_xlim(-1, 1) + self.ax.set_ylim(-1, 1) + self.ax.set_zlim(-1, 1) + plt.show() + +if __name__ == "__main__": + n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], + [math.pi/2, math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .4], + [0., math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .321], + [0., math.pi/2, 0., 0.], + [0., 0., 0., 0.]]) + + print(n_link_arm.forward_kinematics()) + n_link_arm.set_joint_angles([1, 1, 1, 1, 1, 1, 1]) + n_link_arm.plot() diff --git a/ArmNavigation/n_joint_arm_3d/random_joint_angles.py b/ArmNavigation/n_joint_arm_3d/random_joint_angles.py new file mode 100644 index 0000000000..54be155ef1 --- /dev/null +++ b/ArmNavigation/n_joint_arm_3d/random_joint_angles.py @@ -0,0 +1,20 @@ +import math +from NLinkArm import NLinkArm +import random +import time + +def random_val(min_val, max_val): + return min_val + random.random() * (max_val - min_val) + +for i in range(10): + n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], + [math.pi/2, math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .4], + [0., math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .321], + [0., math.pi/2, 0., 0.], + [0., 0., 0., 0.]]) + + n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) + n_link_arm.plot() + From c49f982923cb415e80291ae7d743fd42532713d8 Mon Sep 17 00:00:00 2001 From: Takayuki Murooka Date: Fri, 25 Jan 2019 21:39:32 +0900 Subject: [PATCH 002/909] implement inverse kinematics --- ArmNavigation/n_joint_arm_3d/NLinkArm.py | 65 ++++++++++++++++--- ...angles.py => random_forward_kinematics.py} | 0 .../random_inverse_kinematics.py | 17 +++++ 3 files changed, 72 insertions(+), 10 deletions(-) rename ArmNavigation/n_joint_arm_3d/{random_joint_angles.py => random_forward_kinematics.py} (100%) create mode 100644 ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py index d50f9d5c1b..f6b9c28788 100644 --- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py @@ -7,7 +7,7 @@ class Link: def __init__(self, dh_params): self.dh_params_ = dh_params - def calc_transformation_matrix(self): + def transformation_matrix(self): theta = self.dh_params_[0] alpha = self.dh_params_[1] a = self.dh_params_[2] @@ -21,6 +21,14 @@ def calc_transformation_matrix(self): return trans + def basic_jacobian(self, trans_prev, ee_pose): + pos_prev = np.array([trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]]) + z_axis_prev = np.array([trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]]) + + basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pose - pos_prev), z_axis_prev)) + return basic_jacobian + + class NLinkArm: def __init__(self, dh_params_list): self.link_list = [] @@ -30,18 +38,14 @@ def __init__(self, dh_params_list): self.fig = plt.figure() self.ax = Axes3D(self.fig) - def calc_transformation_matrix(self): - trans = np.array([[1, 0, 0, 0], - [0, 1, 0, 0], - [0, 0, 1, 0], - [0, 0, 0, 1]]) + def transformation_matrix(self): + trans = np.identity(4) for i in range(len(self.link_list)): - trans = np.dot(trans, self.link_list[i].calc_transformation_matrix()) - + trans = np.dot(trans, self.link_list[i].transformation_matrix()) return trans def forward_kinematics(self): - trans = self.calc_transformation_matrix() + trans = self.transformation_matrix() x = trans[0, 3] y = trans[1, 3] @@ -52,9 +56,50 @@ def forward_kinematics(self): return [x, y, z, alpha, beta, gamma] + def basic_jacobian(self, ref_ee_pose): + basic_jacobian_mat = [] + + trans = np.identity(4) + for i in range(len(self.link_list)): + trans = np.dot(trans, self.link_list[i].transformation_matrix()) + basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ref_ee_pose[0:3])) + + #print(np.array(basic_jacobian_mat).T) + return np.array(basic_jacobian_mat).T + + def inverse_kinematics(self, ref_ee_pose): + ee_pose = self.forward_kinematics() + diff_pose = ee_pose - np.array(ref_ee_pose) + + for cnt in range(1000): + basic_jacobian_mat = self.basic_jacobian(ref_ee_pose) + alpha, beta, gamma = self.calc_euler_angle() + + K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)], + [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)], + [1, 0, math.cos(beta)]]) + K_alpha = np.identity(6) + K_alpha[3:, 3:] = K_zyz + + theta_dot = np.dot(np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha), np.array(diff_pose)) + self.update_joint_angles(theta_dot) + + def calc_euler_angle(self): + trans = self.transformation_matrix() + + alpha = math.atan2(trans[1][2], trans[0][2]) + beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2]) + gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha)) + + return alpha, beta, gamma + def set_joint_angles(self, joint_angle_list): for i in range(len(self.link_list)): self.link_list[i].dh_params_[0] = joint_angle_list[i] + + def update_joint_angles(self, diff_joint_angle_list): + for i in range(len(self.link_list)): + self.link_list[i].dh_params_[0] += diff_joint_angle_list[i] def plot(self): x_list = [] @@ -67,7 +112,7 @@ def plot(self): y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) for i in range(len(self.link_list)): - trans = np.dot(trans, self.link_list[i].calc_transformation_matrix()) + trans = np.dot(trans, self.link_list[i].transformation_matrix()) x_list.append(trans[0, 3]) y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) diff --git a/ArmNavigation/n_joint_arm_3d/random_joint_angles.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py similarity index 100% rename from ArmNavigation/n_joint_arm_3d/random_joint_angles.py rename to ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py new file mode 100644 index 0000000000..a728647547 --- /dev/null +++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py @@ -0,0 +1,17 @@ +import math +from NLinkArm import NLinkArm +import random +import time + + +n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], + [math.pi/2, math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .4], + [0., math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .321], + [0., math.pi/2, 0., 0.], + [0., 0., 0., 0.]]) + +#n_link_arm.inverse_kinematics([-0.621, 0., 0., 0., 0., math.pi / 2]) +n_link_arm.inverse_kinematics([-0.5, 0., 0.1, 0., 0., math.pi / 2]) +n_link_arm.plot() From 3504833748d7e5ebb0d9b8ca82b4ab854bd25572 Mon Sep 17 00:00:00 2001 From: Takayuki Murooka Date: Sat, 26 Jan 2019 02:23:53 +0900 Subject: [PATCH 003/909] fix bug of NLinkArm.py and modify random exapmle scripts --- ArmNavigation/n_joint_arm_3d/NLinkArm.py | 106 +++++++++++++++--- .../random_forward_kinematics.py | 16 ++- .../random_inverse_kinematics.py | 32 ++++-- 3 files changed, 121 insertions(+), 33 deletions(-) diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py index f6b9c28788..e1d7369731 100644 --- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py @@ -13,11 +13,21 @@ def transformation_matrix(self): a = self.dh_params_[2] d = self.dh_params_[3] + ''' trans = np.array( [[math.cos(theta), -math.sin(theta), 0, a], [math.cos(alpha) * math.sin(theta), math.cos(alpha) * math.cos(theta), -math.sin(alpha), -d * math.sin(alpha)], [math.sin(alpha) * math.sin(theta), math.sin(alpha) * math.cos(theta), math.cos(alpha), d * math.cos(alpha)], [0, 0, 0, 1]]) + ''' + st = math.sin(theta) + ct = math.cos(theta) + sa = math.sin(alpha) + ca = math.cos(alpha) + trans = np.array([[ct, -st * ca, st * sa, a * ct], + [st, ct * ca, -ct * sa, a * st], + [0, sa, ca, d], + [0, 0, 0, 1]]) return trans @@ -35,44 +45,66 @@ def __init__(self, dh_params_list): for i in range(len(dh_params_list)): self.link_list.append(Link(dh_params_list[i])) - self.fig = plt.figure() - self.ax = Axes3D(self.fig) - def transformation_matrix(self): trans = np.identity(4) for i in range(len(self.link_list)): trans = np.dot(trans, self.link_list[i].transformation_matrix()) return trans - def forward_kinematics(self): + def forward_kinematics(self, plot=False): trans = self.transformation_matrix() x = trans[0, 3] y = trans[1, 3] z = trans[2, 3] - alpha = math.atan2(trans[1, 2], trans[1, 3]) - beta = math.atan2(trans[0, 2] * math.cos(alpha) + trans[1, 2] * math.sin(alpha), trans[2, 2]) - gamma = math.atan2(-trans[0, 0] * math.sin(alpha) + trans[1, 0] * math.cos(alpha), -trans[0, 1] * math.sin(alpha) + trans[1, 1] * math.cos(alpha)) + alpha, beta, gamma = self.calc_euler_angle() + + if plot: + self.fig = plt.figure() + self.ax = Axes3D(self.fig) + + x_list = [] + y_list = [] + z_list = [] + + trans = np.identity(4) + + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + for i in range(len(self.link_list)): + trans = np.dot(trans, self.link_list[i].transformation_matrix()) + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + + self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5) + self.ax.plot([0], [0], [0], "o") + + self.ax.set_xlim(-1, 1) + self.ax.set_ylim(-1, 1) + self.ax.set_zlim(-1, 1) + + plt.show() return [x, y, z, alpha, beta, gamma] - def basic_jacobian(self, ref_ee_pose): + def basic_jacobian(self, ee_pose): basic_jacobian_mat = [] trans = np.identity(4) for i in range(len(self.link_list)): + basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pose[0:3])) trans = np.dot(trans, self.link_list[i].transformation_matrix()) - basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ref_ee_pose[0:3])) - #print(np.array(basic_jacobian_mat).T) return np.array(basic_jacobian_mat).T - def inverse_kinematics(self, ref_ee_pose): - ee_pose = self.forward_kinematics() - diff_pose = ee_pose - np.array(ref_ee_pose) + def inverse_kinematics(self, ref_ee_pose, plot=False): + for cnt in range(500): + ee_pose = self.forward_kinematics() + diff_pose = np.array(ref_ee_pose) - ee_pose - for cnt in range(1000): - basic_jacobian_mat = self.basic_jacobian(ref_ee_pose) + basic_jacobian_mat = self.basic_jacobian(ee_pose) alpha, beta, gamma = self.calc_euler_angle() K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)], @@ -82,12 +114,45 @@ def inverse_kinematics(self, ref_ee_pose): K_alpha[3:, 3:] = K_zyz theta_dot = np.dot(np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha), np.array(diff_pose)) - self.update_joint_angles(theta_dot) - + self.update_joint_angles(theta_dot / 100.) + + if plot: + self.fig = plt.figure() + self.ax = Axes3D(self.fig) + + x_list = [] + y_list = [] + z_list = [] + + trans = np.identity(4) + + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + for i in range(len(self.link_list)): + trans = np.dot(trans, self.link_list[i].transformation_matrix()) + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + + self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5) + self.ax.plot([0], [0], [0], "o") + + self.ax.set_xlim(-1, 1) + self.ax.set_ylim(-1, 1) + self.ax.set_zlim(-1, 1) + + self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]], "o") + plt.show() + def calc_euler_angle(self): trans = self.transformation_matrix() alpha = math.atan2(trans[1][2], trans[0][2]) + if -math.pi / 2 <= alpha and alpha <= math.pi / 2: + alpha = math.atan2(trans[1][2], trans[0][2]) + math.pi + if -math.pi / 2 <= alpha and alpha <= math.pi / 2: + alpha = math.atan2(trans[1][2], trans[0][2]) - math.pi beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2]) gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha)) @@ -102,6 +167,9 @@ def update_joint_angles(self, diff_joint_angle_list): self.link_list[i].dh_params_[0] += diff_joint_angle_list[i] def plot(self): + self.fig = plt.figure() + self.ax = Axes3D(self.fig) + x_list = [] y_list = [] z_list = [] @@ -119,6 +187,10 @@ def plot(self): self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5) self.ax.plot([0], [0], [0], "o") + + self.ax.set_xlabel("x") + self.ax.set_ylabel("y") + self.ax.set_zlabel("z") self.ax.set_xlim(-1, 1) self.ax.set_ylim(-1, 1) diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py index 54be155ef1..df037fb5ea 100644 --- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py @@ -1,12 +1,15 @@ import math from NLinkArm import NLinkArm import random -import time def random_val(min_val, max_val): return min_val + random.random() * (max_val - min_val) -for i in range(10): + +if __name__ == "__main__": + print("Start solving Forward Kinematics 10 times") + + # init NLinkArm with Denavit-Hartenberg parameters of PR2 n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], [math.pi/2, math.pi/2, 0., 0.], [0., -math.pi/2, 0., .4], @@ -14,7 +17,10 @@ def random_val(min_val, max_val): [0., -math.pi/2, 0., .321], [0., math.pi/2, 0., 0.], [0., 0., 0., 0.]]) - - n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) - n_link_arm.plot() + + for i in range(10): + n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) + + ee_pose = n_link_arm.forward_kinematics(plot=True) + print(ee_pose) diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py index a728647547..dd51381678 100644 --- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py @@ -1,17 +1,27 @@ import math from NLinkArm import NLinkArm import random -import time -n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], - [math.pi/2, math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .4], - [0., math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .321], - [0., math.pi/2, 0., 0.], - [0., 0., 0., 0.]]) +def random_val(min_val, max_val): + return min_val + random.random() * (max_val - min_val) -#n_link_arm.inverse_kinematics([-0.621, 0., 0., 0., 0., math.pi / 2]) -n_link_arm.inverse_kinematics([-0.5, 0., 0.1, 0., 0., math.pi / 2]) -n_link_arm.plot() +if __name__ == "__main__": + print("Start solving Inverse Kinematics 10 times") + + # init NLinkArm with Denavit-Hartenberg parameters of PR2 + n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], + [math.pi/2, math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .4], + [0., math.pi/2, 0., 0.], + [0., -math.pi/2, 0., .321], + [0., math.pi/2, 0., 0.], + [0., 0., 0., 0.]]) + + for i in range(10): + n_link_arm.inverse_kinematics([random_val(-0.5, 0.5), + random_val(-0.5, 0.5), + random_val(-0.5, 0.5), + random_val(-0.5, 0.5), + random_val(-0.5, 0.5), + random_val(-0.5, 0.5)], plot=True) From b543623cada7f6f425df6463b44027fdc4ec211d Mon Sep 17 00:00:00 2001 From: Takayuki Murooka Date: Sat, 26 Jan 2019 02:32:49 +0900 Subject: [PATCH 004/909] minor change --- ArmNavigation/n_joint_arm_3d/NLinkArm.py | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py index e1d7369731..19b1a32a5b 100644 --- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py @@ -13,13 +13,6 @@ def transformation_matrix(self): a = self.dh_params_[2] d = self.dh_params_[3] - ''' - trans = np.array( - [[math.cos(theta), -math.sin(theta), 0, a], - [math.cos(alpha) * math.sin(theta), math.cos(alpha) * math.cos(theta), -math.sin(alpha), -d * math.sin(alpha)], - [math.sin(alpha) * math.sin(theta), math.sin(alpha) * math.cos(theta), math.cos(alpha), d * math.cos(alpha)], - [0, 0, 0, 1]]) - ''' st = math.sin(theta) ct = math.cos(theta) sa = math.sin(alpha) @@ -196,16 +189,3 @@ def plot(self): self.ax.set_ylim(-1, 1) self.ax.set_zlim(-1, 1) plt.show() - -if __name__ == "__main__": - n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], - [math.pi/2, math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .4], - [0., math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .321], - [0., math.pi/2, 0., 0.], - [0., 0., 0., 0.]]) - - print(n_link_arm.forward_kinematics()) - n_link_arm.set_joint_angles([1, 1, 1, 1, 1, 1, 1]) - n_link_arm.plot() From 33e7b67db0130f6ec5277fdf7e630738d13fb2dd Mon Sep 17 00:00:00 2001 From: Takayuki Murooka Date: Sat, 26 Jan 2019 03:06:27 +0900 Subject: [PATCH 005/909] minor changes --- ArmNavigation/n_joint_arm_3d/NLinkArm.py | 21 ++++++++++--------- .../random_forward_kinematics.py | 1 - 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py index 19b1a32a5b..d0e32a05da 100644 --- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py @@ -24,11 +24,11 @@ def transformation_matrix(self): return trans - def basic_jacobian(self, trans_prev, ee_pose): + def basic_jacobian(self, trans_prev, ee_pos): pos_prev = np.array([trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]]) z_axis_prev = np.array([trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]]) - basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pose - pos_prev), z_axis_prev)) + basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pos - pos_prev), z_axis_prev)) return basic_jacobian @@ -50,7 +50,7 @@ def forward_kinematics(self, plot=False): x = trans[0, 3] y = trans[1, 3] z = trans[2, 3] - alpha, beta, gamma = self.calc_euler_angle() + alpha, beta, gamma = self.euler_angle() if plot: self.fig = plt.figure() @@ -82,12 +82,13 @@ def forward_kinematics(self, plot=False): return [x, y, z, alpha, beta, gamma] - def basic_jacobian(self, ee_pose): + def basic_jacobian(self): + ee_pos = self.forward_kinematics()[0:3] basic_jacobian_mat = [] trans = np.identity(4) for i in range(len(self.link_list)): - basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pose[0:3])) + basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pos)) trans = np.dot(trans, self.link_list[i].transformation_matrix()) return np.array(basic_jacobian_mat).T @@ -97,8 +98,8 @@ def inverse_kinematics(self, ref_ee_pose, plot=False): ee_pose = self.forward_kinematics() diff_pose = np.array(ref_ee_pose) - ee_pose - basic_jacobian_mat = self.basic_jacobian(ee_pose) - alpha, beta, gamma = self.calc_euler_angle() + basic_jacobian_mat = self.basic_jacobian() + alpha, beta, gamma = self.euler_angle() K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)], [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)], @@ -138,13 +139,13 @@ def inverse_kinematics(self, ref_ee_pose, plot=False): self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]], "o") plt.show() - def calc_euler_angle(self): + def euler_angle(self): trans = self.transformation_matrix() alpha = math.atan2(trans[1][2], trans[0][2]) - if -math.pi / 2 <= alpha and alpha <= math.pi / 2: + if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2): alpha = math.atan2(trans[1][2], trans[0][2]) + math.pi - if -math.pi / 2 <= alpha and alpha <= math.pi / 2: + if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2): alpha = math.atan2(trans[1][2], trans[0][2]) - math.pi beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2]) gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha)) diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py index df037fb5ea..e94795213c 100644 --- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py @@ -22,5 +22,4 @@ def random_val(min_val, max_val): n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) ee_pose = n_link_arm.forward_kinematics(plot=True) - print(ee_pose) From 47b497019a0bcedc2cd257ea8bd48942f2b616d5 Mon Sep 17 00:00:00 2001 From: Takayuki Murooka Date: Sat, 26 Jan 2019 15:28:59 +0900 Subject: [PATCH 006/909] add header comment in each scripts --- ArmNavigation/n_joint_arm_3d/NLinkArm.py | 4 ++++ ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py | 7 ++++++- ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py | 5 +++++ 3 files changed, 15 insertions(+), 1 deletion(-) diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py index d0e32a05da..da562d9f17 100644 --- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py @@ -1,3 +1,7 @@ +""" +Class of n-link arm in 3D +Author: Takayuki Murooka (takayuki5168) +""" import numpy as np import math from mpl_toolkits.mplot3d import Axes3D diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py index e94795213c..87907f803b 100644 --- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py @@ -1,3 +1,7 @@ +""" +Forward Kinematics for an n-link arm in 3D +Author: Takayuki Murooka (takayuki5168) +""" import math from NLinkArm import NLinkArm import random @@ -17,7 +21,8 @@ def random_val(min_val, max_val): [0., -math.pi/2, 0., .321], [0., math.pi/2, 0., 0.], [0., 0., 0., 0.]]) - + + # execute FK 10 times for i in range(10): n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py index dd51381678..44be22709f 100644 --- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py @@ -1,3 +1,7 @@ +""" +Inverse Kinematics for an n-link arm in 3D +Author: Takayuki Murooka (takayuki5168) +""" import math from NLinkArm import NLinkArm import random @@ -18,6 +22,7 @@ def random_val(min_val, max_val): [0., math.pi/2, 0., 0.], [0., 0., 0., 0.]]) + # execute IK 10 times for i in range(10): n_link_arm.inverse_kinematics([random_val(-0.5, 0.5), random_val(-0.5, 0.5), From ae32cb332a91ac706c46e3a1e977f80d948b3e60 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 12 Sep 2019 20:26:48 +0900 Subject: [PATCH 007/909] fix early termination bug. fix #223 --- PathPlanning/RRT/rrt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 887cb1545f..13ca367637 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -74,7 +74,7 @@ def planning(self, animation=True): if animation and i % 5 == 0: self.draw_graph(rnd_node) - if self.calc_dist_to_goal(new_node.x, new_node.y) <= self.expand_dis: + if self.calc_dist_to_goal(self.node_list[-1].x, self.node_list[-1].y) <= self.expand_dis: print("Goal!!") return self.generate_final_course(len(self.node_list) - 1) From 8eef2fd7bbda218d2c381c4a29a17c1f6f710591 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 12 Sep 2019 21:24:20 +0900 Subject: [PATCH 008/909] Update users_comments.md --- users_comments.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/users_comments.md b/users_comments.md index c0165bd2d7..87170eb0c8 100644 --- a/users_comments.md +++ b/users_comments.md @@ -375,6 +375,11 @@ Thanks for your job!
I have learned a lot from it! --- +Dear Atsushi Sakai,
Thank you so much for creating PythonRobotics and documenting it so well. I am a senior in high school trying to learn and better myself in these concepts. + +--Rohan Mathur + + # Citations From 7bafe3d7a572aea4fa49cd0a4ffe667c288921da Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 22 Sep 2019 22:12:57 +0900 Subject: [PATCH 009/909] Update users_comments.md --- users_comments.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/users_comments.md b/users_comments.md index 87170eb0c8..b1a0b66f8e 100644 --- a/users_comments.md +++ b/users_comments.md @@ -398,6 +398,9 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=87 5. Josie Hughes, Masaru Shimizu, and Arnoud Visser, "A Review of Robot Rescue Simulation Platforms for Robotics Education" URL: https://2019.robocup.org/downloads/program/HughesEtAl2019.pdf +6. Hughes, Josie, Masaru Shimizu, and Arnoud Visser. "A review of robot rescue simulation platforms for robotics education." (2019). +URL: https://www.semanticscholar.org/paper/A-Review-of-Robot-Rescue-Simulation-Platforms-for-Hughes-Shimizu/318a4bcb97a44661422ae1430d950efc408097da + # Others - Autonomous Vehicle Readings https://richardkelley.io/readings From a485b0137744a1295292a1caa7abad04da5472e0 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 22 Sep 2019 22:31:17 +0900 Subject: [PATCH 010/909] Update getting_started.rst --- docs/getting_started.rst | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 2f60557f38..93ab70503a 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -3,6 +3,25 @@ Getting Started =============== +What is this? +------------- + +This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. + +The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. + +In this project, the algorithms which are practical and widely used in both academia and industry are selected. + +Each sample code is written in Python3 and only depends on some standard modules for readability and ease of use. + +It includes intuitive animations to understand the behavior of the simulation. + + +See this paper for more details: + +- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703 + + Requirements ------------- From eefa6bf20a2a6d979ed3877b4c6d27bd29a31ef9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 1 Oct 2019 20:10:42 +0900 Subject: [PATCH 011/909] try to check collision check bug --- PathPlanning/RRT/rrt.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 13ca367637..5f7bab54fc 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -10,6 +10,7 @@ import random import matplotlib.pyplot as plt +import numpy as np show_animation = True @@ -104,10 +105,8 @@ def steer(self, from_node, to_node, extend_length=float("inf")): d, _ = self.calc_distance_and_angle(new_node, to_node) if d <= self.path_resolution: - new_node.x = to_node.x - new_node.y = to_node.y - new_node.path_x[-1] = to_node.x - new_node.path_y[-1] = to_node.y + new_node.path_x.append(to_node.x) + new_node.path_y.append(to_node.y) new_node.parent = from_node @@ -142,17 +141,26 @@ def draw_graph(self, rnd=None): plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: if node.parent: - plt.plot(node.path_x, node.path_y, "-g") + plt.plot(node.path_x, node.path_y, "-xg") for (ox, oy, size) in self.obstacle_list: - plt.plot(ox, oy, "ok", ms=30 * size) + self.plot_circle(ox, oy, size) plt.plot(self.start.x, self.start.y, "xr") plt.plot(self.end.x, self.end.y, "xr") + plt.axis("equal") plt.axis([-2, 15, -2, 15]) plt.grid(True) plt.pause(0.01) + @staticmethod + def plot_circle(x, y, size, color="-b"): # pragma: no cover + deg = list(range(0, 360, 5)) + deg.append(0) + xl = [x + size * math.cos(np.deg2rad(d)) for d in deg] + yl = [y + size * math.sin(np.deg2rad(d)) for d in deg] + plt.plot(xl, yl, color) + @staticmethod def get_nearest_node_index(node_list, rnd_node): dlist = [(node.x - rnd_node.x) ** 2 + (node.y - rnd_node.y) @@ -182,7 +190,7 @@ def calc_distance_and_angle(from_node, to_node): return d, theta -def main(gx=5.0, gy=10.0): +def main(gx=6.0, gy=10.0): print("start " + __file__) # ====Search Path with RRT==== @@ -193,7 +201,7 @@ def main(gx=5.0, gy=10.0): (3, 10, 2), (7, 5, 2), (9, 5, 2) - ] # [x,y,size] + ] # [x, y, radius] # Set Initial parameters rrt = RRT(start=[0, 0], goal=[gx, gy], From 97d6dbdd6cc91c7809d8607646e7af830068a9b1 Mon Sep 17 00:00:00 2001 From: Pavel Date: Tue, 1 Oct 2019 23:02:50 +0300 Subject: [PATCH 012/909] cvxpy link fixed --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8e39d6982c..6577cf70b8 100644 --- a/README.md +++ b/README.md @@ -103,7 +103,7 @@ See this paper for more details: - pandas -- [cvxpy](http://www.cvxpy.org/en/latest/) +- [cvxpy](https://www.cvxpy.org/index.html) # Documentation From 3cef87ea3fa28df6b1bab1bca9915aa427858453 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 2 Oct 2019 20:19:33 +0900 Subject: [PATCH 013/909] fixed rrt.py --- PathPlanning/RRT/rrt.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 5f7bab54fc..3bcc622100 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -33,7 +33,7 @@ def __init__(self, x, y): self.parent = None def __init__(self, start, goal, obstacle_list, rand_area, - expand_dis=3.0, path_resolution=1.0, goal_sample_rate=5, max_iter=500): + expand_dis=3.0, path_resolution=0.5, goal_sample_rate=5, max_iter=500): """ Setting Parameter @@ -76,8 +76,9 @@ def planning(self, animation=True): self.draw_graph(rnd_node) if self.calc_dist_to_goal(self.node_list[-1].x, self.node_list[-1].y) <= self.expand_dis: - print("Goal!!") - return self.generate_final_course(len(self.node_list) - 1) + final_node = self.steer(self.node_list[-1], self.end, self.expand_dis) + if self.check_collision(final_node, self.obstacle_list): + return self.generate_final_course(len(self.node_list) - 1) if animation and i % 5: self.draw_graph(rnd_node) @@ -141,7 +142,7 @@ def draw_graph(self, rnd=None): plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: if node.parent: - plt.plot(node.path_x, node.path_y, "-xg") + plt.plot(node.path_x, node.path_y, "-g") for (ox, oy, size) in self.obstacle_list: self.plot_circle(ox, oy, size) @@ -200,7 +201,8 @@ def main(gx=6.0, gy=10.0): (3, 8, 2), (3, 10, 2), (7, 5, 2), - (9, 5, 2) + (9, 5, 2), + (8, 10, 1) ] # [x, y, radius] # Set Initial parameters rrt = RRT(start=[0, 0], From a5c7096c7befd4c1af423b529d62dd440f105243 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 2 Oct 2019 20:28:59 +0900 Subject: [PATCH 014/909] fixed rrt_with_pathsmoothing.py --- PathPlanning/RRT/rrt_with_pathsmoothing.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py index 24b6bfeb6e..9826dba072 100644 --- a/PathPlanning/RRT/rrt_with_pathsmoothing.py +++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py @@ -76,8 +76,6 @@ def line_collision_check(first, second, obstacleList): if d <= size: return False - # print("OK") - return True # OK @@ -127,7 +125,7 @@ def main(): (7, 5, 2), (9, 5, 2) ] # [x,y,size] - rrt = RRT(start=[0, 0], goal=[5, 10], + rrt = RRT(start=[0, 0], goal=[6, 10], rand_area=[-2, 15], obstacle_list=obstacleList) path = rrt.planning(animation=show_animation) @@ -141,7 +139,7 @@ def main(): plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r') plt.plot([x for (x, y) in smoothedPath], [ - y for (x, y) in smoothedPath], '-b') + y for (x, y) in smoothedPath], '-c') plt.grid(True) plt.pause(0.01) # Need for Mac From 295645f4169c4b7031f8cf0cadf3a643d242c50a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 2 Oct 2019 20:51:22 +0900 Subject: [PATCH 015/909] fixed rrt star.py --- PathPlanning/RRTStar/rrt_star.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index eb37964d5c..1c6d682800 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -52,6 +52,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, """ self.connect_circle_dist = connect_circle_dist + self.goal_node = self.Node(goal[0], goal[1]) def planning(self, animation=True, search_until_max_iter=True): """ @@ -121,7 +122,14 @@ def search_best_goal_node(self): dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list] goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expand_dis] - if not goal_inds: + # safe_goal_inds = goal_inds + safe_goal_inds = [] + for goal_ind in goal_inds: + t_node = self.steer(self.node_list[goal_ind], self.goal_node) + if self.check_collision(t_node, self.obstacle_list): + safe_goal_inds.append(goal_ind) + + if not safe_goal_inds: return None min_cost = min([self.node_list[i].cost for i in goal_inds]) @@ -177,12 +185,13 @@ def main(): (3, 8, 2), (3, 10, 2), (7, 5, 2), - (9, 5, 2) + (9, 5, 2), + (8, 10, 1), ] # [x,y,size(radius)] # Set Initial parameters rrt_star = RRTStar(start=[0, 0], - goal=[10, 10], + goal=[6, 10], rand_area=[-2, 15], obstacle_list=obstacle_list) path = rrt_star.planning(animation=show_animation) From 67141071c9e1b351ac6c3819c0d53b58d9582103 Mon Sep 17 00:00:00 2001 From: Naveen Date: Wed, 2 Oct 2019 17:48:51 -0700 Subject: [PATCH 016/909] convert 1D array to scalar for np.matmul operation thrust variable is currently a 1D numpy array so the matmul function throws an error when executed, converting thrust to a scalar before the matmul operation solves the issue. --- .../drone_3d_trajectory_following.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py index 438494a410..e00b3fff48 100644 --- a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py +++ b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py @@ -99,7 +99,7 @@ def quad_sim(x_c, y_c, z_c): R = rotation_matrix(roll, pitch, yaw) acc = (np.matmul(R, np.array( - [0, 0, thrust]).T) - np.array([0, 0, m * g]).T) / m + [0, 0, thrust.item()]).T) - np.array([0, 0, m * g]).T) / m x_acc = acc[0] y_acc = acc[1] z_acc = acc[2] @@ -209,4 +209,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() From d2da3abe25f56bf19f89d54d48c4f902f5922718 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 5 Oct 2019 18:00:18 +0900 Subject: [PATCH 017/909] fix safe index reference --- PathPlanning/RRTStar/rrt_star.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index 1c6d682800..09a421937a 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -59,7 +59,7 @@ def planning(self, animation=True, search_until_max_iter=True): rrt star path planning animation: flag for animation on or off - search_until_maxiter: search until max iteration for path improving or not + search_until_max_iter: search until max iteration for path improving or not """ self.node_list = [self.start] @@ -122,7 +122,6 @@ def search_best_goal_node(self): dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list] goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expand_dis] - # safe_goal_inds = goal_inds safe_goal_inds = [] for goal_ind in goal_inds: t_node = self.steer(self.node_list[goal_ind], self.goal_node) @@ -132,8 +131,8 @@ def search_best_goal_node(self): if not safe_goal_inds: return None - min_cost = min([self.node_list[i].cost for i in goal_inds]) - for i in goal_inds: + min_cost = min([self.node_list[i].cost for i in safe_goal_inds]) + for i in safe_goal_inds: if self.node_list[i].cost == min_cost: return i @@ -187,6 +186,7 @@ def main(): (7, 5, 2), (9, 5, 2), (8, 10, 1), + (6, 12, 1), ] # [x,y,size(radius)] # Set Initial parameters From 45f90ca37f30e6b201ed5b9cb7b18a5f5fd6a3eb Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 6 Oct 2019 08:57:53 +0900 Subject: [PATCH 018/909] try to upgrade 3.7 --- .travis.yml | 4 ++-- README.md | 8 ++++---- appveyor.yml | 4 +--- 3 files changed, 7 insertions(+), 9 deletions(-) diff --git a/.travis.yml b/.travis.yml index eff3df24fa..042c69ee42 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,7 +5,7 @@ matrix: - os: linux python: - - 3.6 + - 3.7 before_install: - sudo apt-get update @@ -18,7 +18,7 @@ before_install: - conda update -q conda # Useful for debugging any issues with conda - conda info -a - - conda install python==3.6.8 +# - conda install python==3.6.8 install: - conda install numpy==1.15 diff --git a/README.md b/README.md index 6577cf70b8..1c455e928b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ - +header pic # PythonRobotics [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics) @@ -93,7 +93,7 @@ See this paper for more details: # Requirements -- Python 3.6.x (2.7 is not supported) +- Python 3.7.x (2.7 is not supported) - numpy @@ -137,7 +137,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation ## Extended Kalman Filter localization - +EKF pic Documentation: [Notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb) @@ -496,7 +496,7 @@ Ref: Path tracking simulation with iterative linear model predictive speed and steering control. - +MPC pic Ref: diff --git a/appveyor.yml b/appveyor.yml index 61f8a0b364..33d4d02970 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -6,9 +6,7 @@ environment: CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd" matrix: - - MINICONDA: C:\Miniconda36-x64 - - + - MINICONDA: C:\Miniconda37-x64 init: - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%" From 0e689bf0e88b51d79928e068afa272a95e693ba3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 6 Oct 2019 09:12:29 +0900 Subject: [PATCH 019/909] removed pycharm markdown setting --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1c455e928b..d97d92adb3 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ -header pic +
+header pic +
# PythonRobotics [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics) From 16891e60d104824fadd908fdb41eb5eea4389d71 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 6 Oct 2019 09:18:35 +0900 Subject: [PATCH 020/909] revert it --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index d97d92adb3..1c455e928b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,4 @@ -
-header pic -
+header pic # PythonRobotics [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics) From b942c430e6df8d854fe00f9a264efaca94cc90c9 Mon Sep 17 00:00:00 2001 From: sszwfy <476020374@qq.com> Date: Sun, 6 Oct 2019 14:32:22 +0800 Subject: [PATCH 021/909] fix error for goal cost angle --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index fa1dcc62c4..a7d74332a8 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -167,7 +167,8 @@ def calc_to_goal_cost(traj, goal, config): dx = goal[0] - traj[-1, 0] dy = goal[1] - traj[-1, 1] error_angle = math.atan2(dy, dx) - cost = abs(error_angle - traj[-1, 2]) + cost_angle = error_angle - traj[-1, 2] + cost = abs(math.atan2(math.sin(cost_angle), math.cos(cost_angle))) return cost From 0c5bb95761c48bfe976395b350cce317e76ff838 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 9 Oct 2019 19:42:51 +0900 Subject: [PATCH 022/909] Update users_comments.md --- users_comments.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/users_comments.md b/users_comments.md index b1a0b66f8e..2cfd7a4a9b 100644 --- a/users_comments.md +++ b/users_comments.md @@ -401,6 +401,9 @@ URL: https://2019.robocup.org/downloads/program/HughesEtAl2019.pdf 6. Hughes, Josie, Masaru Shimizu, and Arnoud Visser. "A review of robot rescue simulation platforms for robotics education." (2019). URL: https://www.semanticscholar.org/paper/A-Review-of-Robot-Rescue-Simulation-Platforms-for-Hughes-Shimizu/318a4bcb97a44661422ae1430d950efc408097da +7. Ghosh, Ritwika, et al. "CyPhyHouse: A Programming, Simulation, and Deployment Toolchain for Heterogeneous Distributed Coordination." arXiv preprint arXiv:1910.01557 (2019). +URL: https://arxiv.org/abs/1910.01557 + # Others - Autonomous Vehicle Readings https://richardkelley.io/readings From 415bf654f1992cf108eb761eb474b55892cef47d Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 11 Oct 2019 20:13:27 +0900 Subject: [PATCH 023/909] Fix #229 --- .../particle_filter/particle_filter.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 8e93893e48..a3dd6bcdb5 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -6,17 +6,18 @@ """ -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np # Estimation parameter of PF -Q = np.diag([0.1])**2 # range error -R = np.diag([1.0, np.deg2rad(40.0)])**2 # input error +Q = np.diag([0.1]) ** 2 # range error +R = np.diag([1.0, np.deg2rad(40.0)]) ** 2 # input error # Simulation parameter -Qsim = np.diag([0.2])**2 -Rsim = np.diag([1.0, np.deg2rad(30.0)])**2 +Qsim = np.diag([0.2]) ** 2 +Rsim = np.diag([1.0, np.deg2rad(30.0)]) ** 2 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] @@ -37,7 +38,6 @@ def calc_input(): def observation(xTrue, xd, u, RFID): - xTrue = motion_model(xTrue, u) # add noise to gps x-y @@ -47,7 +47,7 @@ def observation(xTrue, xd, u, RFID): dx = xTrue[0, 0] - RFID[i, 0] dy = xTrue[1, 0] - RFID[i, 1] - d = math.sqrt(dx**2 + dy**2) + d = math.sqrt(dx ** 2 + dy ** 2) if d <= MAX_RANGE: dn = d + np.random.randn() * Qsim[0, 0] # add noise zi = np.array([[dn, RFID[i, 0], RFID[i, 1]]]) @@ -64,7 +64,6 @@ def observation(xTrue, xd, u, RFID): def motion_model(x, u): - F = np.array([[1.0, 0, 0, 0], [0, 1.0, 0, 0], [0, 0, 1.0, 0], @@ -97,7 +96,7 @@ def calc_covariance(xEst, px, pw): return cov -def pf_localization(px, pw, xEst, PEst, z, u): +def pf_localization(px, pw, z, u): """ Localization with Particle filter """ @@ -105,6 +104,7 @@ def pf_localization(px, pw, xEst, PEst, z, u): for ip in range(NP): x = np.array([px[:, ip]]).T w = pw[0, ip] + # Predict with random input sampling ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] @@ -115,8 +115,8 @@ def pf_localization(px, pw, xEst, PEst, z, u): for i in range(len(z[:, 0])): dx = x[0, 0] - z[i, 1] dy = x[1, 0] - z[i, 2] - prez = math.sqrt(dx**2 + dy**2) - dz = prez - z[i, 0] + pre_z = math.sqrt(dx ** 2 + dy ** 2) + dz = pre_z - z[i, 0] w = w * gauss_likelihood(dz, math.sqrt(Q[0, 0])) px[:, ip] = x[:, 0] @@ -223,7 +223,7 @@ def main(): xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) - xEst, PEst, px, pw = pf_localization(px, pw, xEst, PEst, z, ud) + xEst, PEst, px, pw = pf_localization(px, pw, z, ud) # store data history hxEst = np.hstack((hxEst, xEst)) From 85bf664a9166d9cb7f9463a801931ceb3af5dec9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 11 Oct 2019 20:49:07 +0900 Subject: [PATCH 024/909] fix PF problems --- .../particle_filter/particle_filter.py | 94 +++++++++---------- 1 file changed, 47 insertions(+), 47 deletions(-) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index a3dd6bcdb5..6643116f5a 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -12,12 +12,12 @@ import numpy as np # Estimation parameter of PF -Q = np.diag([0.1]) ** 2 # range error -R = np.diag([1.0, np.deg2rad(40.0)]) ** 2 # input error +Q = np.diag([0.2]) ** 2 # range error +R = np.diag([2.0, np.deg2rad(40.0)]) ** 2 # input error # Simulation parameter -Qsim = np.diag([0.2]) ** 2 -Rsim = np.diag([1.0, np.deg2rad(30.0)]) ** 2 +Q_sim = np.diag([0.2]) ** 2 +R_sim = np.diag([1.0, np.deg2rad(30.0)]) ** 2 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] @@ -32,30 +32,30 @@ def calc_input(): v = 1.0 # [m/s] - yawrate = 0.1 # [rad/s] - u = np.array([[v, yawrate]]).T + yaw_rate = 0.1 # [rad/s] + u = np.array([[v, yaw_rate]]).T return u -def observation(xTrue, xd, u, RFID): +def observation(xTrue, xd, u, RF_ID): xTrue = motion_model(xTrue, u) # add noise to gps x-y z = np.zeros((0, 3)) - for i in range(len(RFID[:, 0])): + for i in range(len(RF_ID[:, 0])): - dx = xTrue[0, 0] - RFID[i, 0] - dy = xTrue[1, 0] - RFID[i, 1] + dx = xTrue[0, 0] - RF_ID[i, 0] + dy = xTrue[1, 0] - RF_ID[i, 1] d = math.sqrt(dx ** 2 + dy ** 2) if d <= MAX_RANGE: - dn = d + np.random.randn() * Qsim[0, 0] # add noise - zi = np.array([[dn, RFID[i, 0], RFID[i, 1]]]) + dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise + zi = np.array([[dn, RF_ID[i, 0], RF_ID[i, 1]]]) z = np.vstack((z, zi)) # add noise to input - ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] - ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5 ud = np.array([[ud1, ud2]]).T xd = motion_model(xd, ud) @@ -92,6 +92,7 @@ def calc_covariance(xEst, px, pw): for i in range(px.shape[1]): dx = (px[:, i] - xEst)[0:3] cov += pw[0, i] * dx.dot(dx.T) + cov /= NP return cov @@ -106,8 +107,8 @@ def pf_localization(px, pw, z, u): w = pw[0, ip] # Predict with random input sampling - ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] - ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + ud1 = u[0, 0] + np.random.randn() * R[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * R[1, 1] ** 0.5 ud = np.array([[ud1, ud2]]).T x = motion_model(x, ud) @@ -127,30 +128,30 @@ def pf_localization(px, pw, z, u): xEst = px.dot(pw.T) PEst = calc_covariance(xEst, px, pw) - px, pw = resampling(px, pw) + px, pw = re_sampling(px, pw) return xEst, PEst, px, pw -def resampling(px, pw): +def re_sampling(px, pw): """ low variance re-sampling """ - Neff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number - if Neff < NTh: - wcum = np.cumsum(pw) + N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number + if N_eff < NTh: + w_cum = np.cumsum(pw) base = np.cumsum(pw * 0.0 + 1 / NP) - 1 / NP - resampleid = base + np.random.rand(base.shape[0]) / NP + re_sample_id = base + np.random.rand(base.shape[0]) / NP - inds = [] + indexes = [] ind = 0 for ip in range(NP): - while resampleid[ip] > wcum[ind]: + while re_sample_id[ip] > w_cum[ind]: ind += 1 - inds.append(ind) + indexes.append(ind) - px = px[:, inds] + px = px[:, indexes] pw = np.zeros((1, NP)) + 1.0 / NP # init weight return px, pw @@ -158,35 +159,35 @@ def resampling(px, pw): def plot_covariance_ellipse(xEst, PEst): # pragma: no cover Pxy = PEst[0:2, 0:2] - eigval, eigvec = np.linalg.eig(Pxy) + eig_val, eig_vec = np.linalg.eig(Pxy) - if eigval[0] >= eigval[1]: - bigind = 0 - smallind = 1 + if eig_val[0] >= eig_val[1]: + big_ind = 0 + small_ind = 1 else: - bigind = 1 - smallind = 0 + big_ind = 1 + small_ind = 0 t = np.arange(0, 2 * math.pi + 0.1, 0.1) - # eigval[bigind] or eiqval[smallind] were occassionally negative numbers extremely + # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely # close to 0 (~10^-20), catch these cases and set the respective variable to 0 try: - a = math.sqrt(eigval[bigind]) + a = math.sqrt(eig_val[big_ind]) except ValueError: a = 0 try: - b = math.sqrt(eigval[smallind]) + b = math.sqrt(eig_val[small_ind]) except ValueError: b = 0 x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] - angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) - R = np.array([[math.cos(angle), math.sin(angle)], - [-math.sin(angle), math.cos(angle)]]) - fx = R.dot(np.array([[x, y]])) + angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0]) + Rot = np.array([[math.cos(angle), -math.sin(angle)], + [math.sin(angle), math.cos(angle)]]) + fx = Rot.dot(np.array([[x, y]])) px = np.array(fx[0, :] + xEst[0, 0]).flatten() py = np.array(fx[1, :] + xEst[1, 0]).flatten() plt.plot(px, py, "--r") @@ -197,16 +198,15 @@ def main(): time = 0.0 - # RFID positions [x, y] - RFID = np.array([[10.0, 0.0], - [10.0, 10.0], - [0.0, 15.0], - [-5.0, 20.0]]) + # RF_ID positions [x, y] + RFi_ID = np.array([[10.0, 0.0], + [10.0, 10.0], + [0.0, 15.0], + [-5.0, 20.0]]) # State Vector [x y yaw v]' xEst = np.zeros((4, 1)) xTrue = np.zeros((4, 1)) - PEst = np.eye(4) px = np.zeros((4, NP)) # Particle store pw = np.zeros((1, NP)) + 1.0 / NP # Particle weight @@ -221,7 +221,7 @@ def main(): time += DT u = calc_input() - xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) + xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFi_ID) xEst, PEst, px, pw = pf_localization(px, pw, z, ud) @@ -235,7 +235,7 @@ def main(): for i in range(len(z[:, 0])): plt.plot([xTrue[0, 0], z[i, 1]], [xTrue[1, 0], z[i, 2]], "-k") - plt.plot(RFID[:, 0], RFID[:, 1], "*k") + plt.plot(RFi_ID[:, 0], RFi_ID[:, 1], "*k") plt.plot(px[0, :], px[1, :], ".r") plt.plot(np.array(hxTrue[0, :]).flatten(), np.array(hxTrue[1, :]).flatten(), "-b") From 46e0506cf57a96d67203a6f50e3dc7ab60f8c5f3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 12 Oct 2019 14:01:54 +0900 Subject: [PATCH 025/909] Start fixing randn --- .../extended_kalman_filter.py | 34 +++++++++---------- 1 file changed, 16 insertions(+), 18 deletions(-) diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py index 3f0589b8c6..7150b497f9 100644 --- a/Localization/extended_kalman_filter/extended_kalman_filter.py +++ b/Localization/extended_kalman_filter/extended_kalman_filter.py @@ -7,21 +7,22 @@ """ import math -import numpy as np + import matplotlib.pyplot as plt +import numpy as np # Covariance for EKF simulation Q = np.diag([ - 0.1, # variance of location on x-axis - 0.1, # variance of location on y-axis - np.deg2rad(1.0), # variance of yaw angle - 1.0 # variance of velocity - ])**2 # predict state covariance -R = np.diag([1.0, 1.0])**2 # Observation x,y position covariance + 0.1, # variance of location on x-axis + 0.1, # variance of location on y-axis + np.deg2rad(1.0), # variance of yaw angle + 1.0 # variance of velocity +]) ** 2 # predict state covariance +R = np.diag([1.0, 1.0]) ** 2 # Observation x,y position covariance # Simulation parameter -INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)])**2 -GPS_NOISE = np.diag([0.5, 0.5])**2 +INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)]) ** 2 +GPS_NOISE = np.diag([0.5, 0.5]) ** 2 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] @@ -37,7 +38,6 @@ def calc_input(): def observation(xTrue, xd, u): - xTrue = motion_model(xTrue, u) # add noise to gps x-y @@ -52,7 +52,6 @@ def observation(xTrue, xd, u): def motion_model(x, u): - F = np.array([[1.0, 0, 0, 0], [0, 1.0, 0, 0], [0, 0, 1.0, 0], @@ -116,20 +115,19 @@ def jacobH(x): def ekf_estimation(xEst, PEst, z, u): - # Predict xPred = motion_model(xEst, u) jF = jacobF(xPred, u) - PPred = jF@PEst@jF.T + Q + PPred = jF @ PEst @ jF.T + Q # Update jH = jacobH(xPred) zPred = observation_model(xPred) y = z - zPred - S = jH@PPred@jH.T + R - K = PPred@jH.T@np.linalg.inv(S) - xEst = xPred + K@y - PEst = (np.eye(len(xEst)) - K@jH)@PPred + S = jH @ PPred @ jH.T + R + K = PPred @ jH.T @ np.linalg.inv(S) + xEst = xPred + K @ y + PEst = (np.eye(len(xEst)) - K @ jH) @ PPred return xEst, PEst @@ -153,7 +151,7 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) R = np.array([[math.cos(angle), math.sin(angle)], [-math.sin(angle), math.cos(angle)]]) - fx = R@(np.array([x, y])) + fx = R @ (np.array([x, y])) px = np.array(fx[0, :] + xEst[0, 0]).flatten() py = np.array(fx[1, :] + xEst[1, 0]).flatten() plt.plot(px, py, "--r") From 8fb4b8a5644904efc4dbbdefa1c6cd62c4cfe2da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Sat, 12 Oct 2019 14:35:00 +0300 Subject: [PATCH 026/909] optimize obstacle cost calculation The obstacle calculation was too slow to work with more obstacles, it is vectorized to speed up the calculation. --- .../dynamic_window_approach.py | 38 ++++++++----------- 1 file changed, 15 insertions(+), 23 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index fa1dcc62c4..1896107220 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -47,8 +47,6 @@ def __init__(self): self.obstacle_cost_gain = 1.0 self.robot_radius = 1.0 # [m] for collision check - - def motion(x, u, dt): """ motion model @@ -138,24 +136,14 @@ def calc_obstacle_cost(traj, ob, config): """ calc obstacle cost inf: collision """ - - skip_n = 2 # for speed up - minr = float("inf") - - for ii in range(0, len(traj[:, 1]), skip_n): - for i in range(len(ob[:, 0])): - ox = ob[i, 0] - oy = ob[i, 1] - dx = traj[ii, 0] - ox - dy = traj[ii, 1] - oy - - r = math.sqrt(dx**2 + dy**2) - if r <= config.robot_radius: - return float("Inf") # collision - - if minr >= r: - minr = r - + ox = ob[:, 0] + oy = ob[:, 1] + dx = traj[:, 0] - ox[:, None] + dy = traj[:, 1] - oy[:, None] + r = np.sqrt(np.square(dx) + np.square(dy)) + if (r <= config.robot_radius).any(): + return float("Inf") + minr = np.min(r) return 1.0 / minr # OK @@ -178,7 +166,7 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1): # pragma: no cover plt.plot(x, y) -def main(gx=10, gy=10): +def main(gx=10.0, gy=10.0): print(__file__ + " start!!") # initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)] x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0]) @@ -194,7 +182,12 @@ def main(gx=10, gy=10): [5.0, 9.0], [8.0, 9.0], [7.0, 9.0], - [12.0, 12.0] + [8.0, 10.0], + [9.0, 11.0], + [12.0, 13.0], + [12.0, 12.0], + [15.0, 15.0], + [13.0, 13.0] ]) # input [forward speed, yawrate] @@ -204,7 +197,6 @@ def main(gx=10, gy=10): while True: u, ptraj = dwa_control(x, u, config, goal, ob) - x = motion(x, u, config.dt) # simulate robot traj = np.vstack((traj, x)) # store state history From bf3b4680ecd56c5478e247e4cfebc4539680e1dc Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 12 Oct 2019 20:48:04 +0900 Subject: [PATCH 027/909] fix randn usage and code clean up --- .../ensemble_kalman_filter.py | 95 +++++++++---------- .../extended_kalman_filter.py | 14 +-- .../unscented_kalman_filter.py | 30 +++--- SLAM/EKFSLAM/ekf_slam.py | 61 ++++++------ SLAM/FastSLAM1/fast_slam1.py | 60 +++++------- SLAM/FastSLAM2/fast_slam2.py | 80 +++++++--------- 6 files changed, 152 insertions(+), 188 deletions(-) diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index 5ca7713b7d..293509157b 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -1,4 +1,3 @@ - """ Ensemble Kalman Filter(EnKF) localization sample @@ -10,13 +9,14 @@ """ -import numpy as np import math + import matplotlib.pyplot as plt +import numpy as np # Simulation parameter -Qsim = np.diag([0.2, np.deg2rad(1.0)])**2 -Rsim = np.diag([1.0, np.deg2rad(30.0)])**2 +Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2 +R_sim = np.diag([1.0, np.deg2rad(30.0)]) ** 2 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] @@ -30,13 +30,12 @@ def calc_input(): v = 1.0 # [m/s] - yawrate = 0.1 # [rad/s] - u = np.array([[v, yawrate]]).T + yaw_rate = 0.1 # [rad/s] + u = np.array([[v, yaw_rate]]).T return u def observation(xTrue, xd, u, RFID): - xTrue = motion_model(xTrue, u) z = np.zeros((0, 4)) @@ -45,18 +44,18 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx**2 + dy**2) + d = math.sqrt(dx ** 2 + dy ** 2) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: - dn = d + np.random.randn() * Qsim[0, 0] # add noise - anglen = angle + np.random.randn() * Qsim[1, 1] # add noise + dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise + anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise zi = np.array([dn, anglen, RFID[i, 0], RFID[i, 1]]) z = np.vstack((z, zi)) # add noise to input ud = np.array([[ - u[0, 0] + np.random.randn() * Rsim[0, 0], - u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T + u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5, + u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5]]).T xd = motion_model(xd, ud) return xTrue, z, xd, ud @@ -77,15 +76,13 @@ def motion_model(x, u): return x -def calc_LM_Pos(x, landmarks): - landmarks_pos = np.zeros((2*landmarks.shape[0], 1)) +def observe_landmark_position(x, landmarks): + landmarks_pos = np.zeros((2 * landmarks.shape[0], 1)) for (i, lm) in enumerate(landmarks): - landmarks_pos[2*i] = x[0, 0] + lm[0] * \ - math.cos(x[2, 0] + lm[1]) + np.random.randn() * \ - Qsim[0, 0]/np.sqrt(2) - landmarks_pos[2*i+1] = x[1, 0] + lm[0] * \ - math.sin(x[2, 0] + lm[1]) + np.random.randn() * \ - Qsim[0, 0]/np.sqrt(2) + landmarks_pos[2 * i] = x[0, 0] + lm[0] * math.cos(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[ + 0, 0] ** 0.5 / np.sqrt(2) + landmarks_pos[2 * i + 1] = x[1, 0] + lm[0] * math.sin(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[ + 0, 0] ** 0.5 / np.sqrt(2) return landmarks_pos @@ -95,25 +92,26 @@ def calc_covariance(xEst, px): for i in range(px.shape[1]): dx = (px[:, i] - xEst)[0:3] cov += dx.dot(dx.T) + cov /= NP return cov -def enkf_localization(px, xEst, PEst, z, u): +def enkf_localization(px, z, u): """ Localization with Ensemble Kalman filter """ - pz = np.zeros((z.shape[0]*2, NP)) # Particle store of z + pz = np.zeros((z.shape[0] * 2, NP)) # Particle store of z for ip in range(NP): x = np.array([px[:, ip]]).T # Predict with random input sampling - ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] - ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5 ud = np.array([[ud1, ud2]]).T x = motion_model(x, ud) px[:, ip] = x[:, 0] - z_pos = calc_LM_Pos(x, z) + z_pos = observe_landmark_position(x, z) pz[:, ip] = z_pos[:, 0] x_ave = np.mean(px, axis=1) @@ -122,12 +120,12 @@ def enkf_localization(px, xEst, PEst, z, u): z_ave = np.mean(pz, axis=1) z_dif = pz - np.tile(z_ave, (NP, 1)).T - U = 1/(NP-1) * x_dif @ z_dif.T - V = 1/(NP-1) * z_dif @ z_dif.T + U = 1 / (NP - 1) * x_dif @ z_dif.T + V = 1 / (NP - 1) * z_dif @ z_dif.T K = U @ np.linalg.inv(V) # Kalman Gain - z_lm_pos = z[:, [2, 3]].reshape(-1,) + z_lm_pos = z[:, [2, 3]].reshape(-1, ) px_hat = px + K @ (np.tile(z_lm_pos, (NP, 1)).T - pz) @@ -139,32 +137,32 @@ def enkf_localization(px, xEst, PEst, z, u): def plot_covariance_ellipse(xEst, PEst): # pragma: no cover Pxy = PEst[0:2, 0:2] - eigval, eigvec = np.linalg.eig(Pxy) + eig_val, eig_vec = np.linalg.eig(Pxy) - if eigval[0] >= eigval[1]: - bigind = 0 - smallind = 1 + if eig_val[0] >= eig_val[1]: + big_ind = 0 + small_ind = 1 else: - bigind = 1 - smallind = 0 + big_ind = 1 + small_ind = 0 t = np.arange(0, 2 * math.pi + 0.1, 0.1) - # eigval[bigind] or eiqval[smallind] were occassionally negative numbers extremely + # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely # close to 0 (~10^-20), catch these cases and set the respective variable to 0 try: - a = math.sqrt(eigval[bigind]) + a = math.sqrt(eig_val[big_ind]) except ValueError: a = 0 try: - b = math.sqrt(eigval[smallind]) + b = math.sqrt(eig_val[small_ind]) except ValueError: b = 0 x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] - angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) + angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0]) R = np.array([[math.cos(angle), math.sin(angle)], [-math.sin(angle), math.cos(angle)]]) fx = R.dot(np.array([[x, y]])) @@ -182,17 +180,15 @@ def main(): time = 0.0 - # RFID positions [x, y] - RFID = np.array([[10.0, 0.0], - [10.0, 10.0], - [0.0, 15.0], - [-5.0, 20.0]]) + # RF_ID positions [x, y] + RF_ID = np.array([[10.0, 0.0], + [10.0, 10.0], + [0.0, 15.0], + [-5.0, 20.0]]) # State Vector [x y yaw v]' xEst = np.zeros((4, 1)) xTrue = np.zeros((4, 1)) - PEst = np.eye(4) - px = np.zeros((4, NP)) # Particle store of x xDR = np.zeros((4, 1)) # Dead reckoning @@ -206,9 +202,9 @@ def main(): time += DT u = calc_input() - xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) + xTrue, z, xDR, ud = observation(xTrue, xDR, u, RF_ID) - xEst, PEst, px = enkf_localization(px, xEst, PEst, z, ud) + xEst, PEst, px = enkf_localization(px, z, ud) # store data history hxEst = np.hstack((hxEst, xEst)) @@ -220,7 +216,7 @@ def main(): for i in range(len(z[:, 0])): plt.plot([xTrue[0, 0], z[i, 2]], [xTrue[1, 0], z[i, 3]], "-k") - plt.plot(RFID[:, 0], RFID[:, 1], "*k") + plt.plot(RF_ID[:, 0], RF_ID[:, 1], "*k") plt.plot(px[0, :], px[1, :], ".r") plt.plot(np.array(hxTrue[0, :]).flatten(), np.array(hxTrue[1, :]).flatten(), "-b") @@ -228,7 +224,7 @@ def main(): np.array(hxDR[1, :]).flatten(), "-k") plt.plot(np.array(hxEst[0, :]).flatten(), np.array(hxEst[1, :]).flatten(), "-r") - #plot_covariance_ellipse(xEst, PEst) + # plot_covariance_ellipse(xEst, PEst) plt.axis("equal") plt.grid(True) plt.pause(0.001) @@ -236,4 +232,3 @@ def main(): if __name__ == '__main__': main() - diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py index 7150b497f9..d2d7178ae6 100644 --- a/Localization/extended_kalman_filter/extended_kalman_filter.py +++ b/Localization/extended_kalman_filter/extended_kalman_filter.py @@ -78,7 +78,7 @@ def observation_model(x): return z -def jacobF(x, u): +def jacob_f(x, u): """ Jacobian of Motion Model @@ -104,7 +104,7 @@ def jacobF(x, u): return jF -def jacobH(x): +def jacob_h(): # Jacobian of Observation Model jH = np.array([ [1, 0, 0, 0], @@ -117,11 +117,11 @@ def jacobH(x): def ekf_estimation(xEst, PEst, z, u): # Predict xPred = motion_model(xEst, u) - jF = jacobF(xPred, u) + jF = jacob_f(xPred, u) PPred = jF @ PEst @ jF.T + Q # Update - jH = jacobH(xPred) + jH = jacob_h() zPred = observation_model(xPred) y = z - zPred S = jH @ PPred @ jH.T + R @@ -149,9 +149,9 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) - R = np.array([[math.cos(angle), math.sin(angle)], - [-math.sin(angle), math.cos(angle)]]) - fx = R @ (np.array([x, y])) + rot = np.array([[math.cos(angle), math.sin(angle)], + [-math.sin(angle), math.cos(angle)]]) + fx = rot @ (np.array([x, y])) px = np.array(fx[0, :] + xEst[0, 0]).flatten() py = np.array(fx[1, :] + xEst[1, 0]).flatten() plt.plot(px, py, "--r") diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py index 78b44754ee..45d5b78132 100644 --- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py +++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py @@ -6,23 +6,24 @@ """ -import numpy as np -import scipy.linalg import math + import matplotlib.pyplot as plt +import numpy as np +import scipy.linalg # Covariance for UKF simulation Q = np.diag([ - 0.1, # variance of location on x-axis - 0.1, # variance of location on y-axis - np.deg2rad(1.0), # variance of yaw angle - 1.0 # variance of velocity - ])**2 # predict state covariance -R = np.diag([1.0, 1.0])**2 # Observation x,y position covariance + 0.1, # variance of location on x-axis + 0.1, # variance of location on y-axis + np.deg2rad(1.0), # variance of yaw angle + 1.0 # variance of velocity +]) ** 2 # predict state covariance +R = np.diag([1.0, 1.0]) ** 2 # Observation x,y position covariance # Simulation parameter -INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)])**2 -GPS_NOISE = np.diag([0.5, 0.5])**2 +INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)]) ** 2 +GPS_NOISE = np.diag([0.5, 0.5]) ** 2 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] @@ -43,7 +44,6 @@ def calc_input(): def observation(xTrue, xd, u): - xTrue = motion_model(xTrue, u) # add noise to gps x-y @@ -58,7 +58,6 @@ def observation(xTrue, xd, u): def motion_model(x, u): - F = np.array([[1.0, 0, 0, 0], [0, 1.0, 0, 0], [0, 0, 1.0, 0], @@ -144,7 +143,6 @@ def calc_pxz(sigma, x, z_sigma, zb, wc): def ukf_estimation(xEst, PEst, z, u, wm, wc, gamma): - # Predict sigma = generate_sigma_points(xEst, PEst, gamma) sigma = predict_sigma_motion(sigma, u) @@ -183,9 +181,9 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) - R = np.array([[math.cos(angle), math.sin(angle)], - [-math.sin(angle), math.cos(angle)]]) - fx = R @ np.array([x, y]) + rot = np.array([[math.cos(angle), math.sin(angle)], + [-math.sin(angle), math.cos(angle)]]) + fx = rot @ np.array([x, y]) px = np.array(fx[0, :] + xEst[0, 0]).flatten() py = np.array(fx[1, :] + xEst[1, 0]).flatten() plt.plot(px, py, "--r") diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index de1b94ec8f..ae39c9e0ea 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -4,16 +4,16 @@ """ import math -import numpy as np -import matplotlib.pyplot as plt +import matplotlib.pyplot as plt +import numpy as np # EKF state covariance -Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2 +Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)]) ** 2 # Simulation parameter -Qsim = np.diag([0.2, np.deg2rad(1.0)])**2 -Rsim = np.diag([1.0, np.deg2rad(10.0)])**2 +Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2 +R_sim = np.diag([1.0, np.deg2rad(10.0)]) ** 2 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] @@ -26,7 +26,6 @@ def ekf_slam(xEst, PEst, u, z): - # Predict S = STATE_SIZE xEst[0:S] = motion_model(xEst[0:S], u) @@ -36,18 +35,18 @@ def ekf_slam(xEst, PEst, u, z): # Update for iz in range(len(z[:, 0])): # for each observation - minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) + minid = search_correspond_landmark_id(xEst, PEst, z[iz, 0:2]) - nLM = calc_n_LM(xEst) + nLM = calc_n_lm(xEst) if minid == nLM: print("New LM") # Extend state and covariance matrix - xAug = np.vstack((xEst, calc_LM_Pos(xEst, z[iz, :]))) + xAug = np.vstack((xEst, calc_landmark_position(xEst, z[iz, :]))) PAug = np.vstack((np.hstack((PEst, np.zeros((len(xEst), LM_SIZE)))), np.hstack((np.zeros((LM_SIZE, len(xEst))), initP)))) xEst = xAug PEst = PAug - lm = get_LM_Pos_from_state(xEst, minid) + lm = get_landmark_position_from_state(xEst, minid) y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid) K = (PEst @ H.T) @ np.linalg.inv(S) @@ -67,7 +66,6 @@ def calc_input(): def observation(xTrue, xd, u, RFID): - xTrue = motion_model(xTrue, u) # add noise to gps x-y @@ -77,25 +75,24 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx**2 + dy**2) + d = math.sqrt(dx ** 2 + dy ** 2) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: - dn = d + np.random.randn() * Qsim[0, 0] # add noise - anglen = angle + np.random.randn() * Qsim[1, 1] # add noise + dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise + anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise zi = np.array([dn, anglen, i]) z = np.vstack((z, zi)) # add noise to input ud = np.array([[ - u[0, 0] + np.random.randn() * Rsim[0, 0], - u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T + u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5, + u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5]]).T xd = motion_model(xd, ud) return xTrue, z, xd, ud def motion_model(x, u): - F = np.array([[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]]) @@ -108,15 +105,14 @@ def motion_model(x, u): return x -def calc_n_LM(x): +def calc_n_lm(x): n = int((len(x) - STATE_SIZE) / LM_SIZE) return n def jacob_motion(x, u): - Fx = np.hstack((np.eye(STATE_SIZE), np.zeros( - (STATE_SIZE, LM_SIZE * calc_n_LM(x))))) + (STATE_SIZE, LM_SIZE * calc_n_lm(x))))) jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])], [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])], @@ -127,35 +123,34 @@ def jacob_motion(x, u): return G, Fx, -def calc_LM_Pos(x, z): +def calc_landmark_position(x, z): zp = np.zeros((2, 1)) zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1]) zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1]) - #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1]) - #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1]) + # zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1]) + # zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1]) return zp -def get_LM_Pos_from_state(x, ind): - +def get_landmark_position_from_state(x, ind): lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :] return lm -def search_correspond_LM_ID(xAug, PAug, zi): +def search_correspond_landmark_id(xAug, PAug, zi): """ Landmark association with Mahalanobis distance """ - nLM = calc_n_LM(xAug) + nLM = calc_n_lm(xAug) mdist = [] for i in range(nLM): - lm = get_LM_Pos_from_state(xAug, i) + lm = get_landmark_position_from_state(xAug, i) y, S, H = calc_innovation(lm, xAug, PAug, zi, i) mdist.append(y.T @ np.linalg.inv(S) @ y) @@ -173,19 +168,19 @@ def calc_innovation(lm, xEst, PEst, z, LMid): zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]]) y = (z - zp).T y[1] = pi_2_pi(y[1]) - H = jacobH(q, delta, xEst, LMid + 1) + H = jacob_h(q, delta, xEst, LMid + 1) S = H @ PEst @ H.T + Cx[0:2, 0:2] return y, S, H -def jacobH(q, delta, x, i): +def jacob_h(q, delta, x, i): sq = math.sqrt(q) G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]], [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]]) G = G / q - nLM = calc_n_LM(x) + nLM = calc_n_lm(x) F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM)))) F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))), np.eye(2), np.zeros((2, 2 * nLM - 2 * i)))) @@ -246,7 +241,7 @@ def main(): plt.plot(xEst[0], xEst[1], ".r") # plot landmark - for i in range(calc_n_LM(xEst)): + for i in range(calc_n_lm(xEst)): plt.plot(xEst[STATE_SIZE + i * 2], xEst[STATE_SIZE + i * 2 + 1], "xg") @@ -262,4 +257,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index 757e9f95ad..c16407e89f 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -6,18 +6,18 @@ """ -import numpy as np import math -import matplotlib.pyplot as plt +import matplotlib.pyplot as plt +import numpy as np # Fast SLAM covariance -Q = np.diag([3.0, np.deg2rad(10.0)])**2 -R = np.diag([1.0, np.deg2rad(20.0)])**2 +Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2 +R = np.diag([1.0, np.deg2rad(20.0)]) ** 2 # Simulation parameter -Qsim = np.diag([0.3, np.deg2rad(2.0)])**2 -Rsim = np.diag([0.5, np.deg2rad(10.0)])**2 +Qsim = np.diag([0.3, np.deg2rad(2.0)]) ** 2 +Rsim = np.diag([0.5, np.deg2rad(10.0)]) ** 2 OFFSET_YAWRATE_NOISE = 0.01 DT = 0.1 # time tick [s] @@ -46,7 +46,6 @@ def __init__(self, N_LM): def fast_slam1(particles, u, z): - particles = predict_particles(particles, u) particles = update_with_observation(particles, z) @@ -57,7 +56,6 @@ def fast_slam1(particles, u, z): def normalize_weight(particles): - sumw = sum([p.w for p in particles]) try: @@ -73,7 +71,6 @@ def normalize_weight(particles): def calc_final_state(particles): - xEst = np.zeros((STATE_SIZE, 1)) particles = normalize_weight(particles) @@ -90,13 +87,12 @@ def calc_final_state(particles): def predict_particles(particles, u): - for i in range(N_PARTICLE): px = np.zeros((STATE_SIZE, 1)) px[0, 0] = particles[i].x px[1, 0] = particles[i].y px[2, 0] = particles[i].yaw - ud = u + (np.random.randn(1, 2) @ R).T # add noise + ud = u + (np.random.randn(1, 2) @ R ** 0.5).T # add noise px = motion_model(px, ud) particles[i].x = px[0, 0] particles[i].y = px[1, 0] @@ -105,8 +101,7 @@ def predict_particles(particles, u): return particles -def add_new_lm(particle, z, Q): - +def add_new_lm(particle, z, Q_cov): r = z[0] b = z[1] lm_id = int(z[2]) @@ -121,15 +116,15 @@ def add_new_lm(particle, z, Q): Gz = np.array([[c, -r * s], [s, r * c]]) - particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q @ Gz.T + particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T return particle -def compute_jacobians(particle, xf, Pf, Q): +def compute_jacobians(particle, xf, Pf, Q_cov): dx = xf[0, 0] - particle.x dy = xf[1, 0] - particle.y - d2 = dx**2 + dy**2 + d2 = dx ** 2 + dy ** 2 d = math.sqrt(d2) zp = np.array( @@ -141,14 +136,14 @@ def compute_jacobians(particle, xf, Pf, Q): Hf = np.array([[dx / d, dy / d], [-dy / d2, dx / d2]]) - Sf = Hf @ Pf @ Hf.T + Q + Sf = Hf @ Pf @ Hf.T + Q_cov return zp, Hv, Hf, Sf -def update_KF_with_cholesky(xf, Pf, v, Q, Hf): +def update_kf_with_cholesky(xf, Pf, v, Q_cov, Hf): PHt = Pf @ Hf.T - S = Hf @ PHt + Q + S = Hf @ PHt + Q_cov S = (S + S.T) * 0.5 SChol = np.linalg.cholesky(S).T @@ -162,8 +157,7 @@ def update_KF_with_cholesky(xf, Pf, v, Q, Hf): return x, P -def update_landmark(particle, z, Q): - +def update_landmark(particle, z, Q_cov): lm_id = int(z[2]) xf = np.array(particle.lm[lm_id, :]).reshape(2, 1) Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2, :]) @@ -173,7 +167,7 @@ def update_landmark(particle, z, Q): dz = z[0:2].reshape(2, 1) - zp dz[1, 0] = pi_2_pi(dz[1, 0]) - xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf) + xf, Pf = update_kf_with_cholesky(xf, Pf, dz, Q_cov, Hf) particle.lm[lm_id, :] = xf.T particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf @@ -181,11 +175,11 @@ def update_landmark(particle, z, Q): return particle -def compute_weight(particle, z, Q): +def compute_weight(particle, z, Q_cov): lm_id = int(z[2]) xf = np.array(particle.lm[lm_id, :]).reshape(2, 1) Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2]) - zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q) + zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov) dx = z[0:2].reshape(2, 1) - zp dx[1, 0] = pi_2_pi(dx[1, 0]) @@ -205,7 +199,6 @@ def compute_weight(particle, z, Q): def update_with_observation(particles, z): - for iz in range(len(z[0, :])): lmid = int(z[2, iz]) @@ -247,7 +240,7 @@ def resampling(particles): inds = [] ind = 0 for ip in range(N_PARTICLE): - while ((ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind])): + while (ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind]): ind += 1 inds.append(ind) @@ -264,7 +257,6 @@ def resampling(particles): def calc_input(time): - if time <= 3.0: # wait at first v = 0.0 yawrate = 0.0 @@ -278,7 +270,6 @@ def calc_input(time): def observation(xTrue, xd, u, RFID): - # calc true state xTrue = motion_model(xTrue, u) @@ -288,17 +279,17 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx**2 + dy**2) + d = math.sqrt(dx ** 2 + dy ** 2) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: - dn = d + np.random.randn() * Qsim[0, 0] # add noise - anglen = angle + np.random.randn() * Qsim[1, 1] # add noise + dn = d + np.random.randn() * Qsim[0, 0] ** 0.5 # add noise + anglen = angle + np.random.randn() * Qsim[1, 1] ** 0.5 # add noise zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1) z = np.hstack((z, zi)) # add noise to input - ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] - ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + OFFSET_YAWRATE_NOISE + ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] ** 0.5 + OFFSET_YAWRATE_NOISE ud = np.array([ud1, ud2]).reshape(2, 1) xd = motion_model(xd, ud) @@ -307,7 +298,6 @@ def observation(xTrue, xd, u, RFID): def motion_model(x, u): - F = np.array([[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]]) @@ -354,7 +344,7 @@ def main(): hxTrue = xTrue hxDR = xTrue - particles = [Particle(N_LM) for i in range(N_PARTICLE)] + particles = [Particle(N_LM) for _ in range(N_PARTICLE)] while SIM_TIME >= time: time += DT diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 3d73a58e30..73624dbf1f 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -6,19 +6,19 @@ """ -import numpy as np import math -import matplotlib.pyplot as plt +import matplotlib.pyplot as plt +import numpy as np # Fast SLAM covariance -Q = np.diag([3.0, np.deg2rad(10.0)])**2 -R = np.diag([1.0, np.deg2rad(20.0)])**2 +Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2 +R = np.diag([1.0, np.deg2rad(20.0)]) ** 2 # Simulation parameter -Qsim = np.diag([0.3, np.deg2rad(2.0)])**2 -Rsim = np.diag([0.5, np.deg2rad(10.0)])**2 -OFFSET_YAWRATE_NOISE = 0.01 +Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2 +R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2 +OFFSET_YAW_RATE_NOISE = 0.01 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] @@ -47,7 +47,6 @@ def __init__(self, N_LM): def fast_slam2(particles, u, z): - particles = predict_particles(particles, u) particles = update_with_observation(particles, z) @@ -58,12 +57,11 @@ def fast_slam2(particles, u, z): def normalize_weight(particles): - - sumw = sum([p.w for p in particles]) + sum_w = sum([p.w for p in particles]) try: for i in range(N_PARTICLE): - particles[i].w /= sumw + particles[i].w /= sum_w except ZeroDivisionError: for i in range(N_PARTICLE): particles[i].w = 1.0 / N_PARTICLE @@ -74,7 +72,6 @@ def normalize_weight(particles): def calc_final_state(particles): - xEst = np.zeros((STATE_SIZE, 1)) particles = normalize_weight(particles) @@ -85,19 +82,17 @@ def calc_final_state(particles): xEst[2, 0] += particles[i].w * particles[i].yaw xEst[2, 0] = pi_2_pi(xEst[2, 0]) - # print(xEst) return xEst def predict_particles(particles, u): - for i in range(N_PARTICLE): px = np.zeros((STATE_SIZE, 1)) px[0, 0] = particles[i].x px[1, 0] = particles[i].y px[2, 0] = particles[i].yaw - ud = u + (np.random.randn(1, 2) @ R).T # add noise + ud = u + (np.random.randn(1, 2) @ R ** 0.5).T # add noise px = motion_model(px, ud) particles[i].x = px[0, 0] particles[i].y = px[1, 0] @@ -106,8 +101,7 @@ def predict_particles(particles, u): return particles -def add_new_lm(particle, z, Q): - +def add_new_lm(particle, z, Q_cov): r = z[0] b = z[1] lm_id = int(z[2]) @@ -122,15 +116,15 @@ def add_new_lm(particle, z, Q): Gz = np.array([[c, -r * s], [s, r * c]]) - particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q @ Gz.T + particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T return particle -def compute_jacobians(particle, xf, Pf, Q): +def compute_jacobians(particle, xf, Pf, Q_cov): dx = xf[0, 0] - particle.x dy = xf[1, 0] - particle.y - d2 = dx**2 + dy**2 + d2 = dx ** 2 + dy ** 2 d = math.sqrt(d2) zp = np.array( @@ -142,14 +136,14 @@ def compute_jacobians(particle, xf, Pf, Q): Hf = np.array([[dx / d, dy / d], [-dy / d2, dx / d2]]) - Sf = Hf @ Pf @ Hf.T + Q + Sf = Hf @ Pf @ Hf.T + Q_cov return zp, Hv, Hf, Sf -def update_KF_with_cholesky(xf, Pf, v, Q, Hf): +def update_kf_with_cholesky(xf, Pf, v, Q_cov, Hf): PHt = Pf @ Hf.T - S = Hf @ PHt + Q + S = Hf @ PHt + Q_cov S = (S + S.T) * 0.5 SChol = np.linalg.cholesky(S).T @@ -163,18 +157,17 @@ def update_KF_with_cholesky(xf, Pf, v, Q, Hf): return x, P -def update_landmark(particle, z, Q): - +def update_landmark(particle, z, Q_cov): lm_id = int(z[2]) xf = np.array(particle.lm[lm_id, :]).reshape(2, 1) Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2]) - zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q) + zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov) dz = z[0:2].reshape(2, 1) - zp dz[1, 0] = pi_2_pi(dz[1, 0]) - xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf) + xf, Pf = update_kf_with_cholesky(xf, Pf, dz, Q, Hf) particle.lm[lm_id, :] = xf.T particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf @@ -182,12 +175,11 @@ def update_landmark(particle, z, Q): return particle -def compute_weight(particle, z, Q): - +def compute_weight(particle, z, Q_cov): lm_id = int(z[2]) xf = np.array(particle.lm[lm_id, :]).reshape(2, 1) Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2]) - zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q) + zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov) dz = z[0:2].reshape(2, 1) - zp dz[1, 0] = pi_2_pi(dz[1, 0]) @@ -205,15 +197,14 @@ def compute_weight(particle, z, Q): return w -def proposal_sampling(particle, z, Q): - +def proposal_sampling(particle, z, Q_cov): lm_id = int(z[2]) xf = particle.lm[lm_id, :].reshape(2, 1) Pf = particle.lmP[2 * lm_id:2 * lm_id + 2] # State x = np.array([particle.x, particle.y, particle.yaw]).reshape(3, 1) P = particle.P - zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q) + zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov) Sfi = np.linalg.inv(Sf) dz = z[0:2].reshape(2, 1) - zp @@ -232,7 +223,6 @@ def proposal_sampling(particle, z, Q): def update_with_observation(particles, z): - for iz in range(len(z[0, :])): lmid = int(z[2, iz]) @@ -265,17 +255,16 @@ def resampling(particles): pw = np.array(pw) Neff = 1.0 / (pw @ pw.T) # Effective particle number - # print(Neff) if Neff < NTH: # resampling wcum = np.cumsum(pw) base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE - resampleid = base + np.random.rand(base.shape[0]) / N_PARTICLE + resamplei_id = base + np.random.rand(base.shape[0]) / N_PARTICLE inds = [] ind = 0 for ip in range(N_PARTICLE): - while ((ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind])): + while (ind < wcum.shape[0] - 1) and (resamplei_id[ip] > wcum[ind]): ind += 1 inds.append(ind) @@ -292,7 +281,6 @@ def resampling(particles): def calc_input(time): - if time <= 3.0: # wait at first v = 0.0 yawrate = 0.0 @@ -306,7 +294,6 @@ def calc_input(time): def observation(xTrue, xd, u, RFID): - # calc true state xTrue = motion_model(xTrue, u) @@ -317,17 +304,17 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx**2 + dy**2) + d = math.sqrt(dx ** 2 + dy ** 2) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: - dn = d + np.random.randn() * Qsim[0, 0] # add noise - anglen = angle + np.random.randn() * Qsim[1, 1] # add noise + dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise + anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1) z = np.hstack((z, zi)) # add noise to input - ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] - ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + OFFSET_YAWRATE_NOISE + ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE ud = np.array([ud1, ud2]).reshape(2, 1) xd = motion_model(xd, ud) @@ -336,7 +323,6 @@ def observation(xTrue, xd, u, RFID): def motion_model(x, u): - F = np.array([[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]]) @@ -383,7 +369,7 @@ def main(): hxTrue = xTrue hxDR = xTrue - particles = [Particle(N_LM) for i in range(N_PARTICLE)] + particles = [Particle(N_LM) for _ in range(N_PARTICLE)] while SIM_TIME >= time: time += DT @@ -409,7 +395,7 @@ def main(): for iz in range(len(z[:, 0])): lmid = int(z[2, iz]) plt.plot([xEst[0], RFID[lmid, 0]], [ - xEst[1], RFID[lmid, 1]], "-k") + xEst[1], RFID[lmid, 1]], "-k") for i in range(N_PARTICLE): plt.plot(particles[i].x, particles[i].y, ".r") From 23c73567d6f20f539e860d03d0ce2e7bbe74cad5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Sat, 12 Oct 2019 15:08:20 +0300 Subject: [PATCH 028/909] Optimize the obstacle cost calculation. The calculation was too slow to work with more obstacles. The calculation function is vectorized to increase the speed. --- .../dynamic_window_approach.py | 24 +++++++------------ 1 file changed, 8 insertions(+), 16 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index a7d74332a8..7b5eba1743 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -139,22 +139,14 @@ def calc_obstacle_cost(traj, ob, config): calc obstacle cost inf: collision """ - skip_n = 2 # for speed up - minr = float("inf") - - for ii in range(0, len(traj[:, 1]), skip_n): - for i in range(len(ob[:, 0])): - ox = ob[i, 0] - oy = ob[i, 1] - dx = traj[ii, 0] - ox - dy = traj[ii, 1] - oy - - r = math.sqrt(dx**2 + dy**2) - if r <= config.robot_radius: - return float("Inf") # collision - - if minr >= r: - minr = r + ox = ob[:, 0] + oy = ob[:, 1] + dx = traj[:, 0] - ox[:, None] + dy = traj[:, 1] - oy[:, None] + r = np.sqrt(np.square(dx) + np.square(dy)) + if (r <= config.robot_radius).any(): + return float("Inf") + minr = np.min(r) return 1.0 / minr # OK From 87a5517c3a6f929c785c6b8a4c4926815e5d55ba Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 15 Oct 2019 21:56:17 +0900 Subject: [PATCH 029/909] code clean up for DWA sample. --- .../dynamic_window_approach.py | 66 +++++++++---------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index f418372c46..fe906e0303 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -7,26 +7,26 @@ """ import math -import numpy as np -import matplotlib.pyplot as plt +import matplotlib.pyplot as plt +import numpy as np show_animation = True -def dwa_control(x, u, config, goal, ob): +def dwa_control(x, config, goal, ob): """ Dynamic Window Approach control """ dw = calc_dynamic_window(x, config) - u, traj = calc_final_input(x, u, dw, config, goal, ob) + u, trajectory = calc_final_input(x, dw, config, goal, ob) - return u, traj + return u, trajectory -class Config(): +class Config: """ simulation parameter class """ @@ -47,6 +47,7 @@ def __init__(self): self.obstacle_cost_gain = 1.0 self.robot_radius = 1.0 # [m] for collision check + def motion(x, u, dt): """ motion model @@ -99,27 +100,26 @@ def predict_trajectory(x_init, v, y, config): return traj -def calc_final_input(x, u, dw, config, goal, ob): +def calc_final_input(x, dw, config, goal, ob): """ - calculation final input with dinamic window + calculation final input with dynamic window """ x_init = x[:] min_cost = float("inf") best_u = [0.0, 0.0] - best_traj = np.array([x]) + best_trajectory = np.array([x]) - # evalucate all trajectory with sampled input in dynamic window + # evaluate all trajectory with sampled input in dynamic window for v in np.arange(dw[0], dw[1], config.v_reso): for y in np.arange(dw[2], dw[3], config.yawrate_reso): - traj = predict_trajectory(x_init, v, y, config) + trajectory = predict_trajectory(x_init, v, y, config) # calc cost - to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(traj, goal, config) - speed_cost = config.speed_cost_gain * \ - (config.max_speed - traj[-1, 3]) - ob_cost = config.obstacle_cost_gain*calc_obstacle_cost(traj, ob, config) + to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(trajectory, goal) + speed_cost = config.speed_cost_gain * (config.max_speed - trajectory[-1, 3]) + ob_cost = config.obstacle_cost_gain * calc_obstacle_cost(trajectory, ob, config) final_cost = to_goal_cost + speed_cost + ob_cost @@ -127,35 +127,35 @@ def calc_final_input(x, u, dw, config, goal, ob): if min_cost >= final_cost: min_cost = final_cost best_u = [v, y] - best_traj = traj + best_trajectory = trajectory - return best_u, best_traj + return best_u, best_trajectory -def calc_obstacle_cost(traj, ob, config): +def calc_obstacle_cost(trajectory, ob, config): """ calc obstacle cost inf: collision """ ox = ob[:, 0] oy = ob[:, 1] - dx = traj[:, 0] - ox[:, None] - dy = traj[:, 1] - oy[:, None] + dx = trajectory[:, 0] - ox[:, None] + dy = trajectory[:, 1] - oy[:, None] r = np.sqrt(np.square(dx) + np.square(dy)) if (r <= config.robot_radius).any(): return float("Inf") - minr = np.min(r) - return 1.0 / minr # OK + min_r = np.min(r) + return 1.0 / min_r # OK -def calc_to_goal_cost(traj, goal, config): +def calc_to_goal_cost(trajectory, goal): """ calc to goal cost with angle difference """ - dx = goal[0] - traj[-1, 0] - dy = goal[1] - traj[-1, 1] + dx = goal[0] - trajectory[-1, 0] + dy = goal[1] - trajectory[-1, 1] error_angle = math.atan2(dy, dx) - cost_angle = error_angle - traj[-1, 2] + cost_angle = error_angle - trajectory[-1, 2] cost = abs(math.atan2(math.sin(cost_angle), math.cos(cost_angle))) return cost @@ -194,16 +194,16 @@ def main(gx=10.0, gy=10.0): # input [forward speed, yawrate] u = np.array([0.0, 0.0]) config = Config() - traj = np.array(x) + trajectory = np.array(x) while True: - u, ptraj = dwa_control(x, u, config, goal, ob) - x = motion(x, u, config.dt) # simulate robot - traj = np.vstack((traj, x)) # store state history + u, predicted_trajectory = dwa_control(x, config, goal, ob) + x = motion(x, u, config.dt) # simulate robot + trajectory = np.vstack((trajectory, x)) # store state history if show_animation: plt.cla() - plt.plot(ptraj[:, 0], ptraj[:, 1], "-g") + plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g") plt.plot(x[0], x[1], "xr") plt.plot(goal[0], goal[1], "xb") plt.plot(ob[:, 0], ob[:, 1], "ok") @@ -213,14 +213,14 @@ def main(gx=10.0, gy=10.0): plt.pause(0.0001) # check reaching goal - dist_to_goal = math.sqrt((x[0] - goal[0])**2 + (x[1] - goal[1])**2) + dist_to_goal = math.sqrt((x[0] - goal[0]) ** 2 + (x[1] - goal[1]) ** 2) if dist_to_goal <= config.robot_radius: print("Goal!!") break print("Done") if show_animation: - plt.plot(traj[:, 0], traj[:, 1], "-r") + plt.plot(trajectory[:, 0], trajectory[:, 1], "-r") plt.pause(0.0001) plt.show() From 4cedb7aad4d2b4f6431536a8c749f1980cc8f84e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Tue, 15 Oct 2019 21:55:56 +0300 Subject: [PATCH 030/909] Add rectangle robot type and collision check Add collision check for rectangle robots Add new function to draw robot depending on the type --- .../dynamic_window_approach.py | 56 ++++++++++++++++++- 1 file changed, 53 insertions(+), 3 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 7b0882ab0c..1fad0e91cf 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -45,8 +45,16 @@ def __init__(self): self.to_goal_cost_gain = 0.15 self.speed_cost_gain = 1.0 self.obstacle_cost_gain = 1.0 + self.robot_type = 'circle' # circle or rectangle + + # if robot_type == circle + # Also used to check if goal is reached in both types self.robot_radius = 1.0 # [m] for collision check + # if robot_type == rectangle + self.robot_width = 0.5 # [m] for collision check + self.robot_length = 1.2 # [m] for collision check + def motion(x, u, dt): """ @@ -141,8 +149,25 @@ def calc_obstacle_cost(trajectory, ob, config): dx = trajectory[:, 0] - ox[:, None] dy = trajectory[:, 1] - oy[:, None] r = np.sqrt(np.square(dx) + np.square(dy)) - if (r <= config.robot_radius).any(): - return float("Inf") + + if config.robot_type == 'rectangle': + yaw = trajectory[:, 2] + rot = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]]) + local_ob = ob[:, None] - trajectory[:, 0:2] + local_ob = local_ob.reshape(-1, local_ob.shape[-1]) + local_ob = np.array([local_ob @ -x for x in rot.T]) + local_ob = local_ob.reshape(-1, local_ob.shape[-1]) + upper_check = local_ob[:, 0] <= config.robot_length/2 + right_check = local_ob[:, 1] <= config.robot_width/2 + bottom_check = local_ob[:, 0] >= -config.robot_length/2 + left_check = local_ob[:, 1] >= -config.robot_width/2 + if (np.logical_and(np.logical_and(upper_check, right_check), + np.logical_and(bottom_check, left_check))).any(): + return float("Inf") + elif config.robot_type == 'circle': + if (r <= config.robot_radius).any(): + return float("Inf") + min_r = np.min(r) return 1.0 / min_r # OK @@ -166,7 +191,30 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1): # pragma: no cover plt.plot(x, y) -def main(gx=10.0, gy=10.0): +def plot_robot(x, y, yaw, config): # pragma: no cover + if config.robot_type == 'rectangle': + outline = np.array([[-config.robot_length/2, config.robot_length/2, + (config.robot_length/2), -config.robot_length/2, + -config.robot_length/2], + [config.robot_width / 2, config.robot_width / 2, + - config.robot_width / 2, -config.robot_width / 2, + config.robot_width / 2]]) + Rot1 = np.array([[math.cos(yaw), math.sin(yaw)], + [-math.sin(yaw), math.cos(yaw)]]) + outline = (outline.T.dot(Rot1)).T + outline[0, :] += x + outline[1, :] += y + plt.plot(np.array(outline[0, :]).flatten(), + np.array(outline[1, :]).flatten(), "-k") + elif config.robot_type == 'circle': + circle = plt.Circle((x, y), config.robot_radius, color="b") + plt.gcf().gca().add_artist(circle) + out_x, out_y = (np.array([x, y]) + + np.array([np.cos(yaw), np.sin(yaw)]) * config.robot_radius) + plt.plot([x, out_x], [y, out_y], "-k") + + +def main(gx=10.0, gy=10.0, robot_type='circle'): print(__file__ + " start!!") # initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)] x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0]) @@ -193,6 +241,7 @@ def main(gx=10.0, gy=10.0): # input [forward speed, yawrate] u = np.array([0.0, 0.0]) config = Config() + config.robot_type = robot_type trajectory = np.array(x) while True: @@ -206,6 +255,7 @@ def main(gx=10.0, gy=10.0): plt.plot(x[0], x[1], "xr") plt.plot(goal[0], goal[1], "xb") plt.plot(ob[:, 0], ob[:, 1], "ok") + plot_robot(x[0], x[1], x[2], config) plot_arrow(x[0], x[1], x[2]) plt.axis("equal") plt.grid(True) From d98604158c56f67d47ad2f9481e6498ee469b415 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Tue, 15 Oct 2019 22:03:28 +0300 Subject: [PATCH 031/909] add test for the rectangle robot type --- tests/test_dynamic_window_approach.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py index ab2e0a27ac..de371f4a2b 100644 --- a/tests/test_dynamic_window_approach.py +++ b/tests/test_dynamic_window_approach.py @@ -17,7 +17,12 @@ def test1(self): m.show_animation = False m.main(gx=1.0, gy=1.0) + def test2(self): + m.show_animation = False + m.main(gx=1.0, gy=1.0, robot_type='rectangle') + if __name__ == '__main__': # pragma: no cover test = Test() test.test1() + test.test2() From bcdf088684b1b47ef9867c39263f624f677fe869 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Wed, 16 Oct 2019 19:19:11 +0300 Subject: [PATCH 032/909] Change robot_type comparison to enum class named RobotType - Instead of using string based comparison for robot_type, use Enum class. - Append authors --- .../dynamic_window_approach.py | 22 +++++++++++-------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 1fad0e91cf..0313b0f1ef 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -2,10 +2,11 @@ Mobile robot motion planning sample with Dynamic Window Approach -author: Atsushi Sakai (@Atsushi_twi) +author: Atsushi Sakai (@Atsushi_twi), Goktug Karakasli """ +from enum import Enum import math import matplotlib.pyplot as plt @@ -25,6 +26,9 @@ def dwa_control(x, config, goal, ob): return u, trajectory +class RobotType(Enum): + circle = 0 + rectangle = 1 class Config: """ @@ -45,13 +49,13 @@ def __init__(self): self.to_goal_cost_gain = 0.15 self.speed_cost_gain = 1.0 self.obstacle_cost_gain = 1.0 - self.robot_type = 'circle' # circle or rectangle + self.robot_type = RobotType.circle - # if robot_type == circle + # if robot_type == RobotType.circle # Also used to check if goal is reached in both types self.robot_radius = 1.0 # [m] for collision check - # if robot_type == rectangle + # if robot_type == RobotType.rectangle self.robot_width = 0.5 # [m] for collision check self.robot_length = 1.2 # [m] for collision check @@ -150,7 +154,7 @@ def calc_obstacle_cost(trajectory, ob, config): dy = trajectory[:, 1] - oy[:, None] r = np.sqrt(np.square(dx) + np.square(dy)) - if config.robot_type == 'rectangle': + if config.robot_type == RobotType.rectangle: yaw = trajectory[:, 2] rot = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]]) local_ob = ob[:, None] - trajectory[:, 0:2] @@ -164,7 +168,7 @@ def calc_obstacle_cost(trajectory, ob, config): if (np.logical_and(np.logical_and(upper_check, right_check), np.logical_and(bottom_check, left_check))).any(): return float("Inf") - elif config.robot_type == 'circle': + elif config.robot_type == RobotType.circle: if (r <= config.robot_radius).any(): return float("Inf") @@ -192,7 +196,7 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1): # pragma: no cover def plot_robot(x, y, yaw, config): # pragma: no cover - if config.robot_type == 'rectangle': + if config.robot_type == RobotType.rectangle: outline = np.array([[-config.robot_length/2, config.robot_length/2, (config.robot_length/2), -config.robot_length/2, -config.robot_length/2], @@ -206,7 +210,7 @@ def plot_robot(x, y, yaw, config): # pragma: no cover outline[1, :] += y plt.plot(np.array(outline[0, :]).flatten(), np.array(outline[1, :]).flatten(), "-k") - elif config.robot_type == 'circle': + elif config.robot_type == RobotType.circle: circle = plt.Circle((x, y), config.robot_radius, color="b") plt.gcf().gca().add_artist(circle) out_x, out_y = (np.array([x, y]) + @@ -214,7 +218,7 @@ def plot_robot(x, y, yaw, config): # pragma: no cover plt.plot([x, out_x], [y, out_y], "-k") -def main(gx=10.0, gy=10.0, robot_type='circle'): +def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): print(__file__ + " start!!") # initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)] x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0]) From 457b4086ef4e900f53a104a6baf36246ef57889e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Wed, 16 Oct 2019 19:24:37 +0300 Subject: [PATCH 033/909] Change string to RobotType enum --- tests/test_dynamic_window_approach.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py index de371f4a2b..890f000475 100644 --- a/tests/test_dynamic_window_approach.py +++ b/tests/test_dynamic_window_approach.py @@ -19,7 +19,7 @@ def test1(self): def test2(self): m.show_animation = False - m.main(gx=1.0, gy=1.0, robot_type='rectangle') + m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle) if __name__ == '__main__': # pragma: no cover From 0ec56812be8e6811cc0a3b2486528b1fb2c64685 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Wed, 16 Oct 2019 20:17:46 +0300 Subject: [PATCH 034/909] Add type checking for robot_type --- .../DynamicWindowApproach/dynamic_window_approach.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 0313b0f1ef..267ca1676d 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -59,6 +59,15 @@ def __init__(self): self.robot_width = 0.5 # [m] for collision check self.robot_length = 1.2 # [m] for collision check + @property + def robot_type(self): + return self._robot_type + + @robot_type.setter + def robot_type(self, value): + if not isinstance(value, RobotType): + raise TypeError("robot_type must be an instance of RobotType") + self._robot_type = value def motion(x, u, dt): """ From adb4e99ceb5628f103c486b9eb84ef35d1c4b3d0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Wed, 16 Oct 2019 21:03:18 +0300 Subject: [PATCH 035/909] Fix transpose error of the rotation matrix --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 267ca1676d..9216892aee 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -166,9 +166,10 @@ def calc_obstacle_cost(trajectory, ob, config): if config.robot_type == RobotType.rectangle: yaw = trajectory[:, 2] rot = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]]) + rot = np.transpose(rot, [2, 0, 1]) local_ob = ob[:, None] - trajectory[:, 0:2] local_ob = local_ob.reshape(-1, local_ob.shape[-1]) - local_ob = np.array([local_ob @ -x for x in rot.T]) + local_ob = np.array([local_ob @ -x for x in rot]) local_ob = local_ob.reshape(-1, local_ob.shape[-1]) upper_check = local_ob[:, 0] <= config.robot_length/2 right_check = local_ob[:, 1] <= config.robot_width/2 From c1ba9bab0a48100aa3f81286a2015c5112bb1f9f Mon Sep 17 00:00:00 2001 From: Francisco Moretti Date: Thu, 17 Oct 2019 09:09:54 -0300 Subject: [PATCH 036/909] fix check segment intersection with obstacle The previous method only worked because the step that checked collision from a point of the line to obstacles was the same as the minimum obstacle radius. If the obstacle radius is very small a great amount of steps would be required. Thus It's better to check the distance from the segment to the obstacles directly and compare with obstacle radius Now there is no need to have two check functions. --- .../InformedRRTStar/informed_rrt_star.py | 54 ++++++++++--------- 1 file changed, 29 insertions(+), 25 deletions(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 4a32801da5..b9769bea53 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -74,10 +74,9 @@ def informed_rrt_star_search(self, animation=True): newNode = self.get_new_node(theta, nind, nearestNode) d = self.line_cost(nearestNode, newNode) - isCollision = self.collision_check(newNode, self.obstacle_list) - isCollisionEx = self.check_collision_extend(nearestNode, theta, d) + isCollision = self.check_collision(nearestNode, theta, d) - if isCollision and isCollisionEx: + if isCollision: nearInds = self.find_near_nodes(newNode) newNode = self.choose_parent(newNode, nearInds) @@ -110,7 +109,7 @@ def choose_parent(self, newNode, nearInds): dy = newNode.y - self.node_list[i].y d = math.sqrt(dx ** 2 + dy ** 2) theta = math.atan2(dy, dx) - if self.check_collision_extend(self.node_list[i], theta, d): + if self.check_collision(self.node_list[i], theta, d): dList.append(self.node_list[i].cost + d) else: dList.append(float('inf')) @@ -194,17 +193,6 @@ def get_nearest_list_index(nodes, rnd): minIndex = dList.index(min(dList)) return minIndex - @staticmethod - def collision_check(newNode, obstacleList): - for (ox, oy, size) in obstacleList: - dx = ox - newNode.x - dy = oy - newNode.y - d = dx * dx + dy * dy - if d <= 1.1 * size ** 2: - return False # collision - - return True # safe - def get_new_node(self, theta, nind, nearestNode): newNode = copy.deepcopy(nearestNode) @@ -234,19 +222,35 @@ def rewire(self, newNode, nearInds): if nearNode.cost > scost: theta = math.atan2(newNode.y - nearNode.y, newNode.x - nearNode.x) - if self.check_collision_extend(nearNode, theta, d): + if self.check_collision(nearNode, theta, d): nearNode.parent = n_node - 1 nearNode.cost = scost - - def check_collision_extend(self, nearNode, theta, d): + + @staticmethod + def distance_squared_point_to_segment(v, w, p): + # Return minimum distance between line segment vw and point p + if (np.array_equal(v, w)): + return (p-v).dot(p-v) # v == w case + l2 = (w-v).dot(w-v) # i.e. |w-v|^2 - avoid a sqrt + # Consider the line extending the segment, parameterized as v + t (w - v). + # We find projection of point p onto the line. + # It falls where t = [(p-v) . (w-v)] / |w-v|^2 + # We clamp t from [0,1] to handle points outside the segment vw. + t = max(0, min(1, (p - v).dot(w - v) / l2)) + projection = v + t * (w - v) # Projection falls on the segment + return (p-projection).dot(p-projection) + + def check_collision(self, nearNode, theta, d): tmpNode = copy.deepcopy(nearNode) - - for i in range(int(d / self.expand_dis)): - tmpNode.x += self.expand_dis * math.cos(theta) - tmpNode.y += self.expand_dis * math.sin(theta) - if not self.collision_check(tmpNode, self.obstacle_list): - return False - + endx = tmpNode.x + math.cos(theta)*d + endy = tmpNode.y + math.sin(theta)*d + for (ox, oy, size) in self.obstacle_list: + dd = self.distance_squared_point_to_segment( + np.array([tmpNode.x, tmpNode.y]), + np.array([endx, endy]), + np.array([ox, oy])) + if dd <= 1.1 * size**2: + return False # collision return True def get_final_course(self, lastIndex): From 35ea4b955cb776718e89440becb5aac8443e962d Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 17 Oct 2019 21:53:39 +0900 Subject: [PATCH 037/909] code clean up for DWA --- .../dynamic_window_approach.py | 33 ++++++++++--------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 9216892aee..c2a8674ae3 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -6,8 +6,8 @@ """ -from enum import Enum import math +from enum import Enum import matplotlib.pyplot as plt import numpy as np @@ -17,7 +17,7 @@ def dwa_control(x, config, goal, ob): """ - Dynamic Window Approach control + Dynamic Window Approach control """ dw = calc_dynamic_window(x, config) @@ -26,10 +26,12 @@ def dwa_control(x, config, goal, ob): return u, trajectory + class RobotType(Enum): circle = 0 rectangle = 1 + class Config: """ simulation parameter class @@ -56,8 +58,8 @@ def __init__(self): self.robot_radius = 1.0 # [m] for collision check # if robot_type == RobotType.rectangle - self.robot_width = 0.5 # [m] for collision check - self.robot_length = 1.2 # [m] for collision check + self.robot_width = 0.5 # [m] for collision check + self.robot_length = 1.2 # [m] for collision check @property def robot_type(self): @@ -69,6 +71,7 @@ def robot_type(self, value): raise TypeError("robot_type must be an instance of RobotType") self._robot_type = value + def motion(x, u, dt): """ motion model @@ -98,7 +101,7 @@ def calc_dynamic_window(x, config): x[4] - config.max_dyawrate * config.dt, x[4] + config.max_dyawrate * config.dt] - # [vmin,vmax, yawrate min, yawrate max] + # [vmin,vmax, yaw_rate min, yaw_rate max] dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]), max(Vs[2], Vd[2]), min(Vs[3], Vd[3])] @@ -171,10 +174,10 @@ def calc_obstacle_cost(trajectory, ob, config): local_ob = local_ob.reshape(-1, local_ob.shape[-1]) local_ob = np.array([local_ob @ -x for x in rot]) local_ob = local_ob.reshape(-1, local_ob.shape[-1]) - upper_check = local_ob[:, 0] <= config.robot_length/2 - right_check = local_ob[:, 1] <= config.robot_width/2 - bottom_check = local_ob[:, 0] >= -config.robot_length/2 - left_check = local_ob[:, 1] >= -config.robot_width/2 + upper_check = local_ob[:, 0] <= config.robot_length / 2 + right_check = local_ob[:, 1] <= config.robot_width / 2 + bottom_check = local_ob[:, 0] >= -config.robot_length / 2 + left_check = local_ob[:, 1] >= -config.robot_width / 2 if (np.logical_and(np.logical_and(upper_check, right_check), np.logical_and(bottom_check, left_check))).any(): return float("Inf") @@ -185,6 +188,7 @@ def calc_obstacle_cost(trajectory, ob, config): min_r = np.min(r) return 1.0 / min_r # OK + def calc_to_goal_cost(trajectory, goal): """ calc to goal cost with angle difference @@ -207,9 +211,9 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1): # pragma: no cover def plot_robot(x, y, yaw, config): # pragma: no cover if config.robot_type == RobotType.rectangle: - outline = np.array([[-config.robot_length/2, config.robot_length/2, - (config.robot_length/2), -config.robot_length/2, - -config.robot_length/2], + outline = np.array([[-config.robot_length / 2, config.robot_length / 2, + (config.robot_length / 2), -config.robot_length / 2, + -config.robot_length / 2], [config.robot_width / 2, config.robot_width / 2, - config.robot_width / 2, -config.robot_width / 2, config.robot_width / 2]]) @@ -252,8 +256,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): [13.0, 13.0] ]) - # input [forward speed, yawrate] - u = np.array([0.0, 0.0]) + # input [forward speed, yaw_rate] config = Config() config.robot_type = robot_type trajectory = np.array(x) @@ -290,4 +293,4 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): if __name__ == '__main__': - main() + main(robot_type=RobotType.rectangle) From 71c124bdd3e15118004c06ed06f62cd7341c963c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 17 Oct 2019 22:00:10 +0900 Subject: [PATCH 038/909] Update issue templates --- .github/ISSUE_TEMPLATE/bug_report.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000000..a643b49571 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,21 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - Python version (This repo only supports Python 3.7.x or higher). + - Each library version From afbcdfc0bdcd8a15b6b0503ae9027eb153d02afc Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 20 Oct 2019 11:27:37 +0900 Subject: [PATCH 039/909] update tests --- tests/test_dynamic_window_approach.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py index 890f000475..197dda2158 100644 --- a/tests/test_dynamic_window_approach.py +++ b/tests/test_dynamic_window_approach.py @@ -1,28 +1,27 @@ +import os +import sys from unittest import TestCase -import sys -import os sys.path.append(os.path.dirname(__file__) + "/../") try: from PathPlanning.DynamicWindowApproach import dynamic_window_approach as m -except: +except ImportError: raise print(__file__) -class Test(TestCase): - - def test1(self): +class TestDynamicWindowApproach(TestCase): + def test_main1(self): m.show_animation = False m.main(gx=1.0, gy=1.0) - def test2(self): + def test_main2(self): m.show_animation = False m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle) if __name__ == '__main__': # pragma: no cover - test = Test() - test.test1() - test.test2() + test = TestDynamicWindowApproach() + test.test_main1() + test.test_main2() From 245838250bc1c2e23ee826026157aeb70d3116a5 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 20 Oct 2019 15:00:57 +0900 Subject: [PATCH 040/909] fix unittests. --- PathPlanning/LQRPlanner/LQRplanner.py | 4 ++-- tests/test_drone_3d_trajectory_following.py | 8 +++++--- tests/test_grid_based_sweep_coverage_path_planner.py | 3 +++ tests/test_n_joint_arm_to_point_control.py | 8 +++++--- 4 files changed, 15 insertions(+), 8 deletions(-) diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py index e2000a957c..5a0b453f78 100644 --- a/PathPlanning/LQRPlanner/LQRplanner.py +++ b/PathPlanning/LQRPlanner/LQRplanner.py @@ -25,7 +25,7 @@ def __init__(self): self.MAX_ITER = 150 self.EPS = 0.01 - def lqr_planning(self, sx, sy, gx, gy, show_animation=SHOW_ANIMATION): + def lqr_planning(self, sx, sy, gx, gy, show_animation=True): rx, ry = [sx], [sy] @@ -129,7 +129,7 @@ def main(): gx = random.uniform(-area, area) gy = random.uniform(-area, area) - rx, ry = lqr_planner.lqr_planning(sx, sy, gx, gy) + rx, ry = lqr_planner.lqr_planning(sx, sy, gx, gy, show_animation=SHOW_ANIMATION) if SHOW_ANIMATION: # pragma: no cover plt.plot(sx, sy, "or") diff --git a/tests/test_drone_3d_trajectory_following.py b/tests/test_drone_3d_trajectory_following.py index 194a329946..dfadcdaee4 100644 --- a/tests/test_drone_3d_trajectory_following.py +++ b/tests/test_drone_3d_trajectory_following.py @@ -1,9 +1,11 @@ +import os +import sys from unittest import TestCase -import sys -sys.path.append("./AerialNavigation/drone_3d_trajectory_following/") +sys.path.append(os.path.dirname(__file__) + "/../AerialNavigation/drone_3d_trajectory_following/") + +import drone_3d_trajectory_following as m -from AerialNavigation.drone_3d_trajectory_following import drone_3d_trajectory_following as m print(__file__) diff --git a/tests/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py index 3933356726..fa2d0a7a64 100644 --- a/tests/test_grid_based_sweep_coverage_path_planner.py +++ b/tests/test_grid_based_sweep_coverage_path_planner.py @@ -9,6 +9,9 @@ except ImportError: raise +grid_based_sweep_coverage_path_planner.do_animation = False + + class TestPlanning(TestCase): def test_planning1(self): diff --git a/tests/test_n_joint_arm_to_point_control.py b/tests/test_n_joint_arm_to_point_control.py index e39883559a..a935216475 100644 --- a/tests/test_n_joint_arm_to_point_control.py +++ b/tests/test_n_joint_arm_to_point_control.py @@ -1,9 +1,11 @@ +import os +import sys from unittest import TestCase -import sys -sys.path.append("./ArmNavigation/n_joint_arm_to_point_control/") +sys.path.append(os.path.dirname(__file__) + "/../ArmNavigation/n_joint_arm_to_point_control/") + +import n_joint_arm_to_point_control as m -from ArmNavigation.n_joint_arm_to_point_control import n_joint_arm_to_point_control as m print(__file__) From 692b7bd68411117204636f3534b4ea1eb661d872 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Wed, 23 Oct 2019 10:14:14 +0300 Subject: [PATCH 041/909] Fix wrong rotation direction - The obstacle rotation for the rectangle obstacle calculation is wrong. It should be positive instead of negative. --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index c2a8674ae3..ebf254e110 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -172,7 +172,7 @@ def calc_obstacle_cost(trajectory, ob, config): rot = np.transpose(rot, [2, 0, 1]) local_ob = ob[:, None] - trajectory[:, 0:2] local_ob = local_ob.reshape(-1, local_ob.shape[-1]) - local_ob = np.array([local_ob @ -x for x in rot]) + local_ob = np.array([local_ob @ x for x in rot]) local_ob = local_ob.reshape(-1, local_ob.shape[-1]) upper_check = local_ob[:, 0] <= config.robot_length / 2 right_check = local_ob[:, 1] <= config.robot_width / 2 From 9f4102378a75b99ec3e8c2f9c465015a5ed2d093 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Wed, 23 Oct 2019 21:46:33 +0300 Subject: [PATCH 042/909] change resampling calculation to match with the book --- Localization/particle_filter/particle_filter.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 6643116f5a..8859b51428 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -141,9 +141,8 @@ def re_sampling(px, pw): N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number if N_eff < NTh: w_cum = np.cumsum(pw) - base = np.cumsum(pw * 0.0 + 1 / NP) - 1 / NP - re_sample_id = base + np.random.rand(base.shape[0]) / NP - + base = np.arange(0.0, 1.0, 1/NP) + re_sample_id = base + np.random.uniform(0, 1/NP) indexes = [] ind = 0 for ip in range(NP): From 7a1784ad4798b4febd82a9a276fdae79a116a57b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Thu, 24 Oct 2019 19:43:22 +0300 Subject: [PATCH 043/909] condition check is moved condition check is moved from the inside of the resampling function to the outside of the function, this way the purpose of the function is much clear. Instead "resampling" every iteration, resample if the condition holds. --- .../particle_filter/particle_filter.py | 31 +++++++++---------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 8859b51428..0ebb5fc3d1 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -128,8 +128,9 @@ def pf_localization(px, pw, z, u): xEst = px.dot(pw.T) PEst = calc_covariance(xEst, px, pw) - px, pw = re_sampling(px, pw) - + N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number + if N_eff < NTh: + px, pw = re_sampling(px, pw) return xEst, PEst, px, pw @@ -138,20 +139,18 @@ def re_sampling(px, pw): low variance re-sampling """ - N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number - if N_eff < NTh: - w_cum = np.cumsum(pw) - base = np.arange(0.0, 1.0, 1/NP) - re_sample_id = base + np.random.uniform(0, 1/NP) - indexes = [] - ind = 0 - for ip in range(NP): - while re_sample_id[ip] > w_cum[ind]: - ind += 1 - indexes.append(ind) - - px = px[:, indexes] - pw = np.zeros((1, NP)) + 1.0 / NP # init weight + w_cum = np.cumsum(pw) + base = np.arange(0.0, 1.0, 1/NP) + re_sample_id = base + np.random.uniform(0, 1/NP) + indexes = [] + ind = 0 + for ip in range(NP): + while re_sample_id[ip] > w_cum[ind]: + ind += 1 + indexes.append(ind) + + px = px[:, indexes] + pw = np.zeros((1, NP)) + 1.0 / NP # init weight return px, pw From bf0652970abe99f42e9e175c55da0d8e5e38f540 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Fri, 25 Oct 2019 15:30:16 +0300 Subject: [PATCH 044/909] optimize nearest_neighbor_assosiation --- .../iterative_closest_point.py | 22 ++++++------------- 1 file changed, 7 insertions(+), 15 deletions(-) diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py index 73651d8ec5..924619fdf1 100644 --- a/SLAM/iterative_closest_point/iterative_closest_point.py +++ b/SLAM/iterative_closest_point/iterative_closest_point.py @@ -39,7 +39,7 @@ def ICP_matching(ppoints, cpoints): plt.plot(cpoints[0, :], cpoints[1, :], ".b") plt.plot(0.0, 0.0, "xr") plt.axis("equal") - plt.pause(1.0) + plt.pause(0.1) inds, error = nearest_neighbor_assosiation(ppoints, cpoints) Rt, Tt = SVD_motion_estimation(ppoints[:, inds], cpoints) @@ -93,18 +93,10 @@ def nearest_neighbor_assosiation(ppoints, cpoints): error = sum(d) # calc index with nearest neighbor assosiation - inds = [] - for i in range(cpoints.shape[1]): - minid = -1 - mind = float("inf") - for ii in range(ppoints.shape[1]): - d = np.linalg.norm(ppoints[:, ii] - cpoints[:, i]) - - if mind >= d: - mind = d - minid = ii - - inds.append(minid) + d = np.linalg.norm( + np.repeat(cpoints, ppoints.shape[1], axis=1) - np.tile(ppoints, (1, + cpoints.shape[1])), axis=0) + inds = np.argmin(d.reshape(cpoints.shape[1], ppoints.shape[1]), axis=1) return inds, error @@ -130,7 +122,7 @@ def main(): print(__file__ + " start!!") # simulation parameters - nPoint = 10 + nPoint = 1000 fieldLength = 50.0 motion = [0.5, 2.0, np.deg2rad(-10.0)] # movement [x[m],y[m],yaw[deg]] @@ -156,4 +148,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main() From b2d65f277ec9d7a122baaec7d2bf4a4b77c5d6a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Sat, 26 Oct 2019 09:11:23 +0300 Subject: [PATCH 045/909] change resampling function to match with the book - changed resampling function so that it chooses random number once for the starting point. - condition check is moved from the inside of the resampling function to the outside of the function, this way the purpose of the function is much clear. Instead "resampling" every iteration, resample if the condition holds. --- .../particle_filter/particle_filter.py | 32 +++++++++---------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 6643116f5a..0ebb5fc3d1 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -128,8 +128,9 @@ def pf_localization(px, pw, z, u): xEst = px.dot(pw.T) PEst = calc_covariance(xEst, px, pw) - px, pw = re_sampling(px, pw) - + N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number + if N_eff < NTh: + px, pw = re_sampling(px, pw) return xEst, PEst, px, pw @@ -138,21 +139,18 @@ def re_sampling(px, pw): low variance re-sampling """ - N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number - if N_eff < NTh: - w_cum = np.cumsum(pw) - base = np.cumsum(pw * 0.0 + 1 / NP) - 1 / NP - re_sample_id = base + np.random.rand(base.shape[0]) / NP - - indexes = [] - ind = 0 - for ip in range(NP): - while re_sample_id[ip] > w_cum[ind]: - ind += 1 - indexes.append(ind) - - px = px[:, indexes] - pw = np.zeros((1, NP)) + 1.0 / NP # init weight + w_cum = np.cumsum(pw) + base = np.arange(0.0, 1.0, 1/NP) + re_sample_id = base + np.random.uniform(0, 1/NP) + indexes = [] + ind = 0 + for ip in range(NP): + while re_sample_id[ip] > w_cum[ind]: + ind += 1 + indexes.append(ind) + + px = px[:, indexes] + pw = np.zeros((1, NP)) + 1.0 / NP # init weight return px, pw From 69772b4ad66cc27f7195659fff14e6c8006203b4 Mon Sep 17 00:00:00 2001 From: Mengge Jin Date: Sun, 27 Oct 2019 16:08:28 +0800 Subject: [PATCH 046/909] Remove PathLen in informed RRT* and instead with cBest --- PathPlanning/InformedRRTStar/informed_rrt_star.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 4a32801da5..6047660fb4 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -40,7 +40,6 @@ def informed_rrt_star_search(self, animation=True): self.node_list = [self.start] # max length we expect to find in our 'informed' sample space, starts as infinite cBest = float('inf') - pathLen = float('inf') solutionSet = set() path = None @@ -89,7 +88,7 @@ def informed_rrt_star_search(self, animation=True): lastIndex = len(self.node_list) - 1 tempPath = self.get_final_course(lastIndex) tempPathLen = self.get_path_len(tempPath) - if tempPathLen < pathLen: + if tempPathLen < cBest: path = tempPath cBest = tempPathLen From 342e671b34f7cced14388b3b33fc4c4dd7dd119f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Fri, 25 Oct 2019 15:30:16 +0300 Subject: [PATCH 047/909] optimize nearest_neighbor_assosiation - vectorize distance calculation - append authors --- .../iterative_closest_point.py | 24 +++++++------------ 1 file changed, 8 insertions(+), 16 deletions(-) diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py index 73651d8ec5..ec81bb1233 100644 --- a/SLAM/iterative_closest_point/iterative_closest_point.py +++ b/SLAM/iterative_closest_point/iterative_closest_point.py @@ -1,6 +1,6 @@ """ Iterative Closest Point (ICP) SLAM example -author: Atsushi Sakai (@Atsushi_twi) +author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı """ import math @@ -39,7 +39,7 @@ def ICP_matching(ppoints, cpoints): plt.plot(cpoints[0, :], cpoints[1, :], ".b") plt.plot(0.0, 0.0, "xr") plt.axis("equal") - plt.pause(1.0) + plt.pause(0.1) inds, error = nearest_neighbor_assosiation(ppoints, cpoints) Rt, Tt = SVD_motion_estimation(ppoints[:, inds], cpoints) @@ -93,18 +93,10 @@ def nearest_neighbor_assosiation(ppoints, cpoints): error = sum(d) # calc index with nearest neighbor assosiation - inds = [] - for i in range(cpoints.shape[1]): - minid = -1 - mind = float("inf") - for ii in range(ppoints.shape[1]): - d = np.linalg.norm(ppoints[:, ii] - cpoints[:, i]) - - if mind >= d: - mind = d - minid = ii - - inds.append(minid) + d = np.linalg.norm( + np.repeat(cpoints, ppoints.shape[1], axis=1) - np.tile(ppoints, (1, + cpoints.shape[1])), axis=0) + inds = np.argmin(d.reshape(cpoints.shape[1], ppoints.shape[1]), axis=1) return inds, error @@ -130,7 +122,7 @@ def main(): print(__file__ + " start!!") # simulation parameters - nPoint = 10 + nPoint = 1000 fieldLength = 50.0 motion = [0.5, 2.0, np.deg2rad(-10.0)] # movement [x[m],y[m],yaw[deg]] @@ -156,4 +148,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main() From 97570de50c222c3362dfda765fd951f37cfb900f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Wed, 23 Oct 2019 10:14:14 +0300 Subject: [PATCH 048/909] Fix wrong rotation direction - The obstacle rotation for the rectangle obstacle calculation is wrong. It should be positive instead of negative. --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index c2a8674ae3..c8a4434958 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -2,7 +2,7 @@ Mobile robot motion planning sample with Dynamic Window Approach -author: Atsushi Sakai (@Atsushi_twi), Goktug Karakasli +author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı """ @@ -172,7 +172,7 @@ def calc_obstacle_cost(trajectory, ob, config): rot = np.transpose(rot, [2, 0, 1]) local_ob = ob[:, None] - trajectory[:, 0:2] local_ob = local_ob.reshape(-1, local_ob.shape[-1]) - local_ob = np.array([local_ob @ -x for x in rot]) + local_ob = np.array([local_ob @ x for x in rot]) local_ob = local_ob.reshape(-1, local_ob.shape[-1]) upper_check = local_ob[:, 0] <= config.robot_length / 2 right_check = local_ob[:, 1] <= config.robot_width / 2 From 49ce57d6f8de1265eb1e578dd0f069c6b7a56ca6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 27 Oct 2019 17:59:08 +0900 Subject: [PATCH 049/909] Code cleanup. --- .../iterative_closest_point.py | 57 +++++++++---------- tests/test_LQR_planner.py | 4 +- 2 files changed, 30 insertions(+), 31 deletions(-) diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py index ec81bb1233..3512ef976b 100644 --- a/SLAM/iterative_closest_point/iterative_closest_point.py +++ b/SLAM/iterative_closest_point/iterative_closest_point.py @@ -4,22 +4,23 @@ """ import math -import numpy as np + import matplotlib.pyplot as plt +import numpy as np # ICP parameters EPS = 0.0001 -MAXITER = 100 +MAX_ITER = 100 show_animation = True -def ICP_matching(ppoints, cpoints): +def icp_matching(previous_points, current_points): """ Iterative Closest Point matching - input - ppoints: 2D points in the previous frame - cpoints: 2D points in the current frame + previous_points: 2D points in the previous frame + current_points: 2D points in the current frame - output R: Rotation matrix T: Translation vector @@ -35,17 +36,17 @@ def ICP_matching(ppoints, cpoints): if show_animation: # pragma: no cover plt.cla() - plt.plot(ppoints[0, :], ppoints[1, :], ".r") - plt.plot(cpoints[0, :], cpoints[1, :], ".b") + plt.plot(previous_points[0, :], previous_points[1, :], ".r") + plt.plot(current_points[0, :], current_points[1, :], ".b") plt.plot(0.0, 0.0, "xr") plt.axis("equal") plt.pause(0.1) - inds, error = nearest_neighbor_assosiation(ppoints, cpoints) - Rt, Tt = SVD_motion_estimation(ppoints[:, inds], cpoints) + indexes, error = nearest_neighbor_association(previous_points, current_points) + Rt, Tt = svd_motion_estimation(previous_points[:, indexes], current_points) # update current points - cpoints = (Rt @ cpoints) + Tt[:, np.newaxis] + current_points = (Rt @ current_points) + Tt[:, np.newaxis] H = update_homogeneous_matrix(H, Rt, Tt) @@ -56,7 +57,7 @@ def ICP_matching(ppoints, cpoints): if dError <= EPS: print("Converge", error, dError, count) break - elif MAXITER <= count: + elif MAX_ITER <= count: print("Not Converge...", error, dError, count) break @@ -85,31 +86,29 @@ def update_homogeneous_matrix(Hin, R, T): return Hin @ H -def nearest_neighbor_assosiation(ppoints, cpoints): +def nearest_neighbor_association(previous_points, current_points): # calc the sum of residual errors - dcpoints = ppoints - cpoints - d = np.linalg.norm(dcpoints, axis=0) + delta_points = previous_points - current_points + d = np.linalg.norm(delta_points, axis=0) error = sum(d) # calc index with nearest neighbor assosiation - d = np.linalg.norm( - np.repeat(cpoints, ppoints.shape[1], axis=1) - np.tile(ppoints, (1, - cpoints.shape[1])), axis=0) - inds = np.argmin(d.reshape(cpoints.shape[1], ppoints.shape[1]), axis=1) - - return inds, error + d = np.linalg.norm(np.repeat(current_points, previous_points.shape[1], axis=1) + - np.tile(previous_points, (1, current_points.shape[1])), axis=0) + indexes = np.argmin(d.reshape(current_points.shape[1], previous_points.shape[1]), axis=1) + return indexes, error -def SVD_motion_estimation(ppoints, cpoints): - pm = np.mean(ppoints, axis=1) - cm = np.mean(cpoints, axis=1) +def svd_motion_estimation(previous_points, current_points): + pm = np.mean(previous_points, axis=1) + cm = np.mean(current_points, axis=1) - pshift = ppoints - pm[:, np.newaxis] - cshift = cpoints - cm[:, np.newaxis] + p_shift = previous_points - pm[:, np.newaxis] + c_shift = current_points - cm[:, np.newaxis] - W = cshift @ pshift.T + W = c_shift @ p_shift.T u, s, vh = np.linalg.svd(W) R = (u @ vh).T @@ -133,16 +132,16 @@ def main(): # previous points px = (np.random.rand(nPoint) - 0.5) * fieldLength py = (np.random.rand(nPoint) - 0.5) * fieldLength - ppoints = np.vstack((px, py)) + previous_points = np.vstack((px, py)) # current points cx = [math.cos(motion[2]) * x - math.sin(motion[2]) * y + motion[0] for (x, y) in zip(px, py)] cy = [math.sin(motion[2]) * x + math.cos(motion[2]) * y + motion[1] for (x, y) in zip(px, py)] - cpoints = np.vstack((cx, cy)) + current_points = np.vstack((cx, cy)) - R, T = ICP_matching(ppoints, cpoints) + R, T = icp_matching(previous_points, current_points) print("R:", R) print("T:", T) diff --git a/tests/test_LQR_planner.py b/tests/test_LQR_planner.py index 5ece27f80b..2bcf828c38 100644 --- a/tests/test_LQR_planner.py +++ b/tests/test_LQR_planner.py @@ -1,6 +1,6 @@ +import sys from unittest import TestCase -import sys sys.path.append("./PathPlanning/LQRPlanner") from PathPlanning.LQRPlanner import LQRplanner as m @@ -11,5 +11,5 @@ class Test(TestCase): def test1(self): - m.show_animation = False + m.SHOW_ANIMATION = False m.main() From 3f14311cc54cb071b530a03b7a0245a61987c300 Mon Sep 17 00:00:00 2001 From: Francisco Moretti Date: Sun, 3 Nov 2019 12:07:56 -0300 Subject: [PATCH 050/909] correct variable name --- PathPlanning/InformedRRTStar/informed_rrt_star.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index b9769bea53..98a239ab6e 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -74,9 +74,9 @@ def informed_rrt_star_search(self, animation=True): newNode = self.get_new_node(theta, nind, nearestNode) d = self.line_cost(nearestNode, newNode) - isCollision = self.check_collision(nearestNode, theta, d) + noCollision = self.check_collision(nearestNode, theta, d) - if isCollision: + if noCollision: nearInds = self.find_near_nodes(newNode) newNode = self.choose_parent(newNode, nearInds) From 178dca3e343749b9e3ec3d1fda7a89f464532cda Mon Sep 17 00:00:00 2001 From: Francisco Moretti Date: Sun, 3 Nov 2019 12:12:53 -0300 Subject: [PATCH 051/909] add collision check to node near to goal --- .../InformedRRTStar/informed_rrt_star.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 98a239ab6e..a8352bc03f 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -84,6 +84,7 @@ def informed_rrt_star_search(self, animation=True): self.rewire(newNode, nearInds) if self.is_near_goal(newNode): + if self.check_segment_collision(newNode.x, newNode.y, self.goal.x , self.goal.y): solutionSet.add(newNode) lastIndex = len(self.node_list) - 1 tempPath = self.get_final_course(lastIndex) @@ -240,19 +241,23 @@ def distance_squared_point_to_segment(v, w, p): projection = v + t * (w - v) # Projection falls on the segment return (p-projection).dot(p-projection) - def check_collision(self, nearNode, theta, d): - tmpNode = copy.deepcopy(nearNode) - endx = tmpNode.x + math.cos(theta)*d - endy = tmpNode.y + math.sin(theta)*d + def check_segment_collision(self, x1, y1, x2, y2): for (ox, oy, size) in self.obstacle_list: dd = self.distance_squared_point_to_segment( - np.array([tmpNode.x, tmpNode.y]), - np.array([endx, endy]), + np.array([x1, y1]), + np.array([x2, y2]), np.array([ox, oy])) - if dd <= 1.1 * size**2: + if dd <= size**2: return False # collision return True + + def check_collision(self, nearNode, theta, d): + tmpNode = copy.deepcopy(nearNode) + endx = tmpNode.x + math.cos(theta)*d + endy = tmpNode.y + math.sin(theta)*d + return self.check_segment_collision(tmpNode.x, tmpNode.y, endx, endy) + def get_final_course(self, lastIndex): path = [[self.goal.x, self.goal.y]] while self.node_list[lastIndex].parent is not None: From 3e5cad819247edd892ae7f8f0192170e8a4c31bd Mon Sep 17 00:00:00 2001 From: Francisco Moretti Date: Sun, 3 Nov 2019 12:23:48 -0300 Subject: [PATCH 052/909] fix indentation --- PathPlanning/InformedRRTStar/informed_rrt_star.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index a8352bc03f..62a9406405 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -85,13 +85,13 @@ def informed_rrt_star_search(self, animation=True): if self.is_near_goal(newNode): if self.check_segment_collision(newNode.x, newNode.y, self.goal.x , self.goal.y): - solutionSet.add(newNode) - lastIndex = len(self.node_list) - 1 - tempPath = self.get_final_course(lastIndex) - tempPathLen = self.get_path_len(tempPath) - if tempPathLen < pathLen: - path = tempPath - cBest = tempPathLen + solutionSet.add(newNode) + lastIndex = len(self.node_list) - 1 + tempPath = self.get_final_course(lastIndex) + tempPathLen = self.get_path_len(tempPath) + if tempPathLen < pathLen: + path = tempPath + cBest = tempPathLen if animation: self.draw_graph(xCenter=xCenter, From 8b31c2bdf0cd1672f2282509e279540f76c2ef3a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 10:25:24 +0900 Subject: [PATCH 053/909] Create greetings.yml --- .github/workflows/greetings.yml | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 .github/workflows/greetings.yml diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml new file mode 100644 index 0000000000..3da4a69458 --- /dev/null +++ b/.github/workflows/greetings.yml @@ -0,0 +1,13 @@ +name: Greetings + +on: [pull_request, issues] + +jobs: + greeting: + runs-on: ubuntu-latest + steps: + - uses: actions/first-interaction@v1 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + issue-message: 'Message that will be displayed on users'' This is first issue for you on this project' + pr-message: 'Message that will be displayed on users'' This is first pr for you on this project' From 25ea5eb6c5a896350e6cd2c3ab2377ea2e9c1daa Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 10:34:19 +0900 Subject: [PATCH 054/909] Create pythonpackage.yml --- .github/workflows/pythonpackage.yml | 33 +++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 .github/workflows/pythonpackage.yml diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml new file mode 100644 index 0000000000..901eb4c696 --- /dev/null +++ b/.github/workflows/pythonpackage.yml @@ -0,0 +1,33 @@ +name: Python package + +on: [push] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + max-parallel: 4 + matrix: + python-version: [3.5, 3.6, 3.7] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + - name: Lint with flake8 + run: | + pip install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + runtests.sh From 8877bc1a42d7853d949f00794385a16b488b3292 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 10:41:28 +0900 Subject: [PATCH 055/909] Update pythonpackage.yml --- .github/workflows/pythonpackage.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 901eb4c696..c49c61de54 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -28,6 +28,6 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - runtests.sh + - name: Test + run: runtests.sh + shell: bash From 4d9ff51f27a11dbf615b5755781a3e1e961bb5a8 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 12:00:00 +0900 Subject: [PATCH 056/909] Update pythonpackage.yml --- .github/workflows/pythonpackage.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index c49c61de54..b9f0adb822 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -28,6 +28,9 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test1 + run: ls + shell: bash - name: Test run: runtests.sh shell: bash From ecdff00fa2ee0d870b0c4b01fd2c92c52389ce21 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 13:15:57 +0900 Subject: [PATCH 057/909] Update pythonpackage.yml --- .github/workflows/pythonpackage.yml | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index b9f0adb822..4d6e3d3b03 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -28,9 +28,5 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test1 - run: ls - shell: bash - name: Test - run: runtests.sh - shell: bash + run: bash runtests.sh From 27b6e19ea6f9d4b4e08b23b5db305e79a624e6bf Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 13:21:14 +0900 Subject: [PATCH 058/909] Update pythonpackage.yml --- .github/workflows/pythonpackage.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 4d6e3d3b03..fbac68ac3b 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -28,5 +28,7 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: install coverage + run: pip insall coverage - name: Test run: bash runtests.sh From 157664857b9eb12181e1405c28b18928d7120a59 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 13:23:30 +0900 Subject: [PATCH 059/909] Update pythonpackage.yml --- .github/workflows/pythonpackage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index fbac68ac3b..fc70845789 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -29,6 +29,6 @@ jobs: # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: install coverage - run: pip insall coverage + run: pip install coverage - name: Test run: bash runtests.sh From 3333b34ce215b0287073fd250aa0c2813ed36e48 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 13:33:50 +0900 Subject: [PATCH 060/909] Update pythonpackage.yml --- .github/workflows/pythonpackage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index fc70845789..a3203ff1ac 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -9,7 +9,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: [3.5, 3.6, 3.7] + python-version: [3.6, 3.7] steps: - uses: actions/checkout@v1 From 0067577cbe500054bafc5ce6dd00a3effc68f88e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 17 Nov 2019 14:33:24 +0900 Subject: [PATCH 061/909] remove PoseOptimizationSLAM --- README.md | 17 ----------- SLAM/PoseOptimizationSLAM2D/README.md | 9 ------ .../PoseOptimizationSLAM2D/data_downloader.py | 28 ------------------- SLAM/PoseOptimizationSLAM3D/README.md | 25 ----------------- .../PoseOptimizationSLAM3D/data_downloader.py | 26 ----------------- 5 files changed, 105 deletions(-) delete mode 100644 SLAM/PoseOptimizationSLAM2D/README.md delete mode 100644 SLAM/PoseOptimizationSLAM2D/data_downloader.py delete mode 100644 SLAM/PoseOptimizationSLAM3D/README.md delete mode 100644 SLAM/PoseOptimizationSLAM3D/data_downloader.py diff --git a/README.md b/README.md index 1c455e928b..e740049b90 100644 --- a/README.md +++ b/README.md @@ -31,8 +31,6 @@ Python codes for robotics algorithm. * [SLAM](#slam) * [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching) * [FastSLAM 1.0](#fastslam-10) - * [Pose Optimization SLAM 2D](#pose-optimization-slam-2d) - * [Pose Optimization SLAM 3D](#pose-optimization-slam-3d) * [Path Planning](#path-planning) * [Dynamic Window Approach](#dynamic-window-approach) * [Grid based search](#grid-based-search) @@ -251,21 +249,6 @@ Ref: - [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm) -## Pose Optimization SLAM 2D - -This is a graph based 2D pose optimization SLAM example. - -![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM2D/animation.gif) - - -## Pose Optimization SLAM 3D - -This is a graph based 3D pose optimization SLAM example. - -![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM3D/animation.gif) - - - # Path Planning ## Dynamic Window Approach diff --git a/SLAM/PoseOptimizationSLAM2D/README.md b/SLAM/PoseOptimizationSLAM2D/README.md deleted file mode 100644 index 612fa5186d..0000000000 --- a/SLAM/PoseOptimizationSLAM2D/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# How to use - -1. Download data - ->$ python data_downloader.py - -2. run SLAM - ->$ python pose_optimization_slam.py diff --git a/SLAM/PoseOptimizationSLAM2D/data_downloader.py b/SLAM/PoseOptimizationSLAM2D/data_downloader.py deleted file mode 100644 index d1b8a2c23b..0000000000 --- a/SLAM/PoseOptimizationSLAM2D/data_downloader.py +++ /dev/null @@ -1,28 +0,0 @@ -""" - -Data down loader for pose optimization - -author: Atsushi Sakai - -""" - - -import subprocess -def main(): - print("start!!") - - cmd = "wget https://www.dropbox.com/s/vcz8cag7bo0zlaj/input_INTEL_g2o.g2o?dl=0 -O intel.g2o -nc" - subprocess.call(cmd, shell=True) - - cmd = "wget https://www.dropbox.com/s/d8fcn1jg1mebx8f/input_MITb_g2o.g2o?dl=0 -O mit_killian.g2o -nc" - - subprocess.call(cmd, shell=True) - cmd = "wget https://www.dropbox.com/s/gmdzo74b3tzvbrw/input_M3500_g2o.g2o?dl=0 -O manhattan3500.g2o -nc" - subprocess.call(cmd, shell=True) - - print("done!!") - - -if __name__ == '__main__': - main() - diff --git a/SLAM/PoseOptimizationSLAM3D/README.md b/SLAM/PoseOptimizationSLAM3D/README.md deleted file mode 100644 index 396a629735..0000000000 --- a/SLAM/PoseOptimizationSLAM3D/README.md +++ /dev/null @@ -1,25 +0,0 @@ -# PoseOptimizationSLAM3D -3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM - -## How to use -1. Download data - -~~~ -python data_downloader.py -~~~ - -2. run SLAM - -~~~ -python pose_optimization_slam_3d.py -~~~ - -## Reference -- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM) - -- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o) - -- [GitHub \- AtsushiSakai/PythonRobotics -/SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/tree/master/SLAM/PoseOptimizationSLAM) - -- [リー代数による3次元ポーズ調整\(Pose Adjustment\)\[PythonRobotics\]\[SLAM\] \- Qiita](https://qiita.com/saitosasaki/items/a0540cba994f08bf5e16#comment-3e6588e6b096cc2567d8) diff --git a/SLAM/PoseOptimizationSLAM3D/data_downloader.py b/SLAM/PoseOptimizationSLAM3D/data_downloader.py deleted file mode 100644 index a0c04fd1ab..0000000000 --- a/SLAM/PoseOptimizationSLAM3D/data_downloader.py +++ /dev/null @@ -1,26 +0,0 @@ -""" - -Data down loader for pose optimization - -author: Atsushi Sakai - -""" - -import subprocess - - -def main(): - print("start!!") - - cmd = "wget https://www.dropbox.com/s/zu23p8d522qccor/parking-garage.g2o?dl=0 -O parking-garage.g2o -nc" - subprocess.call(cmd, shell=True) - cmd = "wget http://www.furo.org/irie/datasets/torus3d_guess.g2o -nc" - subprocess.call(cmd, shell=True) - cmd = "wget http://www.furo.org/irie/datasets/sphere2200_guess.g2o -nc" - subprocess.call(cmd, shell=True) - - print("done!!") - - -if __name__ == '__main__': - main() From 54991efb8f335faf42fa665dce9dfecdc2833810 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 25 Nov 2019 21:54:32 +0900 Subject: [PATCH 062/909] Update FUNDING.yml --- .github/FUNDING.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 80f4984c3f..24f0926299 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1,3 +1,4 @@ # These are supported funding model platforms +github: AtsushiSakai patreon: myenigma custom: https://www.paypal.me/myenigmapay/ From 2fe00f8824842452345cbe180b4303e240c18a6c Mon Sep 17 00:00:00 2001 From: Michael Dobler Date: Thu, 28 Nov 2019 23:35:55 +0100 Subject: [PATCH 063/909] Fix jacobian of observation --- SLAM/EKFSLAM/ekf_slam.ipynb | 2 +- SLAM/EKFSLAM/ekf_slam.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb index 6509373d2b..f800939576 100644 --- a/SLAM/EKFSLAM/ekf_slam.ipynb +++ b/SLAM/EKFSLAM/ekf_slam.ipynb @@ -479,7 +479,7 @@ " \"\"\"\n", " sq = math.sqrt(q)\n", " G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],\n", - " [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])\n", + " [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]])\n", "\n", " G = G / q\n", " nLM = calc_n_LM(x)\n", diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index ae39c9e0ea..84cf4d9701 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -177,7 +177,7 @@ def calc_innovation(lm, xEst, PEst, z, LMid): def jacob_h(q, delta, x, i): sq = math.sqrt(q) G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]], - [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]]) + [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]]) G = G / q nLM = calc_n_lm(x) From 0b07425d2e8d97f61fb456d0969e1a64eeee33ba Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 30 Nov 2019 20:05:52 +0900 Subject: [PATCH 064/909] Code clen up --- SLAM/EKFSLAM/ekf_slam.py | 33 ++++++++++++++++----------------- 1 file changed, 16 insertions(+), 17 deletions(-) diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index 84cf4d9701..27e5035459 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -1,5 +1,6 @@ """ Extended Kalman Filter SLAM example + author: Atsushi Sakai (@Atsushi_twi) """ @@ -35,10 +36,10 @@ def ekf_slam(xEst, PEst, u, z): # Update for iz in range(len(z[:, 0])): # for each observation - minid = search_correspond_landmark_id(xEst, PEst, z[iz, 0:2]) + min_id = search_correspond_landmark_id(xEst, PEst, z[iz, 0:2]) nLM = calc_n_lm(xEst) - if minid == nLM: + if min_id == nLM: print("New LM") # Extend state and covariance matrix xAug = np.vstack((xEst, calc_landmark_position(xEst, z[iz, :]))) @@ -46,8 +47,8 @@ def ekf_slam(xEst, PEst, u, z): np.hstack((np.zeros((LM_SIZE, len(xEst))), initP)))) xEst = xAug PEst = PAug - lm = get_landmark_position_from_state(xEst, minid) - y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid) + lm = get_landmark_position_from_state(xEst, min_id) + y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], min_id) K = (PEst @ H.T) @ np.linalg.inv(S) xEst = xEst + (K @ y) @@ -60,8 +61,8 @@ def ekf_slam(xEst, PEst, u, z): def calc_input(): v = 1.0 # [m/s] - yawrate = 0.1 # [rad/s] - u = np.array([[v, yawrate]]).T + yaw_rate = 0.1 # [rad/s] + u = np.array([[v, yaw_rate]]).T return u @@ -79,8 +80,8 @@ def observation(xTrue, xd, u, RFID): angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise - anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise - zi = np.array([dn, anglen, i]) + angle_n = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise + zi = np.array([dn, angle_n, i]) z = np.vstack((z, zi)) # add noise to input @@ -128,8 +129,6 @@ def calc_landmark_position(x, z): zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1]) zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1]) - # zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1]) - # zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1]) return zp @@ -147,25 +146,25 @@ def search_correspond_landmark_id(xAug, PAug, zi): nLM = calc_n_lm(xAug) - mdist = [] + min_dist = [] for i in range(nLM): lm = get_landmark_position_from_state(xAug, i) y, S, H = calc_innovation(lm, xAug, PAug, zi, i) - mdist.append(y.T @ np.linalg.inv(S) @ y) + min_dist.append(y.T @ np.linalg.inv(S) @ y) - mdist.append(M_DIST_TH) # new landmark + min_dist.append(M_DIST_TH) # new landmark - minid = mdist.index(min(mdist)) + min_id = min_dist.index(min(min_dist)) - return minid + return min_id def calc_innovation(lm, xEst, PEst, z, LMid): delta = lm - xEst[0:2] q = (delta.T @ delta)[0, 0] - zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0] - zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]]) + z_angle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0] + zp = np.array([[math.sqrt(q), pi_2_pi(z_angle)]]) y = (z - zp).T y[1] = pi_2_pi(y[1]) H = jacob_h(q, delta, xEst, LMid + 1) From f26a91d5342c974b78e3cc3c820518c3400a08f7 Mon Sep 17 00:00:00 2001 From: harderthan Date: Tue, 3 Dec 2019 12:46:58 +0900 Subject: [PATCH 065/909] Update Kalmanfilter_basics.rst Edited the misspelling --- docs/modules/Kalmanfilter_basics.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/modules/Kalmanfilter_basics.rst b/docs/modules/Kalmanfilter_basics.rst index ce84fe0db4..7325dafdea 100644 --- a/docs/modules/Kalmanfilter_basics.rst +++ b/docs/modules/Kalmanfilter_basics.rst @@ -180,7 +180,7 @@ Central Limit Theorem ^^^^^^^^^^^^^^^^^^^^^ According to this theorem, the average of n samples of random and -independant variables tends to follow a normal distribution as we +independent variables tends to follow a normal distribution as we increase the sample size.(Generally, for n>=30) .. code-block:: ipython3 From 338b7945b6e12511dc1ae19166c1f32c3e9d313e Mon Sep 17 00:00:00 2001 From: harderthan Date: Tue, 3 Dec 2019 12:49:52 +0900 Subject: [PATCH 066/909] Update Kalmanfilter_basics Edited misspelling --- Localization/Kalmanfilter_basics.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb index 374287911b..b4913f9e7a 100644 --- a/Localization/Kalmanfilter_basics.ipynb +++ b/Localization/Kalmanfilter_basics.ipynb @@ -228,7 +228,7 @@ "\n", "#### Central Limit Theorem\n", "\n", - "According to this theorem, the average of n samples of random and independant variables tends to follow a normal distribution as we increase the sample size.(Generally, for n>=30)" + "According to this theorem, the average of n samples of random and independent variables tends to follow a normal distribution as we increase the sample size.(Generally, for n>=30)" ] }, { From 99c784948d9cfc8145ae208d5301c6bd1144f401 Mon Sep 17 00:00:00 2001 From: Michael Dobler Date: Fri, 6 Dec 2019 11:35:29 +0100 Subject: [PATCH 067/909] Use matrix multiplication instead of elementwise --- SLAM/EKFSLAM/ekf_slam.ipynb | 4 ++-- SLAM/EKFSLAM/ekf_slam.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb index f800939576..ccf5e5bbd6 100644 --- a/SLAM/EKFSLAM/ekf_slam.ipynb +++ b/SLAM/EKFSLAM/ekf_slam.ipynb @@ -309,7 +309,7 @@ " G, Fx = jacob_motion(xEst[0:S], u)\n", " # Fx is an an identity matrix of size (STATE_SIZE)\n", " # sigma = G*sigma*G.T + Noise\n", - " PEst[0:S, 0:S] = G.T * PEst[0:S, 0:S] * G + Fx.T * Cx * Fx\n", + " PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n", " return xEst, PEst, G, Fx" ] }, @@ -584,7 +584,7 @@ " [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],\n", " [0.0, 0.0, 0.0]])\n", "\n", - " G = np.eye(STATE_SIZE) + Fx.T * jF * Fx\n", + " G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx\n", " if calc_n_LM(x) > 0:\n", " print(Fx.shape)\n", " return G, Fx,\n", diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index 27e5035459..22ccd8d659 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -31,7 +31,7 @@ def ekf_slam(xEst, PEst, u, z): S = STATE_SIZE xEst[0:S] = motion_model(xEst[0:S], u) G, Fx = jacob_motion(xEst[0:S], u) - PEst[0:S, 0:S] = G.T * PEst[0:S, 0:S] * G + Fx.T * Cx * Fx + PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx initP = np.eye(2) # Update @@ -119,7 +119,7 @@ def jacob_motion(x, u): [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])], [0.0, 0.0, 0.0]]) - G = np.eye(STATE_SIZE) + Fx.T * jF * Fx + G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx return G, Fx, From dacff1c97c21b915199493492e303a6e11031faa Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 7 Dec 2019 20:23:36 +0900 Subject: [PATCH 068/909] Fixed kmeans clustering bug #251 --- .../kmeans_clustering/kmeans_clustering.py | 174 +++++++----------- 1 file changed, 71 insertions(+), 103 deletions(-) diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py index 8edfa11e3a..bdaa45455e 100644 --- a/Mapping/kmeans_clustering/kmeans_clustering.py +++ b/Mapping/kmeans_clustering/kmeans_clustering.py @@ -6,97 +6,87 @@ """ -import numpy as np import math import matplotlib.pyplot as plt import random +# k means parameters +MAX_LOOP = 10 +DCOST_TH = 0.1 show_animation = True -class Clusters: - - def __init__(self, x, y, nlabel): - self.x = x - self.y = y - self.ndata = len(self.x) - self.nlabel = nlabel - self.labels = [random.randint(0, nlabel - 1) - for _ in range(self.ndata)] - self.cx = [0.0 for _ in range(nlabel)] - self.cy = [0.0 for _ in range(nlabel)] - - def kmeans_clustering(rx, ry, nc): - clusters = Clusters(rx, ry, nc) - clusters = calc_centroid(clusters) + clusters.calc_centroid() - MAX_LOOP = 10 - DCOST_TH = 0.1 - pcost = 100.0 + pre_cost = float("inf") for loop in range(MAX_LOOP): - # print("Loop:", loop) - clusters, cost = update_clusters(clusters) - clusters = calc_centroid(clusters) + print("loop:", loop) + cost = clusters.update_clusters() + clusters.calc_centroid() - dcost = abs(cost - pcost) - if dcost < DCOST_TH: + d_cost = abs(cost - pre_cost) + if d_cost < DCOST_TH: break - pcost = cost + pre_cost = cost return clusters -def calc_centroid(clusters): - - for ic in range(clusters.nlabel): - x, y = calc_labeled_points(ic, clusters) - ndata = len(x) - clusters.cx[ic] = sum(x) / ndata - clusters.cy[ic] = sum(y) / ndata - - return clusters - - -def update_clusters(clusters): - cost = 0.0 - - for ip in range(clusters.ndata): - px = clusters.x[ip] - py = clusters.y[ip] - - dx = [icx - px for icx in clusters.cx] - dy = [icy - py for icy in clusters.cy] - - dlist = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)] - mind = min(dlist) - min_id = dlist.index(mind) - clusters.labels[ip] = min_id - cost += mind - - return clusters, cost - - -def calc_labeled_points(ic, clusters): - - inds = np.array([i for i in range(clusters.ndata) - if clusters.labels[i] == ic]) - tx = np.array(clusters.x) - ty = np.array(clusters.y) - - x = tx[inds] - y = ty[inds] - - return x, y - - -def calc_raw_data(cx, cy, npoints, rand_d): +class Clusters: + def __init__(self, x, y, n_label): + self.x = x + self.y = y + self.n_data = len(self.x) + self.n_label = n_label + self.labels = [random.randint(0, n_label - 1) + for _ in range(self.n_data)] + self.center_x = [0.0 for _ in range(n_label)] + self.center_y = [0.0 for _ in range(n_label)] + + def plot_cluster(self): + for label in set(self.labels): + x, y = self._get_labeled_x_y(label) + plt.plot(x, y, ".") + + def calc_centroid(self): + for label in set(self.labels): + x, y = self._get_labeled_x_y(label) + n_data = len(x) + self.center_x[label] = sum(x) / n_data + self.center_y[label] = sum(y) / n_data + + def update_clusters(self): + cost = 0.0 + + for ip in range(self.n_data): + px = self.x[ip] + py = self.y[ip] + + dx = [icx - px for icx in self.center_x] + dy = [icy - py for icy in self.center_y] + + dist_list = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)] + min_dist = min(dist_list) + min_id = dist_list.index(min_dist) + self.labels[ip] = min_id + cost += min_dist + + return cost + + def _get_labeled_x_y(self, target_label): + x = [self.x[i] for i, label in enumerate(self.labels) if label == target_label] + y = [self.y[i] for i, label in enumerate(self.labels) if label == target_label] + return x, y + + +def calc_raw_data(cx, cy, n_points, rand_d): rx, ry = [], [] for (icx, icy) in zip(cx, cy): - for _ in range(npoints): + for _ in range(n_points): rx.append(icx + rand_d * (random.random() - 0.5)) ry.append(icy + rand_d * (random.random() - 0.5)) @@ -104,48 +94,28 @@ def calc_raw_data(cx, cy, npoints, rand_d): def update_positions(cx, cy): - + # object moving parameters DX1 = 0.4 DY1 = 0.5 - - cx[0] += DX1 - cy[0] += DY1 - DX2 = -0.3 DY2 = -0.5 + cx[0] += DX1 + cy[0] += DY1 cx[1] += DX2 cy[1] += DY2 return cx, cy -def calc_association(cx, cy, clusters): - - inds = [] - - for ic, _ in enumerate(cx): - tcx = cx[ic] - tcy = cy[ic] - - dx = [icx - tcx for icx in clusters.cx] - dy = [icy - tcy for icy in clusters.cy] - - dlist = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)] - min_id = dlist.index(min(dlist)) - inds.append(min_id) - - return inds - - def main(): print(__file__ + " start!!") cx = [0.0, 8.0] cy = [0.0, 8.0] - npoints = 10 + n_points = 10 rand_d = 3.0 - ncluster = 2 + n_cluster = 2 sim_time = 15.0 dt = 1.0 time = 0.0 @@ -154,20 +124,18 @@ def main(): print("Time:", time) time += dt - # simulate objects + # objects moving simulation cx, cy = update_positions(cx, cy) - rx, ry = calc_raw_data(cx, cy, npoints, rand_d) + raw_x, raw_y = calc_raw_data(cx, cy, n_points, rand_d) - clusters = kmeans_clustering(rx, ry, ncluster) + clusters = kmeans_clustering(raw_x, raw_y, n_cluster) # for animation if show_animation: # pragma: no cover plt.cla() - inds = calc_association(cx, cy, clusters) - for ic in inds: - x, y = calc_labeled_points(ic, clusters) - plt.plot(x, y, "x") - plt.plot(cx, cy, "o") + clusters.plot_cluster() + + plt.plot(cx, cy, "or") plt.xlim(-2.0, 10.0) plt.ylim(-2.0, 10.0) plt.pause(dt) From d019e416ba051a08db153c3177ef97a715031ed8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Sat, 7 Dec 2019 14:30:18 +0300 Subject: [PATCH 069/909] exit on key --- AerialNavigation/drone_3d_trajectory_following/Quadrotor.py | 6 ++++-- .../rocket_powered_landing/rocket_powered_landing.py | 2 ++ .../arm_obstacle_navigation/arm_obstacle_navigation.py | 4 +++- .../arm_obstacle_navigation/arm_obstacle_navigation_2.py | 2 ++ ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py | 2 ++ .../two_joint_arm_to_point_control.py | 2 ++ Bipedal/bipedal_planner/bipedal_planner.py | 2 ++ .../ensemble_kalman_filter/ensemble_kalman_filter.py | 2 ++ .../extended_kalman_filter/extended_kalman_filter.py | 2 ++ Localization/histogram_filter/histogram_filter.py | 2 ++ Localization/particle_filter/particle_filter.py | 2 ++ .../unscented_kalman_filter/unscented_kalman_filter.py | 2 ++ Mapping/circle_fitting/circle_fitting.py | 2 ++ Mapping/gaussian_grid_map/gaussian_grid_map.py | 2 ++ Mapping/kmeans_clustering/kmeans_clustering.py | 2 ++ Mapping/raycasting_grid_map/raycasting_grid_map.py | 2 ++ Mapping/rectangle_fitting/rectangle_fitting.py | 2 ++ PathPlanning/AStar/a_star.py | 2 ++ PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py | 2 ++ PathPlanning/ClosedLoopRRTStar/pure_pursuit.py | 2 ++ PathPlanning/Dijkstra/dijkstra.py | 2 ++ PathPlanning/DubinsPath/dubins_path_planning.py | 2 ++ .../DynamicWindowApproach/dynamic_window_approach.py | 2 ++ PathPlanning/Eta3SplinePath/eta3_spline_path.py | 2 ++ .../FrenetOptimalTrajectory/frenet_optimal_trajectory.py | 2 ++ .../grid_based_sweep_coverage_path_planner.py | 4 ++++ PathPlanning/HybridAStar/a_star.py | 4 +++- PathPlanning/HybridAStar/hybrid_a_star.py | 2 ++ PathPlanning/InformedRRTStar/informed_rrt_star.py | 3 ++- PathPlanning/LQRPlanner/LQRplanner.py | 2 ++ PathPlanning/LQRRRTStar/lqr_rrt_star.py | 2 ++ .../PotentialFieldPlanning/potential_field_planning.py | 2 ++ PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py | 2 ++ .../quintic_polynomials_planner.py | 2 ++ PathPlanning/RRT/rrt.py | 2 ++ PathPlanning/RRTDubins/rrt_dubins.py | 2 ++ PathPlanning/RRTStarDubins/rrt_star_dubins.py | 2 ++ PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py | 2 ++ PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py | 2 ++ PathPlanning/VoronoiRoadMap/voronoi_road_map.py | 2 ++ PathTracking/cgmres_nmpc/cgmres_nmpc.py | 2 ++ .../lqr_speed_steer_control/lqr_speed_steer_control.py | 2 ++ PathTracking/lqr_steer_control/lqr_steer_control.py | 2 ++ .../model_predictive_speed_and_steer_control.py | 2 ++ PathTracking/move_to_pose/move_to_pose.py | 3 +++ PathTracking/pure_pursuit/pure_pursuit.py | 2 ++ PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 2 ++ PathTracking/stanley_controller/stanley_controller.py | 2 ++ SLAM/EKFSLAM/ekf_slam.py | 2 ++ SLAM/FastSLAM1/fast_slam1.py | 2 ++ SLAM/FastSLAM2/fast_slam2.py | 2 ++ SLAM/GraphBasedSLAM/graph_based_slam.py | 5 +++-- SLAM/iterative_closest_point/iterative_closest_point.py | 2 ++ 53 files changed, 114 insertions(+), 7 deletions(-) diff --git a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py index 9b38677cbe..89171da212 100644 --- a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py +++ b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py @@ -8,7 +8,6 @@ import numpy as np import matplotlib.pyplot as plt - class Quadrotor(): def __init__(self, x=0, y=0, z=0, roll=0, pitch=0, yaw=0, size=0.25, show_animation=True): self.p1 = np.array([size / 2, 0, 0, 1]).T @@ -24,6 +23,9 @@ def __init__(self, x=0, y=0, z=0, roll=0, pitch=0, yaw=0, size=0.25, show_animat if self.show_animation: plt.ion() fig = plt.figure() + fig.canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + self.ax = fig.add_subplot(111, projection='3d') self.update_pose(x, y, z, roll, pitch, yaw) @@ -81,4 +83,4 @@ def plot(self): # pragma: no cover plt.ylim(-5, 5) self.ax.set_zlim(0, 10) - plt.pause(0.001) \ No newline at end of file + plt.pause(0.001) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 9f1e16fc1d..c7ab998bec 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -567,6 +567,8 @@ def plot_animation(X, U): # pragma: no cover fig = plt.figure() ax = fig.gca(projection='3d') + rig.canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for k in range(K): plt.cla() diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py index 2bcc33d0f7..b435e62d2b 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py @@ -162,6 +162,8 @@ def astar_torus(grid, start_node, goal_node): for i in range(1, len(route)): grid[route[i]] = 6 plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None) plt.show() plt.pause(1e-2) @@ -262,4 +264,4 @@ def plot(self, obstacles=[]): # pragma: no cover if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py index b8c1ce89e9..5a21b806d3 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py @@ -193,6 +193,8 @@ def astar_torus(grid, start_node, goal_node): for i in range(1, len(route)): grid[route[i]] = 6 plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None) plt.show() plt.pause(1e-2) diff --git a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py index 8ed12c565b..576bd29a8f 100644 --- a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py @@ -51,6 +51,8 @@ def update_points(self): def plot(self): # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for i in range(self.n_links + 1): if i is not self.n_links: diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index e674ae8ac2..134583a95c 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -114,6 +114,8 @@ def animation(): def main(): # pragma: no cover fig = plt.figure() fig.canvas.mpl_connect("button_press_event", click) + fig.canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) two_joint_arm() diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py index 3cf2936ee4..afa68afb25 100644 --- a/Bipedal/bipedal_planner/bipedal_planner.py +++ b/Bipedal/bipedal_planner/bipedal_planner.py @@ -111,6 +111,8 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): if c > len(com_trajectory_for_plot): # set up plotter plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) ax.set_zlim(0, z_c * 2) ax.set_aspect('equal', 'datalim') diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index 293509157b..6ac6144f36 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -213,6 +213,8 @@ def main(): if show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for i in range(len(z[:, 0])): plt.plot([xTrue[0, 0], z[i, 2]], [xTrue[1, 0], z[i, 3]], "-k") diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py index d2d7178ae6..029dac4bf8 100644 --- a/Localization/extended_kalman_filter/extended_kalman_filter.py +++ b/Localization/extended_kalman_filter/extended_kalman_filter.py @@ -191,6 +191,8 @@ def main(): if show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(hz[0, :], hz[1, :], ".g") plt.plot(hxTrue[0, :].flatten(), hxTrue[1, :].flatten(), "-b") diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py index bdfbb13fa7..ac07c59fbd 100644 --- a/Localization/histogram_filter/histogram_filter.py +++ b/Localization/histogram_filter/histogram_filter.py @@ -233,6 +233,8 @@ def main(): if show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) draw_heat_map(grid_map.data, mx, my) plt.plot(xTrue[0, :], xTrue[1, :], "xr") plt.plot(RF_ID[:, 0], RF_ID[:, 1], ".k") diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 0ebb5fc3d1..4b9abd78bd 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -230,6 +230,8 @@ def main(): if show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for i in range(len(z[:, 0])): plt.plot([xTrue[0, 0], z[i, 1]], [xTrue[1, 0], z[i, 2]], "-k") diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py index 45d5b78132..0005d12fe7 100644 --- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py +++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py @@ -240,6 +240,8 @@ def main(): if show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(hz[0, :], hz[1, :], ".g") plt.plot(np.array(hxTrue[0, :]).flatten(), np.array(hxTrue[1, :]).flatten(), "-b") diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py index dae9413d27..27ceac21f7 100644 --- a/Mapping/circle_fitting/circle_fitting.py +++ b/Mapping/circle_fitting/circle_fitting.py @@ -124,6 +124,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.axis("equal") plt.plot(0.0, 0.0, "*r") plot_circle(cx, cy, cr) diff --git a/Mapping/gaussian_grid_map/gaussian_grid_map.py b/Mapping/gaussian_grid_map/gaussian_grid_map.py index b520b63944..094b094fdd 100644 --- a/Mapping/gaussian_grid_map/gaussian_grid_map.py +++ b/Mapping/gaussian_grid_map/gaussian_grid_map.py @@ -73,6 +73,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) draw_heatmap(gmap, minx, maxx, miny, maxy, xyreso) plt.plot(ox, oy, "xr") plt.plot(0.0, 0.0, "ob") diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py index 8edfa11e3a..7900e30457 100644 --- a/Mapping/kmeans_clustering/kmeans_clustering.py +++ b/Mapping/kmeans_clustering/kmeans_clustering.py @@ -163,6 +163,8 @@ def main(): # for animation if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) inds = calc_association(cx, cy, clusters) for ic in inds: x, y = calc_labeled_points(ic, clusters) diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/raycasting_grid_map/raycasting_grid_map.py index 997687423e..cae92a9dcb 100644 --- a/Mapping/raycasting_grid_map/raycasting_grid_map.py +++ b/Mapping/raycasting_grid_map/raycasting_grid_map.py @@ -124,6 +124,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) draw_heatmap(pmap, minx, maxx, miny, maxy, xyreso) plt.plot(ox, oy, "xr") plt.plot(0.0, 0.0, "ob") diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index e40fc2e4c7..77c66db250 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -242,6 +242,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.axis("equal") plt.plot(0.0, 0.0, "*r") v1.plot() diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index 6b0392f515..e4f7dd4d97 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -79,6 +79,8 @@ def planning(self, sx, sy, gx, gy): if show_animation: # pragma: no cover plt.plot(self.calc_grid_position(current.x, self.minx), self.calc_grid_position(current.y, self.miny), "xc") + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if len(closed_set.keys()) % 10 == 0: plt.pause(0.001) diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py index 37ac75ada9..46ff3caf5c 100644 --- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py +++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py @@ -534,6 +534,8 @@ def update_graph(self): def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, samples=None, start=None, end=None): plt.clf() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for rnd in samples: if rnd is not None: plt.plot(rnd[0], rnd[1], "^k") diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py index 0490727933..d078338d67 100644 --- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py +++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py @@ -135,6 +135,8 @@ def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal): if target_ind % 1 == 0 and animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") plt.plot(x, y, "ob", label="trajectory") plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index bb73ed501a..8e719f50e2 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -69,6 +69,8 @@ def planning(self, sx, sy, gx, gy): if show_animation: # pragma: no cover plt.plot(self.calc_position(current.x, self.minx), self.calc_position(current.y, self.miny), "xc") + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if len(closedset.keys()) % 10 == 0: plt.pause(0.001) diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py index 706b5a49af..8690fbe8db 100644 --- a/PathPlanning/DubinsPath/dubins_path_planning.py +++ b/PathPlanning/DubinsPath/dubins_path_planning.py @@ -318,6 +318,8 @@ def test(): if show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(px, py, label="final course " + str(mode)) # plotting diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index c8a4434958..c2cc358b13 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -268,6 +268,8 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): if show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g") plt.plot(x[0], x[1], "xr") plt.plot(goal[0], goal[1], "xb") diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py index 072b0bb46c..51ac612c28 100644 --- a/PathPlanning/Eta3SplinePath/eta3_spline_path.py +++ b/PathPlanning/Eta3SplinePath/eta3_spline_path.py @@ -213,6 +213,8 @@ def test1(): if show_animation: # plot the path plt.plot(pos[0, :], pos[1, :]) + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.pause(1.0) if show_animation: diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index 5c756a9dfe..1abda92646 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -347,6 +347,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(tx, ty) plt.plot(ob[:, 0], ob[:, 1], "xk") plt.plot(path.x[1:], path.y[1:], "-or") diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index f22b058fa8..049373ece2 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -213,6 +213,8 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False): if grid_search_animation: fig, ax = plt.subplots() + fig.canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) while True: cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap) @@ -266,6 +268,8 @@ def planning_animation(ox, oy, reso): # pragma: no cover if do_animation: for ipx, ipy in zip(px, py): plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(ox, oy, "-xb") plt.plot(px, py, "-r") plt.plot(ipx, ipy, "or") diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py index c1c82edd0d..918469f0d3 100644 --- a/PathPlanning/HybridAStar/a_star.py +++ b/PathPlanning/HybridAStar/a_star.py @@ -78,6 +78,8 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr): # show graph if show_animation: # pragma: no cover plt.plot(current.x * reso, current.y * reso, "xc") + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if len(closedset.keys()) % 10 == 0: plt.pause(0.001) @@ -228,4 +230,4 @@ def main(): if __name__ == '__main__': show_animation = True - main() \ No newline at end of file + main() diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index e82003ed25..70ffcee865 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -327,6 +327,8 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso): if show_animation: # pragma: no cover plt.plot(current.xlist[-1], current.ylist[-1], "xc") + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if len(closedList.keys()) % 10 == 0: plt.pause(0.001) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 175749183d..ef15022aca 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -266,8 +266,9 @@ def get_final_course(self, lastIndex): return path def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None): - plt.clf() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd[0], rnd[1], "^k") if cBest != float('inf'): diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py index 5a0b453f78..d5cda0bc50 100644 --- a/PathPlanning/LQRPlanner/LQRplanner.py +++ b/PathPlanning/LQRPlanner/LQRplanner.py @@ -54,6 +54,8 @@ def lqr_planning(self, sx, sy, gx, gy, show_animation=True): # animation if show_animation: # pragma: no cover + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(sx, sy, "or") plt.plot(gx, gy, "ob") plt.plot(rx, ry, "-r") diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py index 2e276b9f9b..c1f45445ff 100644 --- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py +++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py @@ -102,6 +102,8 @@ def planning(self, animation=True, search_until_max_iter=True): def draw_graph(self, rnd=None): plt.clf() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py index 1e918fb158..642f1cbff9 100644 --- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py +++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py @@ -98,6 +98,8 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr): if show_animation: draw_heatmap(pmap) + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(ix, iy, "*k") plt.plot(gix, giy, "*m") diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 351232d849..3fef2d0f7a 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -194,6 +194,8 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): # show graph if show_animation and len(closedset.keys()) % 2 == 0: + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(current.x, current.y, "xg") plt.pause(0.001) diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index ecef7d454c..b8d533d4dc 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -151,6 +151,8 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_ if show_animation: # pragma: no cover for i, _ in enumerate(time): plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.grid(True) plt.axis("equal") plot_arrow(sx, sy, syaw) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 3bcc622100..9d0bc36272 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -138,6 +138,8 @@ def get_random_node(self): def draw_graph(self, rnd=None): plt.clf() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py index 339acd2c3a..2a842aa42c 100644 --- a/PathPlanning/RRTDubins/rrt_dubins.py +++ b/PathPlanning/RRTDubins/rrt_dubins.py @@ -106,6 +106,8 @@ def planning(self, animation=True, search_until_max_iter=True): def draw_graph(self, rnd=None): # pragma: no cover plt.clf() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py index 4d7312933d..d4601c0f12 100644 --- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py +++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py @@ -112,6 +112,8 @@ def planning(self, animation=True, search_until_max_iter=True): def draw_graph(self, rnd=None): plt.clf() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py index 5d78f98f31..d37ae90634 100644 --- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py +++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py @@ -122,6 +122,8 @@ def try_goal_path(self, node): def draw_graph(self, rnd=None): plt.clf() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index 54b5adbe6d..b2f8d4a57d 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -395,6 +395,8 @@ def test(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(px, py, label="final course " + str(mode)) # plotting diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py index 3ac02993b7..86371b2051 100644 --- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py +++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py @@ -185,6 +185,8 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): # show graph if show_animation and len(closedset.keys()) % 2 == 0: # pragma: no cover plt.plot(current.x, current.y, "xg") + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.pause(0.001) if c_id == (len(road_map) - 1): diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py index 74a0f9cc57..1be91b2d20 100644 --- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py +++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py @@ -546,6 +546,8 @@ def animation(plant, controller, dt): steer = math.atan2(controller.history_u_2[t] * WB / v, 1.0) plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(plant.history_x, plant.history_y, "-r", label="trajectory") plot_car(x, y, yaw, steer=steer) plt.axis("equal") diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py index 6dec12bd30..0e22529623 100644 --- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py +++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py @@ -224,6 +224,8 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") plt.plot(x, y, "ob", label="trajectory") plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py index f977cdc2a1..9cc36f231f 100644 --- a/PathTracking/lqr_steer_control/lqr_steer_control.py +++ b/PathTracking/lqr_steer_control/lqr_steer_control.py @@ -203,6 +203,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") plt.plot(x, y, "ob", label="trajectory") plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py index 0f5104e954..8bff2e107d 100644 --- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py @@ -429,6 +429,8 @@ def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if ox is not None: plt.plot(ox, oy, "xr", label="MPC") plt.plot(cx, cy, "-r", label="course") diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py index 0814ef62ef..838b780f48 100644 --- a/PathTracking/move_to_pose/move_to_pose.py +++ b/PathTracking/move_to_pose/move_to_pose.py @@ -93,6 +93,9 @@ def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover plt.plot(x_traj, y_traj, 'b--') + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + plt.xlim(0, 20) plt.ylim(0, 20) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index 38a84affce..f9624956c4 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -166,6 +166,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plot_arrow(state.x, state.y, state.yaw) plt.plot(cx, cy, "-r", label="course") plt.plot(x, y, "-b", label="trajectory") diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 285d9404e3..155eeec2ce 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -149,6 +149,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") plt.plot(x, y, "ob", label="trajectory") plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index addd5ad3a1..cbfa4c667e 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -186,6 +186,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, ".r", label="course") plt.plot(x, y, "-b", label="trajectory") plt.plot(cx[target_idx], cy[target_idx], "xg", label="target") diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index 27e5035459..33a9b1ddb1 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -235,6 +235,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") plt.plot(xEst[0], xEst[1], ".r") diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index c16407e89f..ef1282995b 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -365,6 +365,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") for i in range(N_PARTICLE): diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 73624dbf1f..f8958b8c73 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -390,6 +390,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") for iz in range(len(z[:, 0])): diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py index b40ad6494e..e73e8d0d4a 100644 --- a/SLAM/GraphBasedSLAM/graph_based_slam.py +++ b/SLAM/GraphBasedSLAM/graph_based_slam.py @@ -303,7 +303,8 @@ def main(): if show_animation: # pragma: no cover plt.cla() - + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") plt.plot(hxTrue[0, :].flatten(), @@ -319,4 +320,4 @@ def main(): if __name__ == '__main__': - main() \ No newline at end of file + main() diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py index 3512ef976b..4a4d29eb5e 100644 --- a/SLAM/iterative_closest_point/iterative_closest_point.py +++ b/SLAM/iterative_closest_point/iterative_closest_point.py @@ -36,6 +36,8 @@ def icp_matching(previous_points, current_points): if show_animation: # pragma: no cover plt.cla() + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(previous_points[0, :], previous_points[1, :], ".r") plt.plot(current_points[0, :], current_points[1, :], ".b") plt.plot(0.0, 0.0, "xr") From 8c701762b40e38318a4dd03485e9824504de458b Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 13:54:35 +0100 Subject: [PATCH 070/909] Fixed typo in stanley_controller.py --- PathTracking/stanley_controller/stanley_controller.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index addd5ad3a1..42a446dda3 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -21,7 +21,7 @@ k = 0.5 # control gain -Kp = 1.0 # speed propotional gain +Kp = 1.0 # speed proportional gain dt = 0.1 # [s] time difference L = 2.9 # [m] Wheel base of vehicle max_steer = np.radians(30.0) # [rad] max steering angle From 22e92ed5583875fbc4e46fce044059f936b0be4e Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 14:40:28 +0100 Subject: [PATCH 071/909] Simplified calc_target_index function in stanley_controller.py Used: - np.hypot to replace np.sqrt(x**2 + y**2) - np.argmin to find location of a minimum in a numpy array --- PathTracking/stanley_controller/stanley_controller.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index 42a446dda3..9b808dd963 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -127,20 +127,19 @@ def calc_target_index(state, cx, cy): :param cy: [float] :return: (int, float) """ - # Calc front axle position + # Calc front axle position fx = state.x + L * np.cos(state.yaw) fy = state.y + L * np.sin(state.yaw) # Search nearest point index dx = [fx - icx for icx in cx] dy = [fy - icy for icy in cy] - d = [np.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)] - closest_error = min(d) - target_idx = d.index(closest_error) + d = np.hypot(dx, dy) + target_idx = np.argmin(d) # Project RMS error onto front axle vector front_axle_vec = [-np.cos(state.yaw + np.pi / 2), - - np.sin(state.yaw + np.pi / 2)] + -np.sin(state.yaw + np.pi / 2)] error_front_axle = np.dot([dx[target_idx], dy[target_idx]], front_axle_vec) return target_idx, error_front_axle From 652c32fba9eadfeb1e2200b2f7e475de1acfc5bb Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:10:53 +0100 Subject: [PATCH 072/909] Replaced sqrt(x**2+y**2) with hypot in SLAM/GraphBasedSLAM/graph_based_slam.py --- SLAM/GraphBasedSLAM/graph_based_slam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py index b40ad6494e..41b9d52591 100644 --- a/SLAM/GraphBasedSLAM/graph_based_slam.py +++ b/SLAM/GraphBasedSLAM/graph_based_slam.py @@ -216,7 +216,7 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx**2 + dy**2) + d = math.hypot(dx, dy) angle = pi_2_pi(math.atan2(dy, dx)) - xTrue[2, 0] phi = pi_2_pi(math.atan2(dy, dx)) if d <= MAX_RANGE: From 1d03241d5d6319fc1cdc42f6a11b560ee23dc0d2 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:11:28 +0100 Subject: [PATCH 073/909] Replaced sqrt(x**2+y**2) with hypot in SLAM/FastSLAM2/fast_slam2.py --- SLAM/FastSLAM2/fast_slam2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 73624dbf1f..b9df694830 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -304,7 +304,7 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise From f1926bdd9e08733f7dce997fab3f0e6f4e97476f Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:11:57 +0100 Subject: [PATCH 074/909] Replaced sqrt(x**2+y**2) with hypot in SLAM/FastSLAM2/fast_slam1.py --- SLAM/FastSLAM1/fast_slam1.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index c16407e89f..298cc0363a 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -279,7 +279,7 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: dn = d + np.random.randn() * Qsim[0, 0] ** 0.5 # add noise From 02f14e425efab343a26acbbf2feeb139cbe337b6 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:13:20 +0100 Subject: [PATCH 075/909] Replaced sqrt(x**2+y**2) with hypot in SLAM/EKFSLAM/ekf_slam.py --- SLAM/EKFSLAM/ekf_slam.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index 22ccd8d659..66234b57ec 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -76,7 +76,7 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise From 41bcdf8573e4cd3b1773934f8b30d0363edda0f3 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:14:48 +0100 Subject: [PATCH 076/909] Replaced sqrt(x**2+y**2) with hypot in PathTracking/rear_wheel_feedback/rear_wheel_feedback.py --- PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 285d9404e3..bf4f31b39a 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -136,7 +136,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): # check goal dx = state.x - goal[0] dy = state.y - goal[1] - if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis: + if math.hypot(dx, dy) <= goal_dis: print("Goal") goal_flag = True break From e500f65d7ff6f8ddc82b5c9375fe8e423f8af387 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:16:12 +0100 Subject: [PATCH 077/909] Replaced sqrt(x**2+y**2) with hypot in PathTracking/pure_pursuit/pure_pursuit.py --- PathTracking/pure_pursuit/pure_pursuit.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index 38a84affce..370cac96cc 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -77,7 +77,7 @@ def calc_distance(state, point_x, point_y): dx = state.rear_x - point_x dy = state.rear_y - point_y - return math.sqrt(dx ** 2 + dy ** 2) + return math.hypot(dx, dy) def calc_target_index(state, cx, cy): @@ -88,7 +88,7 @@ def calc_target_index(state, cx, cy): # search nearest point index dx = [state.rear_x - icx for icx in cx] dy = [state.rear_y - icy for icy in cy] - d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)] + d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)] ind = d.index(min(d)) old_nearest_point_index = ind else: @@ -110,7 +110,7 @@ def calc_target_index(state, cx, cy): while Lf > L and (ind + 1) < len(cx): dx = cx[ind] - state.rear_x dy = cy[ind] - state.rear_y - L = math.sqrt(dx ** 2 + dy ** 2) + L = math.hypot(dx, dy) ind += 1 return ind From 4ef4658f82c4baa4784b430e67847ed24aa8dd48 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:18:00 +0100 Subject: [PATCH 078/909] Replaced sqrt(x**2+y**2) with hypot in PathTracking/move_to_pose/move_to_pose.py --- PathTracking/move_to_pose/move_to_pose.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py index 0814ef62ef..e161b4616d 100644 --- a/PathTracking/move_to_pose/move_to_pose.py +++ b/PathTracking/move_to_pose/move_to_pose.py @@ -40,7 +40,7 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal): x_traj, y_traj = [], [] - rho = np.sqrt(x_diff**2 + y_diff**2) + rho = np.hypot(x_diff, y_diff) while rho > 0.001: x_traj.append(x) y_traj.append(y) @@ -52,7 +52,7 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal): # [-pi, pi] to prevent unstable behavior e.g. difference going # from 0 rad to 2*pi rad with slight turn - rho = np.sqrt(x_diff**2 + y_diff**2) + rho = np.hypot(x_diff, y_diff) alpha = (np.arctan2(y_diff, x_diff) - theta + np.pi) % (2 * np.pi) - np.pi beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi From 9f5b0a25cc4c0727087dddde445862e727f283f5 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:19:05 +0100 Subject: [PATCH 079/909] Replaced sqrt(x**2+y**2) with hypot in PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py --- .../model_predictive_speed_and_steer_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py index 0f5104e954..a16768e36b 100644 --- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py @@ -348,7 +348,7 @@ def check_goal(state, goal, tind, nind): # check goal dx = state.x - goal[0] dy = state.y - goal[1] - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) isgoal = (d <= GOAL_DIS) From db78f07c0bc084748632bf36af713d249bf0647c Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:19:41 +0100 Subject: [PATCH 080/909] Replaced sqrt(x**2+y**2) with hypot in PathTracking/lqr_steer_control/lqr_steer_control.py --- PathTracking/lqr_steer_control/lqr_steer_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py index f977cdc2a1..b69577d3aa 100644 --- a/PathTracking/lqr_steer_control/lqr_steer_control.py +++ b/PathTracking/lqr_steer_control/lqr_steer_control.py @@ -191,7 +191,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): # check goal dx = state.x - goal[0] dy = state.y - goal[1] - if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis: + if math.hypot(dx, dy) <= goal_dis: print("Goal") break From bc2b7c969cd37b572372c5cdaa36b680a02f7c56 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:20:12 +0100 Subject: [PATCH 081/909] Replaced sqrt(x**2+y**2) with hypot in PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py --- PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py index 6dec12bd30..c7c8e0dd97 100644 --- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py +++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py @@ -212,7 +212,7 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal): # check goal dx = state.x - goal[0] dy = state.y - goal[1] - if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis: + if math.hypot(dx, dy) <= goal_dis: print("Goal") break From 26cff8e69fdbe7c11a55b83a3668e85e24aa952d Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:20:58 +0100 Subject: [PATCH 082/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/RRT/rrt.py --- PathPlanning/RRT/rrt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 3bcc622100..5b6f3252ae 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -126,7 +126,7 @@ def generate_final_course(self, goal_ind): def calc_dist_to_goal(self, x, y): dx = x - self.end.x dy = y - self.end.y - return math.sqrt(dx ** 2 + dy ** 2) + return math.hypot(dx, dy) def get_random_node(self): if random.randint(0, 100) > self.goal_sample_rate: @@ -186,7 +186,7 @@ def check_collision(node, obstacleList): def calc_distance_and_angle(from_node, to_node): dx = to_node.x - from_node.x dy = to_node.y - from_node.y - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) theta = math.atan2(dy, dx) return d, theta From 5221a9df2a82d9a527930c59057cfaa398c6f403 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:21:29 +0100 Subject: [PATCH 083/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/InformedRRTStar/informed_rrt_star.py --- PathPlanning/InformedRRTStar/informed_rrt_star.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 175749183d..065868f2d2 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -106,7 +106,7 @@ def choose_parent(self, newNode, nearInds): for i in nearInds: dx = newNode.x - self.node_list[i].x dy = newNode.y - self.node_list[i].y - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) theta = math.atan2(dy, dx) if self.check_collision(self.node_list[i], theta, d): dList.append(self.node_list[i].cost + d) @@ -224,7 +224,7 @@ def rewire(self, newNode, nearInds): if self.check_collision(nearNode, theta, d): nearNode.parent = n_node - 1 nearNode.cost = scost - + @staticmethod def distance_squared_point_to_segment(v, w, p): # Return minimum distance between line segment vw and point p @@ -232,7 +232,7 @@ def distance_squared_point_to_segment(v, w, p): return (p-v).dot(p-v) # v == w case l2 = (w-v).dot(w-v) # i.e. |w-v|^2 - avoid a sqrt # Consider the line extending the segment, parameterized as v + t (w - v). - # We find projection of point p onto the line. + # We find projection of point p onto the line. # It falls where t = [(p-v) . (w-v)] / |w-v|^2 # We clamp t from [0,1] to handle points outside the segment vw. t = max(0, min(1, (p - v).dot(w - v) / l2)) From 06cbcc3ee55598f54b612c2f842cfb052f8b751e Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:22:14 +0100 Subject: [PATCH 084/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py --- .../GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index f22b058fa8..9d7e1616f3 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -118,7 +118,7 @@ def find_sweep_direction_and_start_posi(ox, oy): for i in range(len(ox) - 1): dx = ox[i + 1] - ox[i] dy = oy[i + 1] - oy[i] - d = np.sqrt(dx ** 2 + dy ** 2) + d = np.hypot(dx, dy) if d > max_dist: max_dist = d From 1eb3ebbf26be7f000275f2a897d46a008613814e Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:22:47 +0100 Subject: [PATCH 085/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/DynamicWindowApproach/dynamic_window_approach.py --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index c8a4434958..5c3d128dc4 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -164,7 +164,7 @@ def calc_obstacle_cost(trajectory, ob, config): oy = ob[:, 1] dx = trajectory[:, 0] - ox[:, None] dy = trajectory[:, 1] - oy[:, None] - r = np.sqrt(np.square(dx) + np.square(dy)) + r = np.hypot(dx, dy) if config.robot_type == RobotType.rectangle: yaw = trajectory[:, 2] From 00f8fd37d367aa6409b70e945356f1aa5abc4fde Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:24:27 +0100 Subject: [PATCH 086/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/ClosedLoopRRTStar/pure_pursuit.py --- PathPlanning/ClosedLoopRRTStar/pure_pursuit.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py index 0490727933..32faa51c11 100644 --- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py +++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py @@ -68,17 +68,17 @@ def calc_target_index(state, cx, cy): dx = [state.x - icx for icx in cx] dy = [state.y - icy for icy in cy] - d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)] + d = np.hypot(dx, dy) mindis = min(d) - ind = d.index(mindis) + ind = np.argmin(d) L = 0.0 while Lf > L and (ind + 1) < len(cx): dx = cx[ind + 1] - cx[ind] dy = cy[ind + 1] - cy[ind] - L += math.sqrt(dx ** 2 + dy ** 2) + L += math.hypot(dx, dy) ind += 1 # print(mindis) @@ -121,7 +121,7 @@ def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal): # check goal dx = state.x - goal[0] dy = state.y - goal[1] - if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis: + if math.hypot(dx, dy) <= goal_dis: find_goal = True break From 1fcfa72133522ce004b99ad079d9f7361ee8f981 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:30:04 +0100 Subject: [PATCH 087/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py --- PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py index a07dc48af0..aea0080112 100644 --- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py +++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py @@ -6,7 +6,6 @@ """ -import math import os import sys @@ -119,9 +118,8 @@ def check_tracking_path_is_feasible(self, path): print("final angle is bad") find_goal = False - travel = sum([abs(iv) * unicycle_model.dt for iv in v]) - origin_travel = sum([math.sqrt(dx ** 2 + dy ** 2) - for (dx, dy) in zip(np.diff(cx), np.diff(cy))]) + travel = unicycle_model.dt * sum(np.abs(v)) + origin_travel = sum(np.hypot(np.diff(cx), np.diff(cy))) if (travel / origin_travel) >= self.invalid_travel_ratio: print("path is too long") From 0aaf6406d82f7067af460160f4d75495d59fa80f Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:30:39 +0100 Subject: [PATCH 088/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/BezierPath/bezier_path.py --- PathPlanning/BezierPath/bezier_path.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/BezierPath/bezier_path.py b/PathPlanning/BezierPath/bezier_path.py index 516a681e3b..46faf1f310 100644 --- a/PathPlanning/BezierPath/bezier_path.py +++ b/PathPlanning/BezierPath/bezier_path.py @@ -26,7 +26,7 @@ def calc_4points_bezier_path(sx, sy, syaw, ex, ey, eyaw, offset): :param offset: (float) :return: (numpy array, numpy array) """ - dist = np.sqrt((sx - ex) ** 2 + (sy - ey) ** 2) / offset + dist = np.hypot(sx - ex, sy - ey) / offset control_points = np.array( [[sx, sy], [sx + dist * np.cos(syaw), sy + dist * np.sin(syaw)], From 72a28b4d4b850e3c2ac7e13ffacc2ea01736f0db Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:31:05 +0100 Subject: [PATCH 089/909] Replaced sqrt(x**2+y**2) with hypot in Mapping/rectangle_fitting/rectangle_fitting.py --- Mapping/rectangle_fitting/rectangle_fitting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index e40fc2e4c7..2018f02514 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -162,7 +162,7 @@ def _adoptive_range_segmentation(self, ox, oy): C = set() R = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]]) for j, _ in enumerate(ox): - d = np.sqrt((ox[i] - ox[j])**2 + (oy[i] - oy[j])**2) + d = np.hypot(ox[i] - ox[j], oy[i] - oy[j]) if d <= R: C.add(j) S.append(C) From 2cb9e37792180fed4d73ce5db35a071ab10d2d17 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:32:02 +0100 Subject: [PATCH 090/909] Replaced sqrt(x**2+y**2) with hypot in Mapping/kmeans_clustering/kmeans_clustering.py --- Mapping/kmeans_clustering/kmeans_clustering.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py index bdaa45455e..c2518b80c0 100644 --- a/Mapping/kmeans_clustering/kmeans_clustering.py +++ b/Mapping/kmeans_clustering/kmeans_clustering.py @@ -68,7 +68,7 @@ def update_clusters(self): dx = [icx - px for icx in self.center_x] dy = [icy - py for icy in self.center_y] - dist_list = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)] + dist_list = [math.hypot(idx, idy) for (idx, idy) in zip(dx, dy)] min_dist = min(dist_list) min_id = dist_list.index(min_dist) self.labels[ip] = min_id From 4545084da430bd353239565715bb22dde1c1c96f Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:32:40 +0100 Subject: [PATCH 091/909] Replaced sqrt(x**2+y**2) with hypot in Localization/particle_filter/particle_filter.py --- Localization/particle_filter/particle_filter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 0ebb5fc3d1..d14b7c0697 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -47,7 +47,7 @@ def observation(xTrue, xd, u, RF_ID): dx = xTrue[0, 0] - RF_ID[i, 0] dy = xTrue[1, 0] - RF_ID[i, 1] - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise zi = np.array([[dn, RF_ID[i, 0], RF_ID[i, 1]]]) @@ -116,7 +116,7 @@ def pf_localization(px, pw, z, u): for i in range(len(z[:, 0])): dx = x[0, 0] - z[i, 1] dy = x[1, 0] - z[i, 2] - pre_z = math.sqrt(dx ** 2 + dy ** 2) + pre_z = math.hypot(dx, dy) dz = pre_z - z[i, 0] w = w * gauss_likelihood(dz, math.sqrt(Q[0, 0])) From f66a10f42eca2a57cb8004f16403fd67e0788d58 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:33:21 +0100 Subject: [PATCH 092/909] Replaced sqrt(x**2+y**2) with hypot in Localization/histogram_filter/histogram_filter.py --- Localization/histogram_filter/histogram_filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py index bdfbb13fa7..8404646ca5 100644 --- a/Localization/histogram_filter/histogram_filter.py +++ b/Localization/histogram_filter/histogram_filter.py @@ -126,7 +126,7 @@ def observation(xTrue, u, RFID): dx = xTrue[0, 0] - RFID[i, 0] dy = xTrue[1, 0] - RFID[i, 1] - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) if d <= MAX_RANGE: # add noise to range observation dn = d + np.random.randn() * NOISE_RANGE From e289d1f9a9ea1a1284ab3f4e34cd89c5c21c72b8 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:33:52 +0100 Subject: [PATCH 093/909] Replaced sqrt(x**2+y**2) with hypot in Localization/ensemble_kalman_filter/ensemble_kalman_filter.py --- Localization/ensemble_kalman_filter/ensemble_kalman_filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index 293509157b..3e2af749e7 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -44,7 +44,7 @@ def observation(xTrue, xd, u, RFID): dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] - d = math.sqrt(dx ** 2 + dy ** 2) + d = math.hypot(dx, dy) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise From 84aead6149b885b44ad661b92cd8fa2ac6431a41 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:35:06 +0100 Subject: [PATCH 094/909] Replaced sqrt(x**2+y**2) with hypot in ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py --- .../two_joint_arm_to_point_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index e674ae8ac2..0e91be0ff6 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -55,7 +55,7 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0): wrist = plot_arm(theta1, theta2, x, y) # check goal - d2goal = np.math.sqrt((wrist[0] - x)**2 + (wrist[1] - y)**2) + d2goal = np.hypot(wrist[0] - x, wrist[1] - y) if abs(d2goal) < GOAL_TH and x is not None: return theta1, theta2 From d979bb24d9cf6272a24bd000f5642cc1443c7b86 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:35:44 +0100 Subject: [PATCH 095/909] Replaced sqrt(x**2+y**2) with hypot in ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py --- .../n_joint_arm_to_point_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py index b232ab33f3..3972474b8d 100644 --- a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py +++ b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py @@ -148,7 +148,7 @@ def jacobian_inverse(link_lengths, joint_angles): def distance_to_goal(current_pos, goal_pos): x_diff = goal_pos[0] - current_pos[0] y_diff = goal_pos[1] - current_pos[1] - return np.array([x_diff, y_diff]).T, np.math.sqrt(x_diff**2 + y_diff**2) + return np.array([x_diff, y_diff]).T, np.hypot(x_diff, y_diff) def ang_diff(theta1, theta2): From d26eb04ae2c2f1047271e913977f27dfc129aad3 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:36:25 +0100 Subject: [PATCH 096/909] Removed useless zero in erialNavigation/rocket_powered_landing/rocket_powered_landing.py --- .../rocket_powered_landing/rocket_powered_landing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 9f1e16fc1d..dde76f19bd 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -195,8 +195,8 @@ def B_func(self, x, u): [0, 0, 0], [0, 0, 0], [0, 0, 0], - [0, 0, 1.00000000000000], - [0, -1.00000000000000, 0] + [0, 0, 1.0], + [0, -1.0, 0] ]) def euler_to_quat(self, a): From e467f4fad1d055b947f565ac5d27e6dd21528621 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:07 +0100 Subject: [PATCH 097/909] Replaced sqrt(x**2+y**2) with hypot in Localization/histogram_filter/histogram_filter.py --- Localization/histogram_filter/histogram_filter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py index 8404646ca5..db715a6d1e 100644 --- a/Localization/histogram_filter/histogram_filter.py +++ b/Localization/histogram_filter/histogram_filter.py @@ -68,7 +68,7 @@ def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std): # predicted range x = ix * gmap.xy_reso + gmap.minx y = iy * gmap.xy_reso + gmap.miny - d = math.sqrt((x - z[iz, 1]) ** 2 + (y - z[iz, 2]) ** 2) + d = math.hypot(x - z[iz, 1], y - z[iz, 2]) # likelihood pdf = (1.0 - norm.cdf(abs(d - z[iz, 0]), 0.0, std)) From ccf047135e558332f9e460e21f8263bfb3e2974a Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:07 +0100 Subject: [PATCH 098/909] Replaced sqrt(x**2+y**2) with hypot in Mapping/gaussian_grid_map/gaussian_grid_map.py --- Mapping/gaussian_grid_map/gaussian_grid_map.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Mapping/gaussian_grid_map/gaussian_grid_map.py b/Mapping/gaussian_grid_map/gaussian_grid_map.py index b520b63944..9c1aa27320 100644 --- a/Mapping/gaussian_grid_map/gaussian_grid_map.py +++ b/Mapping/gaussian_grid_map/gaussian_grid_map.py @@ -31,7 +31,7 @@ def generate_gaussian_grid_map(ox, oy, xyreso, std): # Search minimum distance mindis = float("inf") for (iox, ioy) in zip(ox, oy): - d = math.sqrt((iox - x)**2 + (ioy - y)**2) + d = math.hypot(iox - x, ioy - y) if mindis >= d: mindis = d From 1273934a7ea2723375422dc29081a014f649dbb8 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:07 +0100 Subject: [PATCH 099/909] Replaced sqrt(x**2+y**2) with hypot in Mapping/raycasting_grid_map/raycasting_grid_map.py --- Mapping/raycasting_grid_map/raycasting_grid_map.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/raycasting_grid_map/raycasting_grid_map.py index 997687423e..a0d9060916 100644 --- a/Mapping/raycasting_grid_map/raycasting_grid_map.py +++ b/Mapping/raycasting_grid_map/raycasting_grid_map.py @@ -57,7 +57,7 @@ def precasting(minx, miny, xw, yw, xyreso, yawreso): px = ix * xyreso + minx py = iy * xyreso + miny - d = math.sqrt(px**2 + py**2) + d = math.hypot(px, py) angle = atan_zero_to_twopi(py, px) angleid = int(math.floor(angle / yawreso)) @@ -85,7 +85,7 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso): for (x, y) in zip(ox, oy): - d = math.sqrt(x**2 + y**2) + d = math.hypot(x, y) angle = atan_zero_to_twopi(y, x) angleid = int(math.floor(angle / yawreso)) From d433748ef8ab6fba34f7f7882253e2c1f0c31611 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 100/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/AStar/a_star.py --- PathPlanning/AStar/a_star.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index 6b0392f515..061aafbeff 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -136,7 +136,7 @@ def calc_final_path(self, ngoal, closedset): @staticmethod def calc_heuristic(n1, n2): w = 1.0 # weight of heuristic - d = w * math.sqrt((n1.x - n2.x) ** 2 + (n1.y - n2.y) ** 2) + d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) return d def calc_grid_position(self, index, minp): @@ -199,7 +199,7 @@ def calc_obstacle_map(self, ox, oy): for iy in range(self.ywidth): y = self.calc_grid_position(iy, self.miny) for iox, ioy in zip(ox, oy): - d = math.sqrt((iox - x) ** 2 + (ioy - y) ** 2) + d = math.hypot(iox - x, ioy - y) if d <= self.rr: self.obmap[ix][iy] = True break From ed9b84dfc3310e6a45e19c05c38a485560da5c15 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 101/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py --- PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py index 37ac75ada9..8e7af39f30 100644 --- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py +++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py @@ -180,8 +180,8 @@ def setup_planning(self): cBest = self.g_scores[self.goalId] # Computing the sampling space - cMin = math.sqrt(pow(self.start[0] - self.goal[0], 2) - + pow(self.start[1] - self.goal[1], 2)) / 1.5 + cMin = math.hypot(self.start[0] - self.goal[0], + self.start[1] - self.goal[1]) / 1.5 xCenter = np.array([[(self.start[0] + self.goal[0]) / 2.0], [(self.start[1] + self.goal[1]) / 2.0], [0]]) a1 = np.array([[(self.goal[0] - self.start[0]) / cMin], From cf538756e69713fe5d3d23a9c99a09ba5f62c35d Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 102/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/ClosedLoopRRTStar/pure_pursuit.py --- PathPlanning/ClosedLoopRRTStar/pure_pursuit.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py index 32faa51c11..be1d223815 100644 --- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py +++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py @@ -161,7 +161,7 @@ def set_stop_point(target_speed, cx, cy, cyaw): for i in range(len(cx) - 1): dx = cx[i + 1] - cx[i] dy = cy[i + 1] - cy[i] - d.append(math.sqrt(dx ** 2.0 + dy ** 2.0)) + d.append(math.hypot(dx, dy)) iyaw = cyaw[i] move_direction = math.atan2(dy, dx) is_back = abs(move_direction - iyaw) >= math.pi / 2.0 From 4c73cada9b1fccbcf929a4520735c590f188a85c Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 103/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/CubicSpline/cubic_spline_planner.py --- PathPlanning/CubicSpline/cubic_spline_planner.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py index 071b117185..239ce16738 100644 --- a/PathPlanning/CubicSpline/cubic_spline_planner.py +++ b/PathPlanning/CubicSpline/cubic_spline_planner.py @@ -140,8 +140,7 @@ def __init__(self, x, y): def __calc_s(self, x, y): dx = np.diff(x) dy = np.diff(y) - self.ds = [math.sqrt(idx ** 2 + idy ** 2) - for (idx, idy) in zip(dx, dy)] + self.ds = np.hypot(dx, dy) s = [0] s.extend(np.cumsum(self.ds)) return s From 4c6fe30fb33bff9b85238f26e95e8ae7b9a973dd Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 104/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/Dijkstra/dijkstra.py --- PathPlanning/Dijkstra/dijkstra.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index bb73ed501a..5c06f53aed 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -123,7 +123,7 @@ def calc_final_path(self, ngoal, closedset): def calc_heuristic(self, n1, n2): w = 1.0 # weight of heuristic - d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2) + d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) return d def calc_position(self, index, minp): @@ -178,7 +178,7 @@ def calc_obstacle_map(self, ox, oy): for iy in range(self.ywidth): y = self.calc_position(iy, self.miny) for iox, ioy in zip(ox, oy): - d = math.sqrt((iox - x)**2 + (ioy - y)**2) + d = math.hypot(iox - x, ioy - y) if d <= self.rr: self.obmap[ix][iy] = True break From 367c2c6dc635644b3fa37b5f921b3ebabb1e97c8 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 105/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/DubinsPath/dubins_path_planning.py --- PathPlanning/DubinsPath/dubins_path_planning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py index 706b5a49af..96c9c8e475 100644 --- a/PathPlanning/DubinsPath/dubins_path_planning.py +++ b/PathPlanning/DubinsPath/dubins_path_planning.py @@ -141,7 +141,7 @@ def dubins_path_planning_from_origin(ex, ey, eyaw, c, D_ANGLE): # normalize dx = ex dy = ey - D = math.sqrt(dx ** 2.0 + dy ** 2.0) + D = math.hypot(dx, dy) d = D * c # print(dx, dy, D, d) From 60387ee5f8b5f7898dbe73cc671f59503042a182 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 106/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/DynamicWindowApproach/dynamic_window_approach.py --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 5c3d128dc4..891e68cbe8 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -279,7 +279,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): plt.pause(0.0001) # check reaching goal - dist_to_goal = math.sqrt((x[0] - goal[0]) ** 2 + (x[1] - goal[1]) ** 2) + dist_to_goal = math.hypot(x[0] - goal[0], x[1] - goal[1]) if dist_to_goal <= config.robot_radius: print("Goal!!") break From 077120201b41a8c4808a81170679aac7cd077acd Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 107/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py --- .../FrenetOptimalTrajectory/frenet_optimal_trajectory.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index 5c756a9dfe..f3ae2a439b 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -230,7 +230,7 @@ def calc_global_paths(fplist, csp): dx = fp.x[i + 1] - fp.x[i] dy = fp.y[i + 1] - fp.y[i] fp.yaw.append(math.atan2(dy, dx)) - fp.ds.append(math.sqrt(dx**2 + dy**2)) + fp.ds.append(math.hypot(dx, dy)) fp.yaw.append(fp.yaw[-1]) fp.ds.append(fp.ds[-1]) From d20f1e336e9b2536eacea224d12c59858a9c74bd Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 108/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py --- .../ProbabilisticRoadMap/probabilistic_road_map.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 351232d849..8090e3364f 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -97,7 +97,7 @@ def is_collision(sx, sy, gx, gy, rr, okdtree): dx = gx - sx dy = gy - sy yaw = math.atan2(gy - sy, gx - sx) - d = math.sqrt(dx**2 + dy**2) + d = math.hypot(dx, dy) if d >= MAX_EDGE_LEN: return True @@ -171,7 +171,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): road_map: ??? [m] sample_x: ??? [m] sample_y: ??? [m] - + @return: Two lists of path coordinates ([x1, x2, ...], [y1, y2, ...]), empty list when no path was found """ @@ -182,7 +182,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): openset[len(road_map) - 2] = nstart path_found = True - + while True: if not openset: print("Cannot find path") @@ -213,7 +213,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): n_id = road_map[c_id][i] dx = sample_x[n_id] - current.x dy = sample_y[n_id] - current.y - d = math.sqrt(dx**2 + dy**2) + d = math.hypot(dx, dy) node = Node(sample_x[n_id], sample_y[n_id], current.cost + d, c_id) @@ -226,7 +226,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): openset[n_id].pind = c_id else: openset[n_id] = node - + if path_found is False: return [], [] From c63a80b0ef148d5fb4bf75b1b0da41ba994c84ad Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 7 Dec 2019 22:53:08 +0100 Subject: [PATCH 109/909] Replaced sqrt(x**2+y**2) with hypot in PathPlanning/VoronoiRoadMap/voronoi_road_map.py --- PathPlanning/VoronoiRoadMap/voronoi_road_map.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py index 3ac02993b7..c73d650951 100644 --- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py +++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py @@ -95,7 +95,7 @@ def is_collision(sx, sy, gx, gy, rr, okdtree): dx = gx - sx dy = gy - sy yaw = math.atan2(gy - sy, gx - sx) - d = math.sqrt(dx**2 + dy**2) + d = math.hypot(dx, dy) if d >= MAX_EDGE_LEN: return True @@ -203,7 +203,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): n_id = road_map[c_id][i] dx = sample_x[n_id] - current.x dy = sample_y[n_id] - current.y - d = math.sqrt(dx**2 + dy**2) + d = math.hypot(dx, dy) node = Node(sample_x[n_id], sample_y[n_id], current.cost + d, c_id) From bf86cc8096482745acaff354da1f8a56fff4a5d4 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Dec 2019 10:25:08 +0900 Subject: [PATCH 110/909] fixed lgtm warning --- PathPlanning/HybridAStar/hybrid_a_star.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index e82003ed25..ea6a466113 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -150,7 +150,7 @@ def calc_next_node(current, steer, direction, config, ox, oy, kdtree): arc_l = XY_GRID_RESOLUTION * 1.5 xlist, ylist, yawlist = [], [], [] - for dist in np.arange(0, arc_l, MOTION_RESOLUTION): + for _ in np.arange(0, arc_l, MOTION_RESOLUTION): x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION * direction, steer) xlist.append(x) ylist.append(y) From 25cffd70d21389b76760b44da2697cde35175472 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Sat, 14 Dec 2019 14:39:45 +0300 Subject: [PATCH 111/909] fix typo --- .../rocket_powered_landing/rocket_powered_landing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index c7ab998bec..51e09b4e3c 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -567,7 +567,7 @@ def plot_animation(X, U): # pragma: no cover fig = plt.figure() ax = fig.gca(projection='3d') - rig.canvas.mpl_connect('key_release_event', + fig.canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) for k in range(K): From 9ca7d8f14808f535b5e698bc57e5ab163b3dae4f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?= <20567087+goktug97@users.noreply.github.com> Date: Sat, 14 Dec 2019 14:44:01 +0300 Subject: [PATCH 112/909] add comment for stopping the simulation --- AerialNavigation/drone_3d_trajectory_following/Quadrotor.py | 1 + .../rocket_powered_landing/rocket_powered_landing.py | 1 + .../arm_obstacle_navigation/arm_obstacle_navigation.py | 1 + .../arm_obstacle_navigation/arm_obstacle_navigation_2.py | 1 + ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py | 1 + .../two_joint_arm_to_point_control.py | 1 + Bipedal/bipedal_planner/bipedal_planner.py | 1 + Localization/ensemble_kalman_filter/ensemble_kalman_filter.py | 1 + Localization/extended_kalman_filter/extended_kalman_filter.py | 1 + Localization/histogram_filter/histogram_filter.py | 1 + Localization/particle_filter/particle_filter.py | 1 + Localization/unscented_kalman_filter/unscented_kalman_filter.py | 1 + Mapping/circle_fitting/circle_fitting.py | 1 + Mapping/gaussian_grid_map/gaussian_grid_map.py | 1 + Mapping/kmeans_clustering/kmeans_clustering.py | 1 + Mapping/raycasting_grid_map/raycasting_grid_map.py | 1 + Mapping/rectangle_fitting/rectangle_fitting.py | 1 + PathPlanning/AStar/a_star.py | 1 + PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py | 1 + PathPlanning/ClosedLoopRRTStar/pure_pursuit.py | 1 + PathPlanning/Dijkstra/dijkstra.py | 1 + PathPlanning/DubinsPath/dubins_path_planning.py | 1 + PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 1 + PathPlanning/Eta3SplinePath/eta3_spline_path.py | 1 + .../FrenetOptimalTrajectory/frenet_optimal_trajectory.py | 1 + .../GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py | 2 ++ PathPlanning/HybridAStar/a_star.py | 1 + PathPlanning/HybridAStar/hybrid_a_star.py | 1 + PathPlanning/InformedRRTStar/informed_rrt_star.py | 1 + PathPlanning/LQRPlanner/LQRplanner.py | 1 + PathPlanning/LQRRRTStar/lqr_rrt_star.py | 1 + PathPlanning/PotentialFieldPlanning/potential_field_planning.py | 1 + PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py | 1 + .../QuinticPolynomialsPlanner/quintic_polynomials_planner.py | 1 + PathPlanning/RRT/rrt.py | 1 + PathPlanning/RRTDubins/rrt_dubins.py | 1 + PathPlanning/RRTStarDubins/rrt_star_dubins.py | 1 + PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py | 1 + PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py | 1 + PathPlanning/VoronoiRoadMap/voronoi_road_map.py | 1 + PathTracking/cgmres_nmpc/cgmres_nmpc.py | 1 + PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py | 1 + PathTracking/lqr_steer_control/lqr_steer_control.py | 1 + .../model_predictive_speed_and_steer_control.py | 1 + PathTracking/move_to_pose/move_to_pose.py | 1 + PathTracking/pure_pursuit/pure_pursuit.py | 1 + PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 1 + PathTracking/stanley_controller/stanley_controller.py | 1 + SLAM/EKFSLAM/ekf_slam.py | 1 + SLAM/FastSLAM1/fast_slam1.py | 1 + SLAM/FastSLAM2/fast_slam2.py | 1 + SLAM/GraphBasedSLAM/graph_based_slam.py | 1 + SLAM/iterative_closest_point/iterative_closest_point.py | 1 + 53 files changed, 54 insertions(+) diff --git a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py index 89171da212..413a8625a5 100644 --- a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py +++ b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py @@ -23,6 +23,7 @@ def __init__(self, x=0, y=0, z=0, roll=0, pitch=0, yaw=0, size=0.25, show_animat if self.show_animation: plt.ion() fig = plt.figure() + # for stopping simulation with the esc key. fig.canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 51e09b4e3c..ee0f5e26a5 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -567,6 +567,7 @@ def plot_animation(X, U): # pragma: no cover fig = plt.figure() ax = fig.gca(projection='3d') + # for stopping simulation with the esc key. fig.canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py index b435e62d2b..f3bd25577c 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py @@ -162,6 +162,7 @@ def astar_torus(grid, start_node, goal_node): for i in range(1, len(route)): grid[route[i]] = 6 plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None) diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py index 5a21b806d3..429cd4d211 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py @@ -193,6 +193,7 @@ def astar_torus(grid, start_node, goal_node): for i in range(1, len(route)): grid[route[i]] = 6 plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None) diff --git a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py index 576bd29a8f..854ade9038 100644 --- a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py @@ -51,6 +51,7 @@ def update_points(self): def plot(self): # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index 134583a95c..60a7cd5f0f 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -114,6 +114,7 @@ def animation(): def main(): # pragma: no cover fig = plt.figure() fig.canvas.mpl_connect("button_press_event", click) + # for stopping simulation with the esc key. fig.canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) two_joint_arm() diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py index afa68afb25..6502ce5bed 100644 --- a/Bipedal/bipedal_planner/bipedal_planner.py +++ b/Bipedal/bipedal_planner/bipedal_planner.py @@ -111,6 +111,7 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): if c > len(com_trajectory_for_plot): # set up plotter plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) ax.set_zlim(0, z_c * 2) diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index 6ac6144f36..244cfccd9c 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -213,6 +213,7 @@ def main(): if show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py index 029dac4bf8..2b057e21de 100644 --- a/Localization/extended_kalman_filter/extended_kalman_filter.py +++ b/Localization/extended_kalman_filter/extended_kalman_filter.py @@ -191,6 +191,7 @@ def main(): if show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(hz[0, :], hz[1, :], ".g") diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py index ac07c59fbd..79302c8abb 100644 --- a/Localization/histogram_filter/histogram_filter.py +++ b/Localization/histogram_filter/histogram_filter.py @@ -233,6 +233,7 @@ def main(): if show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) draw_heat_map(grid_map.data, mx, my) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 4b9abd78bd..3e901f8da0 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -230,6 +230,7 @@ def main(): if show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py index 0005d12fe7..7bf279ced0 100644 --- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py +++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py @@ -240,6 +240,7 @@ def main(): if show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(hz[0, :], hz[1, :], ".g") diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py index 27ceac21f7..c331d56796 100644 --- a/Mapping/circle_fitting/circle_fitting.py +++ b/Mapping/circle_fitting/circle_fitting.py @@ -124,6 +124,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.axis("equal") diff --git a/Mapping/gaussian_grid_map/gaussian_grid_map.py b/Mapping/gaussian_grid_map/gaussian_grid_map.py index 094b094fdd..7893d6a5ee 100644 --- a/Mapping/gaussian_grid_map/gaussian_grid_map.py +++ b/Mapping/gaussian_grid_map/gaussian_grid_map.py @@ -73,6 +73,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) draw_heatmap(gmap, minx, maxx, miny, maxy, xyreso) diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py index 5345813096..d658b84f48 100644 --- a/Mapping/kmeans_clustering/kmeans_clustering.py +++ b/Mapping/kmeans_clustering/kmeans_clustering.py @@ -133,6 +133,7 @@ def main(): # for animation if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) clusters.plot_cluster() diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/raycasting_grid_map/raycasting_grid_map.py index cae92a9dcb..372e31eccd 100644 --- a/Mapping/raycasting_grid_map/raycasting_grid_map.py +++ b/Mapping/raycasting_grid_map/raycasting_grid_map.py @@ -124,6 +124,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) draw_heatmap(pmap, minx, maxx, miny, maxy, xyreso) diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index 77c66db250..1c4d412207 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -242,6 +242,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.axis("equal") diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index e4f7dd4d97..d8ef09de85 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -79,6 +79,7 @@ def planning(self, sx, sy, gx, gy): if show_animation: # pragma: no cover plt.plot(self.calc_grid_position(current.x, self.minx), self.calc_grid_position(current.y, self.miny), "xc") + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if len(closed_set.keys()) % 10 == 0: diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py index 46ff3caf5c..e84f65d617 100644 --- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py +++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py @@ -534,6 +534,7 @@ def update_graph(self): def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, samples=None, start=None, end=None): plt.clf() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) for rnd in samples: diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py index d078338d67..5833f6e91e 100644 --- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py +++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py @@ -135,6 +135,7 @@ def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal): if target_ind % 1 == 0 and animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index 8e719f50e2..a8f6441328 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -69,6 +69,7 @@ def planning(self, sx, sy, gx, gy): if show_animation: # pragma: no cover plt.plot(self.calc_position(current.x, self.minx), self.calc_position(current.y, self.miny), "xc") + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if len(closedset.keys()) % 10 == 0: diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py index 8690fbe8db..de5db2a5aa 100644 --- a/PathPlanning/DubinsPath/dubins_path_planning.py +++ b/PathPlanning/DubinsPath/dubins_path_planning.py @@ -318,6 +318,7 @@ def test(): if show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(px, py, label="final course " + str(mode)) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index c2cc358b13..b0d020302f 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -268,6 +268,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): if show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g") diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py index 51ac612c28..414f6b4534 100644 --- a/PathPlanning/Eta3SplinePath/eta3_spline_path.py +++ b/PathPlanning/Eta3SplinePath/eta3_spline_path.py @@ -213,6 +213,7 @@ def test1(): if show_animation: # plot the path plt.plot(pos[0, :], pos[1, :]) + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.pause(1.0) diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index 1abda92646..861260e9c9 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -347,6 +347,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(tx, ty) diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index 049373ece2..5c39a1ee81 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -213,6 +213,7 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False): if grid_search_animation: fig, ax = plt.subplots() + # for stopping simulation with the esc key. fig.canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) @@ -268,6 +269,7 @@ def planning_animation(ox, oy, reso): # pragma: no cover if do_animation: for ipx, ipy in zip(px, py): plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(ox, oy, "-xb") diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py index 918469f0d3..a27293288c 100644 --- a/PathPlanning/HybridAStar/a_star.py +++ b/PathPlanning/HybridAStar/a_star.py @@ -78,6 +78,7 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr): # show graph if show_animation: # pragma: no cover plt.plot(current.x * reso, current.y * reso, "xc") + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if len(closedset.keys()) % 10 == 0: diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 70ffcee865..8a87be85a3 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -327,6 +327,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso): if show_animation: # pragma: no cover plt.plot(current.xlist[-1], current.ylist[-1], "xc") + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if len(closedList.keys()) % 10 == 0: diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index ef15022aca..a77b1cea74 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -267,6 +267,7 @@ def get_final_course(self, lastIndex): def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None): plt.clf() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py index d5cda0bc50..ba01526a2c 100644 --- a/PathPlanning/LQRPlanner/LQRplanner.py +++ b/PathPlanning/LQRPlanner/LQRplanner.py @@ -54,6 +54,7 @@ def lqr_planning(self, sx, sy, gx, gy, show_animation=True): # animation if show_animation: # pragma: no cover + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(sx, sy, "or") diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py index c1f45445ff..177131ac96 100644 --- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py +++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py @@ -102,6 +102,7 @@ def planning(self, animation=True, search_until_max_iter=True): def draw_graph(self, rnd=None): plt.clf() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py index 642f1cbff9..31904fa45d 100644 --- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py +++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py @@ -98,6 +98,7 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr): if show_animation: draw_heatmap(pmap) + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(ix, iy, "*k") diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 3fef2d0f7a..0a8ea037d1 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -194,6 +194,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): # show graph if show_animation and len(closedset.keys()) % 2 == 0: + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(current.x, current.y, "xg") diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index b8d533d4dc..406dbc1ee2 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -151,6 +151,7 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_ if show_animation: # pragma: no cover for i, _ in enumerate(time): plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.grid(True) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 9d0bc36272..f8e9908287 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -138,6 +138,7 @@ def get_random_node(self): def draw_graph(self, rnd=None): plt.clf() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py index 2a842aa42c..4599904cc1 100644 --- a/PathPlanning/RRTDubins/rrt_dubins.py +++ b/PathPlanning/RRTDubins/rrt_dubins.py @@ -106,6 +106,7 @@ def planning(self, animation=True, search_until_max_iter=True): def draw_graph(self, rnd=None): # pragma: no cover plt.clf() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py index d4601c0f12..65fac54c78 100644 --- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py +++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py @@ -112,6 +112,7 @@ def planning(self, animation=True, search_until_max_iter=True): def draw_graph(self, rnd=None): plt.clf() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py index d37ae90634..6ea66dc729 100644 --- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py +++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py @@ -122,6 +122,7 @@ def try_goal_path(self, node): def draw_graph(self, rnd=None): plt.clf() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index b2f8d4a57d..fbf9495790 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -395,6 +395,7 @@ def test(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(px, py, label="final course " + str(mode)) diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py index 86371b2051..7517669628 100644 --- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py +++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py @@ -185,6 +185,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): # show graph if show_animation and len(closedset.keys()) % 2 == 0: # pragma: no cover plt.plot(current.x, current.y, "xg") + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.pause(0.001) diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py index 1be91b2d20..98a12ab10c 100644 --- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py +++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py @@ -546,6 +546,7 @@ def animation(plant, controller, dt): steer = math.atan2(controller.history_u_2[t] * WB / v, 1.0) plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(plant.history_x, plant.history_y, "-r", label="trajectory") diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py index 0e22529623..77409c7f42 100644 --- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py +++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py @@ -224,6 +224,7 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py index 9cc36f231f..fe440a1734 100644 --- a/PathTracking/lqr_steer_control/lqr_steer_control.py +++ b/PathTracking/lqr_steer_control/lqr_steer_control.py @@ -203,6 +203,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py index 8bff2e107d..85aea2ed1b 100644 --- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py @@ -429,6 +429,7 @@ def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) if ox is not None: diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py index 838b780f48..685df1979d 100644 --- a/PathTracking/move_to_pose/move_to_pose.py +++ b/PathTracking/move_to_pose/move_to_pose.py @@ -93,6 +93,7 @@ def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover plt.plot(x_traj, y_traj, 'b--') + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index f9624956c4..473feea5da 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -166,6 +166,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plot_arrow(state.x, state.y, state.yaw) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 155eeec2ce..c6a3501368 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -149,6 +149,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index cbfa4c667e..604ed93f01 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -186,6 +186,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, ".r", label="course") diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index b6698be228..4bcafef2fa 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -235,6 +235,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index ef1282995b..892010c53d 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -365,6 +365,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index f8958b8c73..25712fc0cb 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -390,6 +390,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py index e73e8d0d4a..2a5acfab1f 100644 --- a/SLAM/GraphBasedSLAM/graph_based_slam.py +++ b/SLAM/GraphBasedSLAM/graph_based_slam.py @@ -303,6 +303,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py index 4a4d29eb5e..0b2802603c 100644 --- a/SLAM/iterative_closest_point/iterative_closest_point.py +++ b/SLAM/iterative_closest_point/iterative_closest_point.py @@ -36,6 +36,7 @@ def icp_matching(previous_points, current_points): if show_animation: # pragma: no cover plt.cla() + # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(previous_points[0, :], previous_points[1, :], ".r") From c09e20bc45b0c4408878b2d02cf17cc9d333dd16 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 14 Dec 2019 21:41:04 +0900 Subject: [PATCH 113/909] Update users_comments.md --- users_comments.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/users_comments.md b/users_comments.md index 2cfd7a4a9b..792f3a4d0d 100644 --- a/users_comments.md +++ b/users_comments.md @@ -404,6 +404,9 @@ URL: https://www.semanticscholar.org/paper/A-Review-of-Robot-Rescue-Simulation-P 7. Ghosh, Ritwika, et al. "CyPhyHouse: A Programming, Simulation, and Deployment Toolchain for Heterogeneous Distributed Coordination." arXiv preprint arXiv:1910.01557 (2019). URL: https://arxiv.org/abs/1910.01557 +8. Hahn, Carsten, et al. "Dynamic Path Planning with Stable Growing Neural Gas." (2019). +URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5.pdf + # Others - Autonomous Vehicle Readings https://richardkelley.io/readings From ca49613b53d8e45b169a3585eb7d0c8bf5057e64 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 23 Dec 2019 20:44:41 +0900 Subject: [PATCH 114/909] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index e740049b90..f48e9c0ef3 100644 --- a/README.md +++ b/README.md @@ -605,5 +605,8 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/ - [Ryohei Sasaki](https://github.com/rsasaki0109) +- [Göktuğ Karakaşlı](https://github.com/goktug97) + + From 6132629a70b9b47ec9a00dcf338650e22368d0f6 Mon Sep 17 00:00:00 2001 From: YILUN CHEN Date: Mon, 30 Dec 2019 17:52:05 +0800 Subject: [PATCH 115/909] fix environment dependencies --- environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/environment.yml b/environment.yml index 28ff9f1b4e..6866ed93ca 100644 --- a/environment.yml +++ b/environment.yml @@ -1,9 +1,11 @@ name: python_robotics dependencies: - python +- pip - matplotlib - scipy - numpy==1.15 - pandas +- coverage - pip: - cvxpy From 164f82c0dc022e71625b53f8e899e7b361a50c17 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 3 Jan 2020 08:47:18 +0900 Subject: [PATCH 116/909] Delete greetings.yml --- .github/workflows/greetings.yml | 13 ------------- 1 file changed, 13 deletions(-) delete mode 100644 .github/workflows/greetings.yml diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml deleted file mode 100644 index 3da4a69458..0000000000 --- a/.github/workflows/greetings.yml +++ /dev/null @@ -1,13 +0,0 @@ -name: Greetings - -on: [pull_request, issues] - -jobs: - greeting: - runs-on: ubuntu-latest - steps: - - uses: actions/first-interaction@v1 - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - issue-message: 'Message that will be displayed on users'' This is first issue for you on this project' - pr-message: 'Message that will be displayed on users'' This is first pr for you on this project' From 0c708766e13a167e5afe4a96ea6d4527c74a3e56 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Fri, 3 Jan 2020 14:01:47 +0100 Subject: [PATCH 117/909] :pencil: Removed unused class attribute in quintic_polynomials_planner.py --- .../quintic_polynomials_planner.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index 406dbc1ee2..e7c744d1fe 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -26,14 +26,7 @@ class QuinticPolynomial: def __init__(self, xs, vxs, axs, xe, vxe, axe, T): - # calc coefficient of quinic polynomial - self.xs = xs - self.vxs = vxs - self.axs = axs - self.xe = xe - self.vxe = vxe - self.axe = axe - + # calc coefficient of quintic polynomial self.a0 = xs self.a1 = vxs self.a2 = axs / 2.0 From 48cdd0817365ac10fc4031a39110dabf06cf4b75 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Fri, 3 Jan 2020 21:53:40 +0100 Subject: [PATCH 118/909] :pencil: Removed unused attribute in classes in frenet_optimal_trajectory.py --- .../frenet_optimal_trajectory.py | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index 25fdd7dbcf..1b62ea0187 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -49,13 +49,6 @@ class quintic_polynomial: def __init__(self, xs, vxs, axs, xe, vxe, axe, T): # calc coefficient of quintic polynomial - self.xs = xs - self.vxs = vxs - self.axs = axs - self.xe = xe - self.vxe = vxe - self.axe = axe - self.a0 = xs self.a1 = vxs self.a2 = axs / 2.0 @@ -99,12 +92,7 @@ class quartic_polynomial: def __init__(self, xs, vxs, axs, vxe, axe, T): - # calc coefficient of quintic polynomial - self.xs = xs - self.vxs = vxs - self.axs = axs - self.vxe = vxe - self.axe = axe + # calc coefficient of quartic polynomial self.a0 = xs self.a1 = vxs @@ -184,7 +172,7 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): fp.d_dd = [lat_qp.calc_second_derivative(t) for t in fp.t] fp.d_ddd = [lat_qp.calc_third_derivative(t) for t in fp.t] - # Loongitudinal motion planning (Velocity keeping) + # Longitudinal motion planning (Velocity keeping) for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S): tfp = copy.deepcopy(fp) lon_qp = quartic_polynomial(s0, c_speed, 0.0, tv, 0.0, Ti) From 1ac858b075b5db19ef4c6130ecd426d42a23eb77 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 4 Jan 2020 09:43:21 +0900 Subject: [PATCH 119/909] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f48e9c0ef3..ec42812753 100644 --- a/README.md +++ b/README.md @@ -607,6 +607,6 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/ - [Göktuğ Karakaşlı](https://github.com/goktug97) - +- [Guillaume Jacquenot](https://github.com/Gjacquenot) From 811bdc1d49ac5d04ebd05c18de906c0b3c7243dc Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Fri, 3 Jan 2020 23:13:36 +0100 Subject: [PATCH 120/909] :hammer: Refactored pure_pursuit.py - Added new method to state class - Removed global variable old_nearest_point_index - Created a class States that store the results --- PathTracking/pure_pursuit/pure_pursuit.py | 161 ++++++++++++---------- 1 file changed, 85 insertions(+), 76 deletions(-) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index dfe8a8e17e..49e32677f5 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -17,7 +17,6 @@ L = 2.9 # [m] wheel base of vehicle -old_nearest_point_index = None show_animation = True @@ -31,89 +30,106 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0): self.rear_x = self.x - ((L / 2) * math.cos(self.yaw)) self.rear_y = self.y - ((L / 2) * math.sin(self.yaw)) + def update(self, a, delta): -def update(state, a, delta): + self.x += self.v * math.cos(self.yaw) * dt + self.y += self.v * math.sin(self.yaw) * dt + self.yaw += self.v / L * math.tan(delta) * dt + self.v += a * dt + self.rear_x = self.x - ((L / 2) * math.cos(self.yaw)) + self.rear_y = self.y - ((L / 2) * math.sin(self.yaw)) - state.x = state.x + state.v * math.cos(state.yaw) * dt - state.y = state.y + state.v * math.sin(state.yaw) * dt - state.yaw = state.yaw + state.v / L * math.tan(delta) * dt - state.v = state.v + a * dt - state.rear_x = state.x - ((L / 2) * math.cos(state.yaw)) - state.rear_y = state.y - ((L / 2) * math.sin(state.yaw)) + def calc_distance(self, point_x, point_y): - return state + dx = self.rear_x - point_x + dy = self.rear_y - point_y + return math.hypot(dx, dy) -def PIDControl(target, current): - a = Kp * (target - current) +class States: - return a + def __init__(self): + self.x = [] + self.y = [] + self.yaw = [] + self.v = [] + self.t = [] + def append(self, t , state): + self.x.append(state.x) + self.y.append(state.y) + self.yaw.append(state.yaw) + self.v.append(state.v) + self.t.append(t) -def pure_pursuit_control(state, cx, cy, pind): - ind = calc_target_index(state, cx, cy) +def PIDControl(target, current): + a = Kp * (target - current) - if pind >= ind: - ind = pind + return a - if ind < len(cx): - tx = cx[ind] - ty = cy[ind] - else: - tx = cx[-1] - ty = cy[-1] - ind = len(cx) - 1 - alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw +class Trajectory: + def __init__(self, cx, cy): + self.cx = cx + self.cy = cy + self.old_nearest_point_index = None - Lf = k * state.v + Lfc + def __call__(self, state): + if self.old_nearest_point_index is None: + # search nearest point index + dx = [state.rear_x - icx for icx in self.cx] + dy = [state.rear_y - icy for icy in self.cy] + d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)] + ind = d.index(min(d)) + self.old_nearest_point_index = ind + else: + ind = self.old_nearest_point_index + distance_this_index = state.calc_distance(self.cx[ind], self.cy[ind]) + while True: + ind = ind + 1 if (ind + 1) < len(self.cx) else ind + distance_next_index = state.calc_distance(self.cx[ind], self.cy[ind]) + if distance_this_index < distance_next_index: + break + distance_this_index = distance_next_index + self.old_nearest_point_index = ind - delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0) + L = 0.0 - return delta, ind + Lf = k * state.v + Lfc -def calc_distance(state, point_x, point_y): + # search look ahead target point index + while Lf > L and (ind + 1) < len(self.cx): + dx = self.cx[ind] - state.rear_x + dy = self.cy[ind] - state.rear_y + L = math.hypot(dx, dy) + ind += 1 - dx = state.rear_x - point_x - dy = state.rear_y - point_y - return math.hypot(dx, dy) + return ind -def calc_target_index(state, cx, cy): +def pure_pursuit_control(state, trajectory, pind): - global old_nearest_point_index + ind = trajectory(state) + + if pind >= ind: + ind = pind - if old_nearest_point_index is None: - # search nearest point index - dx = [state.rear_x - icx for icx in cx] - dy = [state.rear_y - icy for icy in cy] - d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)] - ind = d.index(min(d)) - old_nearest_point_index = ind + if ind < len(trajectory.cx): + tx = trajectory.cx[ind] + ty = trajectory.cy[ind] else: - ind = old_nearest_point_index - distance_this_index = calc_distance(state, cx[ind], cy[ind]) - while True: - ind = ind + 1 if (ind + 1) < len(cx) else ind - distance_next_index = calc_distance(state, cx[ind], cy[ind]) - if distance_this_index < distance_next_index: - break - distance_this_index = distance_next_index - old_nearest_point_index = ind - - L = 0.0 + tx = trajectory.cx[-1] + ty = trajectory.cy[-1] + ind = len(trajectory.cx) - 1 + + alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw Lf = k * state.v + Lfc - # search look ahead target point index - while Lf > L and (ind + 1) < len(cx): - dx = cx[ind] - state.rear_x - dy = cy[ind] - state.rear_y - L = math.hypot(dx, dy) - ind += 1 + delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0) - return ind + return delta, ind def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): @@ -144,25 +160,18 @@ def main(): lastIndex = len(cx) - 1 time = 0.0 - x = [state.x] - y = [state.y] - yaw = [state.yaw] - v = [state.v] - t = [0.0] - target_ind = calc_target_index(state, cx, cy) + states = States() + states.append(time, state) + trajectory = Trajectory(cx, cy) + target_ind = trajectory(state) while T >= time and lastIndex > target_ind: ai = PIDControl(target_speed, state.v) - di, target_ind = pure_pursuit_control(state, cx, cy, target_ind) - state = update(state, ai, di) - - time = time + dt + di, target_ind = pure_pursuit_control(state, trajectory, target_ind) + state.update(ai, di) - x.append(state.x) - y.append(state.y) - yaw.append(state.yaw) - v.append(state.v) - t.append(time) + time += dt + states.append(time, state) if show_animation: # pragma: no cover plt.cla() @@ -171,7 +180,7 @@ def main(): lambda event: [exit(0) if event.key == 'escape' else None]) plot_arrow(state.x, state.y, state.yaw) plt.plot(cx, cy, "-r", label="course") - plt.plot(x, y, "-b", label="trajectory") + plt.plot(states.x, states.y, "-b", label="trajectory") plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") plt.axis("equal") plt.grid(True) @@ -184,7 +193,7 @@ def main(): if show_animation: # pragma: no cover plt.cla() plt.plot(cx, cy, ".r", label="course") - plt.plot(x, y, "-b", label="trajectory") + plt.plot(states.x, states.y, "-b", label="trajectory") plt.legend() plt.xlabel("x[m]") plt.ylabel("y[m]") @@ -192,7 +201,7 @@ def main(): plt.grid(True) plt.subplots(1) - plt.plot(t, [iv * 3.6 for iv in v], "-r") + plt.plot(states.t, [iv * 3.6 for iv in states.v], "-r") plt.xlabel("Time[s]") plt.ylabel("Speed[km/h]") plt.grid(True) From f81140e65bcf4648c8aae4bdf14e48080bfbd706 Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 4 Jan 2020 09:45:05 +0100 Subject: [PATCH 121/909] :pencil: Renamed method name __call__ with search_target_index in Trajectory class Thanks to @AtsushiSakai remarks https://github.com/AtsushiSakai/PythonRobotics/pull/269#discussion_r363005681 --- PathTracking/pure_pursuit/pure_pursuit.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index 49e32677f5..d7cf8fe9f8 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -75,7 +75,7 @@ def __init__(self, cx, cy): self.cy = cy self.old_nearest_point_index = None - def __call__(self, state): + def search_target_index(self, state): if self.old_nearest_point_index is None: # search nearest point index dx = [state.rear_x - icx for icx in self.cx] @@ -110,7 +110,7 @@ def __call__(self, state): def pure_pursuit_control(state, trajectory, pind): - ind = trajectory(state) + ind = trajectory.search_target_index(state) if pind >= ind: ind = pind @@ -163,7 +163,7 @@ def main(): states = States() states.append(time, state) trajectory = Trajectory(cx, cy) - target_ind = trajectory(state) + target_ind = trajectory.search_target_index(state) while T >= time and lastIndex > target_ind: ai = PIDControl(target_speed, state.v) From bfc874492390a803e803992324777376a6b12e8c Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 4 Jan 2020 09:50:41 +0100 Subject: [PATCH 122/909] :hammer: Refcatored modification in pure_pursuit.py Thanks to @AtsushiSakai remark https://github.com/AtsushiSakai/PythonRobotics/pull/269#discussion_r363005775 --- PathTracking/pure_pursuit/pure_pursuit.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index d7cf8fe9f8..4e8b1bedef 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -80,8 +80,8 @@ def search_target_index(self, state): # search nearest point index dx = [state.rear_x - icx for icx in self.cx] dy = [state.rear_y - icy for icy in self.cy] - d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)] - ind = d.index(min(d)) + d = np.hypot(dx, dy) + ind = np.argmin(d) self.old_nearest_point_index = ind else: ind = self.old_nearest_point_index @@ -100,9 +100,7 @@ def search_target_index(self, state): # search look ahead target point index while Lf > L and (ind + 1) < len(self.cx): - dx = self.cx[ind] - state.rear_x - dy = self.cy[ind] - state.rear_y - L = math.hypot(dx, dy) + L = state.calc_distance(self.cx[ind], self.cy[ind]) ind += 1 return ind From 3bdf2555345b93c89068549fc1fd87ce7a7e92dc Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 4 Jan 2020 09:57:30 +0100 Subject: [PATCH 123/909] :hammer: Removed unnecessary parenthesis in pure_pursuit.py --- PathTracking/pure_pursuit/pure_pursuit.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index 4e8b1bedef..bfc3c7ccf9 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -136,7 +136,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): """ if not isinstance(x, float): - for (ix, iy, iyaw) in zip(x, y, yaw): + for ix, iy, iyaw in zip(x, y, yaw): plot_arrow(ix, iy, iyaw) else: plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), From 08428bed153dfe49085e63becd6f399a713dd57c Mon Sep 17 00:00:00 2001 From: Guillaume Jacquenot Date: Sat, 4 Jan 2020 09:57:47 +0100 Subject: [PATCH 124/909] :pencil: Added Gjacquenot as author as suggested by @AtsushiSakai --- PathTracking/pure_pursuit/pure_pursuit.py | 1 + 1 file changed, 1 insertion(+) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index bfc3c7ccf9..bcaf869726 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -3,6 +3,7 @@ Path tracking simulation with pure pursuit steering control and PID speed control. author: Atsushi Sakai (@Atsushi_twi) + Guillaume Jacquenot (@Gjacquenot) """ import numpy as np From f19fdacb014af4af1395519746b2cd7f6c89b8cc Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 5 Jan 2020 13:47:32 +0900 Subject: [PATCH 125/909] change default mode for DWA --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 891e68cbe8..2dab2f301f 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -293,4 +293,4 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): if __name__ == '__main__': - main(robot_type=RobotType.rectangle) + main(robot_type=RobotType.circle) From 5ceb83783e7c2e561afac87f9bb899609fe7770e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 5 Jan 2020 13:53:49 +0900 Subject: [PATCH 126/909] Add node valid check to fix #271 --- PathPlanning/RRT/rrt.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index b590d7b1cb..4acea0c10e 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -140,7 +140,7 @@ def draw_graph(self, rnd=None): plt.clf() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: @@ -175,6 +175,10 @@ def get_nearest_node_index(node_list, rnd_node): @staticmethod def check_collision(node, obstacleList): + + if node is None: + return False + for (ox, oy, size) in obstacleList: dx_list = [ox - x for x in node.path_x] dy_list = [oy - y for y in node.path_y] From fbeeefb52204ec50dc5686d36b78b83867e41080 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 5 Jan 2020 20:45:12 +0900 Subject: [PATCH 127/909] code cleanup for quintic_polynomials_planner.py --- .../quintic_polynomials_planner.py | 25 +++++++++---------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index e7c744d1fe..8600f1d7bd 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -24,18 +24,17 @@ class QuinticPolynomial: - def __init__(self, xs, vxs, axs, xe, vxe, axe, T): - + def __init__(self, xs, vxs, axs, xe, vxe, axe, time): # calc coefficient of quintic polynomial self.a0 = xs self.a1 = vxs self.a2 = axs / 2.0 - A = np.array([[T**3, T**4, T**5], - [3 * T ** 2, 4 * T ** 3, 5 * T ** 4], - [6 * T, 12 * T ** 2, 20 * T ** 3]]) - b = np.array([xe - self.a0 - self.a1 * T - self.a2 * T**2, - vxe - self.a1 - 2 * self.a2 * T, + A = np.array([[time ** 3, time ** 4, time ** 5], + [3 * time ** 2, 4 * time ** 3, 5 * time ** 4], + [6 * time, 12 * time ** 2, 20 * time ** 3]]) + b = np.array([xe - self.a0 - self.a1 * time - self.a2 * time ** 2, + vxe - self.a1 - 2 * self.a2 * time, axe - 2 * self.a2]) x = np.linalg.solve(A, b) @@ -44,24 +43,24 @@ def __init__(self, xs, vxs, axs, xe, vxe, axe, T): self.a5 = x[2] def calc_point(self, t): - xt = self.a0 + self.a1 * t + self.a2 * t**2 + \ - self.a3 * t**3 + self.a4 * t**4 + self.a5 * t**5 + xt = self.a0 + self.a1 * t + self.a2 * t ** 2 + \ + self.a3 * t ** 3 + self.a4 * t ** 4 + self.a5 * t ** 5 return xt def calc_first_derivative(self, t): xt = self.a1 + 2 * self.a2 * t + \ - 3 * self.a3 * t**2 + 4 * self.a4 * t**3 + 5 * self.a5 * t**4 + 3 * self.a3 * t ** 2 + 4 * self.a4 * t ** 3 + 5 * self.a5 * t ** 4 return xt def calc_second_derivative(self, t): - xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2 + 20 * self.a5 * t**3 + xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t ** 2 + 20 * self.a5 * t ** 3 return xt def calc_third_derivative(self, t): - xt = 6 * self.a3 + 24 * self.a4 * t + 60 * self.a5 * t**2 + xt = 6 * self.a3 + 24 * self.a4 * t + 60 * self.a5 * t ** 2 return xt @@ -146,7 +145,7 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_ plt.cla() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [exit(0) if event.key == 'escape' else None]) plt.grid(True) plt.axis("equal") plot_arrow(sx, sy, syaw) From c50bba7c0489493dc144e9202aad65ab13abbcaa Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 5 Jan 2020 22:12:37 +0900 Subject: [PATCH 128/909] add jupyter docs --- .../quintic_polynomials_planner.ipynb | 56 +++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb new file mode 100644 index 0000000000..bc829f2e13 --- /dev/null +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Quintic polynomials planner" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Quintic polynomials for one dimension robot motion\n", + "\n", + "$e^{i\\pi} + 1 = 0$\n", + "\n", + "\n" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From e4fb755471bc476d7fe21f17f1125bc311466baf Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 5 Jan 2020 22:24:46 +0900 Subject: [PATCH 129/909] update jupyter docs --- .../quintic_polynomials_planner.ipynb | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb index bc829f2e13..7b83a87b7e 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb @@ -12,7 +12,21 @@ { "cell_type": "markdown", "source": [ - "## Quintic polynomials for one dimension robot motion\n", + "## Quintic polynomials for one dimensional robot motion\n", + "\n", + "We assume a dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n", + "\n", + "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4$\n", + "\n", + "a_0, a_1. a_2, a_3 are parameters of the quintic polynomial.\n", + "\n", + "So, when time is 0,\n", + "\n", + "$x(0) = a_0 = x_s$\n", + "\n", + "x_s is a start x position.\n", + "\n", + "\n", "\n", "$e^{i\\pi} + 1 = 0$\n", "\n", From 763ca4fd15f4ae4bd272709bf5e187475ca4e4a3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 5 Jan 2020 22:37:08 +0900 Subject: [PATCH 130/909] update jupyter docs --- .../quintic_polynomials_planner.ipynb | 12 +++++++++--- .../quintic_polynomials_planner.py | 1 + 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb index 7b83a87b7e..804b7fba13 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb @@ -24,13 +24,19 @@ "\n", "$x(0) = a_0 = x_s$\n", "\n", - "x_s is a start x position.\n", + "$x_s$ is a start x position.\n", "\n", + "Then, differentiating this equation with t, \n", "\n", + "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3$\n", "\n", - "$e^{i\\pi} + 1 = 0$\n", + "So, when time is 0,\n", + "\n", + "$x'(0) = a_1 = v_s$\n", + "\n", + "$v_s$ is a initial speed for x axis.\n", "\n", - "\n" + "=== TBD ==== \n" ], "metadata": { "collapsed": false diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index 8600f1d7bd..5538731dcd 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -26,6 +26,7 @@ class QuinticPolynomial: def __init__(self, xs, vxs, axs, xe, vxe, axe, time): # calc coefficient of quintic polynomial + # See jupyter notebook document for derivation of this equation. self.a0 = xs self.a1 = vxs self.a2 = axs / 2.0 From 0de06534d22e22d84b4fe65ad973525e16e312c0 Mon Sep 17 00:00:00 2001 From: Yilun Chen Date: Fri, 17 Jan 2020 19:17:28 -0500 Subject: [PATCH 131/909] cleanup in dynamic window approach --- .../DynamicWindowApproach/dynamic_window_approach.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 92fd08bb40..9e9d34d721 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -22,7 +22,7 @@ def dwa_control(x, config, goal, ob): dw = calc_dynamic_window(x, config) - u, trajectory = calc_final_input(x, dw, config, goal, ob) + u, trajectory = calc_control_and_trajectory(x, dw, config, goal, ob) return u, trajectory @@ -77,9 +77,9 @@ def motion(x, u, dt): motion model """ - x[2] += u[1] * dt x[0] += u[0] * math.cos(x[2]) * dt x[1] += u[0] * math.sin(x[2]) * dt + x[2] += u[1] * dt x[3] = u[0] x[4] = u[1] @@ -101,7 +101,7 @@ def calc_dynamic_window(x, config): x[4] - config.max_dyawrate * config.dt, x[4] + config.max_dyawrate * config.dt] - # [vmin,vmax, yaw_rate min, yaw_rate max] + # [vmin, vmax, yaw_rate min, yaw_rate max] dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]), max(Vs[2], Vd[2]), min(Vs[3], Vd[3])] @@ -124,7 +124,7 @@ def predict_trajectory(x_init, v, y, config): return traj -def calc_final_input(x, dw, config, goal, ob): +def calc_control_and_trajectory(x, dw, config, goal, ob): """ calculation final input with dynamic window """ From dc612e6f2000a67b5542cc48303bf0b505350926 Mon Sep 17 00:00:00 2001 From: Yilun Chen Date: Sun, 19 Jan 2020 11:25:52 -0500 Subject: [PATCH 132/909] minor fix in dijkstra.py --- .gitignore | 1 + PathPlanning/Dijkstra/dijkstra.py | 18 ++++++++---------- 2 files changed, 9 insertions(+), 10 deletions(-) diff --git a/.gitignore b/.gitignore index 272dc63b6d..c971b8f9c5 100644 --- a/.gitignore +++ b/.gitignore @@ -70,3 +70,4 @@ target/ .ipynb_checkpoints matplotrecorder/* +.vscode/settings.json diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index 62da6d761b..ee88b3f9f1 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -33,7 +33,7 @@ def __init__(self, x, y, cost, pind): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.pind = pind + self.pind = pind # index of previous Node def __str__(self): return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind) @@ -88,10 +88,10 @@ def planning(self, sx, sy, gx, gy): closedset[c_id] = current # expand search grid based on motion model - for i, _ in enumerate(self.motion): - node = self.Node(current.x + self.motion[i][0], - current.y + self.motion[i][1], - current.cost + self.motion[i][2], c_id) + for move_x, move_y, move_cost in self.motion: + node = self.Node(current.x + move_x, + current.y + move_y, + current.cost + move_cost, c_id) n_id = self.calc_index(node) if n_id in closedset: @@ -124,11 +124,6 @@ def calc_final_path(self, ngoal, closedset): return rx, ry - def calc_heuristic(self, n1, n2): - w = 1.0 # weight of heuristic - d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) - return d - def calc_position(self, index, minp): pos = index*self.reso+minp return pos @@ -242,6 +237,9 @@ def main(): dijkstra = Dijkstra(ox, oy, grid_size, robot_radius) rx, ry = dijkstra.planning(sx, sy, gx, gy) + print(rx) + print(ry) + if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") plt.show() From f261da52f0301041829dc20d8845372cf98a9a73 Mon Sep 17 00:00:00 2001 From: Yilun Chen Date: Sun, 19 Jan 2020 11:29:05 -0500 Subject: [PATCH 133/909] remove print --- PathPlanning/Dijkstra/dijkstra.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index ee88b3f9f1..bb1fb3aa63 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -237,9 +237,6 @@ def main(): dijkstra = Dijkstra(ox, oy, grid_size, robot_radius) rx, ry = dijkstra.planning(sx, sy, gx, gy) - print(rx) - print(ry) - if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") plt.show() From 1c081ed3e0ccfbc20214a505b2fbd925427e0fa1 Mon Sep 17 00:00:00 2001 From: Yilun Chen Date: Sun, 19 Jan 2020 18:35:52 -0500 Subject: [PATCH 134/909] address the comment --- PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 9e9d34d721..b8395e5ff8 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -77,9 +77,9 @@ def motion(x, u, dt): motion model """ + x[2] += u[1] * dt x[0] += u[0] * math.cos(x[2]) * dt x[1] += u[0] * math.sin(x[2]) * dt - x[2] += u[1] * dt x[3] = u[0] x[4] = u[1] From cd65de271462b9cc50a130c708f1d2c0e005e953 Mon Sep 17 00:00:00 2001 From: Atsushi Date: Tue, 21 Jan 2020 10:58:02 +0900 Subject: [PATCH 135/909] add math descriptions --- .../quintic_polynomials_planner.ipynb | 119 ++++++++++++++---- 1 file changed, 93 insertions(+), 26 deletions(-) diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb index 804b7fba13..f094ae2c15 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb @@ -2,45 +2,112 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Quintic polynomials planner" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Quintic polynomials for one dimensional robot motion\n", "\n", - "We assume a dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n", + "We assume a one-dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n", "\n", - "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4$\n", + "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(1)\n", "\n", - "a_0, a_1. a_2, a_3 are parameters of the quintic polynomial.\n", + "$a_0, a_1. a_2, a_3, a_4, a_5$ are parameters of the quintic polynomial.\n", "\n", - "So, when time is 0,\n", + "It is assumed that terminal states (start and end) are known as boundary conditions.\n", + "\n", + "Start position, velocity, and acceleration are $x_s, v_s, a_s$ respectively.\n", "\n", - "$x(0) = a_0 = x_s$\n", + "End position, velocity, and acceleration are $x_e, v_e, a_e$ respectively.\n", "\n", - "$x_s$ is a start x position.\n", + "So, when time is 0.\n", "\n", - "Then, differentiating this equation with t, \n", + "$x(0) = a_0 = x_s$ -- (2)\n", "\n", - "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3$\n", + "Then, differentiating the equation (1) with t, \n", + "\n", + "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4$ -- (3)\n", "\n", "So, when time is 0,\n", "\n", - "$x'(0) = a_1 = v_s$\n", + "$x'(0) = a_1 = v_s$ -- (4)\n", "\n", - "$v_s$ is a initial speed for x axis.\n", + "Then, differentiating the equation (3) with t again, \n", "\n", - "=== TBD ==== \n" - ], - "metadata": { - "collapsed": false - } + "$x''(t) = 2a_2+6a_3t+12a_4t^2$ -- (5)\n", + "\n", + "So, when time is 0,\n", + "\n", + "$x''(0) = 2a_2 = a_s$ -- (6)\n", + "\n", + "so, we can calculate $a_0$, $a_1$, $a_2$ with eq. (2), (4), (6) and boundary conditions.\n", + "\n", + "$a_3, a_4, a_5$ are still unknown in eq(1).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We assume that the end time for a maneuver is $T$, we can get these equations from eq (1), (3), (5):\n", + "\n", + "$x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e$ -- (7)\n", + "\n", + "$x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e$ -- (8)\n", + "\n", + "$x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e$ -- (9)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From eq (7), (8), (9), we can calculate $a_3, a_4, a_5$ to solve the linear equations.\n", + "\n", + "$Ax=b$\n", + "\n", + "$\\begin{bmatrix} T^3 & T^4 & T^5 \\\\ 3T^2 & 4T^3 & 5T^4 \\\\ 6T & 12T^2 & 20T^3 \\end{bmatrix}\n", + "\\begin{bmatrix} a_3\\\\ a_4\\\\ a_5\\end{bmatrix}=\\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\\\ v_e-v_s-a_sT\\\\ a_e-a_s\\end{bmatrix}$\n", + "\n", + "We can get all unknown parameters now" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quintic polynomials for two dimensional robot motion (x-y)\n", + "\n", + "If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane.\n", + "\n", + "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(10)\n", + "\n", + "$y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5$ --(11)\n", + "\n", + "It is assumed that terminal states (start and end) are known as boundary conditions.\n", + "\n", + "Start position, orientation, velocity, and acceleration are $x_s, y_s, \\theta_s, v_s, a_s$ respectively.\n", + "\n", + "End position, orientation, velocity, and acceleration are $x_e, y_e. \\theta_e, v_e, a_e$ respectively.\n", + "\n", + "Each velocity and acceleration boundary condition can be calculated with each orientation.\n", + "\n", + "$v_{xs}=v_scos(\\theta_s), v_{ys}=v_ssin(\\theta_s)$\n", + "\n", + "$v_{xe}=v_ecos(\\theta_e), v_{ye}=v_esin(\\theta_e)$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -52,25 +119,25 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.7.5" }, "pycharm": { "stem_cell": { "cell_type": "raw", - "source": [], "metadata": { "collapsed": false - } + }, + "source": [] } } }, "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "nbformat_minor": 1 +} From 33f4b944129b176c760879de7015f6183bb32950 Mon Sep 17 00:00:00 2001 From: Atsushi Date: Tue, 21 Jan 2020 11:12:15 +0900 Subject: [PATCH 136/909] keep DRY --- .../frenet_optimal_trajectory.py | 58 +++++-------------- 1 file changed, 13 insertions(+), 45 deletions(-) diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index 1b62ea0187..b113bcbe32 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -17,6 +17,17 @@ import copy import math import cubic_spline_planner +import sys +import os + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../QuinticPolynomialsPlanner/") + +try: + from quintic_polynomials_planner import QuinticPolynomial +except ImportError: + raise + SIM_LOOP = 500 @@ -44,50 +55,6 @@ show_animation = True -class quintic_polynomial: - - def __init__(self, xs, vxs, axs, xe, vxe, axe, T): - - # calc coefficient of quintic polynomial - self.a0 = xs - self.a1 = vxs - self.a2 = axs / 2.0 - - A = np.array([[T**3, T**4, T**5], - [3 * T ** 2, 4 * T ** 3, 5 * T ** 4], - [6 * T, 12 * T ** 2, 20 * T ** 3]]) - b = np.array([xe - self.a0 - self.a1 * T - self.a2 * T**2, - vxe - self.a1 - 2 * self.a2 * T, - axe - 2 * self.a2]) - x = np.linalg.solve(A, b) - - self.a3 = x[0] - self.a4 = x[1] - self.a5 = x[2] - - def calc_point(self, t): - xt = self.a0 + self.a1 * t + self.a2 * t**2 + \ - self.a3 * t**3 + self.a4 * t**4 + self.a5 * t**5 - - return xt - - def calc_first_derivative(self, t): - xt = self.a1 + 2 * self.a2 * t + \ - 3 * self.a3 * t**2 + 4 * self.a4 * t**3 + 5 * self.a5 * t**4 - - return xt - - def calc_second_derivative(self, t): - xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2 + 20 * self.a5 * t**3 - - return xt - - def calc_third_derivative(self, t): - xt = 6 * self.a3 + 24 * self.a4 * t + 60 * self.a5 * t**2 - - return xt - - class quartic_polynomial: def __init__(self, xs, vxs, axs, vxe, axe, T): @@ -164,7 +131,8 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): for Ti in np.arange(MINT, MAXT, DT): fp = Frenet_path() - lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti) + # lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti) + lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti) fp.t = [t for t in np.arange(0.0, Ti, DT)] fp.d = [lat_qp.calc_point(t) for t in fp.t] From d4cb0f981d688d8df2c65ca846544a4d8494f9df Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 27 Jan 2020 17:39:10 +0900 Subject: [PATCH 137/909] fix cal_covariance func and clean up code and added jupyter doc --- .../particle_filter/particle_filter.ipynb | 62 +++++++++++ .../particle_filter/particle_filter.py | 100 +++++++++--------- 2 files changed, 114 insertions(+), 48 deletions(-) create mode 100644 Localization/particle_filter/particle_filter.ipynb diff --git a/Localization/particle_filter/particle_filter.ipynb b/Localization/particle_filter/particle_filter.ipynb new file mode 100644 index 0000000000..46825b639d --- /dev/null +++ b/Localization/particle_filter/particle_filter.ipynb @@ -0,0 +1,62 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Particle Filter\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## How to calculate covariance matrix from particles\n", + "\n", + "$\\Xi_{j,k}=\\frac{1}{1-\\sum^N_{i=1}(w^i)^2}\\sum^N_{i=1}w^i(x^i_j-\\mu_j)(x^i_k-\\mu_k)$\n", + "\n", + "Ref:\n", + "\n", + "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://www.visiondummy.com/2014/04/draw-error-ellipse-representing-covariance-matrix/)\n" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index bfef3ed929..6eca4eb53b 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -37,20 +37,20 @@ def calc_input(): return u -def observation(xTrue, xd, u, RF_ID): - xTrue = motion_model(xTrue, u) +def observation(x_true, xd, u, rf_id): + x_true = motion_model(x_true, u) # add noise to gps x-y z = np.zeros((0, 3)) - for i in range(len(RF_ID[:, 0])): + for i in range(len(rf_id[:, 0])): - dx = xTrue[0, 0] - RF_ID[i, 0] - dy = xTrue[1, 0] - RF_ID[i, 1] + dx = x_true[0, 0] - rf_id[i, 0] + dy = x_true[1, 0] - rf_id[i, 1] d = math.hypot(dx, dy) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise - zi = np.array([[dn, RF_ID[i, 0], RF_ID[i, 1]]]) + zi = np.array([[dn, rf_id[i, 0], rf_id[i, 1]]]) z = np.vstack((z, zi)) # add noise to input @@ -60,7 +60,7 @@ def observation(xTrue, xd, u, RF_ID): xd = motion_model(xd, ud) - return xTrue, z, xd, ud + return x_true, z, xd, ud def motion_model(x, u): @@ -86,13 +86,17 @@ def gauss_likelihood(x, sigma): return p -def calc_covariance(xEst, px, pw): +def calc_covariance(x_est, px, pw): + """ + calculate covariance matrix + see ipynb doc + """ cov = np.zeros((3, 3)) - - for i in range(px.shape[1]): - dx = (px[:, i] - xEst)[0:3] - cov += pw[0, i] * dx.dot(dx.T) - cov /= NP + n_particle = px.shape[1] + for i in range(n_particle): + dx = (px[:, i:i + 1] - x_est)[0:3] + cov += pw[0, i] * dx @ dx.T + cov *= 1.0 / (1.0 - pw @ pw.T) return cov @@ -125,13 +129,13 @@ def pf_localization(px, pw, z, u): pw = pw / pw.sum() # normalize - xEst = px.dot(pw.T) - PEst = calc_covariance(xEst, px, pw) + x_est = px.dot(pw.T) + p_est = calc_covariance(x_est, px, pw) N_eff = 1.0 / (pw.dot(pw.T))[0, 0] # Effective particle number if N_eff < NTh: px, pw = re_sampling(px, pw) - return xEst, PEst, px, pw + return x_est, p_est, px, pw def re_sampling(px, pw): @@ -140,8 +144,8 @@ def re_sampling(px, pw): """ w_cum = np.cumsum(pw) - base = np.arange(0.0, 1.0, 1/NP) - re_sample_id = base + np.random.uniform(0, 1/NP) + base = np.arange(0.0, 1.0, 1 / NP) + re_sample_id = base + np.random.uniform(0, 1 / NP) indexes = [] ind = 0 for ip in range(NP): @@ -155,9 +159,9 @@ def re_sampling(px, pw): return px, pw -def plot_covariance_ellipse(xEst, PEst): # pragma: no cover - Pxy = PEst[0:2, 0:2] - eig_val, eig_vec = np.linalg.eig(Pxy) +def plot_covariance_ellipse(x_est, p_est): # pragma: no cover + p_xy = p_est[0:2, 0:2] + eig_val, eig_vec = np.linalg.eig(p_xy) if eig_val[0] >= eig_val[1]: big_ind = 0 @@ -186,8 +190,8 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover Rot = np.array([[math.cos(angle), -math.sin(angle)], [math.sin(angle), math.cos(angle)]]) fx = Rot.dot(np.array([[x, y]])) - px = np.array(fx[0, :] + xEst[0, 0]).flatten() - py = np.array(fx[1, :] + xEst[1, 0]).flatten() + px = np.array(fx[0, :] + x_est[0, 0]).flatten() + py = np.array(fx[1, :] + x_est[1, 0]).flatten() plt.plot(px, py, "--r") @@ -197,54 +201,54 @@ def main(): time = 0.0 # RF_ID positions [x, y] - RFi_ID = np.array([[10.0, 0.0], - [10.0, 10.0], - [0.0, 15.0], - [-5.0, 20.0]]) + rf_id = np.array([[10.0, 0.0], + [10.0, 10.0], + [0.0, 15.0], + [-5.0, 20.0]]) # State Vector [x y yaw v]' - xEst = np.zeros((4, 1)) - xTrue = np.zeros((4, 1)) + x_est = np.zeros((4, 1)) + x_true = np.zeros((4, 1)) px = np.zeros((4, NP)) # Particle store pw = np.zeros((1, NP)) + 1.0 / NP # Particle weight - xDR = np.zeros((4, 1)) # Dead reckoning + x_dr = np.zeros((4, 1)) # Dead reckoning # history - hxEst = xEst - hxTrue = xTrue - hxDR = xTrue + h_x_est = x_est + h_x_true = x_true + h_x_dr = x_true while SIM_TIME >= time: time += DT u = calc_input() - xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFi_ID) + x_true, z, x_dr, ud = observation(x_true, x_dr, u, rf_id) - xEst, PEst, px, pw = pf_localization(px, pw, z, ud) + x_est, PEst, px, pw = pf_localization(px, pw, z, ud) # store data history - hxEst = np.hstack((hxEst, xEst)) - hxDR = np.hstack((hxDR, xDR)) - hxTrue = np.hstack((hxTrue, xTrue)) + h_x_est = np.hstack((h_x_est, x_est)) + h_x_dr = np.hstack((h_x_dr, x_dr)) + h_x_true = np.hstack((h_x_true, x_true)) if show_animation: plt.cla() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [exit(0) if event.key == 'escape' else None]) for i in range(len(z[:, 0])): - plt.plot([xTrue[0, 0], z[i, 1]], [xTrue[1, 0], z[i, 2]], "-k") - plt.plot(RFi_ID[:, 0], RFi_ID[:, 1], "*k") + plt.plot([x_true[0, 0], z[i, 1]], [x_true[1, 0], z[i, 2]], "-k") + plt.plot(rf_id[:, 0], rf_id[:, 1], "*k") plt.plot(px[0, :], px[1, :], ".r") - plt.plot(np.array(hxTrue[0, :]).flatten(), - np.array(hxTrue[1, :]).flatten(), "-b") - plt.plot(np.array(hxDR[0, :]).flatten(), - np.array(hxDR[1, :]).flatten(), "-k") - plt.plot(np.array(hxEst[0, :]).flatten(), - np.array(hxEst[1, :]).flatten(), "-r") - plot_covariance_ellipse(xEst, PEst) + plt.plot(np.array(h_x_true[0, :]).flatten(), + np.array(h_x_true[1, :]).flatten(), "-b") + plt.plot(np.array(h_x_dr[0, :]).flatten(), + np.array(h_x_dr[1, :]).flatten(), "-k") + plt.plot(np.array(h_x_est[0, :]).flatten(), + np.array(h_x_est[1, :]).flatten(), "-r") + plot_covariance_ellipse(x_est, PEst) plt.axis("equal") plt.grid(True) plt.pause(0.001) From 123c4f777ccfc49a0a11a8b99c9a63013d46b9a7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 27 Jan 2020 19:57:48 +0900 Subject: [PATCH 138/909] update ipynb doc. --- Localization/particle_filter/particle_filter.ipynb | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/Localization/particle_filter/particle_filter.ipynb b/Localization/particle_filter/particle_filter.ipynb index 46825b639d..65f1e026df 100644 --- a/Localization/particle_filter/particle_filter.ipynb +++ b/Localization/particle_filter/particle_filter.ipynb @@ -9,7 +9,7 @@ } }, "source": [ - "# Particle Filter\n", + "# Particle Filter Localization\n", "\n" ] }, @@ -18,11 +18,21 @@ "source": [ "## How to calculate covariance matrix from particles\n", "\n", + "The covariance matrix $\\Xi$ from particle information is calculated by the following equation: \n", + "\n", "$\\Xi_{j,k}=\\frac{1}{1-\\sum^N_{i=1}(w^i)^2}\\sum^N_{i=1}w^i(x^i_j-\\mu_j)(x^i_k-\\mu_k)$\n", "\n", + "- $\\Xi_{j,k}$ is covariance matrix element at row $i$ and column $k$.\n", + "\n", + "- $w^i$ is the weight of the $i$ th particle. \n", + "\n", + "- $x^i_j$ is the $j$ th state of the $i$ th particle. \n", + "\n", + "- $\\mu_j$ is the $j$ th mean state of particles.\n", + "\n", "Ref:\n", "\n", - "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://www.visiondummy.com/2014/04/draw-error-ellipse-representing-covariance-matrix/)\n" + "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://arxiv.org/pdf/1801.07000.pdf)\n" ], "metadata": { "collapsed": false From 4e323212e51f32b2e11a94a4c35bd1ca9e1ef4ef Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 27 Jan 2020 20:25:40 +0900 Subject: [PATCH 139/909] start fix the bug. --- PathPlanning/DubinsPath/dubins_path_planning.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py index 410e8db01a..ce32a4668b 100644 --- a/PathPlanning/DubinsPath/dubins_path_planning.py +++ b/PathPlanning/DubinsPath/dubins_path_planning.py @@ -212,7 +212,6 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0) def generate_course(length, mode, c, D_ANGLE): - px = [0.0] py = [0.0] pyaw = [0.0] @@ -299,7 +298,6 @@ def main(): def test(): - NTEST = 5 for i in range(NTEST): @@ -320,7 +318,7 @@ def test(): plt.cla() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(px, py, label="final course " + str(mode)) # plotting From 18ac00dced6448f12241c8d9d42d0e06709cc17a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 28 Jan 2020 21:35:49 +0900 Subject: [PATCH 140/909] fix the dubins planning bug and code clean up --- .../DubinsPath/dubins_path_planning.py | 208 +++++++++--------- PathPlanning/RRTDubins/rrt_dubins.py | 3 + PathPlanning/RRTStarDubins/rrt_star_dubins.py | 5 +- tests/test_dubins_path_planning.py | 36 ++- 4 files changed, 143 insertions(+), 109 deletions(-) diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py index ce32a4668b..b72801a6b3 100644 --- a/PathPlanning/DubinsPath/dubins_path_planning.py +++ b/PathPlanning/DubinsPath/dubins_path_planning.py @@ -21,7 +21,7 @@ def pi_2_pi(angle): return (angle + math.pi) % (2 * math.pi) - math.pi -def LSL(alpha, beta, d): +def left_straight_left(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -38,12 +38,11 @@ def LSL(alpha, beta, d): t = mod2pi(-alpha + tmp1) p = math.sqrt(p_squared) q = mod2pi(beta - tmp1) - # print(np.rad2deg(t), p, np.rad2deg(q)) return t, p, q, mode -def RSR(alpha, beta, d): +def right_straight_right(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -63,7 +62,7 @@ def RSR(alpha, beta, d): return t, p, q, mode -def LSR(alpha, beta, d): +def left_straight_right(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -82,7 +81,7 @@ def LSR(alpha, beta, d): return t, p, q, mode -def RSL(alpha, beta, d): +def right_straight_left(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -101,7 +100,7 @@ def RSL(alpha, beta, d): return t, p, q, mode -def RLR(alpha, beta, d): +def right_left_right(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -119,7 +118,7 @@ def RLR(alpha, beta, d): return t, p, q, mode -def LRL(alpha, beta, d): +def left_right_left(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -137,23 +136,21 @@ def LRL(alpha, beta, d): return t, p, q, mode -def dubins_path_planning_from_origin(ex, ey, eyaw, c, D_ANGLE): - # normalize - dx = ex - dy = ey +def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size): + dx = end_x + dy = end_y D = math.hypot(dx, dy) - d = D * c - # print(dx, dy, D, d) + d = D * curvature theta = mod2pi(math.atan2(dy, dx)) alpha = mod2pi(- theta) - beta = mod2pi(eyaw - theta) - # print(theta, alpha, beta, d) + beta = mod2pi(end_yaw - theta) - planners = [LSL, RSR, LSR, RSL, RLR, LRL] + planners = [left_straight_left, right_straight_right, left_straight_right, right_straight_left, right_left_right, + left_right_left] - bcost = float("inf") - bt, bp, bq, bmode = None, None, None, None + best_cost = float("inf") + bt, bp, bq, best_mode = None, None, None, None for planner in planners: t, p, q, mode = planner(alpha, beta, d) @@ -161,17 +158,48 @@ def dubins_path_planning_from_origin(ex, ey, eyaw, c, D_ANGLE): continue cost = (abs(t) + abs(p) + abs(q)) - if bcost > cost: - bt, bp, bq, bmode = t, p, q, mode - bcost = cost - - # print(bmode) - px, py, pyaw = generate_course([bt, bp, bq], bmode, c, D_ANGLE) + if best_cost > cost: + bt, bp, bq, best_mode = t, p, q, mode + best_cost = cost + lengths = [bt, bp, bq] + + px, py, pyaw, directions = generate_local_course( + sum(lengths), lengths, best_mode, curvature, step_size) + + return px, py, pyaw, best_mode, best_cost + + +def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions): + if mode == "S": + path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw) + path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw) + path_yaw[ind] = origin_yaw + else: # curve + ldx = math.sin(length) / max_curvature + ldy = 0.0 + if mode == "L": # left turn + ldy = (1.0 - math.cos(length)) / max_curvature + elif mode == "R": # right turn + ldy = (1.0 - math.cos(length)) / -max_curvature + gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy + gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy + path_x[ind] = origin_x + gdx + path_y[ind] = origin_y + gdy + + if mode == "L": # left turn + path_yaw[ind] = origin_yaw + length + elif mode == "R": # right turn + path_yaw[ind] = origin_yaw - length + + if length > 0.0: + directions[ind] = 1 + else: + directions[ind] = -1 - return px, py, pyaw, bmode, bcost + return path_x, path_y, path_yaw, directions -def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0)): +def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, step_size=0.1): """ Dubins path plannner @@ -200,7 +228,7 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0) leyaw = eyaw - syaw lpx, lpy, lpyaw, mode, clen = dubins_path_planning_from_origin( - lex, ley, leyaw, c, D_ANGLE) + lex, ley, leyaw, c, step_size) px = [math.cos(-syaw) * x + math.sin(-syaw) * y + sx for x, y in zip(lpx, lpy)] @@ -211,44 +239,60 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0) return px, py, pyaw, mode, clen -def generate_course(length, mode, c, D_ANGLE): - px = [0.0] - py = [0.0] - pyaw = [0.0] - - for m, l in zip(mode, length): - pd = 0.0 - if m == "S": - d = 1.0 * c - else: # turning couse - d = D_ANGLE - - while pd < abs(l - d): - # print(pd, l) - px.append(px[-1] + d / c * math.cos(pyaw[-1])) - py.append(py[-1] + d / c * math.sin(pyaw[-1])) - - if m == "L": # left turn - pyaw.append(pyaw[-1] + d) - elif m == "S": # Straight - pyaw.append(pyaw[-1]) - elif m == "R": # right turn - pyaw.append(pyaw[-1] - d) +def generate_local_course(total_length, lengths, mode, max_curvature, step_size): + n_point = math.trunc(total_length / step_size) + len(lengths) + 4 + + path_x = [0.0 for _ in range(n_point)] + path_y = [0.0 for _ in range(n_point)] + path_yaw = [0.0 for _ in range(n_point)] + directions = [0.0 for _ in range(n_point)] + ind = 1 + + if lengths[0] > 0.0: + directions[0] = 1 + else: + directions[0] = -1 + + ll = 0.0 + + for (m, l, i) in zip(mode, lengths, range(len(mode))): + if l > 0.0: + d = step_size + else: + d = -step_size + + # set origin state + origin_x, origin_y, origin_yaw = path_x[ind], path_y[ind], path_yaw[ind] + + ind -= 1 + if i >= 1 and (lengths[i - 1] * lengths[i]) > 0: + pd = - d - ll + else: + pd = d - ll + + while abs(pd) <= abs(l): + ind += 1 + path_x, path_y, path_yaw, directions = interpolate( + ind, pd, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions) pd += d - d = l - pd - px.append(px[-1] + d / c * math.cos(pyaw[-1])) - py.append(py[-1] + d / c * math.sin(pyaw[-1])) + ll = l - pd - d # calc remain length + + ind += 1 + path_x, path_y, path_yaw, directions = interpolate( + ind, l, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions) + + if len(path_x) <= 1: + return [], [], [], [] - if m == "L": # left turn - pyaw.append(pyaw[-1] + d) - elif m == "S": # Straight - pyaw.append(pyaw[-1]) - elif m == "R": # right turn - pyaw.append(pyaw[-1] - d) - pd += d + # remove unused data + while len(path_x) >= 1 and path_x[-1] == 0.0: + path_x.pop() + path_y.pop() + path_yaw.pop() + directions.pop() - return px, py, pyaw + return path_x, path_y, path_yaw, directions def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): # pragma: no cover @@ -288,53 +332,11 @@ def main(): plot_arrow(start_x, start_y, start_yaw) plot_arrow(end_x, end_y, end_yaw) - # for (ix, iy, iyaw) in zip(px, py, pyaw): - # plot_arrow(ix, iy, iyaw, fc="b") - plt.legend() plt.grid(True) plt.axis("equal") plt.show() -def test(): - NTEST = 5 - - for i in range(NTEST): - start_x = (np.random.rand() - 0.5) * 10.0 # [m] - start_y = (np.random.rand() - 0.5) * 10.0 # [m] - start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] - - end_x = (np.random.rand() - 0.5) * 10.0 # [m] - end_y = (np.random.rand() - 0.5) * 10.0 # [m] - end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] - - curvature = 1.0 / (np.random.rand() * 5.0) - - px, py, pyaw, mode, clen = dubins_path_planning( - start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) - - if show_animation: - plt.cla() - # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) - plt.plot(px, py, label="final course " + str(mode)) - - # plotting - plot_arrow(start_x, start_y, start_yaw) - plot_arrow(end_x, end_y, end_yaw) - - plt.legend() - plt.grid(True) - plt.axis("equal") - plt.xlim(-10, 10) - plt.ylim(-10, 10) - plt.pause(1.0) - - print("Test done") - - if __name__ == '__main__': - test() main() diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py index 4599904cc1..55cea0bde3 100644 --- a/PathPlanning/RRTDubins/rrt_dubins.py +++ b/PathPlanning/RRTDubins/rrt_dubins.py @@ -137,6 +137,9 @@ def steer(self, from_node, to_node): from_node.x, from_node.y, from_node.yaw, to_node.x, to_node.y, to_node.yaw, self.curvature) + if len(px) <= 1: # cannot find a dubins path + return None + new_node = copy.deepcopy(from_node) new_node.x = px[-1] new_node.y = py[-1] diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py index 65fac54c78..9cfd4e692a 100644 --- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py +++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py @@ -114,7 +114,7 @@ def draw_graph(self, rnd=None): plt.clf() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: @@ -143,6 +143,9 @@ def steer(self, from_node, to_node): from_node.x, from_node.y, from_node.yaw, to_node.x, to_node.y, to_node.yaw, self.curvature) + if len(px) <= 1: # cannot find a dubins path + return None + new_node = copy.deepcopy(from_node) new_node.x = px[-1] new_node.y = py[-1] diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py index ad140b6c3a..72305530b9 100644 --- a/tests/test_dubins_path_planning.py +++ b/tests/test_dubins_path_planning.py @@ -7,6 +7,18 @@ class Test(TestCase): + @staticmethod + def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw): + assert (abs(px[0] - start_x) <= 0.01) + assert (abs(py[0] - start_y) <= 0.01) + assert (abs(pyaw[0] - start_yaw) <= 0.01) + print("x", px[-1], end_x) + assert (abs(px[-1] - end_x) <= 0.01) + print("y", py[-1], end_y) + assert (abs(py[-1] - end_y) <= 0.01) + print("yaw", pyaw[-1], end_yaw) + assert (abs(pyaw[-1] - end_yaw) <= 0.01) + def test1(self): start_x = 1.0 # [m] start_y = 1.0 # [m] @@ -21,14 +33,28 @@ def test1(self): px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning( start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) - assert (abs(px[-1] - end_x) <= 0.5) - assert (abs(py[-1] - end_y) <= 0.5) - assert(abs(pyaw[-1] - end_yaw) <= 0.1) + self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw) def test2(self): dubins_path_planning.show_animation = False dubins_path_planning.main() def test3(self): - dubins_path_planning.show_animation = False - dubins_path_planning.test() + N_TEST = 10 + + for i in range(N_TEST): + start_x = (np.random.rand() - 0.5) * 10.0 # [m] + start_y = (np.random.rand() - 0.5) * 10.0 # [m] + start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] + + end_x = (np.random.rand() - 0.5) * 10.0 # [m] + end_y = (np.random.rand() - 0.5) * 10.0 # [m] + end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] + + curvature = 1.0 / (np.random.rand() * 5.0) + + px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning( + start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) + + self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw) + From 067304acdbac043cf302a2eb006ee6c767341826 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 28 Jan 2020 21:38:19 +0900 Subject: [PATCH 141/909] reformat codes --- tests/test_dubins_path_planning.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py index 72305530b9..f9349217a1 100644 --- a/tests/test_dubins_path_planning.py +++ b/tests/test_dubins_path_planning.py @@ -57,4 +57,3 @@ def test3(self): start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw) - From aa7d48fa5b84259c43ff58e36ec6a5b5a9197f38 Mon Sep 17 00:00:00 2001 From: Peteski <38994044+YAOS5@users.noreply.github.com> Date: Wed, 29 Jan 2020 14:28:27 +1100 Subject: [PATCH 142/909] IMPROVEMENT: Fixed typo --- SLAM/FastSLAM1/FastSLAM1.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/SLAM/FastSLAM1/FastSLAM1.ipynb b/SLAM/FastSLAM1/FastSLAM1.ipynb index 9ce48238f9..60faed6e88 100644 --- a/SLAM/FastSLAM1/FastSLAM1.ipynb +++ b/SLAM/FastSLAM1/FastSLAM1.ipynb @@ -93,7 +93,7 @@ "metadata": {}, "source": [ "### 1- Predict\n", - "The following equations and code snippets we can see how the particles distribution evolves in case we provide only the control $(v,w)$, which are the linear and angular velocity repsectively. \n", + "The following equations and code snippets we can see how the particles distribution evolves in case we provide only the control $(v,w)$, which are the linear and angular velocity respectively. \n", "\n", "$\\begin{equation*}\n", "F=\n", @@ -668,7 +668,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.7.5" } }, "nbformat": 4, From 29c02fae58bb16dc7ef963aa99a668079d2d8b03 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 29 Jan 2020 21:49:30 +0900 Subject: [PATCH 143/909] code clean up --- .../reeds_shepp_path_planning.py | 183 +++++++----------- tests/test_reeds_shepp_path_planning.py | 33 ++++ 2 files changed, 99 insertions(+), 117 deletions(-) diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index fbf9495790..9c72a0c9b2 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -49,15 +49,15 @@ def mod2pi(x): return v -def SLS(x, y, phi): +def straight_left_straight(x, y, phi): phi = mod2pi(phi) - if y > 0.0 and phi > 0.0 and phi < math.pi * 0.99: + if y > 0.0 and 0.0 < phi < math.pi * 0.99: xd = - y / math.tan(phi) + x t = xd - math.tan(phi / 2.0) u = phi v = math.sqrt((x - xd) ** 2 + y ** 2) - math.tan(phi / 2.0) return True, t, u, v - elif y < 0.0 and phi > 0.0 and phi < math.pi * 0.99: + elif y < 0.0 < phi < math.pi * 0.99: xd = - y / math.tan(phi) + x t = xd - math.tan(phi / 2.0) u = phi @@ -68,7 +68,6 @@ def SLS(x, y, phi): def set_path(paths, lengths, ctypes): - path = Path() path.ctypes = ctypes path.lengths = lengths @@ -89,12 +88,12 @@ def set_path(paths, lengths, ctypes): return paths -def SCS(x, y, phi, paths): - flag, t, u, v = SLS(x, y, phi) +def straight_curve_straight(x, y, phi, paths): + flag, t, u, v = straight_left_straight(x, y, phi) if flag: paths = set_path(paths, [t, u, v], ["S", "L", "S"]) - flag, t, u, v = SLS(x, -y, -phi) + flag, t, u, v = straight_left_straight(x, -y, -phi) if flag: paths = set_path(paths, [t, u, v], ["S", "R", "S"]) @@ -107,7 +106,7 @@ def polar(x, y): return r, theta -def LSL(x, y, phi): +def left_straight_left(x, y, phi): u, t = polar(x - math.sin(phi), y - 1.0 + math.cos(phi)) if t >= 0.0: v = mod2pi(phi - t) @@ -117,7 +116,7 @@ def LSL(x, y, phi): return False, 0.0, 0.0, 0.0 -def LRL(x, y, phi): +def left_right_left(x, y, phi): u1, t1 = polar(x - math.sin(phi), y - 1.0 + math.cos(phi)) if u1 <= 4.0: @@ -125,91 +124,89 @@ def LRL(x, y, phi): t = mod2pi(t1 + 0.5 * u + math.pi) v = mod2pi(phi - t + u) - if t >= 0.0 and u <= 0.0: + if t >= 0.0 >= u: return True, t, u, v return False, 0.0, 0.0, 0.0 -def CCC(x, y, phi, paths): - - flag, t, u, v = LRL(x, y, phi) +def curve_curve_curve(x, y, phi, paths): + flag, t, u, v = left_right_left(x, y, phi) if flag: paths = set_path(paths, [t, u, v], ["L", "R", "L"]) - flag, t, u, v = LRL(-x, y, -phi) + flag, t, u, v = left_right_left(-x, y, -phi) if flag: paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"]) - flag, t, u, v = LRL(x, -y, -phi) + flag, t, u, v = left_right_left(x, -y, -phi) if flag: paths = set_path(paths, [t, u, v], ["R", "L", "R"]) - flag, t, u, v = LRL(-x, -y, phi) + flag, t, u, v = left_right_left(-x, -y, phi) if flag: paths = set_path(paths, [-t, -u, -v], ["R", "L", "R"]) # backwards xb = x * math.cos(phi) + y * math.sin(phi) yb = x * math.sin(phi) - y * math.cos(phi) - # println(xb, ",", yb,",",x,",",y) - flag, t, u, v = LRL(xb, yb, phi) + flag, t, u, v = left_right_left(xb, yb, phi) if flag: paths = set_path(paths, [v, u, t], ["L", "R", "L"]) - flag, t, u, v = LRL(-xb, yb, -phi) + flag, t, u, v = left_right_left(-xb, yb, -phi) if flag: paths = set_path(paths, [-v, -u, -t], ["L", "R", "L"]) - flag, t, u, v = LRL(xb, -yb, -phi) + flag, t, u, v = left_right_left(xb, -yb, -phi) if flag: paths = set_path(paths, [v, u, t], ["R", "L", "R"]) - flag, t, u, v = LRL(-xb, -yb, phi) + flag, t, u, v = left_right_left(-xb, -yb, phi) if flag: paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"]) return paths -def CSC(x, y, phi, paths): - flag, t, u, v = LSL(x, y, phi) +def curve_straight_curve(x, y, phi, paths): + flag, t, u, v = left_straight_left(x, y, phi) if flag: paths = set_path(paths, [t, u, v], ["L", "S", "L"]) - flag, t, u, v = LSL(-x, y, -phi) + flag, t, u, v = left_straight_left(-x, y, -phi) if flag: paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"]) - flag, t, u, v = LSL(x, -y, -phi) + flag, t, u, v = left_straight_left(x, -y, -phi) if flag: paths = set_path(paths, [t, u, v], ["R", "S", "R"]) - flag, t, u, v = LSL(-x, -y, phi) + flag, t, u, v = left_straight_left(-x, -y, phi) if flag: paths = set_path(paths, [-t, -u, -v], ["R", "S", "R"]) - flag, t, u, v = LSR(x, y, phi) + flag, t, u, v = left_straight_right(x, y, phi) if flag: paths = set_path(paths, [t, u, v], ["L", "S", "R"]) - flag, t, u, v = LSR(-x, y, -phi) + flag, t, u, v = left_straight_right(-x, y, -phi) if flag: paths = set_path(paths, [-t, -u, -v], ["L", "S", "R"]) - flag, t, u, v = LSR(x, -y, -phi) + flag, t, u, v = left_straight_right(x, -y, -phi) if flag: paths = set_path(paths, [t, u, v], ["R", "S", "L"]) - flag, t, u, v = LSR(-x, -y, phi) + flag, t, u, v = left_straight_right(-x, -y, phi) if flag: paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"]) return paths -def LSR(x, y, phi): +def left_straight_right(x, y, phi): u1, t1 = polar(x + math.sin(phi), y - 1.0 - math.cos(phi)) u1 = u1 ** 2 if u1 >= 4.0: @@ -224,60 +221,60 @@ def LSR(x, y, phi): return False, 0.0, 0.0, 0.0 -def generate_path(q0, q1, maxc): +def generate_path(q0, q1, max_curvature): dx = q1[0] - q0[0] dy = q1[1] - q0[1] dth = q1[2] - q0[2] c = math.cos(q0[2]) s = math.sin(q0[2]) - x = (c * dx + s * dy) * maxc - y = (-s * dx + c * dy) * maxc + x = (c * dx + s * dy) * max_curvature + y = (-s * dx + c * dy) * max_curvature paths = [] - paths = SCS(x, y, dth, paths) - paths = CSC(x, y, dth, paths) - paths = CCC(x, y, dth, paths) + paths = straight_curve_straight(x, y, dth, paths) + paths = curve_straight_curve(x, y, dth, paths) + paths = curve_curve_curve(x, y, dth, paths) return paths -def interpolate(ind, l, m, maxc, ox, oy, oyaw, px, py, pyaw, directions): - - if m == "S": - px[ind] = ox + l / maxc * math.cos(oyaw) - py[ind] = oy + l / maxc * math.sin(oyaw) - pyaw[ind] = oyaw +def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions): + if mode == "S": + path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw) + path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw) + path_yaw[ind] = origin_yaw else: # curve - ldx = math.sin(l) / maxc - if m == "L": # left turn - ldy = (1.0 - math.cos(l)) / maxc - elif m == "R": # right turn - ldy = (1.0 - math.cos(l)) / -maxc - gdx = math.cos(-oyaw) * ldx + math.sin(-oyaw) * ldy - gdy = -math.sin(-oyaw) * ldx + math.cos(-oyaw) * ldy - px[ind] = ox + gdx - py[ind] = oy + gdy - - if m == "L": # left turn - pyaw[ind] = oyaw + l - elif m == "R": # right turn - pyaw[ind] = oyaw - l - - if l > 0.0: + ldx = math.sin(length) / max_curvature + ldy = 0.0 + if mode == "L": # left turn + ldy = (1.0 - math.cos(length)) / max_curvature + elif mode == "R": # right turn + ldy = (1.0 - math.cos(length)) / -max_curvature + gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy + gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy + path_x[ind] = origin_x + gdx + path_y[ind] = origin_y + gdy + + if mode == "L": # left turn + path_yaw[ind] = origin_yaw + length + elif mode == "R": # right turn + path_yaw[ind] = origin_yaw - length + + if length > 0.0: directions[ind] = 1 else: directions[ind] = -1 - return px, py, pyaw, directions + return path_x, path_y, path_yaw, directions -def generate_local_course(L, lengths, mode, maxc, step_size): - npoint = math.trunc(L / step_size) + len(lengths) + 4 +def generate_local_course(total_length, lengths, mode, max_curvature, step_size): + n_point = math.trunc(total_length / step_size) + len(lengths) + 4 - px = [0.0 for i in range(npoint)] - py = [0.0 for i in range(npoint)] - pyaw = [0.0 for i in range(npoint)] - directions = [0.0 for i in range(npoint)] + px = [0.0 for _ in range(n_point)] + py = [0.0 for _ in range(n_point)] + pyaw = [0.0 for _ in range(n_point)] + directions = [0.0 for _ in range(n_point)] ind = 1 if lengths[0] > 0.0: @@ -305,14 +302,14 @@ def generate_local_course(L, lengths, mode, maxc, step_size): while abs(pd) <= abs(l): ind += 1 px, py, pyaw, directions = interpolate( - ind, pd, m, maxc, ox, oy, oyaw, px, py, pyaw, directions) + ind, pd, m, max_curvature, ox, oy, oyaw, px, py, pyaw, directions) pd += d ll = l - pd - d # calc remain length ind += 1 px, py, pyaw, directions = interpolate( - ind, l, m, maxc, ox, oy, oyaw, px, py, pyaw, directions) + ind, l, m, max_curvature, ox, oy, oyaw, px, py, pyaw, directions) # remove unused data while px[-1] == 0.0: @@ -344,22 +341,17 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size): * iy + q0[1] for (ix, iy) in zip(x, y)] path.yaw = [pi_2_pi(iyaw + q0[2]) for iyaw in yaw] path.directions = directions - path.lengths = [l / maxc for l in path.lengths] + path.lengths = [length / maxc for length in path.lengths] path.L = path.L / maxc - # print(paths) - return paths def reeds_shepp_path_planning(sx, sy, syaw, gx, gy, gyaw, maxc, step_size=0.2): - paths = calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size) if not paths: - # print("No path") - # print(sx, sy, syaw, gx, gy, gyaw) return None, None, None, None, None minL = float("Inf") @@ -374,48 +366,6 @@ def reeds_shepp_path_planning(sx, sy, syaw, return bpath.x, bpath.y, bpath.yaw, bpath.ctypes, bpath.lengths -def test(): - - NTEST = 5 - - for i in range(NTEST): - start_x = (np.random.rand() - 0.5) * 10.0 # [m] - start_y = (np.random.rand() - 0.5) * 10.0 # [m] - start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] - - end_x = (np.random.rand() - 0.5) * 10.0 # [m] - end_y = (np.random.rand() - 0.5) * 10.0 # [m] - end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] - - curvature = 1.0 / (np.random.rand() * 20.0) - step_size = 0.1 - - px, py, pyaw, mode, clen = reeds_shepp_path_planning( - start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature, step_size) - - if show_animation: # pragma: no cover - plt.cla() - # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) - plt.plot(px, py, label="final course " + str(mode)) - - # plotting - plot_arrow(start_x, start_y, start_yaw) - plot_arrow(end_x, end_y, end_yaw) - - plt.legend() - plt.grid(True) - plt.axis("equal") - plt.xlim(-10, 10) - plt.ylim(-10, 10) - plt.pause(1.0) - - # plt.show() - - print("Test done") - - def main(): print("Reeds Shepp path planner sample start!!") @@ -451,5 +401,4 @@ def main(): if __name__ == '__main__': - test() main() diff --git a/tests/test_reeds_shepp_path_planning.py b/tests/test_reeds_shepp_path_planning.py index bc4a038cbb..2c3b38dc31 100644 --- a/tests/test_reeds_shepp_path_planning.py +++ b/tests/test_reeds_shepp_path_planning.py @@ -1,9 +1,42 @@ from unittest import TestCase from PathPlanning.ReedsSheppPath import reeds_shepp_path_planning as m +import numpy as np + class Test(TestCase): + @staticmethod + def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw): + assert (abs(px[0] - start_x) <= 0.01) + assert (abs(py[0] - start_y) <= 0.01) + assert (abs(pyaw[0] - start_yaw) <= 0.01) + print("x", px[-1], end_x) + assert (abs(px[-1] - end_x) <= 0.01) + print("y", py[-1], end_y) + assert (abs(py[-1] - end_y) <= 0.01) + print("yaw", pyaw[-1], end_yaw) + assert (abs(pyaw[-1] - end_yaw) <= 0.01) + def test1(self): m.show_animation = False m.main() + + def test2(self): + N_TEST = 10 + + for i in range(N_TEST): + start_x = (np.random.rand() - 0.5) * 10.0 # [m] + start_y = (np.random.rand() - 0.5) * 10.0 # [m] + start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] + + end_x = (np.random.rand() - 0.5) * 10.0 # [m] + end_y = (np.random.rand() - 0.5) * 10.0 # [m] + end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0) # [rad] + + curvature = 1.0 / (np.random.rand() * 5.0) + + px, py, pyaw, mode, clen = m.reeds_shepp_path_planning( + start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) + + self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw) From c6c0a61d495f234c1cff77f1f79a0676f6aa9f8a Mon Sep 17 00:00:00 2001 From: "Kenta Yonekura(a.k.a. yoneken)" Date: Sat, 1 Feb 2020 22:59:52 +0900 Subject: [PATCH 144/909] [Kalman Basics] Align some subscripts on the text --- Localization/Kalmanfilter_basics.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb index 374287911b..bbc10ce5f1 100644 --- a/Localization/Kalmanfilter_basics.ipynb +++ b/Localization/Kalmanfilter_basics.ipynb @@ -354,14 +354,14 @@ "\n", "Here for the numerator, $P(Z \\mid X),P(X)$ both are gaussian.\n", "\n", - "$N(\\bar\\mu, \\bar\\sigma^1)$ and $N(\\bar\\mu, \\bar\\sigma^2)$ are their mean and variances.\n", + "$N(\\mu_1, \\sigma_1^2)$ and $N(\\mu_2, \\sigma_2^2)$ are their mean and variances.\n", "\n", "New mean is \n", "\n", - "$$\\mu_\\mathtt{new} = \\frac{\\sigma_z^2\\bar\\mu + \\bar\\sigma^2z}{\\bar\\sigma^2+\\sigma_z^2}$$\n", + "$$\\mu_\\mathtt{new} = \\frac{\\mu_1 \\sigma_2^2 + \\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}$$\n", "New variance is\n", "$$\n", - "\\sigma_\\mathtt{new} = \\frac{\\sigma_z^2\\bar\\sigma^2}{\\bar\\sigma^2+\\sigma_z^2}\n", + "\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}\n", "$$" ] }, @@ -759,4 +759,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file From 74b723dc47798e9b5681828066e9e8ab87267bb7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 2 Feb 2020 14:12:39 +0900 Subject: [PATCH 145/909] #fix 281 and doc clean up --- Localization/Kalmanfilter_basics.ipynb | 13 ++++++++++--- Localization/Kalmanfilter_basics_2.ipynb | 13 +++++++++++-- 2 files changed, 21 insertions(+), 5 deletions(-) diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb index 86966c8d54..c501b117de 100644 --- a/Localization/Kalmanfilter_basics.ipynb +++ b/Localization/Kalmanfilter_basics.ipynb @@ -360,9 +360,7 @@ "\n", "$$\\mu_\\mathtt{new} = \\frac{\\mu_1 \\sigma_2^2 + \\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}$$\n", "New variance is\n", - "$$\n", - "\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}\n", - "$$" + "$$\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}$$" ] }, { @@ -755,6 +753,15 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } } }, "nbformat": 4, diff --git a/Localization/Kalmanfilter_basics_2.ipynb b/Localization/Kalmanfilter_basics_2.ipynb index ac3101d518..43f34ec6f2 100644 --- a/Localization/Kalmanfilter_basics_2.ipynb +++ b/Localization/Kalmanfilter_basics_2.ipynb @@ -35,7 +35,7 @@ "\n", "If $x_{t}$ is complete, then:\n", "\n", - "$$p(z_{t} | x-_{0:t},z_{1:t-1},u_{1:t})=p(z_{t} | x_{t})$$\n", + "$$p(z_{t} | x_{0:t},z_{1:t-1},u_{1:t})=p(z_{t} | x_{t})$$\n", "\n", "$x_{t}$ is **complete** means that the past states, controls or measurements carry no additional information to predict future.\n", "\n", @@ -365,8 +365,17 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.6" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file From c632666603c870fa26d895238e3bcbd4f8200e84 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 3 Feb 2020 20:41:35 +0900 Subject: [PATCH 146/909] #fix 281 and doc clean up --- README.md | 20 +++++--------------- 1 file changed, 5 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index ec42812753..ab62c9af3c 100644 --- a/README.md +++ b/README.md @@ -119,8 +119,6 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation > git clone https://github.com/AtsushiSakai/PythonRobotics.git -> cd PythonRobotics/ - 2. Install the required libraries. You can use environment.yml with conda command. @@ -553,7 +551,7 @@ MIT # Use-case -If this project helps your robotics project, please let me know with [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai). +If this project helps your robotics project, please let me know with creating an issue. Your robot's video, which is using PythonRobotics, is very welcome!! @@ -561,7 +559,7 @@ This is a list of other user's comment and references:[users\_comments](https:// # Contribution -A small PR like bug fix is welcome. +Any contribution is welcome!! If your PR is merged multiple times, I will add your account to the author list. @@ -575,23 +573,15 @@ If you use this project's code in industry, we'd love to hear from you as well; If you or your company would like to support this project, please consider: +- [Sponsor @AtsushiSakai on GitHub Sponsors](https://github.com/sponsors/AtsushiSakai) + - [Become a backer or sponsor on Patreon](https://www.patreon.com/myenigma) - [One-time donation via PayPal](https://www.paypal.me/myenigmapay/) -You can add your name or your company logo in README if you are a patron. - -E-mail consultant is also available. - - 　 - -Your comment using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai) is also welcome. - -This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md) - # Authors -- [Atsushi Sakai](https://github.com/AtsushiSakai/) ([@Atsushi_twi](https://twitter.com/Atsushi_twi)) +- [Atsushi Sakai](https://github.com/AtsushiSakai/) - [Daniel Ingram](https://github.com/daniel-s-ingram) From a061e162d6ceaf83db57e3c48e7792e94f36ad3c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 4 Feb 2020 20:41:51 +0900 Subject: [PATCH 147/909] start fixing. --- SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb index 632bf57992..554ccd1a64 100644 --- a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb +++ b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb @@ -6,12 +6,10 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "import copy\n", "import math\n", "import itertools\n", "import numpy as np \n", - "import cvxpy as cp\n", "import matplotlib.pyplot as plt\n", "from graph_based_slam import calc_rotational_matrix, calc_jacobian, cal_observation_sigma, \\\n", " calc_input, observation, motion_model, Edge, pi_2_pi\n", @@ -650,8 +648,17 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "source": [], + "metadata": { + "collapsed": false + } + } } }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file From 994e0e354637d7bf540b1da8abaec312325b9f74 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 4 Feb 2020 21:13:12 +0900 Subject: [PATCH 148/909] fixing. --- SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb | 156 +++++++++++++----------- 1 file changed, 87 insertions(+), 69 deletions(-) diff --git a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb index 554ccd1a64..53364e3276 100644 --- a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb +++ b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb @@ -2,8 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": 11, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "import copy\n", @@ -38,15 +42,17 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 12, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [ { "data": { - "image/png": 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\n", 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\n" }, "metadata": { "needs_background": "light" @@ -55,23 +61,21 @@ }, { "name": "stdout", - "output_type": "stream", "text": [ "The determinant of H: 0.0\n", - "The determinant of H after anchoring constraint: 18.75\n", + "The determinant of H after anchoring constraint: 18.750000000000007\n", "Running graphSLAM ...\n", "Odometry values after optimzation: \n", - " [[0. ]\n", - " [0.947]\n", - " [1.939]]\n" - ] + " [[-0. ]\n", + " [ 0.9]\n", + " [ 1.9]]\n" + ], + "output_type": "stream" }, { "data": { - "image/png": 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\n", 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te68x6cJJdAh3wDA6hDsw6cJJvPa91xQOTYxutSEiKdX99O48cNkDPHCZLmJr6rQFISJJ0Rx3V7dUyRoLBYSIJCwjI4O9e/cqJJoAd2fv3r1kZGQkvC7tYhKRhHXr1o2SkhJ2797d2KUI0cDu1q1bwutRQIhIwsLhML17927sMiTJtItJREQCKSBERCSQAkJERAIpIEREJJACQkREAikgREQkkAJCREQCKSBERCSQAkJERAIpIEREJJACQkREAiUlIMxsjJl9bGbbzGxOwPK2ZrYwtvw9M+sVm3+dmW0ws3/Efo9KRj0iIpK4hAPCzNKBp4BvA/2Bm8ysf61mtwFfuvs3gceBR2Pz9wDfc/dMIBf4r0TrERGR5EjGFsQwYJu7F7n7EeBloPY3f48D5sceLwJGm5m5+/vu/llsfiFwmpm1TUJNIiKSoGQExPlAcY3pkti8wDbuHgEOAJ1qtfk3IN/dv05CTSIikqAm8X0QZjaA6G6n6+tocwdwB0CPHj1SVJmISOuVjC2InUD3GtPdYvMC25hZCDgD2Bub7gYsBqa5+ycnehF3f8bds909u3PnzkkoW0RE6pKMgFgP9DWz3mbWBpgCLK3VZinRg9AAk4CV7u5mdibw38Acd38nCbWIiEiSJBwQsWMKM4E3gM3AK+5eaGY/N7OxsWZ/BDqZ2TZgFnD0VNiZwDeBB82sIPZzbqI1iYhI4szdG7uGBsvOzva8vLzGLkNEpFkxsw3unh1ve11JLSIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoFCjV1AKqwvXMH8d37J+vAuDptxmjuXVnQh98oHuHTA6MYuT+K1rwjefRI+fAWOlEKbDpA1Ga6YCWf3CXzKjr1l/GFtEUve/4yyryO0bxti/OBv8P3hfejZqX2KO5BaxQeLmb9pPsuKlnGo4hDtwu3I6ZNDbv9cup/evbHLa5hWPPaNOY7m7qf0BU6F7Oxsz8vLi6vtgr89wm93vUjEIGJWPT/kTsjhni43M+0795+qUiVZti6HV6ZBZQVUVfxrfloY0sMweQH0ve6Yp6z6+AtmPJ9PRWUVkap//TsPpRnh9DSevmUI1/Q7N1U9SKm1JWuZtXoWkcoIEY9Uzw9ZiFB6iHkj5jG82/BGrLABWvHYJ3sczWyDu2fH2z4pu5jMbIyZfWxm28xsTsDytma2MLb8PTPrVWPZfbH5H5vZDcmo56j1hSv47a4XKU+zY8IBomFRnmb8dteLrC9ckcyXlWTbVxR9g6g4dOwbBESnKw5Fl+8rqp69Y28ZM57P53BF5TFvEACRKudwRSUzns9nx96yVPQgpYoPFjNr9SzKI+XHvKkARDxCeaScWatnUXywuJEqbIBWPPZNYRwTDggzSweeAr4N9AduMrP+tZrdBnzp7t8EHgcejT23PzAFGACMAZ6OrS8p5r/zSyJWd5uIwYJ3fpmsl5RT4d0no58e61JZAf/zVPXkH9YWUVFZVedTKiqreHbtP5NRYZMyf9N8IpWROttEKiMs2LQgRRUloBWPfVMYx2RsQQwDtrl7kbsfAV4GxtVqMw6YH3u8CBhtZhab/7K7f+3u/wS2xdaXFOvDu47bcqgtYsb68K5kvaScCh++cvynx9qqKuDDhdWTS97/7LhPj7VFqpzF7+9MRoVNyrKiZcd94qwt4hGWFS1LUUUJaMVj3xTGMRkBcT5QcxunJDYvsI27R4ADQKc4nwuAmd1hZnlmlrd79+64CjtcTzgcdSjOdtJIjpQ2uF3Z13X/x6pudyS+ds3JoYpDcbUrq2jau1iAVj32TWEcm81pru7+jLtnu3t2586d43rOaXEegG/XDA/UtyptOjS4Xfu28Z2g175NyzuRr124XVzt2oebwZk8rXjsm8I4JiMgdgI1z7XqFpsX2MbMQsAZwN44n3vSLq3oQqieN/9Q7JRXacKyJkfPWKlLWhiybqyeHD/4G4TS6t4yDKUZEwYHbrA2azl9cghZ3W9+IQuR0ycnRRUloBWPfVMYx2QExHqgr5n1NrM2RA86L63VZimQG3s8CVjp0fNrlwJTYmc59Qb6AuuSUBMAuVc+QKiejYOQw7QrH0jWS8qpcMXM6OmMdUkPw+V3V09+f3gfwul1//MOp6dx+/DeyaiwScntn0sovZ43lvQQ0/pPS1FFCWjFY98UxjHhgIgdU5gJvAFsBl5x90Iz+7mZjY01+yPQycy2AbOAObHnFgKvAJuAvwN3u3tlojUddemA0dzT5WYyqvy4LYmQOxlVzj1dbtbFck3d2X2i57qH2x3/aTItHJ0/ecExF0z17NSep28Zwmnh9OM+TYbSjNPC6Tx9y5BmdcFUvLqf3p15I+aREco47hNoyEJkhDKYN2Je87hYrhWPfVMYxxZ/oRxEr4dYELuS+pAZ7WK7labpSurmZV9R9HTGDxfWuJr2xuinxzqupn127T9Z/P5Oyo5EaN8mxITB53P78N5N/g0iUcUHi1mwaQHLipZRVlFG+3B7cvrkMK3/tOYRDjW14rFP5jg29EK5VhEQIiLSSFdSi4hIy6OAEBGRQAoIEREJpIAQEZFACggREQmkgBARkUAKCBERCaSAEBGRQAoIEREJpIAQEZFACggREQmkgBARkUAKCBERCaSAEBGRQAoIEREJpIAQEZFACggREQmkgBARkUAKCBERCaSAEBGRQAoIEREJpIAQEZFACggREQmkgBARkUAKCBERCaSAEBGRQAoIEREJpIAQEZFACggREQmUUECY2dlmttzMtsZ+n3WCdrmxNlvNLDc2r52Z/beZfWRmhWY2N5FaREQkuRLdgpgDrHD3vsCK2PQxzOxs4CHgW8Aw4KEaQfJrd78IGAxcaWbfTrAeERFJkkQDYhwwP/Z4PjA+oM0NwHJ33+fuXwLLgTHufsjdVwG4+xEgH+iWYD0iIpIkiQZEF3f/PPb4f4EuAW3OB4prTJfE5lUzszOB7xHdChERkSYgVF8DM3sTOC9g0QM1J9zdzcwbWoCZhYCXgP9096I62t0B3AHQo0ePhr6MiIg0UL0B4e7XnmiZme0ys67u/rmZdQW+CGi2ExhZY7ob8FaN6WeAre7+RD11PBNrS3Z2doODSEREGibRXUxLgdzY41zgLwFt3gCuN7OzYgenr4/Nw8z+AzgD+GGCdYiISJIlGhBzgevMbCtwbWwaM8s2s2cB3H0f8Atgfezn5+6+z8y6Ed1N1R/IN7MCM7s9wXpERCRJzL357a3Jzs72vLy8xi5DRKRZMbMN7p4db3tdSS0iIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoEUECIiEkgBISIigRQQIiISSAEhIiKBFBAiIhJIASEiIoESCggzO9vMlpvZ1tjvs07QLjfWZquZ5QYsX2pmGxOpRUREkivRLYg5wAp37wusiE0fw8zOBh4CvgUMAx6qGSRmNhEoTbAOERFJskQDYhwwP/Z4PjA+oM0NwHJ33+fuXwLLgTEAZtYBmAX8R4J1iIhIkiUaEF3c/fPY4/8FugS0OR8orjFdEpsH8AvgN8Ch+l7IzO4wszwzy9u9e3cCJYuISDxC9TUwszeB8wIWPVBzwt3dzDzeFzazQcAF7v4jM+tVX3t3fwZ4BiA7Ozvu1xERkZNTb0C4+7UnWmZmu8ysq7t/bmZdgS8Cmu0ERtaY7ga8BVwOZJvZ9lgd55rZW+4+EhERaXSJ7mJaChw9KykX+EtAmzeA683srNjB6euBN9z9d+7+DXfvBVwFbFE4iIg0HYkGxFzgOjPbClwbm8bMss3sWQB330f0WMP62M/PY/NERKQJM/fmtzs/Ozvb8/LyGrsMEZFmxcw2uHt2vO11JbWIiARSQIiISCAFhIiIBFJAiIhIIAWEiIgEUkCIiEggBYSIiARSQIiISCAFhIiIBFJAiIhIIAWEiIgEUkCIiEggBYSIiARSQIiISCAFhIiIBFJAiIhIIAWEiIgEUkCIiEggBYSIiARSQIiISCAFhIiIBFJAiIhIIAWEiIgEUkCIiEggc/fGrqHBzGw3sOMkn34OsCeJ5TQnrbnv0Lr735r7Dq27/zX73tPdO8f7xGYZEIkwszx3z27sOhpDa+47tO7+t+a+Q+vufyJ91y4mEREJpIAQEZFArTEgnmnsAhpRa+47tO7+t+a+Q+vu/0n3vdUdgxARkfi0xi0IERGJgwJCREQCtdiAMLMxZvaxmW0zszkBy9ua2cLY8vfMrFfqqzw14uj7dDPbbWYFsZ/bG6POU8HM/mRmX5jZxhMsNzP7z9jf5kMzG5LqGk+VOPo+0swO1Bj3B1Nd46liZt3NbJWZbTKzQjP7QUCbljz28fS/4ePv7i3uB0gHPgH6AG2AD4D+tdrMAH4fezwFWNjYdaew79OBJxu71lPU/6uBIcDGEyz/DvA6YMBlwHuNXXMK+z4SWNbYdZ6ivncFhsQedwS2BPy7b8ljH0//Gzz+LXULYhiwzd2L3P0I8DIwrlabccD82ONFwGgzsxTWeKrE0/cWy93XAPvqaDIOWOBR/w8408y6pqa6UyuOvrdY7v65u+fHHn8FbAbOr9WsJY99PP1vsJYaEOcDxTWmSzj+j1Xdxt0jwAGgU0qqO7Xi6TvAv8U2sxeZWffUlNYkxPv3aakuN7MPzOx1MxvQ2MWcCrHdxYOB92otahVjX0f/oYHj31IDQur2V6CXu2cBy/nXlpS0bPlE78VzCfBbYEkj15N0ZtYB+DPwQ3c/2Nj1pFo9/W/w+LfUgNgJ1PxU3C02L7CNmYWAM4C9Kanu1Kq37+6+192/jk0+CwxNUW1NQTz/Nlokdz/o7qWxx38DwmZ2TiOXlTRmFib65viCu78W0KRFj319/T+Z8W+pAbEe6Gtmvc2sDdGD0EtrtVkK5MYeTwJWeuxITjNXb99r7XcdS3R/ZWuxFJgWO6PlMuCAu3/e2EWlgpmdd/Q4m5kNI/r/vyV8KCLWrz8Cm9193gmatdixj6f/JzP+oWQX2hS4e8TMZgJvED2r50/uXmhmPwfy3H0p0T/mf5nZNqIH9qY0XsXJE2ff/6+ZjQUiRPs+vdEKTjIze4no2RrnmFkJ8BAQBnD33wN/I3o2yzbgEHBr41SafHH0fRJwl5lFgMPAlBbyoQjgSuD/AP8ws4LYvPuBHtDyx574+t/g8detNkREJFBL3cUkIiIJUkCIiEggBYSIiARSQIiISCAFhIiIBFJAiIhIIAWEiIgE+v8XlbT44BGi/QAAAABJRU5ErkJggg==\n" }, "metadata": { "needs_background": "light" @@ -138,8 +142,8 @@ " H[t2,t1] -= omegas[(t1,t2)]\n", " H[t1,t2] -= omegas[(t1,t2)]\n", "\n", - " b[t1] += measure_constraints[(t1,t2)]\n", - " b[t2] -= measure_constraints[(t1,t2)]\n", + " b[t1] += omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n", + " b[t2] -= omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n", "\n", " return H, b\n", "\n", @@ -206,15 +210,17 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 13, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [ { "data": { - "image/png": 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\n", 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\n" }, "metadata": { "needs_background": "light" @@ -291,8 +297,12 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 14, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [ "# Copy the data to have a consistent naming with the .py file\n", @@ -312,18 +322,22 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 15, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [ { "name": "stdout", - "output_type": "stream", "text": [ "Node combinations: \n", " [(0, 1)]\n", "Node 0 observed landmark with ID 0.0\n", "Node 1 observed landmark with ID 0.0\n" - ] + ], + "output_type": "stream" } ], "source": [ @@ -354,12 +368,15 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 16, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [ { "name": "stdout", - "output_type": "stream", "text": [ "0.0 1.427649841628278 -2.0126109674819155 -3.524048014922737\n", "For nodes (0, 1)\n", @@ -367,14 +384,13 @@ " [[-0.02 ]\n", " [-0.084]\n", " [ 0. ]]\n" - ] + ], + "output_type": "stream" }, { "data": { - "image/png": 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\n", 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\n" }, "metadata": { "needs_background": "light" @@ -460,41 +476,35 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 17, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [ { "name": "stdout", - "output_type": "stream", "text": [ - "The determinant of H: 0.0\n" - ] + "The determinant of H: 0.0\n", + "The determinant of H after origin constraint: 716.1972439134893\n" + ], + "output_type": "stream" }, { "data": { - "image/png": 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- "text/plain": [ - "
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H7kwAUuA0E0AKhBmAFAgzAG+ZT8uebD9m+5jtSu95I8wASJqXy56ekLSh6saEGYAp82rZU0TslvRa1e0JMwBTzrfsqdVQLbNGmAFIgTADMKWnlz0RZgCm9PSyJ8IMgKTJZU+SppY9HZT0VIdlT0XZ/rKk70laa3vc9sdn3J7lTAAyYGYGIAXCDEAKhBmAFAgzACkQZgBSIMwApECYAUjh/wGp9kxGxr36PwAAAABJRU5ErkJggg==\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The determinant of H after origin constraint: 716.1972439134918\n" - ] - }, { "data": { - "image/png": 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}, "metadata": { "needs_background": "light" @@ -559,15 +569,18 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 18, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [ { "name": "stdout", - "output_type": "stream", "text": [ "dx: \n", - " [[ 0. ]\n", + " [[-0. ]\n", " [-0. ]\n", " [ 0. ]\n", " [ 0.02 ]\n", @@ -582,13 +595,14 @@ " [0. 1.428]\n", " [0.785 0.976]]\n", "SLAM: \n", - " [[ 0. 1.448]\n", + " [[-0. 1.448]\n", " [-0. 1.512]\n", " [ 0.785 0.976]]\n", "\n", - "graphSLAM localization error: 0.0107290727510571\n", + "graphSLAM localization error: 0.010729072751057656\n", "Odom localization error: 0.0004460978857535104\n" - ] + ], + "output_type": "stream" } ], "source": [ @@ -625,8 +639,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 18, + "metadata": { + "pycharm": { + "is_executing": false + } + }, "outputs": [], "source": [] } From 53b5b43a75b1593e8fcbd8f72cd11e7da6178d3b Mon Sep 17 00:00:00 2001 From: Marwan Taher Date: Tue, 4 Feb 2020 15:38:57 +0000 Subject: [PATCH 149/909] Fixed typo in planning method docstring --- PathPlanning/AStar/a_star.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index b0c718c9a2..2229d63306 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -51,7 +51,7 @@ def planning(self, sx, sy, gx, gy): sx: start x position [m] sy: start y position [m] gx: goal x position [m] - gx: goal x position [m] + gy: goal y position [m] output: rx: x position list of the final path From b3af4fcf507e9944265e6587c6c991a3b67f371d Mon Sep 17 00:00:00 2001 From: Peteski <38994044+YAOS5@users.noreply.github.com> Date: Thu, 6 Feb 2020 17:31:34 +1100 Subject: [PATCH 150/909] IMPROVEMENT: Fixed typo --- SLAM/FastSLAM2/fast_slam2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 868812225a..4780b57982 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -25,7 +25,7 @@ MAX_RANGE = 20.0 # maximum observation range M_DIST_TH = 2.0 # Threshold of Mahalanobis distance for data association. STATE_SIZE = 3 # State size [x,y,yaw] -LM_SIZE = 2 # LM srate size [x,y] +LM_SIZE = 2 # LM state size [x,y] N_PARTICLE = 100 # number of particle NTH = N_PARTICLE / 1.5 # Number of particle for re-sampling From 03e8969e9d152cd3d0744c7478580fe7bb716b94 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 7 Feb 2020 20:32:12 +0900 Subject: [PATCH 151/909] inverted pendulum mpc control is added --- .../inverted_pendulum_mpc_control.py | 171 ++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 InvertedPendulumCart/inverted_pendulum_mpc_control.py diff --git a/InvertedPendulumCart/inverted_pendulum_mpc_control.py b/InvertedPendulumCart/inverted_pendulum_mpc_control.py new file mode 100644 index 0000000000..d807a688c5 --- /dev/null +++ b/InvertedPendulumCart/inverted_pendulum_mpc_control.py @@ -0,0 +1,171 @@ +""" +Inverted Pendulum MPC control +author: Atsushi Sakai +""" + +import matplotlib.pyplot as plt +import numpy as np +import math +import time +import cvxpy + +# Model parameters + +l_bar = 2.0 # length of bar +M = 1.0 # [kg] +m = 0.3 # [kg] +g = 9.8 # [m/s^2] + +Q = np.diag([0.0, 1.0, 1.0, 0.0]) +R = np.diag([0.01]) +nx = 4 # number of state +nu = 1 # number of input +T = 30 # Horizon length +delta_t = 0.1 # time tick + +animation = True + + +def main(): + x0 = np.array([ + [0.0], + [0.0], + [0.3], + [0.0] + ]) + + x = np.copy(x0) + + for i in range(50): + + # calc control input + optimized_x, optimized_delta_x, optimized_theta, optimized_delta_theta, optimized_input = mpc_control(x) + + # get input + u = optimized_input[0] + + # simulate inverted pendulum cart + x = simulation(x, u) + + if animation: + plt.clf() + px = float(x[0]) + theta = float(x[2]) + plot_cart(px, theta) + plt.xlim([-5.0, 2.0]) + plt.pause(0.001) + + +def simulation(x, u): + A, B = get_model_matrix() + + x = np.dot(A, x) + np.dot(B, u) + + return x + + +def mpc_control(x0): + x = cvxpy.Variable((nx, T + 1)) + u = cvxpy.Variable((nu, T)) + + A, B = get_model_matrix() + + cost = 0.0 + constr = [] + for t in range(T): + cost += cvxpy.quad_form(x[:, t + 1], Q) + cost += cvxpy.quad_form(u[:, t], R) + constr += [x[:, t + 1] == A * x[:, t] + B * u[:, t]] + + constr += [x[:, 0] == x0[:, 0]] + prob = cvxpy.Problem(cvxpy.Minimize(cost), constr) + + start = time.time() + prob.solve(verbose=False) + elapsed_time = time.time() - start + print("calc time:{0} [sec]".format(elapsed_time)) + + if prob.status == cvxpy.OPTIMAL: + ox = get_nparray_from_matrix(x.value[0, :]) + dx = get_nparray_from_matrix(x.value[1, :]) + theta = get_nparray_from_matrix(x.value[2, :]) + dtheta = get_nparray_from_matrix(x.value[3, :]) + + ou = get_nparray_from_matrix(u.value[0, :]) + + return ox, dx, theta, dtheta, ou + + +def get_nparray_from_matrix(x): + """ + get build-in list from matrix + """ + return np.array(x).flatten() + + +def get_model_matrix(): + A = np.array([ + [0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, m * g / M, 0.0], + [0.0, 0.0, 0.0, 1.0], + [0.0, 0.0, g * (M + m) / (l_bar * M), 0.0] + ]) + A = np.eye(nx) + delta_t * A + + B = np.array([ + [0.0], + [1.0 / M], + [0.0], + [1.0 / (l_bar * M)] + ]) + B = delta_t * B + + return A, B + + +def flatten(a): + return np.array(a).flatten() + + +def plot_cart(xt, theta): + cart_w = 1.0 + cart_h = 0.5 + radius = 0.1 + + cx = np.array([-cart_w / 2.0, cart_w / 2.0, cart_w / + 2.0, -cart_w / 2.0, -cart_w / 2.0]) + cy = np.array([0.0, 0.0, cart_h, cart_h, 0.0]) + cy += radius * 2.0 + + cx = cx + xt + + bx = np.array([0.0, l_bar * math.sin(-theta)]) + bx += xt + by = np.array([cart_h, l_bar * math.cos(-theta) + cart_h]) + by += radius * 2.0 + + angles = np.arange(0.0, math.pi * 2.0, math.radians(3.0)) + ox = [radius * math.cos(a) for a in angles] + oy = [radius * math.sin(a) for a in angles] + + rwx = np.copy(ox) + cart_w / 4.0 + xt + rwy = np.copy(oy) + radius + lwx = np.copy(ox) - cart_w / 4.0 + xt + lwy = np.copy(oy) + radius + + wx = np.copy(ox) + float(bx[0, -1]) + wy = np.copy(oy) + float(by[0, -1]) + + plt.plot(flatten(cx), flatten(cy), "-b") + plt.plot(flatten(bx), flatten(by), "-k") + plt.plot(flatten(rwx), flatten(rwy), "-k") + plt.plot(flatten(lwx), flatten(lwy), "-k") + plt.plot(flatten(wx), flatten(wy), "-k") + plt.title("x:" + str(round(xt, 2)) + ",theta:" + + str(round(math.degrees(theta), 2))) + + plt.axis("equal") + + +if __name__ == '__main__': + main() From bd5269b3d2f2e3e73739e51334868adac32e1665 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 7 Feb 2020 20:45:15 +0900 Subject: [PATCH 152/909] inverted pendulum mpc control is added --- .../inverted_pendulum_mpc_control.py | 8 ++++---- tests/test_inverted_pendulum_mpc_control.py | 15 +++++++++++++++ 2 files changed, 19 insertions(+), 4 deletions(-) create mode 100644 tests/test_inverted_pendulum_mpc_control.py diff --git a/InvertedPendulumCart/inverted_pendulum_mpc_control.py b/InvertedPendulumCart/inverted_pendulum_mpc_control.py index d807a688c5..caf0c23e77 100644 --- a/InvertedPendulumCart/inverted_pendulum_mpc_control.py +++ b/InvertedPendulumCart/inverted_pendulum_mpc_control.py @@ -145,16 +145,16 @@ def plot_cart(xt, theta): by += radius * 2.0 angles = np.arange(0.0, math.pi * 2.0, math.radians(3.0)) - ox = [radius * math.cos(a) for a in angles] - oy = [radius * math.sin(a) for a in angles] + ox = np.array([radius * math.cos(a) for a in angles]) + oy = np.array([radius * math.sin(a) for a in angles]) rwx = np.copy(ox) + cart_w / 4.0 + xt rwy = np.copy(oy) + radius lwx = np.copy(ox) - cart_w / 4.0 + xt lwy = np.copy(oy) + radius - wx = np.copy(ox) + float(bx[0, -1]) - wy = np.copy(oy) + float(by[0, -1]) + wx = np.copy(ox) + bx[-1] + wy = np.copy(oy) + by[-1] plt.plot(flatten(cx), flatten(cy), "-b") plt.plot(flatten(bx), flatten(by), "-k") diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py new file mode 100644 index 0000000000..bf6c8576e4 --- /dev/null +++ b/tests/test_inverted_pendulum_mpc_control.py @@ -0,0 +1,15 @@ +from unittest import TestCase + +import sys +if 'cvxpy' in sys.modules: # pragma: no cover + sys.path.append("./InvertedPendulumCart/inverted_pendulum_mpc_control/") + + import inverted_pendulum_mpc_control as m + + print(__file__) + + class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main() From cacb6db028332e9ef1b48757d6b7e53e6080324b Mon Sep 17 00:00:00 2001 From: Shilong Date: Sun, 9 Feb 2020 16:24:21 -0800 Subject: [PATCH 153/909] fix RRT rewire --- PathPlanning/RRTStar/rrt_star.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index 09a421937a..7b46029791 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -34,7 +34,7 @@ def __init__(self, x, y): self.cost = 0.0 def __init__(self, start, goal, obstacle_list, rand_area, - expand_dis=3.0, + expand_dis=30.0, path_resolution=1.0, goal_sample_rate=20, max_iter=300, @@ -141,6 +141,9 @@ def search_best_goal_node(self): def find_near_nodes(self, new_node): nnode = len(self.node_list) + 1 r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode)) + # if expand_dist exists, search vertices in a range no more than expand_dist + if hasattr(self, 'expand_dis'): + r = min(r, self.expand_dis) dist_list = [(node.x - new_node.x) ** 2 + (node.y - new_node.y) ** 2 for node in self.node_list] near_inds = [dist_list.index(i) for i in dist_list if i <= r ** 2] @@ -158,8 +161,7 @@ def rewire(self, new_node, near_inds): improved_cost = near_node.cost > edge_node.cost if no_collision and improved_cost: - near_node = edge_node - near_node.parent = new_node + self.node_list[i] = edge_node self.propagate_cost_to_leaves(new_node) def calc_new_cost(self, from_node, to_node): From 41d744c67f71de511d22179f2b8999ac41e789e6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 22 Feb 2020 21:36:51 +0900 Subject: [PATCH 154/909] add error handling. But there is still bug. --- Mapping/grid_map_lib/grid_map_lib.py | 4 +- .../grid_based_sweep_coverage_path_planner.py | 82 ++++++++++++------- 2 files changed, 54 insertions(+), 32 deletions(-) diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py index fd016a288f..4787238f07 100644 --- a/Mapping/grid_map_lib/grid_map_lib.py +++ b/Mapping/grid_map_lib/grid_map_lib.py @@ -51,7 +51,7 @@ def get_value_from_xy_index(self, x_ind, y_ind): grid_ind = self.calc_grid_index_from_xy_index(x_ind, y_ind) - if 0 <= grid_ind <= self.ndata: + if 0 <= grid_ind < self.ndata: return self.data[grid_ind] else: return None @@ -163,7 +163,7 @@ def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0): val = self.get_value_from_xy_index(xind, yind) - if val >= occupied_val: + if val is None or val >= occupied_val: return True else: return False diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index 0d2239a9ae..c51e9393cb 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -30,22 +30,23 @@ class MovingDirection(IntEnum): RIGHT = 1 LEFT = -1 - def __init__(self, mdirection, sdirection, xinds_goaly, goaly): - self.moving_direction = mdirection - self.sweep_direction = sdirection + def __init__(self, moving_direction, sweep_direction, x_inds_goal_y, goal_y): + self.moving_direction = moving_direction + self.sweep_direction = sweep_direction self.turing_window = [] self.update_turning_window() - self.xinds_goaly = xinds_goaly - self.goaly = goaly + self.xinds_goaly = x_inds_goal_y + self.goaly = goal_y def move_target_grid(self, cxind, cyind, gmap): nxind = self.moving_direction + cxind nyind = cyind # found safe grid - if not gmap.check_occupied_from_xy_index(nxind, nyind, occupied_val=0.5): + if not gmap.check_occupied_from_xy_index(nxind, nyind, + occupied_val=0.5): return nxind, nyind - else: # occupided + else: # occupied ncxind, ncyind = self.find_safe_turning_grid(cxind, cyind, gmap) if (ncxind is None) and (ncyind is None): # moving backward @@ -56,27 +57,31 @@ def move_target_grid(self, cxind, cyind, gmap): return None, None else: # keep moving until end - while not gmap.check_occupied_from_xy_index(ncxind + self.moving_direction, ncyind, occupied_val=0.5): + while not gmap.check_occupied_from_xy_index( + ncxind + self.moving_direction, ncyind, + occupied_val=0.5): ncxind += self.moving_direction self.swap_moving_direction() return ncxind, ncyind def find_safe_turning_grid(self, cxind, cyind, gmap): - for (dxind, dyind) in self.turing_window: + for (d_x_ind, d_y_ind) in self.turing_window: - nxind = dxind + cxind - nyind = dyind + cyind + next_x_ind = d_x_ind + cxind + next_y_ind = d_y_ind + cyind # found safe grid - if not gmap.check_occupied_from_xy_index(nxind, nyind, occupied_val=0.5): - return nxind, nyind + if not gmap.check_occupied_from_xy_index(next_x_ind, next_y_ind, + occupied_val=0.5): + return next_x_ind, next_y_ind return None, None - def is_search_done(self, gmap): + def is_search_done(self, grid_map): for ix in self.xinds_goaly: - if not gmap.check_occupied_from_xy_index(ix, self.goaly, occupied_val=0.5): + if not grid_map.check_occupied_from_xy_index(ix, self.goaly, + occupied_val=0.5): return False # all lower grid is occupied @@ -95,22 +100,24 @@ def swap_moving_direction(self): self.update_turning_window() def search_start_grid(self, grid_map): - xinds = [] + x_inds = [] y_ind = 0 if self.sweep_direction == self.SweepDirection.DOWN: - xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=True) + x_inds, y_ind = search_free_grid_index_at_edge_y( + grid_map, from_upper=True) elif self.sweep_direction == self.SweepDirection.UP: - xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=False) + x_inds, y_ind = search_free_grid_index_at_edge_y( + grid_map, from_upper=False) if self.moving_direction == self.MovingDirection.RIGHT: - return min(xinds), y_ind + return min(x_inds), y_ind elif self.moving_direction == self.MovingDirection.LEFT: - return max(xinds), y_ind + return max(x_inds), y_ind raise ValueError("self.moving direction is invalid ") -def find_sweep_direction_and_start_posi(ox, oy): +def find_sweep_direction_and_start_position(ox, oy): # find sweep_direction max_dist = 0.0 vec = [0.0, 0.0] @@ -194,9 +201,11 @@ def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10): xinds_goaly = [] goaly = 0 if sweep_direction == SweepSearcher.SweepDirection.UP: - xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, from_upper=True) + xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, + from_upper=True) elif sweep_direction == SweepSearcher.SweepDirection.DOWN: - xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, from_upper=False) + xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, + from_upper=False) return grid_map, xinds_goaly, goaly @@ -211,16 +220,19 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False): x, y = gmap.calc_grid_central_xy_position_from_xy_index(cxind, cyind) px, py = [x], [y] + fig, ax = None, None if grid_search_animation: fig, ax = plt.subplots() # for stopping simulation with the esc key. fig.canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [ + exit(0) if event.key == 'escape' else None]) while True: cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap) - if sweep_searcher.is_search_done(gmap) or (cxind is None or cyind is None): + if sweep_searcher.is_search_done(gmap) or ( + cxind is None or cyind is None): print("Done") break @@ -245,13 +257,16 @@ def planning(ox, oy, reso, moving_direction=SweepSearcher.MovingDirection.RIGHT, sweeping_direction=SweepSearcher.SweepDirection.UP, ): - sweep_vec, sweep_start_posi = find_sweep_direction_and_start_posi(ox, oy) + sweep_vec, sweep_start_posi = find_sweep_direction_and_start_position( + ox, oy) rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi) - gmap, xinds_goaly, goaly = setup_grid_map(rox, roy, reso, sweeping_direction) + gmap, xinds_goaly, goaly = setup_grid_map(rox, roy, reso, + sweeping_direction) - sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, xinds_goaly, goaly) + sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, + xinds_goaly, goaly) px, py = sweep_path_search(sweep_searcher, gmap) @@ -270,8 +285,9 @@ def planning_animation(ox, oy, reso): # pragma: no cover for ipx, ipy in zip(px, py): plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(ox, oy, "-xb") plt.plot(px, py, "-r") plt.plot(ipx, ipy, "or") @@ -285,11 +301,17 @@ def planning_animation(ox, oy, reso): # pragma: no cover plt.axis("equal") plt.grid(True) plt.pause(0.1) + plt.close() def main(): # pragma: no cover print("start!!") + ox = [0.0, 50.0, 50.0, 0.0, 0.0] + oy = [0.0, 0.0, 50.0, 50.0, 0.0] + reso = 0.4 + planning_animation(ox, oy, reso) + ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0] oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0] reso = 5.0 From 7c8a940ebfd44217ae325470c48b47d9ed35f0f3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 22 Feb 2020 23:17:25 +0900 Subject: [PATCH 155/909] fixed the bug and code clean up. --- Mapping/grid_map_lib/grid_map_lib.py | 21 +++++--- .../grid_based_sweep_coverage_path_planner.py | 48 +++++++++---------- 2 files changed, 36 insertions(+), 33 deletions(-) diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py index 4787238f07..10651cc673 100644 --- a/Mapping/grid_map_lib/grid_map_lib.py +++ b/Mapping/grid_map_lib/grid_map_lib.py @@ -32,10 +32,8 @@ def __init__(self, width, height, resolution, self.center_x = center_x self.center_y = center_y - self.left_lower_x = self.center_x - \ - (self.width / 2.0) * self.resolution - self.left_lower_y = self.center_y - \ - (self.height / 2.0) * self.resolution + self.left_lower_x = self.center_x - self.width / 2.0 * self.resolution + self.left_lower_y = self.center_y - self.height / 2.0 * self.resolution self.ndata = self.width * self.height self.data = [init_val] * self.ndata @@ -99,7 +97,6 @@ def set_value_from_xy_index(self, x_ind, y_ind, val): """ if (x_ind is None) or (y_ind is None): - print(x_ind, y_ind) return False, False grid_ind = int(y_ind * self.width + x_ind) @@ -200,12 +197,22 @@ def check_inside_polygon(iox, ioy, x, y): if not min_x < iox < max_x: continue - if (y[i1] + (y[i2] - y[i1]) / (x[i2] - x[i1]) - * (iox - x[i1]) - ioy) > 0.0: + tmp1 = (y[i2] - y[i1]) / (x[i2] - x[i1]) + if (y[i1] + tmp1 * (iox - x[i1]) - ioy) > 0.0: inside = not inside return inside + def print_grid_map_info(self): + print("width:", self.width) + print("height:", self.height) + print("resolution:", self.resolution) + print("center_x:", self.center_x) + print("center_y:", self.center_y) + print("left_lower_x:", self.left_lower_x) + print("left_lower_y:", self.left_lower_y) + print("ndata:", self.ndata) + def plot_grid_map(self, ax=None): grid_data = np.reshape(np.array(self.data), (self.height, self.width)) diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index c51e9393cb..8d9ccd2e2f 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -30,7 +30,8 @@ class MovingDirection(IntEnum): RIGHT = 1 LEFT = -1 - def __init__(self, moving_direction, sweep_direction, x_inds_goal_y, goal_y): + def __init__(self, + moving_direction, sweep_direction, x_inds_goal_y, goal_y): self.moving_direction = moving_direction self.sweep_direction = sweep_direction self.turing_window = [] @@ -88,6 +89,8 @@ def is_search_done(self, grid_map): return True def update_turning_window(self): + # turning window definition + # robot can move grid based on it. self.turing_window = [ (self.moving_direction, 0.0), (self.moving_direction, self.sweep_direction), @@ -189,35 +192,34 @@ def search_free_grid_index_at_edge_y(grid_map, from_upper=False): def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10): width = math.ceil((max(ox) - min(ox)) / reso) + offset_grid height = math.ceil((max(oy) - min(oy)) / reso) + offset_grid - center_x = np.mean(ox) - center_y = np.mean(oy) + center_x = (np.max(ox)+np.min(ox))/2.0 + center_y = (np.max(oy)+np.min(oy))/2.0 grid_map = GridMap(width, height, reso, center_x, center_y) - + grid_map.print_grid_map_info() grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False) - grid_map.expand_grid() - xinds_goaly = [] - goaly = 0 + x_inds_goal_y = [] + goal_y = 0 if sweep_direction == SweepSearcher.SweepDirection.UP: - xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, + x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map, from_upper=True) elif sweep_direction == SweepSearcher.SweepDirection.DOWN: - xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, + x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map, from_upper=False) - return grid_map, xinds_goaly, goaly + return grid_map, x_inds_goal_y, goal_y -def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False): +def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False): # search start grid - cxind, cyind = sweep_searcher.search_start_grid(gmap) - if not gmap.set_value_from_xy_index(cxind, cyind, 0.5): + cxind, cyind = sweep_searcher.search_start_grid(grid_map) + if not grid_map.set_value_from_xy_index(cxind, cyind, 0.5): print("Cannot find start grid") return [], [] - x, y = gmap.calc_grid_central_xy_position_from_xy_index(cxind, cyind) + x, y = grid_map.calc_grid_central_xy_position_from_xy_index(cxind, cyind) px, py = [x], [y] fig, ax = None, None @@ -229,26 +231,26 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False): exit(0) if event.key == 'escape' else None]) while True: - cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap) + cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, grid_map) - if sweep_searcher.is_search_done(gmap) or ( + if sweep_searcher.is_search_done(grid_map) or ( cxind is None or cyind is None): print("Done") break - x, y = gmap.calc_grid_central_xy_position_from_xy_index( + x, y = grid_map.calc_grid_central_xy_position_from_xy_index( cxind, cyind) px.append(x) py.append(y) - gmap.set_value_from_xy_index(cxind, cyind, 0.5) + grid_map.set_value_from_xy_index(cxind, cyind, 0.5) if grid_search_animation: - gmap.plot_grid_map(ax=ax) + grid_map.plot_grid_map(ax=ax) plt.pause(1.0) - gmap.plot_grid_map() + grid_map.plot_grid_map() return px, py @@ -307,11 +309,6 @@ def planning_animation(ox, oy, reso): # pragma: no cover def main(): # pragma: no cover print("start!!") - ox = [0.0, 50.0, 50.0, 0.0, 0.0] - oy = [0.0, 0.0, 50.0, 50.0, 0.0] - reso = 0.4 - planning_animation(ox, oy, reso) - ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0] oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0] reso = 5.0 @@ -328,7 +325,6 @@ def main(): # pragma: no cover planning_animation(ox, oy, reso) plt.show() - print("done!!") From 065536a47b0c8e692638df288539eb301e3bb086 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 23 Feb 2020 20:35:09 +0900 Subject: [PATCH 156/909] start implementation --- PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py new file mode 100644 index 0000000000..e69de29bb2 From 6f0d9bfd5c6cd74bbd65891570568652c0160690 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 24 Feb 2020 16:15:17 +0900 Subject: [PATCH 157/909] clean up voronoi road map --- .../VoronoiRoadMap/dijkstra_search.py | 106 +++++++ PathPlanning/VoronoiRoadMap/kdtree.py | 49 +++ .../VoronoiRoadMap/voronoi_road_map.py | 294 +++++------------- 3 files changed, 239 insertions(+), 210 deletions(-) create mode 100644 PathPlanning/VoronoiRoadMap/dijkstra_search.py create mode 100644 PathPlanning/VoronoiRoadMap/kdtree.py diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py new file mode 100644 index 0000000000..4d294ee5e5 --- /dev/null +++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py @@ -0,0 +1,106 @@ +""" + +Dijkstra Search library + +author: Atsushi Sakai (@Atsushi_twi) + +""" + +import matplotlib.pyplot as plt +import math + + +class DijkstraSearch: + + class Node: + """ + Node class for dijkstra search + """ + + def __init__(self, x, y, cost, parent): + self.x = x + self.y = y + self.cost = cost + self.parent = parent + + def __str__(self): + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.parent) + + def __init__(self, show_animation): + self.show_animation = show_animation + + def search(self, sx, sy, gx, gy, road_map, sample_x, sample_y): + """ + gx: goal x position [m] + gx: goal x position [m] + ox: x position list of Obstacles [m] + oy: y position list of Obstacles [m] + reso: grid resolution [m] + rr: robot radius[m] + """ + + start_node = self.Node(sx, sy, 0.0, -1) + goal_node = self.Node(gx, gy, 0.0, -1) + + open_set, close_set = dict(), dict() + open_set[len(road_map) - 2] = start_node + + while True: + if not open_set: + print("Cannot find path") + break + + c_id = min(open_set, key=lambda o: open_set[o].cost) + current = open_set[c_id] + + # show graph + if self.show_animation and len( + close_set.keys()) % 2 == 0: # pragma: no cover + plt.plot(current.x, current.y, "xg") + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + plt.pause(0.001) + + if c_id == (len(road_map) - 1): + print("goal is found!") + goal_node.parent = current.parent + goal_node.cost = current.cost + break + + # Remove the item from the open set + del open_set[c_id] + # Add it to the closed set + close_set[c_id] = current + + # expand search grid based on motion model + for i in range(len(road_map[c_id])): + n_id = road_map[c_id][i] + dx = sample_x[n_id] - current.x + dy = sample_y[n_id] - current.y + d = math.hypot(dx, dy) + node = self.Node(sample_x[n_id], sample_y[n_id], + current.cost + d, c_id) + + if n_id in close_set: + continue + # Otherwise if it is already in the open set + if n_id in open_set: + if open_set[n_id].cost > node.cost: + open_set[n_id].cost = node.cost + open_set[n_id].parent = c_id + else: + open_set[n_id] = node + + # generate final course + rx, ry = [goal_node.x], [goal_node.y] + parent = goal_node.parent + while parent != -1: + n = close_set[parent] + rx.append(n.x) + ry.append(n.y) + parent = n.parent + + return rx, ry diff --git a/PathPlanning/VoronoiRoadMap/kdtree.py b/PathPlanning/VoronoiRoadMap/kdtree.py new file mode 100644 index 0000000000..d3dc01b1f6 --- /dev/null +++ b/PathPlanning/VoronoiRoadMap/kdtree.py @@ -0,0 +1,49 @@ +""" + +Kd tree Search library + +author: Atsushi Sakai (@Atsushi_twi) + +""" + +import scipy.spatial + + +class KDTree: + """ + Nearest neighbor search class with KDTree + """ + + def __init__(self, data): + # store kd-tree + self.tree = scipy.spatial.cKDTree(data) + + def search(self, inp, k=1): + """ + Search NN + + inp: input data, single frame or multi frame + + """ + + if len(inp.shape) >= 2: # multi input + index = [] + dist = [] + + for i in inp.T: + idist, iindex = self.tree.query(i, k=k) + index.append(iindex) + dist.append(idist) + + return index, dist + + dist, index = self.tree.query(inp, k=k) + return index, dist + + def search_in_distance(self, inp, r): + """ + find points with in a distance r + """ + + index = self.tree.query_ball_point(inp, r) + return index diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py index 6db4bb26ce..dfac7258f8 100644 --- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py +++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py @@ -10,252 +10,124 @@ import numpy as np import scipy.spatial import matplotlib.pyplot as plt - -# parameter -N_KNN = 10 # number of edge from one sampled point -MAX_EDGE_LEN = 30.0 # [m] Maximum edge length +from dijkstra_search import DijkstraSearch +from kdtree import KDTree show_animation = True -class Node: - """ - Node class for dijkstra search - """ - - def __init__(self, x, y, cost, pind): - self.x = x - self.y = y - self.cost = cost - self.pind = pind - - def __str__(self): - return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind) - - -class KDTree: - """ - Nearest neighbor search class with KDTree - """ - - def __init__(self, data): - # store kd-tree - self.tree = scipy.spatial.cKDTree(data) - - def search(self, inp, k=1): - """ - Search NN - - inp: input data, single frame or multi frame - - """ - - if len(inp.shape) >= 2: # multi input - index = [] - dist = [] - - for i in inp.T: - idist, iindex = self.tree.query(i, k=k) - index.append(iindex) - dist.append(idist) - - return index, dist - - dist, index = self.tree.query(inp, k=k) - return index, dist - - def search_in_distance(self, inp, r): - """ - find points with in a distance r - """ - - index = self.tree.query_ball_point(inp, r) - return index - +class VoronoiRoadMapPlanner: -def VRM_planning(sx, sy, gx, gy, ox, oy, rr): + def __init__(self): + # parameter + self.N_KNN = 10 # number of edge from one sampled point + self.MAX_EDGE_LEN = 30.0 # [m] Maximum edge length - obkdtree = KDTree(np.vstack((ox, oy)).T) + def planning(self, sx, sy, gx, gy, ox, oy, rr): + obstacle_tree = KDTree(np.vstack((ox, oy)).T) - sample_x, sample_y = sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree) - if show_animation: # pragma: no cover - plt.plot(sample_x, sample_y, ".b") + sample_x, sample_y = self.voronoi_sampling(sx, sy, gx, gy, ox, oy) + if show_animation: # pragma: no cover + plt.plot(sample_x, sample_y, ".b") - road_map = generate_roadmap(sample_x, sample_y, rr, obkdtree) + road_map = self.generate_road_map(sample_x, sample_y, rr, obstacle_tree) - rx, ry = dijkstra_planning( - sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y) + rx, ry = DijkstraSearch(show_animation).search(sx, sy, gx, gy, road_map, + sample_x, sample_y) - return rx, ry + return rx, ry + def is_collision(self, sx, sy, gx, gy, rr, obstacle_kdtree): + x = sx + y = sy + dx = gx - sx + dy = gy - sy + yaw = math.atan2(gy - sy, gx - sx) + d = math.hypot(dx, dy) -def is_collision(sx, sy, gx, gy, rr, okdtree): - x = sx - y = sy - dx = gx - sx - dy = gy - sy - yaw = math.atan2(gy - sy, gx - sx) - d = math.hypot(dx, dy) + if d >= self.MAX_EDGE_LEN: + return True - if d >= MAX_EDGE_LEN: - return True + D = rr + n_step = round(d / D) - D = rr - nstep = round(d / D) + for i in range(n_step): + ids, dist = obstacle_kdtree.search(np.array([x, y]).reshape(2, 1)) + if dist[0] <= rr: + return True # collision + x += D * math.cos(yaw) + y += D * math.sin(yaw) - for i in range(nstep): - idxs, dist = okdtree.search(np.array([x, y]).reshape(2, 1)) + # goal point check + ids, dist = obstacle_kdtree.search(np.array([gx, gy]).reshape(2, 1)) if dist[0] <= rr: return True # collision - x += D * math.cos(yaw) - y += D * math.sin(yaw) - - # goal point check - idxs, dist = okdtree.search(np.array([gx, gy]).reshape(2, 1)) - if dist[0] <= rr: - return True # collision - - return False # OK - - -def generate_roadmap(sample_x, sample_y, rr, obkdtree): - """ - Road map generation - - sample_x: [m] x positions of sampled points - sample_y: [m] y positions of sampled points - rr: Robot Radius[m] - obkdtree: KDTree object of obstacles - """ - - road_map = [] - nsample = len(sample_x) - skdtree = KDTree(np.vstack((sample_x, sample_y)).T) - - for (i, ix, iy) in zip(range(nsample), sample_x, sample_y): - - index, dists = skdtree.search( - np.array([ix, iy]).reshape(2, 1), k=nsample) - - inds = index[0] - edge_id = [] - # print(index) - - for ii in range(1, len(inds)): - nx = sample_x[inds[ii]] - ny = sample_y[inds[ii]] - if not is_collision(ix, iy, nx, ny, rr, obkdtree): - edge_id.append(inds[ii]) + return False # OK - if len(edge_id) >= N_KNN: - break - - road_map.append(edge_id) - - # plot_road_map(road_map, sample_x, sample_y) - - return road_map - - -def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): - """ - gx: goal x position [m] - gx: goal x position [m] - ox: x position list of Obstacles [m] - oy: y position list of Obstacles [m] - reso: grid resolution [m] - rr: robot radius[m] - """ - - nstart = Node(sx, sy, 0.0, -1) - ngoal = Node(gx, gy, 0.0, -1) + def generate_road_map(self, node_x, node_y, rr, obstacle_tree): + """ + Road map generation - openset, closedset = dict(), dict() - openset[len(road_map) - 2] = nstart + sample_x: [m] x positions of sampled points + sample_y: [m] y positions of sampled points + rr: Robot Radius[m] + obstacle_tree: KDTree object of obstacles + """ - while True: - if not openset: - print("Cannot find path") - break + road_map = [] + n_sample = len(node_x) + node_tree = KDTree(np.vstack((node_x, node_y)).T) - c_id = min(openset, key=lambda o: openset[o].cost) - current = openset[c_id] + for (i, ix, iy) in zip(range(n_sample), node_x, node_y): - # show graph - if show_animation and len(closedset.keys()) % 2 == 0: # pragma: no cover - plt.plot(current.x, current.y, "xg") - # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) - plt.pause(0.001) + index, dists = node_tree.search( + np.array([ix, iy]).reshape(2, 1), k=n_sample) - if c_id == (len(road_map) - 1): - print("goal is found!") - ngoal.pind = current.pind - ngoal.cost = current.cost - break + inds = index[0] + edge_id = [] - # Remove the item from the open set - del openset[c_id] - # Add it to the closed set - closedset[c_id] = current + for ii in range(1, len(inds)): + nx = node_x[inds[ii]] + ny = node_y[inds[ii]] - # expand search grid based on motion model - for i in range(len(road_map[c_id])): - n_id = road_map[c_id][i] - dx = sample_x[n_id] - current.x - dy = sample_y[n_id] - current.y - d = math.hypot(dx, dy) - node = Node(sample_x[n_id], sample_y[n_id], - current.cost + d, c_id) + if not self.is_collision(ix, iy, nx, ny, rr, obstacle_tree): + edge_id.append(inds[ii]) - if n_id in closedset: - continue - # Otherwise if it is already in the open set - if n_id in openset: - if openset[n_id].cost > node.cost: - openset[n_id].cost = node.cost - openset[n_id].pind = c_id - else: - openset[n_id] = node + if len(edge_id) >= self.N_KNN: + break - # generate final course - rx, ry = [ngoal.x], [ngoal.y] - pind = ngoal.pind - while pind != -1: - n = closedset[pind] - rx.append(n.x) - ry.append(n.y) - pind = n.pind + road_map.append(edge_id) - return rx, ry + # plot_road_map(road_map, sample_x, sample_y) + return road_map -def plot_road_map(road_map, sample_x, sample_y): # pragma: no cover + @staticmethod + def plot_road_map(road_map, sample_x, sample_y): # pragma: no cover - for i, _ in enumerate(road_map): - for ii in range(len(road_map[i])): - ind = road_map[i][ii] + for i, _ in enumerate(road_map): + for ii in range(len(road_map[i])): + ind = road_map[i][ii] - plt.plot([sample_x[i], sample_x[ind]], - [sample_y[i], sample_y[ind]], "-k") + plt.plot([sample_x[i], sample_x[ind]], + [sample_y[i], sample_y[ind]], "-k") + @staticmethod + def voronoi_sampling(sx, sy, gx, gy, ox, oy): + oxy = np.vstack((ox, oy)).T -def sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree): - oxy = np.vstack((ox, oy)).T + # generate voronoi point + vor = scipy.spatial.Voronoi(oxy) + sample_x = [ix for [ix, _] in vor.vertices] + sample_y = [iy for [_, iy] in vor.vertices] - # generate voronoi point - vor = scipy.spatial.Voronoi(oxy) - sample_x = [ix for [ix, iy] in vor.vertices] - sample_y = [iy for [ix, iy] in vor.vertices] + sample_x.append(sx) + sample_y.append(sy) + sample_x.append(gx) + sample_y.append(gy) - sample_x.append(sx) - sample_y.append(sy) - sample_x.append(gx) - sample_y.append(gy) - - return sample_x, sample_y + return sample_x, sample_y def main(): @@ -297,12 +169,14 @@ def main(): plt.grid(True) plt.axis("equal") - rx, ry = VRM_planning(sx, sy, gx, gy, ox, oy, robot_size) + rx, ry = VoronoiRoadMapPlanner().planning(sx, sy, gx, gy, ox, oy, + robot_size) assert rx, 'Cannot found path' if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") + plt.pause(0.1) plt.show() From ae85e66d07dedc37739a7f81910befce2e4065cc Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 24 Feb 2020 16:40:57 +0900 Subject: [PATCH 158/909] fix tests --- .../visibility_graph_planner.py | 90 +++++++++++++++++++ tests/test_voronoi_path_planner.py | 6 ++ 2 files changed, 96 insertions(+) diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py index e69de29bb2..06a4d59834 100644 --- a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py +++ b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py @@ -0,0 +1,90 @@ +""" + +Visibility Graph Planner + +author: Atsushi Sakai (@Atsushi_twi) + +""" + +import matplotlib.pyplot as plt + +show_animation = True + +class VisibilityGraphPlanner: + + def __init__(self, robot_radius): + self.robot_radius = robot_radius + + def planning(self, start_x, start_y, goal_x, goal_y, obstacles): + nodes = self.exstract_graph_node(start_x, start_y, goal_x, goal_y, + obstacles) + + graph = self.generate_graph(nodes, obstacles) + + rx, ry = dijkstra_search(graph) + + return rx, ry + + def exstract_graph_node(self, start_x, start_y, goal_x, goal_x, obstacles): + nodes = [] + + return nodes + + def generate_graph(self, nodes, obstacles): + + graph = [] + + return graph + + +class ObstaclePolygon: + + def __init__(self, x_list, y_list): + self.x_list = x_list + self.y_list = y_list + + self.close_polygon() + + def close_polygon(self): + is_x_same = self.x_list[0] == self.x_list[-1] + is_y_same = self.y_list[0] == self.y_list[-1] + if is_x_same and is_y_same: + return # no need to close + + self.x_list.append(self.x_list[0]) + self.y_list.append(self.y_list[0]) + + def plot(self): + plt.plot(self.x_list, self.y_list, "-b") + + +def main(): + print(__file__ + " start!!") + + # start and goal position + sx, sy = 10.0, 10.0 # [m] + gx, gy = 50.0, 50.0 # [m] + robot_radius = 5.0 # [m] + + obstacles = [ObstaclePolygon( + [20.0, 30.0, 15.0], + [20.0, 20.0, 30.0], + ), ObstaclePolygon( + [30.0, 45.0, 50.0, 40.0], + [50.0, 40.0, 20.0, 40.0], + )] + + rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy, + obstacles) + assert rx, 'Cannot found path' + if show_animation: # pragma: no cover + plt.plot(sx, sy, "or") + plt.plot(gx, gy, "ob") + [ob.plot() for ob in obstacles] + plt.plot(rx, ry, "-r") + plt.axis("equal") + plt.show() + + +if __name__ == '__main__': + main() diff --git a/tests/test_voronoi_path_planner.py b/tests/test_voronoi_path_planner.py index 3e0ff961d8..5295769b40 100644 --- a/tests/test_voronoi_path_planner.py +++ b/tests/test_voronoi_path_planner.py @@ -1,6 +1,12 @@ +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../PathPlanning/VoronoiRoadMap/") + from unittest import TestCase from PathPlanning.VoronoiRoadMap import voronoi_road_map as m + print(__file__) From cfd83841d753956ae6957ca101d42cff5e5596a5 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 26 Feb 2020 20:37:45 +0900 Subject: [PATCH 159/909] for good measure --- .../visibility_graph_planner.py | 126 ++++++++++++++++-- .../VoronoiRoadMap/dijkstra_search.py | 2 +- tests/test_rrt_star_dubins.py | 1 + 3 files changed, 119 insertions(+), 10 deletions(-) diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py index 06a4d59834..d00e8f43c6 100644 --- a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py +++ b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py @@ -6,36 +6,127 @@ """ +import os +import sys + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../VoronoiRoadMap/") + +import math +import numpy as np import matplotlib.pyplot as plt +from dijkstra_search import DijkstraSearch show_animation = True + class VisibilityGraphPlanner: def __init__(self, robot_radius): self.robot_radius = robot_radius def planning(self, start_x, start_y, goal_x, goal_y, obstacles): - nodes = self.exstract_graph_node(start_x, start_y, goal_x, goal_y, - obstacles) - + nodes = self.extract_graph_node(start_x, start_y, goal_x, goal_y, + obstacles) graph = self.generate_graph(nodes, obstacles) - rx, ry = dijkstra_search(graph) + # rx, ry = DijkstraSearch().search(graph) + + rx, ry = [], [] return rx, ry - def exstract_graph_node(self, start_x, start_y, goal_x, goal_x, obstacles): - nodes = [] + def extract_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles): + + # add start and goal as nodes + nodes = [DijkstraSearch.Node(start_x, start_y), + DijkstraSearch.Node(goal_x, goal_y, 0, None)] + + # add vertexes in configuration space as nodes + for obstacle in obstacles: + + cvx_list, cvy_list = self.calc_vertexes_in_configuration_space( + obstacle.x_list, obstacle.y_list) + + for (vx, vy) in zip(cvx_list, cvy_list): + nodes.append(DijkstraSearch.Node(vx, vy)) + + for node in nodes: + plt.plot(node.x, node.y, "xr") return nodes + def calc_vertexes_in_configuration_space(self, x_list, y_list): + x_list=x_list[0:-1] + y_list=y_list[0:-1] + cvx_list, cvy_list = [], [] + + n_data=len(x_list) + + for index in range(n_data): + offset_x, offset_y = self.calc_offset_xy( + x_list[index - 1], y_list[index - 1], + x_list[index], y_list[index], + x_list[(index + 1) % n_data], y_list[(index + 1) % n_data], + ) + cvx_list.append(offset_x) + cvy_list.append(offset_y) + + return cvx_list, cvy_list + def generate_graph(self, nodes, obstacles): graph = [] + for target_node in nodes: + for node in nodes: + for obstacle in obstacles: + if not self.is_edge_valid(target_node, node, obstacle): + print("bb") + break + print(target_node, node) + print("aa") + plt.plot([target_node.x, node.x],[target_node.y, node.y], "-r") + return graph + def is_edge_valid(self, target_node, node, obstacle): + + for i in range(len(obstacle.x_list)-1): + p1 = np.array([target_node.x, target_node.y]) + p2 = np.array([node.x, node.y]) + p3 = np.array([obstacle.x_list[i], obstacle.y_list[i]]) + p4 = np.array([obstacle.y_list[i+1], obstacle.y_list[i+1]]) + + if is_seg_intersect(p1, p2, p3, p4): + return False + + return True + + def calc_offset_xy(self, px, py, x, y, nx, ny): + p_vec = math.atan2(y - py, x - px) + n_vec = math.atan2(ny - y, nx - x) + offset_vec = math.atan2(math.sin(p_vec) + math.sin(n_vec), + math.cos(p_vec) + math.cos( + n_vec))+math.pi/2.0 + offset_x = x + self.robot_radius * math.cos(offset_vec) + offset_y = y + self.robot_radius * math.sin(offset_vec) + return offset_x, offset_y + + +def is_seg_intersect(a1, a2, b1, b2): + + xdiff = [a1[0] - a2[0], b1[0] - b2[0]] + ydiff = [a1[1] - a2[1], b1[1] - b2[1]] + + def det(a, b): + return a[0] * b[1] - a[1] * b[0] + + div = det(xdiff, ydiff) + if div == 0: + return False + else: + return True class ObstaclePolygon: @@ -44,6 +135,21 @@ def __init__(self, x_list, y_list): self.y_list = y_list self.close_polygon() + self.make_clockwise() + + def make_clockwise(self): + if not self.is_clockwise(): + self.x_list = list(reversed(self.x_list)) + self.y_list = list(reversed(self.y_list)) + + def is_clockwise(self): + n_data = len(self.x_list) + eval_sum = sum([(self.x_list[i + 1] - self.x_list[i]) * + (self.y_list[i + 1] + self.y_list[i]) + for i in range(n_data - 1)]) + eval_sum += (self.x_list[0] - self.x_list[n_data - 1]) * \ + (self.y_list[0] + self.y_list[n_data - 1]) + return eval_sum >= 0 def close_polygon(self): is_x_same = self.x_list[0] == self.x_list[-1] @@ -74,13 +180,15 @@ def main(): [50.0, 40.0, 20.0, 40.0], )] - rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy, - obstacles) - assert rx, 'Cannot found path' if show_animation: # pragma: no cover plt.plot(sx, sy, "or") plt.plot(gx, gy, "ob") [ob.plot() for ob in obstacles] + + rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy, + obstacles) + # assert rx, 'Cannot found path' + if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") plt.axis("equal") plt.show() diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py index 4d294ee5e5..7f248cd3cf 100644 --- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py +++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py @@ -17,7 +17,7 @@ class Node: Node class for dijkstra search """ - def __init__(self, x, y, cost, parent): + def __init__(self, x, y, cost=None, parent=None): self.x = x self.y = y self.cost = cost diff --git a/tests/test_rrt_star_dubins.py b/tests/test_rrt_star_dubins.py index 8ee3d38788..db1ca2bfa4 100644 --- a/tests/test_rrt_star_dubins.py +++ b/tests/test_rrt_star_dubins.py @@ -2,6 +2,7 @@ import sys import os + sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../PathPlanning/RRTStarDubins/") From 2e188f866b0de3e46cca5e3b9f807003be8bc305 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 26 Feb 2020 22:27:25 +0900 Subject: [PATCH 160/909] To fix #298 --- PathTracking/pure_pursuit/pure_pursuit.py | 83 ++++++++++++----------- 1 file changed, 43 insertions(+), 40 deletions(-) diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py index bcaf869726..ff995033a9 100644 --- a/PathTracking/pure_pursuit/pure_pursuit.py +++ b/PathTracking/pure_pursuit/pure_pursuit.py @@ -1,6 +1,6 @@ """ -Path tracking simulation with pure pursuit steering control and PID speed control. +Path tracking simulation with pure pursuit steering and PID speed control. author: Atsushi Sakai (@Atsushi_twi) Guillaume Jacquenot (@Gjacquenot) @@ -10,13 +10,12 @@ import math import matplotlib.pyplot as plt - +# Parameters k = 0.1 # look forward gain -Lfc = 2.0 # look-ahead distance +Lfc = 2.0 # [m] look-ahead distance Kp = 1.0 # speed proportional gain -dt = 0.1 # [s] -L = 2.9 # [m] wheel base of vehicle - +dt = 0.1 # [s] time tick +WB = 2.9 # [m] wheel base of vehicle show_animation = True @@ -28,20 +27,18 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0): self.y = y self.yaw = yaw self.v = v - self.rear_x = self.x - ((L / 2) * math.cos(self.yaw)) - self.rear_y = self.y - ((L / 2) * math.sin(self.yaw)) + self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw)) + self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw)) def update(self, a, delta): - self.x += self.v * math.cos(self.yaw) * dt self.y += self.v * math.sin(self.yaw) * dt - self.yaw += self.v / L * math.tan(delta) * dt + self.yaw += self.v / WB * math.tan(delta) * dt self.v += a * dt - self.rear_x = self.x - ((L / 2) * math.cos(self.yaw)) - self.rear_y = self.y - ((L / 2) * math.sin(self.yaw)) + self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw)) + self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw)) def calc_distance(self, point_x, point_y): - dx = self.rear_x - point_x dy = self.rear_y - point_y return math.hypot(dx, dy) @@ -56,7 +53,7 @@ def __init__(self): self.v = [] self.t = [] - def append(self, t , state): + def append(self, t, state): self.x.append(state.x) self.y.append(state.y) self.yaw.append(state.yaw) @@ -64,19 +61,22 @@ def append(self, t , state): self.t.append(t) -def PIDControl(target, current): +def proportional_control(target, current): a = Kp * (target - current) return a -class Trajectory: +class TargetCourse: + def __init__(self, cx, cy): self.cx = cx self.cy = cy self.old_nearest_point_index = None def search_target_index(self, state): + + # To speed up nearest point search, doing it at only first time. if self.old_nearest_point_index is None: # search nearest point index dx = [state.rear_x - icx for icx in self.cx] @@ -86,30 +86,30 @@ def search_target_index(self, state): self.old_nearest_point_index = ind else: ind = self.old_nearest_point_index - distance_this_index = state.calc_distance(self.cx[ind], self.cy[ind]) + distance_this_index = state.calc_distance(self.cx[ind], + self.cy[ind]) while True: - ind = ind + 1 if (ind + 1) < len(self.cx) else ind - distance_next_index = state.calc_distance(self.cx[ind], self.cy[ind]) + distance_next_index = state.calc_distance(self.cx[ind + 1], + self.cy[ind + 1]) if distance_this_index < distance_next_index: break + ind = ind + 1 if (ind + 1) < len(self.cx) else ind distance_this_index = distance_next_index self.old_nearest_point_index = ind - L = 0.0 - - Lf = k * state.v + Lfc + Lf = k * state.v + Lfc # update look ahead distance # search look ahead target point index - while Lf > L and (ind + 1) < len(self.cx): - L = state.calc_distance(self.cx[ind], self.cy[ind]) + while Lf > state.calc_distance(self.cx[ind], self.cy[ind]): + if (ind + 1) >= len(self.cx): + break # not exceed goal ind += 1 - return ind + return ind, Lf -def pure_pursuit_control(state, trajectory, pind): - - ind = trajectory.search_target_index(state) +def pure_pursuit_steer_control(state, trajectory, pind): + ind, Lf = trajectory.search_target_index(state) if pind >= ind: ind = pind @@ -117,16 +117,14 @@ def pure_pursuit_control(state, trajectory, pind): if ind < len(trajectory.cx): tx = trajectory.cx[ind] ty = trajectory.cy[ind] - else: + else: # toward goal tx = trajectory.cx[-1] ty = trajectory.cy[-1] ind = len(trajectory.cx) - 1 alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw - Lf = k * state.v + Lfc - - delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0) + delta = math.atan2(2.0 * WB * math.sin(alpha) / Lf, 1.0) return delta, ind @@ -147,7 +145,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): def main(): # target course - cx = np.arange(0, 50, 0.1) + cx = np.arange(0, 50, 0.5) cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx] target_speed = 10.0 / 3.6 # [m/s] @@ -161,13 +159,17 @@ def main(): time = 0.0 states = States() states.append(time, state) - trajectory = Trajectory(cx, cy) - target_ind = trajectory.search_target_index(state) + target_course = TargetCourse(cx, cy) + target_ind, _ = target_course.search_target_index(state) while T >= time and lastIndex > target_ind: - ai = PIDControl(target_speed, state.v) - di, target_ind = pure_pursuit_control(state, trajectory, target_ind) - state.update(ai, di) + + # Calc control input + ai = proportional_control(target_speed, state.v) + di, target_ind = pure_pursuit_steer_control( + state, target_course, target_ind) + + state.update(ai, di) # Control vehicle time += dt states.append(time, state) @@ -175,8 +177,9 @@ def main(): if show_animation: # pragma: no cover plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plot_arrow(state.x, state.y, state.yaw) plt.plot(cx, cy, "-r", label="course") plt.plot(states.x, states.y, "-b", label="trajectory") From ee423e8b94f8ef5b20faa18e510546ebfc05b4a2 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 29 Feb 2020 14:46:43 +0900 Subject: [PATCH 161/909] implement visibility_road_map planner --- PathPlanning/Dijkstra/dijkstra.py | 157 ++++++++++-------- PathPlanning/VisibilityRoadMap/__init__.py | 0 PathPlanning/VisibilityRoadMap/geometry.py | 44 +++++ .../visibility_road_map.py} | 135 ++++++++------- .../VoronoiRoadMap/dijkstra_search.py | 93 +++++++---- .../VoronoiRoadMap/voronoi_road_map.py | 23 +-- ...py => test_visibility_road_map_planner.py} | 0 tests/test_voronoi_road_map_planner.py | 17 ++ 8 files changed, 300 insertions(+), 169 deletions(-) create mode 100644 PathPlanning/VisibilityRoadMap/__init__.py create mode 100644 PathPlanning/VisibilityRoadMap/geometry.py rename PathPlanning/{VisibilityGraphPlanner/visibility_graph_planner.py => VisibilityRoadMap/visibility_road_map.py} (57%) rename tests/{test_voronoi_path_planner.py => test_visibility_road_map_planner.py} (100%) create mode 100644 tests/test_voronoi_road_map_planner.py diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index bb1fb3aa63..ad3e3a85c8 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -11,32 +11,42 @@ show_animation = True + class Dijkstra: - def __init__(self, ox, oy, reso, rr): + def __init__(self, ox, oy, resolution, robot_radius): """ Initialize map for a star planning ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ - self.reso = reso - self.rr = rr + self.resolution = resolution + self.robot_radius = robot_radius self.calc_obstacle_map(ox, oy) self.motion = self.get_motion_model() + self.min_x = None + self.min_y = None + self.max_x = None + self.max_y = None + self.x_width = None + self.y_width = None + self.obstacle_map = None + class Node: - def __init__(self, x, y, cost, pind): + def __init__(self, x, y, cost, parent): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.pind = pind # index of previous Node + self.parent = parent # index of previous Node def __str__(self): - return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind) + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.parent) def planning(self, sx, sy, gx, gy): """ @@ -53,39 +63,40 @@ def planning(self, sx, sy, gx, gy): ry: y position list of the final path """ - nstart = self.Node(self.calc_xyindex(sx, self.minx), - self.calc_xyindex(sy, self.miny), 0.0, -1) - ngoal = self.Node(self.calc_xyindex(gx, self.minx), - self.calc_xyindex(gy, self.miny), 0.0, -1) + start_node = self.Node(self.calc_xy_index(sx, self.min_x), + self.calc_xy_index(sy, self.min_y), 0.0, -1) + goal_node = self.Node(self.calc_xy_index(gx, self.min_x), + self.calc_xy_index(gy, self.min_y), 0.0, -1) - openset, closedset = dict(), dict() - openset[self.calc_index(nstart)] = nstart + open_set, closed_set = dict(), dict() + open_set[self.calc_index(start_node)] = start_node while 1: - c_id = min(openset, key=lambda o: openset[o].cost) - current = openset[c_id] + c_id = min(open_set, key=lambda o: open_set[o].cost) + current = open_set[c_id] # show graph if show_animation: # pragma: no cover - plt.plot(self.calc_position(current.x, self.minx), - self.calc_position(current.y, self.miny), "xc") + plt.plot(self.calc_position(current.x, self.min_x), + self.calc_position(current.y, self.min_y), "xc") # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) - if len(closedset.keys()) % 10 == 0: + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + if len(closed_set.keys()) % 10 == 0: plt.pause(0.001) - if current.x == ngoal.x and current.y == ngoal.y: + if current.x == goal_node.x and current.y == goal_node.y: print("Find goal") - ngoal.pind = current.pind - ngoal.cost = current.cost + goal_node.parent = current.parent + goal_node.cost = current.cost break # Remove the item from the open set - del openset[c_id] + del open_set[c_id] # Add it to the closed set - closedset[c_id] = current + closed_set[c_id] = current # expand search grid based on motion model for move_x, move_y, move_cost in self.motion: @@ -94,94 +105,95 @@ def planning(self, sx, sy, gx, gy): current.cost + move_cost, c_id) n_id = self.calc_index(node) - if n_id in closedset: + if n_id in closed_set: continue if not self.verify_node(node): continue - if n_id not in openset: - openset[n_id] = node # Discover a new node + if n_id not in open_set: + open_set[n_id] = node # Discover a new node else: - if openset[n_id].cost >= node.cost: + if open_set[n_id].cost >= node.cost: # This path is the best until now. record it! - openset[n_id] = node + open_set[n_id] = node - rx, ry = self.calc_final_path(ngoal, closedset) + rx, ry = self.calc_final_path(goal_node, closed_set) return rx, ry - def calc_final_path(self, ngoal, closedset): + def calc_final_path(self, goal_node, closed_set): # generate final course - rx, ry = [self.calc_position(ngoal.x, self.minx)], [ - self.calc_position(ngoal.y, self.miny)] - pind = ngoal.pind - while pind != -1: - n = closedset[pind] - rx.append(self.calc_position(n.x, self.minx)) - ry.append(self.calc_position(n.y, self.miny)) - pind = n.pind + rx, ry = [self.calc_position(goal_node.x, self.min_x)], [ + self.calc_position(goal_node.y, self.min_y)] + parent = goal_node.parent + while parent != -1: + n = closed_set[parent] + rx.append(self.calc_position(n.x, self.min_x)) + ry.append(self.calc_position(n.y, self.min_y)) + parent = n.parent return rx, ry def calc_position(self, index, minp): - pos = index*self.reso+minp + pos = index * self.resolution + minp return pos - def calc_xyindex(self, position, minp): - return round((position - minp)/self.reso) + def calc_xy_index(self, position, minp): + return round((position - minp) / self.resolution) def calc_index(self, node): - return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + return (node.y - self.min_y) * self.x_width + (node.x - self.min_x) def verify_node(self, node): - px = self.calc_position(node.x, self.minx) - py = self.calc_position(node.y, self.miny) + px = self.calc_position(node.x, self.min_x) + py = self.calc_position(node.y, self.min_y) - if px < self.minx: + if px < self.min_x: return False - elif py < self.miny: + elif py < self.min_y: return False - elif px >= self.maxx: + elif px >= self.max_x: return False - elif py >= self.maxy: + elif py >= self.max_y: return False - if self.obmap[node.x][node.y]: + if self.obstacle_map[node.x][node.y]: return False return True def calc_obstacle_map(self, ox, oy): - self.minx = round(min(ox)) - self.miny = round(min(oy)) - self.maxx = round(max(ox)) - self.maxy = round(max(oy)) - print("minx:", self.minx) - print("miny:", self.miny) - print("maxx:", self.maxx) - print("maxy:", self.maxy) + self.min_x = round(min(ox)) + self.min_y = round(min(oy)) + self.max_x = round(max(ox)) + self.max_y = round(max(oy)) + print("minx:", self.min_x) + print("miny:", self.min_y) + print("maxx:", self.max_x) + print("maxy:", self.max_y) - self.xwidth = round((self.maxx - self.minx)/self.reso) - self.ywidth = round((self.maxy - self.miny)/self.reso) - print("xwidth:", self.xwidth) - print("ywidth:", self.ywidth) + self.x_width = round((self.max_x - self.min_x) / self.resolution) + self.y_width = round((self.max_y - self.min_y) / self.resolution) + print("xwidth:", self.x_width) + print("ywidth:", self.y_width) # obstacle map generation - self.obmap = [[False for i in range(self.ywidth)] - for i in range(self.xwidth)] - for ix in range(self.xwidth): - x = self.calc_position(ix, self.minx) - for iy in range(self.ywidth): - y = self.calc_position(iy, self.miny) + self.obstacle_map = [[False for _ in range(self.y_width)] + for _ in range(self.x_width)] + for ix in range(self.x_width): + x = self.calc_position(ix, self.min_x) + for iy in range(self.y_width): + y = self.calc_position(iy, self.min_y) for iox, ioy in zip(ox, oy): d = math.hypot(iox - x, ioy - y) - if d <= self.rr: - self.obmap[ix][iy] = True + if d <= self.robot_radius: + self.obstacle_map[ix][iy] = True break - def get_motion_model(self): + @staticmethod + def get_motion_model(): # dx, dy, cost motion = [[1, 0, 1], [0, 1, 1], @@ -239,6 +251,7 @@ def main(): if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") + plt.pause(0.01) plt.show() diff --git a/PathPlanning/VisibilityRoadMap/__init__.py b/PathPlanning/VisibilityRoadMap/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/PathPlanning/VisibilityRoadMap/geometry.py b/PathPlanning/VisibilityRoadMap/geometry.py new file mode 100644 index 0000000000..053a2bd70e --- /dev/null +++ b/PathPlanning/VisibilityRoadMap/geometry.py @@ -0,0 +1,44 @@ +class Geometry: + class Point: + def __init__(self, x, y): + self.x = x + self.y = y + + @staticmethod + def is_seg_intersect(p1, q1, p2, q2): + + def on_segment(p, q, r): + if ((q.x <= max(p.x, r.x)) and (q.x >= min(p.x, r.x)) and + (q.y <= max(p.y, r.y)) and (q.y >= min(p.y, r.y))): + return True + return False + + def orientation(p, q, r): + val = (float(q.y - p.y) * (r.x - q.x)) - ( + float(q.x - p.x) * (r.y - q.y)) + if val > 0: + return 1 + elif val < 0: + return 2 + else: + return 0 + + # Find the 4 orientations required for + # the general and special cases + o1 = orientation(p1, q1, p2) + o2 = orientation(p1, q1, q2) + o3 = orientation(p2, q2, p1) + o4 = orientation(p2, q2, q1) + + if (o1 != o2) and (o3 != o4): + return True + if (o1 == 0) and on_segment(p1, p2, q1): + return True + if (o2 == 0) and on_segment(p1, q2, q1): + return True + if (o3 == 0) and on_segment(p2, p1, q2): + return True + if (o4 == 0) and on_segment(p2, q1, q2): + return True + + return False \ No newline at end of file diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py similarity index 57% rename from PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py rename to PathPlanning/VisibilityRoadMap/visibility_road_map.py index d00e8f43c6..bf9b520a75 100644 --- a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py +++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py @@ -1,6 +1,6 @@ """ -Visibility Graph Planner +Visibility Road Map Planner author: Atsushi Sakai (@Atsushi_twi) @@ -8,35 +8,47 @@ import os import sys - -sys.path.append(os.path.dirname(os.path.abspath(__file__)) + - "/../VoronoiRoadMap/") - import math import numpy as np import matplotlib.pyplot as plt + +from PathPlanning.VisibilityRoadMap.geometry import Geometry + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../VoronoiRoadMap/") from dijkstra_search import DijkstraSearch show_animation = True -class VisibilityGraphPlanner: +class VisibilityRoadMap: - def __init__(self, robot_radius): + def __init__(self, robot_radius, do_plot=False): self.robot_radius = robot_radius + self.do_plot = do_plot def planning(self, start_x, start_y, goal_x, goal_y, obstacles): - nodes = self.extract_graph_node(start_x, start_y, goal_x, goal_y, - obstacles) - graph = self.generate_graph(nodes, obstacles) - # rx, ry = DijkstraSearch().search(graph) + nodes = self.generate_graph_node(start_x, start_y, goal_x, goal_y, + obstacles) + + road_map_info = self.generate_road_map_info(nodes, obstacles) - rx, ry = [], [] + if show_animation: + self.plot_road_map(nodes, road_map_info) + plt.pause(1.0) + + rx, ry = DijkstraSearch(show_animation).search( + start_x, start_y, + goal_x, goal_y, + [node.x for node in nodes], + [node.y for node in nodes], + road_map_info + ) return rx, ry - def extract_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles): + def generate_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles): # add start and goal as nodes nodes = [DijkstraSearch.Node(start_x, start_y), @@ -57,11 +69,11 @@ def extract_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles): return nodes def calc_vertexes_in_configuration_space(self, x_list, y_list): - x_list=x_list[0:-1] - y_list=y_list[0:-1] + x_list = x_list[0:-1] + y_list = y_list[0:-1] cvx_list, cvy_list = [], [] - n_data=len(x_list) + n_data = len(x_list) for index in range(n_data): offset_x, offset_y = self.calc_offset_xy( @@ -74,31 +86,39 @@ def calc_vertexes_in_configuration_space(self, x_list, y_list): return cvx_list, cvy_list - def generate_graph(self, nodes, obstacles): + def generate_road_map_info(self, nodes, obstacles): - graph = [] + road_map_info_list = [] for target_node in nodes: - for node in nodes: + road_map_info = [] + for node_id, node in enumerate(nodes): + if np.hypot(target_node.x - node.x, + target_node.y - node.y) <= 0.1: + continue + + is_valid = True for obstacle in obstacles: if not self.is_edge_valid(target_node, node, obstacle): - print("bb") + is_valid = False break - print(target_node, node) - print("aa") - plt.plot([target_node.x, node.x],[target_node.y, node.y], "-r") + if is_valid: + road_map_info.append(node_id) + + road_map_info_list.append(road_map_info) - return graph + return road_map_info_list - def is_edge_valid(self, target_node, node, obstacle): + @staticmethod + def is_edge_valid(target_node, node, obstacle): - for i in range(len(obstacle.x_list)-1): - p1 = np.array([target_node.x, target_node.y]) - p2 = np.array([node.x, node.y]) - p3 = np.array([obstacle.x_list[i], obstacle.y_list[i]]) - p4 = np.array([obstacle.y_list[i+1], obstacle.y_list[i+1]]) + for i in range(len(obstacle.x_list) - 1): + p1 = Geometry.Point(target_node.x, target_node.y) + p2 = Geometry.Point(node.x, node.y) + p3 = Geometry.Point(obstacle.x_list[i], obstacle.y_list[i]) + p4 = Geometry.Point(obstacle.x_list[i + 1], obstacle.y_list[i + 1]) - if is_seg_intersect(p1, p2, p3, p4): + if Geometry.is_seg_intersect(p1, p2, p3, p4): return False return True @@ -108,25 +128,18 @@ def calc_offset_xy(self, px, py, x, y, nx, ny): n_vec = math.atan2(ny - y, nx - x) offset_vec = math.atan2(math.sin(p_vec) + math.sin(n_vec), math.cos(p_vec) + math.cos( - n_vec))+math.pi/2.0 + n_vec)) + math.pi / 2.0 offset_x = x + self.robot_radius * math.cos(offset_vec) offset_y = y + self.robot_radius * math.sin(offset_vec) return offset_x, offset_y + @staticmethod + def plot_road_map(nodes, road_map_info_list): + for i, node in enumerate(nodes): + for index in road_map_info_list[i]: + plt.plot([node.x, nodes[index].x], + [node.y, nodes[index].y], "-b") -def is_seg_intersect(a1, a2, b1, b2): - - xdiff = [a1[0] - a2[0], b1[0] - b2[0]] - ydiff = [a1[1] - a2[1], b1[1] - b2[1]] - - def det(a, b): - return a[0] * b[1] - a[1] * b[0] - - div = det(xdiff, ydiff) - if div == 0: - return False - else: - return True class ObstaclePolygon: @@ -161,7 +174,7 @@ def close_polygon(self): self.y_list.append(self.y_list[0]) def plot(self): - plt.plot(self.x_list, self.y_list, "-b") + plt.plot(self.x_list, self.y_list, "-k") def main(): @@ -170,26 +183,36 @@ def main(): # start and goal position sx, sy = 10.0, 10.0 # [m] gx, gy = 50.0, 50.0 # [m] + robot_radius = 5.0 # [m] - obstacles = [ObstaclePolygon( - [20.0, 30.0, 15.0], - [20.0, 20.0, 30.0], - ), ObstaclePolygon( - [30.0, 45.0, 50.0, 40.0], - [50.0, 40.0, 20.0, 40.0], - )] + obstacles = [ + ObstaclePolygon( + [20.0, 30.0, 15.0], + [20.0, 20.0, 30.0], + ), + ObstaclePolygon( + [40.0, 45.0, 50.0, 40.0], + [50.0, 40.0, 20.0, 40.0], + ), + ObstaclePolygon( + [20.0, 30.0, 30.0, 20.0], + [40.0, 45.0, 70.0, 50.0], + ) + ] if show_animation: # pragma: no cover plt.plot(sx, sy, "or") plt.plot(gx, gy, "ob") [ob.plot() for ob in obstacles] + plt.pause(1.0) + + rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning( + sx, sy, gx, gy, obstacles) - rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy, - obstacles) - # assert rx, 'Cannot found path' if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") + plt.pause(0.1) plt.axis("equal") plt.show() diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py index 7f248cd3cf..f7b8fc48c1 100644 --- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py +++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py @@ -8,20 +8,21 @@ import matplotlib.pyplot as plt import math +import numpy as np class DijkstraSearch: - class Node: """ Node class for dijkstra search """ - def __init__(self, x, y, cost=None, parent=None): + def __init__(self, x, y, cost=None, parent=None, edge_ids=None): self.x = x self.y = y self.cost = cost self.parent = parent + self.edge_ids = edge_ids def __str__(self): return str(self.x) + "," + str(self.y) + "," + str( @@ -30,71 +31,79 @@ def __str__(self): def __init__(self, show_animation): self.show_animation = show_animation - def search(self, sx, sy, gx, gy, road_map, sample_x, sample_y): + def search(self, sx, sy, gx, gy, node_x, node_y, edge_ids_list): """ + Search shortest path + + sx: start x positions [m] + sy: start y positions [m] gx: goal x position [m] gx: goal x position [m] - ox: x position list of Obstacles [m] - oy: y position list of Obstacles [m] - reso: grid resolution [m] - rr: robot radius[m] + node_x: node x position + node_y: node y position + edge_ids_list: edge_list each item includes a list of edge ids """ start_node = self.Node(sx, sy, 0.0, -1) goal_node = self.Node(gx, gy, 0.0, -1) + current_node = None open_set, close_set = dict(), dict() - open_set[len(road_map) - 2] = start_node + open_set[self.find_id(node_x, node_y, start_node)] = start_node while True: - if not open_set: + if self.has_node_in_set(close_set, goal_node): + print("goal is found!") + goal_node.parent = current_node.parent + goal_node.cost = current_node.cost + break + elif not open_set: print("Cannot find path") break - c_id = min(open_set, key=lambda o: open_set[o].cost) - current = open_set[c_id] + current_id = min(open_set, key=lambda o: open_set[o].cost) + current_node = open_set[current_id] # show graph if self.show_animation and len( close_set.keys()) % 2 == 0: # pragma: no cover - plt.plot(current.x, current.y, "xg") + plt.plot(current_node.x, current_node.y, "xg") # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect( 'key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) - plt.pause(0.001) - - if c_id == (len(road_map) - 1): - print("goal is found!") - goal_node.parent = current.parent - goal_node.cost = current.cost - break + plt.pause(0.1) # Remove the item from the open set - del open_set[c_id] + del open_set[current_id] # Add it to the closed set - close_set[c_id] = current + close_set[current_id] = current_node # expand search grid based on motion model - for i in range(len(road_map[c_id])): - n_id = road_map[c_id][i] - dx = sample_x[n_id] - current.x - dy = sample_y[n_id] - current.y + for i in range(len(edge_ids_list[current_id])): + n_id = edge_ids_list[current_id][i] + dx = node_x[n_id] - current_node.x + dy = node_y[n_id] - current_node.y d = math.hypot(dx, dy) - node = self.Node(sample_x[n_id], sample_y[n_id], - current.cost + d, c_id) + node = self.Node(node_x[n_id], node_y[n_id], + current_node.cost + d, current_id) if n_id in close_set: continue # Otherwise if it is already in the open set if n_id in open_set: if open_set[n_id].cost > node.cost: - open_set[n_id].cost = node.cost - open_set[n_id].parent = c_id + open_set[n_id] = node else: open_set[n_id] = node # generate final course + rx, ry = self.generate_final_path(close_set, goal_node) + + return rx, ry + + @staticmethod + def generate_final_path(close_set, goal_node): rx, ry = [goal_node.x], [goal_node.y] parent = goal_node.parent while parent != -1: @@ -102,5 +111,29 @@ def search(self, sx, sy, gx, gy, road_map, sample_x, sample_y): rx.append(n.x) ry.append(n.y) parent = n.parent - + rx, ry = rx[::-1], ry[::-1] # reverse it return rx, ry + + def has_node_in_set(self, target_set, node): + for key in target_set: + if self.is_same_node(target_set[key], node): + return True + return False + + def find_id(self, node_x_list, node_y_list, target_node): + for i in range(len(node_x_list)): + if self.is_same_node_with_xy(node_x_list[i], node_y_list[i], + target_node): + return i + + @staticmethod + def is_same_node_with_xy(node_x, node_y, node_b): + dist = np.hypot(node_x - node_b.x, + node_y - node_b.y) + return dist <= 0.1 + + @staticmethod + def is_same_node(node_a, node_b): + dist = np.hypot(node_a.x - node_b.x, + node_b.y - node_b.y) + return dist <= 0.1 diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py index dfac7258f8..d060c8e46c 100644 --- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py +++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py @@ -23,21 +23,22 @@ def __init__(self): self.N_KNN = 10 # number of edge from one sampled point self.MAX_EDGE_LEN = 30.0 # [m] Maximum edge length - def planning(self, sx, sy, gx, gy, ox, oy, rr): + def planning(self, sx, sy, gx, gy, ox, oy, robot_radius): obstacle_tree = KDTree(np.vstack((ox, oy)).T) sample_x, sample_y = self.voronoi_sampling(sx, sy, gx, gy, ox, oy) if show_animation: # pragma: no cover plt.plot(sample_x, sample_y, ".b") - road_map = self.generate_road_map(sample_x, sample_y, rr, obstacle_tree) - - rx, ry = DijkstraSearch(show_animation).search(sx, sy, gx, gy, road_map, - sample_x, sample_y) + road_map_info = self.generate_road_map_info( + sample_x, sample_y, robot_radius, obstacle_tree) + rx, ry = DijkstraSearch(show_animation).search(sx, sy, gx, gy, + sample_x, sample_y, + road_map_info) return rx, ry - def is_collision(self, sx, sy, gx, gy, rr, obstacle_kdtree): + def is_collision(self, sx, sy, gx, gy, rr, obstacle_kd_tree): x = sx y = sy dx = gx - sx @@ -52,25 +53,25 @@ def is_collision(self, sx, sy, gx, gy, rr, obstacle_kdtree): n_step = round(d / D) for i in range(n_step): - ids, dist = obstacle_kdtree.search(np.array([x, y]).reshape(2, 1)) + ids, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1)) if dist[0] <= rr: return True # collision x += D * math.cos(yaw) y += D * math.sin(yaw) # goal point check - ids, dist = obstacle_kdtree.search(np.array([gx, gy]).reshape(2, 1)) + ids, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1)) if dist[0] <= rr: return True # collision return False # OK - def generate_road_map(self, node_x, node_y, rr, obstacle_tree): + def generate_road_map_info(self, node_x, node_y, rr, obstacle_tree): """ Road map generation - sample_x: [m] x positions of sampled points - sample_y: [m] y positions of sampled points + node_x: [m] x positions of sampled points + node_y: [m] y positions of sampled points rr: Robot Radius[m] obstacle_tree: KDTree object of obstacles """ diff --git a/tests/test_voronoi_path_planner.py b/tests/test_visibility_road_map_planner.py similarity index 100% rename from tests/test_voronoi_path_planner.py rename to tests/test_visibility_road_map_planner.py diff --git a/tests/test_voronoi_road_map_planner.py b/tests/test_voronoi_road_map_planner.py new file mode 100644 index 0000000000..a1f860a108 --- /dev/null +++ b/tests/test_voronoi_road_map_planner.py @@ -0,0 +1,17 @@ +import os +import sys + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../PathPlanning/VisibilityRoadMap/") + +from unittest import TestCase +from PathPlanning.VisibilityRoadMap import visibility_road_map as m + +print(__file__) + + +class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main() From ac1a903ca794ef3e943ce123f06d1bf5443c7a9f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 29 Feb 2020 14:59:40 +0900 Subject: [PATCH 162/909] fixed CI --- PathPlanning/Dijkstra/dijkstra.py | 28 +++++++++---------- PathPlanning/VisibilityRoadMap/geometry.py | 5 ++-- .../VisibilityRoadMap/visibility_road_map.py | 3 +- .../VoronoiRoadMap/dijkstra_search.py | 2 +- 4 files changed, 19 insertions(+), 19 deletions(-) diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index ad3e3a85c8..0ece712ae7 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -24,11 +24,6 @@ def __init__(self, ox, oy, resolution, robot_radius): rr: robot radius[m] """ - self.resolution = resolution - self.robot_radius = robot_radius - self.calc_obstacle_map(ox, oy) - self.motion = self.get_motion_model() - self.min_x = None self.min_y = None self.max_x = None @@ -37,6 +32,11 @@ def __init__(self, ox, oy, resolution, robot_radius): self.y_width = None self.obstacle_map = None + self.resolution = resolution + self.robot_radius = robot_radius + self.calc_obstacle_map(ox, oy) + self.motion = self.get_motion_model() + class Node: def __init__(self, x, y, cost, parent): self.x = x # index of grid @@ -151,11 +151,11 @@ def verify_node(self, node): if px < self.min_x: return False - elif py < self.min_y: + if py < self.min_y: return False - elif px >= self.max_x: + if px >= self.max_x: return False - elif py >= self.max_y: + if py >= self.max_y: return False if self.obstacle_map[node.x][node.y]: @@ -169,15 +169,15 @@ def calc_obstacle_map(self, ox, oy): self.min_y = round(min(oy)) self.max_x = round(max(ox)) self.max_y = round(max(oy)) - print("minx:", self.min_x) - print("miny:", self.min_y) - print("maxx:", self.max_x) - print("maxy:", self.max_y) + print("min_x:", self.min_x) + print("min_y:", self.min_y) + print("max_x:", self.max_x) + print("max_y:", self.max_y) self.x_width = round((self.max_x - self.min_x) / self.resolution) self.y_width = round((self.max_y - self.min_y) / self.resolution) - print("xwidth:", self.x_width) - print("ywidth:", self.y_width) + print("x_width:", self.x_width) + print("y_width:", self.y_width) # obstacle map generation self.obstacle_map = [[False for _ in range(self.y_width)] diff --git a/PathPlanning/VisibilityRoadMap/geometry.py b/PathPlanning/VisibilityRoadMap/geometry.py index 053a2bd70e..c4d73837c9 100644 --- a/PathPlanning/VisibilityRoadMap/geometry.py +++ b/PathPlanning/VisibilityRoadMap/geometry.py @@ -18,10 +18,9 @@ def orientation(p, q, r): float(q.x - p.x) * (r.y - q.y)) if val > 0: return 1 - elif val < 0: + if val < 0: return 2 - else: - return 0 + return 0 # Find the 4 orientations required for # the general and special cases diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py index bf9b520a75..fdd214f757 100644 --- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py +++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py @@ -204,7 +204,8 @@ def main(): if show_animation: # pragma: no cover plt.plot(sx, sy, "or") plt.plot(gx, gy, "ob") - [ob.plot() for ob in obstacles] + for ob in obstacles: + ob.plot() plt.pause(1.0) rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning( diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py index f7b8fc48c1..7bf5425ebf 100644 --- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py +++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py @@ -121,7 +121,7 @@ def has_node_in_set(self, target_set, node): return False def find_id(self, node_x_list, node_y_list, target_node): - for i in range(len(node_x_list)): + for i, _ in enumerate(node_x_list): if self.is_same_node_with_xy(node_x_list[i], node_y_list[i], target_node): return i From 881308bc42764ee2652d4f17d5a99d0891fb8e93 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 29 Feb 2020 20:37:03 +0900 Subject: [PATCH 163/909] fixed CI --- PathPlanning/VisibilityRoadMap/visibility_road_map.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py index fdd214f757..a677804440 100644 --- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py +++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py @@ -34,7 +34,7 @@ def planning(self, start_x, start_y, goal_x, goal_y, obstacles): road_map_info = self.generate_road_map_info(nodes, obstacles) - if show_animation: + if self.do_plot: self.plot_road_map(nodes, road_map_info) plt.pause(1.0) @@ -197,7 +197,7 @@ def main(): ), ObstaclePolygon( [20.0, 30.0, 30.0, 20.0], - [40.0, 45.0, 70.0, 50.0], + [40.0, 45.0, 60.0, 50.0], ) ] @@ -206,6 +206,7 @@ def main(): plt.plot(gx, gy, "ob") for ob in obstacles: ob.plot() + plt.axis("equal") plt.pause(1.0) rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning( @@ -214,7 +215,6 @@ def main(): if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") plt.pause(0.1) - plt.axis("equal") plt.show() From 487a7e4141dc4e2a0ae887e7fec98251900362b7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 29 Feb 2020 21:12:30 +0900 Subject: [PATCH 164/909] Update users_comments.md --- users_comments.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/users_comments.md b/users_comments.md index 792f3a4d0d..056a896e94 100644 --- a/users_comments.md +++ b/users_comments.md @@ -23,6 +23,8 @@ If you found other users, please make an issue to let me know. - [CSCI/ARTI 4530/6530: Introduction to Robotics (Fall 2018), University of Georgia ](http://cobweb.cs.uga.edu/~ramviyas/csci_x530.html) +- [CIT Modules & Programmes \- COMP9073 \- Automation with Python](https://courses.cit.ie/index.cfm/page/module/moduleId/14416) + # Stargazers location map You can check stargazer's locations of this project from: @@ -415,3 +417,4 @@ URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5. - Real-time Model Predictive Control (MPC), ACADO, Python | Work-is-Playing http://grauonline.de/wordpress/?page_id=3244 +- 💡 Greatest of GitHub - Python Robotics (Amazing!) - YouTube https://www.youtube.com/watch?v=f_pPaYx6Gu0&feature=emb_logo From 80ebc55c2f8c986ef58aae28c43dcf0bbbaa85d1 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 1 Mar 2020 21:05:04 +0900 Subject: [PATCH 165/909] clean up codes --- PathPlanning/BSplinePath/bspline_path.py | 25 +++++++++++------------- 1 file changed, 11 insertions(+), 14 deletions(-) diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py index 0c453ad436..2c3d7ad68e 100644 --- a/PathPlanning/BSplinePath/bspline_path.py +++ b/PathPlanning/BSplinePath/bspline_path.py @@ -1,6 +1,6 @@ """ -Path Plannting with B-Spline +Path Planning with B-Spline author: Atsushi Sakai (@Atsushi_twi) @@ -8,16 +8,13 @@ import numpy as np import matplotlib.pyplot as plt -import scipy.interpolate as si +import scipy.interpolate as scipy_interpolate -# parameter -N = 3 # B Spline order - -def bspline_planning(x, y, sn): +def b_spline_planning(x, y, sn, degree=3): t = range(len(x)) - x_tup = si.splrep(t, x, k=N) - y_tup = si.splrep(t, y, k=N) + x_tup = scipy_interpolate.splrep(t, x, k=degree) + y_tup = scipy_interpolate.splrep(t, y, k=degree) x_list = list(x_tup) xl = x.tolist() @@ -28,8 +25,8 @@ def bspline_planning(x, y, sn): y_list[1] = yl + [0.0, 0.0, 0.0, 0.0] ipl_t = np.linspace(0.0, len(x) - 1, sn) - rx = si.splev(ipl_t, x_list) - ry = si.splev(ipl_t, y_list) + rx = scipy_interpolate.splev(ipl_t, x_list) + ry = scipy_interpolate.splev(ipl_t, y_list) return rx, ry @@ -37,14 +34,14 @@ def bspline_planning(x, y, sn): def main(): print(__file__ + " start!!") # way points - x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0]) - y = np.array([0.0, -3.0, 1.0, 1.0, 3.0]) + way_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0]) + way_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0]) sn = 100 # sampling number - rx, ry = bspline_planning(x, y, sn) + rx, ry = b_spline_planning(way_x, way_y, sn) # show results - plt.plot(x, y, '-og', label="Waypoints") + plt.plot(way_x, way_y, '-og', label="way points") plt.plot(rx, ry, '-r', label="B-Spline path") plt.grid(True) plt.legend() From 869650ffa93c444bafd8eea2a6f9868c0d53bb61 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 7 Mar 2020 21:57:26 +0900 Subject: [PATCH 166/909] Update users_comments.md --- users_comments.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/users_comments.md b/users_comments.md index 056a896e94..44e9355ec1 100644 --- a/users_comments.md +++ b/users_comments.md @@ -409,6 +409,9 @@ URL: https://arxiv.org/abs/1910.01557 8. Hahn, Carsten, et al. "Dynamic Path Planning with Stable Growing Neural Gas." (2019). URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5.pdf +9. Brijen Thananjeyan, et al. "ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions" +URL: https://arxiv.org/pdf/2003.01410.pdf + # Others - Autonomous Vehicle Readings https://richardkelley.io/readings From 6cb903a8143064c8eaba811b7f894bd1539ee2ae Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 22:27:07 +0900 Subject: [PATCH 167/909] add mypy setting and update bspline_path.py --- .github/workflows/mypycheck.yml | 3 ++ PathPlanning/BSplinePath/bspline_path.py | 57 ++++++++++++++++++------ mypy.ini | 2 + 3 files changed, 49 insertions(+), 13 deletions(-) create mode 100644 .github/workflows/mypycheck.yml create mode 100644 mypy.ini diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml new file mode 100644 index 0000000000..26e5566015 --- /dev/null +++ b/.github/workflows/mypycheck.yml @@ -0,0 +1,3 @@ +- name: Mypy Check + uses: jpetrucciani/mypy-check@0.761 + diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py index 2c3d7ad68e..d4b24c7645 100644 --- a/PathPlanning/BSplinePath/bspline_path.py +++ b/PathPlanning/BSplinePath/bspline_path.py @@ -1,6 +1,6 @@ """ -Path Planning with B-Spline +Path Planner with B-Spline author: Atsushi Sakai (@Atsushi_twi) @@ -11,38 +11,69 @@ import scipy.interpolate as scipy_interpolate -def b_spline_planning(x, y, sn, degree=3): +def approximate_b_spline_path(x: list, y: list, n_path_points: int, + degree: int = 3) -> tuple: + """ + approximate points with a B-Spline path + + :param x: x position list of approximated points + :param y: y position list of approximated points + :param n_path_points: number of path points + :param degree: (Optional) B Spline curve degree + :return: x and y position list of the result path + """ t = range(len(x)) x_tup = scipy_interpolate.splrep(t, x, k=degree) y_tup = scipy_interpolate.splrep(t, y, k=degree) x_list = list(x_tup) - xl = x.tolist() - x_list[1] = xl + [0.0, 0.0, 0.0, 0.0] + x_list[1] = x + [0.0, 0.0, 0.0, 0.0] y_list = list(y_tup) - yl = y.tolist() - y_list[1] = yl + [0.0, 0.0, 0.0, 0.0] + y_list[1] = y + [0.0, 0.0, 0.0, 0.0] - ipl_t = np.linspace(0.0, len(x) - 1, sn) + ipl_t = np.linspace(0.0, len(x) - 1, n_path_points) rx = scipy_interpolate.splev(ipl_t, x_list) ry = scipy_interpolate.splev(ipl_t, y_list) return rx, ry +def interpolate_b_spline_path(x: list, y: list, n_path_points: int, + degree: int = 3) -> tuple: + """ + interpolate points with a B-Spline path + + :param x: x positions of interpolated points + :param y: y positions of interpolated points + :param n_path_points: number of path points + :param degree: B-Spline degree + :return: x and y position list of the result path + """ + ipl_t = np.linspace(0.0, len(x) - 1, len(x)) + spl_i_x = scipy_interpolate.make_interp_spline(ipl_t, x, k=degree) + spl_i_y = scipy_interpolate.make_interp_spline(ipl_t, y, k=degree) + + travel = np.linspace(0.0, len(x) - 1, n_path_points) + return spl_i_x(travel), spl_i_y(travel) + + def main(): print(__file__ + " start!!") # way points - way_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0]) - way_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0]) - sn = 100 # sampling number + way_point_x = [-1.0, 3.0, 4.0, 2.0, 1.0] + way_point_y = [0.0, -3.0, 1.0, 1.0, 3.0] + n_course_point = 100 # sampling number - rx, ry = b_spline_planning(way_x, way_y, sn) + rax, ray = approximate_b_spline_path(way_point_x, way_point_y, + n_course_point) + rix, riy = interpolate_b_spline_path(way_point_x, way_point_y, + n_course_point) # show results - plt.plot(way_x, way_y, '-og', label="way points") - plt.plot(rx, ry, '-r', label="B-Spline path") + plt.plot(way_point_x, way_point_y, '-og', label="way points") + plt.plot(rax, ray, '-r', label="Approximated B-Spline path") + plt.plot(rix, riy, '-b', label="Interpolated B-Spline path") plt.grid(True) plt.legend() plt.axis("equal") diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 0000000000..1215375ed9 --- /dev/null +++ b/mypy.ini @@ -0,0 +1,2 @@ +[mypy] +ignore_missing_imports = True \ No newline at end of file From ba13817862c503c79da0e847d1afd7555cb8015e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 22:29:03 +0900 Subject: [PATCH 168/909] fixed git hub action --- .github/workflows/mypycheck.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml index 26e5566015..649028674a 100644 --- a/.github/workflows/mypycheck.yml +++ b/.github/workflows/mypycheck.yml @@ -1,3 +1,3 @@ -- name: Mypy Check - uses: jpetrucciani/mypy-check@0.761 +name: Mypy Check +uses: jpetrucciani/mypy-check@0.761 From 5f253ef56229b44cfb5e6ba5a317de5c042efad9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 22:31:17 +0900 Subject: [PATCH 169/909] fixed git hub action --- .github/workflows/mypycheck.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml index 649028674a..0e26c1e241 100644 --- a/.github/workflows/mypycheck.yml +++ b/.github/workflows/mypycheck.yml @@ -1,3 +1,4 @@ name: Mypy Check +on: [push] uses: jpetrucciani/mypy-check@0.761 From aad8d4face65a0adf571b01a54b32f307876dcb2 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 22:34:00 +0900 Subject: [PATCH 170/909] fixed git hub action --- .github/workflows/mypycheck.yml | 4 ---- .github/workflows/pythonpackage.yml | 6 +++++- 2 files changed, 5 insertions(+), 5 deletions(-) delete mode 100644 .github/workflows/mypycheck.yml diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml deleted file mode 100644 index 0e26c1e241..0000000000 --- a/.github/workflows/mypycheck.yml +++ /dev/null @@ -1,4 +0,0 @@ -name: Mypy Check -on: [push] -uses: jpetrucciani/mypy-check@0.761 - diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index a3203ff1ac..d03d5ea14c 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -29,6 +29,10 @@ jobs: # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: install coverage - run: pip install coverage + run: pip install coverage + - name: Mypy Check + uses: jpetrucciani/mypy-check@0.761 - name: Test run: bash runtests.sh + + From d7dddc8772b04a6895902ea6e3f72be1ebf21a86 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 22:51:47 +0900 Subject: [PATCH 171/909] mypy fail test --- PathPlanning/BSplinePath/bspline_path.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py index d4b24c7645..7c59e99bbb 100644 --- a/PathPlanning/BSplinePath/bspline_path.py +++ b/PathPlanning/BSplinePath/bspline_path.py @@ -11,7 +11,7 @@ import scipy.interpolate as scipy_interpolate -def approximate_b_spline_path(x: list, y: list, n_path_points: int, +def approximate_b_spline_path(x: int, y: list, n_path_points: int, degree: int = 3) -> tuple: """ approximate points with a B-Spline path From 0b9a0c88d6c5fc029bf9b0fb305e977c3b641d3c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 23:00:32 +0900 Subject: [PATCH 172/909] mypy fail test --- .github/workflows/pythonpackage.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index d03d5ea14c..af761ec7b3 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -32,6 +32,8 @@ jobs: run: pip install coverage - name: Mypy Check uses: jpetrucciani/mypy-check@0.761 + with: + path: './AerialNavigation ./PathPlanning' - name: Test run: bash runtests.sh From 8dc23fec2135f8c376d7aaa7696104d3deb40a8b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 23:15:13 +0900 Subject: [PATCH 173/909] mypy fail test --- .github/workflows/pythonpackage.yml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index af761ec7b3..c81ce8930d 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -30,11 +30,12 @@ jobs: flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: install coverage run: pip install coverage - - name: Mypy Check - uses: jpetrucciani/mypy-check@0.761 - with: - path: './AerialNavigation ./PathPlanning' - - name: Test + - name: install mypy + run: pip install mypy + - name: mypy check + run: | + - find . -name "*.py" | xargs mypy + - name: do all unit tests run: bash runtests.sh From c36aa27d605628238ff4d05e66b2f59d91fc30b3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 Mar 2020 23:20:10 +0900 Subject: [PATCH 174/909] mypy fail test --- .github/workflows/pythonpackage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index c81ce8930d..1844e53820 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -34,7 +34,7 @@ jobs: run: pip install mypy - name: mypy check run: | - - find . -name "*.py" | xargs mypy + find . -name "*.py" | xargs mypy - name: do all unit tests run: bash runtests.sh From 14ffc4a2d4e202c49c0ae385b3ba62144a1cf86c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 9 Mar 2020 22:57:35 +0900 Subject: [PATCH 175/909] mypy fix test --- .../{NLinkArm.py => NLinkArm3d.py} | 116 +++++---- ArmNavigation/n_joint_arm_3d/__init__py.py | 0 .../random_forward_kinematics.py | 21 +- .../random_inverse_kinematics.py | 15 +- .../n_joint_arm_to_point_control/__init__.py | 0 PathPlanning/AStar/a_star.py | 12 +- PathPlanning/BSplinePath/bspline_path.py | 2 +- PathPlanning/CubicSpline/__init__.py | 0 .../cubic_spline_planner.py | 239 ------------------ .../frenet_optimal_trajectory.py | 109 ++++---- PathPlanning/HybridAStar/__init__.py | 0 .../{a_star.py => a_star_heuristic.py} | 0 PathPlanning/HybridAStar/hybrid_a_star.py | 2 +- 13 files changed, 150 insertions(+), 366 deletions(-) rename ArmNavigation/n_joint_arm_3d/{NLinkArm.py => NLinkArm3d.py} (75%) create mode 100644 ArmNavigation/n_joint_arm_3d/__init__py.py create mode 100644 ArmNavigation/n_joint_arm_to_point_control/__init__.py create mode 100644 PathPlanning/CubicSpline/__init__.py delete mode 100644 PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py create mode 100644 PathPlanning/HybridAStar/__init__.py rename PathPlanning/HybridAStar/{a_star.py => a_star_heuristic.py} (100%) diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py similarity index 75% rename from ArmNavigation/n_joint_arm_3d/NLinkArm.py rename to ArmNavigation/n_joint_arm_3d/NLinkArm3d.py index da562d9f17..b411cd7114 100644 --- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py @@ -7,6 +7,7 @@ from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt + class Link: def __init__(self, dh_params): self.dh_params_ = dh_params @@ -28,16 +29,22 @@ def transformation_matrix(self): return trans - def basic_jacobian(self, trans_prev, ee_pos): - pos_prev = np.array([trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]]) - z_axis_prev = np.array([trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]]) + @staticmethod + def basic_jacobian(trans_prev, ee_pos): + pos_prev = np.array( + [trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]]) + z_axis_prev = np.array( + [trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]]) - basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pos - pos_prev), z_axis_prev)) + basic_jacobian = np.hstack( + (np.cross(z_axis_prev, ee_pos - pos_prev), z_axis_prev)) return basic_jacobian - + class NLinkArm: def __init__(self, dh_params_list): + self.fig = plt.figure() + self.ax = Axes3D(self.fig) self.link_list = [] for i in range(len(dh_params_list)): self.link_list.append(Link(dh_params_list[i])) @@ -47,7 +54,7 @@ def transformation_matrix(self): for i in range(len(self.link_list)): trans = np.dot(trans, self.link_list[i].transformation_matrix()) return trans - + def forward_kinematics(self, plot=False): trans = self.transformation_matrix() @@ -57,15 +64,12 @@ def forward_kinematics(self, plot=False): alpha, beta, gamma = self.euler_angle() if plot: - self.fig = plt.figure() - self.ax = Axes3D(self.fig) - x_list = [] y_list = [] z_list = [] - + trans = np.identity(4) - + x_list.append(trans[0, 3]) y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) @@ -74,14 +78,15 @@ def forward_kinematics(self, plot=False): x_list.append(trans[0, 3]) y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) - - self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5) + + self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, + mew=0.5) self.ax.plot([0], [0], [0], "o") - + self.ax.set_xlim(-1, 1) self.ax.set_ylim(-1, 1) self.ax.set_zlim(-1, 1) - + plt.show() return [x, y, z, alpha, beta, gamma] @@ -89,41 +94,45 @@ def forward_kinematics(self, plot=False): def basic_jacobian(self): ee_pos = self.forward_kinematics()[0:3] basic_jacobian_mat = [] - - trans = np.identity(4) + + trans = np.identity(4) for i in range(len(self.link_list)): - basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pos)) + basic_jacobian_mat.append( + self.link_list[i].basic_jacobian(trans, ee_pos)) trans = np.dot(trans, self.link_list[i].transformation_matrix()) - + return np.array(basic_jacobian_mat).T def inverse_kinematics(self, ref_ee_pose, plot=False): for cnt in range(500): ee_pose = self.forward_kinematics() diff_pose = np.array(ref_ee_pose) - ee_pose - + basic_jacobian_mat = self.basic_jacobian() alpha, beta, gamma = self.euler_angle() - - K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)], - [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)], - [1, 0, math.cos(beta)]]) + + K_zyz = np.array( + [[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)], + [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)], + [1, 0, math.cos(beta)]]) K_alpha = np.identity(6) K_alpha[3:, 3:] = K_zyz - theta_dot = np.dot(np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha), np.array(diff_pose)) + theta_dot = np.dot( + np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha), + np.array(diff_pose)) self.update_joint_angles(theta_dot / 100.) - + if plot: self.fig = plt.figure() self.ax = Axes3D(self.fig) - + x_list = [] y_list = [] z_list = [] - + trans = np.identity(4) - + x_list.append(trans[0, 3]) y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) @@ -132,30 +141,36 @@ def inverse_kinematics(self, ref_ee_pose, plot=False): x_list.append(trans[0, 3]) y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) - - self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5) + + self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, + mew=0.5) self.ax.plot([0], [0], [0], "o") - + self.ax.set_xlim(-1, 1) self.ax.set_ylim(-1, 1) self.ax.set_zlim(-1, 1) - - self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]], "o") + + self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]], + "o") plt.show() - + def euler_angle(self): trans = self.transformation_matrix() - + alpha = math.atan2(trans[1][2], trans[0][2]) - if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2): + if not (-math.pi / 2 <= alpha <= math.pi / 2): alpha = math.atan2(trans[1][2], trans[0][2]) + math.pi - if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2): + if not (-math.pi / 2 <= alpha <= math.pi / 2): alpha = math.atan2(trans[1][2], trans[0][2]) - math.pi - beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2]) - gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha)) - + beta = math.atan2( + trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), + trans[2][2]) + gamma = math.atan2( + -trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), + -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha)) + return alpha, beta, gamma - + def set_joint_angles(self, joint_angle_list): for i in range(len(self.link_list)): self.link_list[i].dh_params_[0] = joint_angle_list[i] @@ -163,17 +178,17 @@ def set_joint_angles(self, joint_angle_list): def update_joint_angles(self, diff_joint_angle_list): for i in range(len(self.link_list)): self.link_list[i].dh_params_[0] += diff_joint_angle_list[i] - + def plot(self): self.fig = plt.figure() self.ax = Axes3D(self.fig) - + x_list = [] y_list = [] z_list = [] trans = np.identity(4) - + x_list.append(trans[0, 3]) y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) @@ -182,15 +197,16 @@ def plot(self): x_list.append(trans[0, 3]) y_list.append(trans[1, 3]) z_list.append(trans[2, 3]) - - self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5) + + self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, + mew=0.5) self.ax.plot([0], [0], [0], "o") self.ax.set_xlabel("x") self.ax.set_ylabel("y") - self.ax.set_zlabel("z") - + self.ax.set_zlabel("z") + self.ax.set_xlim(-1, 1) self.ax.set_ylim(-1, 1) - self.ax.set_zlim(-1, 1) + self.ax.set_zlim(-1, 1) plt.show() diff --git a/ArmNavigation/n_joint_arm_3d/__init__py.py b/ArmNavigation/n_joint_arm_3d/__init__py.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py index 87907f803b..b3426e53e8 100644 --- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py @@ -3,9 +3,10 @@ Author: Takayuki Murooka (takayuki5168) """ import math -from NLinkArm import NLinkArm +from NLinkArm3d import NLinkArm import random + def random_val(min_val, max_val): return min_val + random.random() * (max_val - min_val) @@ -14,17 +15,17 @@ def random_val(min_val, max_val): print("Start solving Forward Kinematics 10 times") # init NLinkArm with Denavit-Hartenberg parameters of PR2 - n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], - [math.pi/2, math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .4], - [0., math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .321], - [0., math.pi/2, 0., 0.], + n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.], + [math.pi / 2, math.pi / 2, 0., 0.], + [0., -math.pi / 2, 0., .4], + [0., math.pi / 2, 0., 0.], + [0., -math.pi / 2, 0., .321], + [0., math.pi / 2, 0., 0.], [0., 0., 0., 0.]]) # execute FK 10 times for i in range(10): - n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) - - ee_pose = n_link_arm.forward_kinematics(plot=True) + n_link_arm.set_joint_angles( + [random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) + ee_pose = n_link_arm.forward_kinematics(plot=True) diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py index 44be22709f..a3838a6a38 100644 --- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py @@ -3,23 +3,24 @@ Author: Takayuki Murooka (takayuki5168) """ import math -from NLinkArm import NLinkArm +from NLinkArm3d import NLinkArm import random def random_val(min_val, max_val): return min_val + random.random() * (max_val - min_val) + if __name__ == "__main__": print("Start solving Inverse Kinematics 10 times") # init NLinkArm with Denavit-Hartenberg parameters of PR2 - n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.], - [math.pi/2, math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .4], - [0., math.pi/2, 0., 0.], - [0., -math.pi/2, 0., .321], - [0., math.pi/2, 0., 0.], + n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.], + [math.pi / 2, math.pi / 2, 0., 0.], + [0., -math.pi / 2, 0., .4], + [0., math.pi / 2, 0., 0.], + [0., -math.pi / 2, 0., .321], + [0., math.pi / 2, 0., 0.], [0., 0., 0., 0.]]) # execute IK 10 times diff --git a/ArmNavigation/n_joint_arm_to_point_control/__init__.py b/ArmNavigation/n_joint_arm_to_point_control/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index 2229d63306..55881f4e49 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -41,7 +41,8 @@ def __init__(self, x, y, cost, pind): self.pind = pind def __str__(self): - return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind) + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.pind) def planning(self, sx, sy, gx, gy): """ @@ -72,7 +73,10 @@ def planning(self, sx, sy, gx, gy): break c_id = min( - open_set, key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal, open_set[o])) + open_set, + key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal, + open_set[ + o])) current = open_set[c_id] # show graph @@ -81,7 +85,8 @@ def planning(self, sx, sy, gx, gy): self.calc_grid_position(current.y, self.miny), "xc") # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [exit( + 0) if event.key == 'escape' else None]) if len(closed_set.keys()) % 10 == 0: plt.pause(0.001) @@ -104,7 +109,6 @@ def planning(self, sx, sy, gx, gy): current.cost + self.motion[i][2], c_id) n_id = self.calc_grid_index(node) - # If the node is not safe, do nothing if not self.verify_node(node): continue diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py index 7c59e99bbb..d4b24c7645 100644 --- a/PathPlanning/BSplinePath/bspline_path.py +++ b/PathPlanning/BSplinePath/bspline_path.py @@ -11,7 +11,7 @@ import scipy.interpolate as scipy_interpolate -def approximate_b_spline_path(x: int, y: list, n_path_points: int, +def approximate_b_spline_path(x: list, y: list, n_path_points: int, degree: int = 3) -> tuple: """ approximate points with a B-Spline path diff --git a/PathPlanning/CubicSpline/__init__.py b/PathPlanning/CubicSpline/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py b/PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py deleted file mode 100644 index 98ef8c4ecf..0000000000 --- a/PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py +++ /dev/null @@ -1,239 +0,0 @@ -""" -cubic spline planner - -Author: Atsushi Sakai - -""" -import math -import numpy as np -import bisect - - -class Spline: - """ - Cubic Spline class - """ - - def __init__(self, x, y): - self.b, self.c, self.d, self.w = [], [], [], [] - - self.x = x - self.y = y - - self.nx = len(x) # dimension of x - h = np.diff(x) - - # calc coefficient c - self.a = [iy for iy in y] - - # calc coefficient c - A = self.__calc_A(h) - B = self.__calc_B(h) - self.c = np.linalg.solve(A, B) - # print(self.c1) - - # calc spline coefficient b and d - for i in range(self.nx - 1): - self.d.append((self.c[i + 1] - self.c[i]) / (3.0 * h[i])) - tb = (self.a[i + 1] - self.a[i]) / h[i] - h[i] * \ - (self.c[i + 1] + 2.0 * self.c[i]) / 3.0 - self.b.append(tb) - - def calc(self, t): - """ - Calc position - - if t is outside of the input x, return None - - """ - - if t < self.x[0]: - return None - elif t > self.x[-1]: - return None - - i = self.__search_index(t) - dx = t - self.x[i] - result = self.a[i] + self.b[i] * dx + \ - self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0 - - return result - - def calcd(self, t): - """ - Calc first derivative - - if t is outside of the input x, return None - """ - - if t < self.x[0]: - return None - elif t > self.x[-1]: - return None - - i = self.__search_index(t) - dx = t - self.x[i] - result = self.b[i] + 2.0 * self.c[i] * dx + 3.0 * self.d[i] * dx ** 2.0 - return result - - def calcdd(self, t): - """ - Calc second derivative - """ - - if t < self.x[0]: - return None - elif t > self.x[-1]: - return None - - i = self.__search_index(t) - dx = t - self.x[i] - result = 2.0 * self.c[i] + 6.0 * self.d[i] * dx - return result - - def __search_index(self, x): - """ - search data segment index - """ - return bisect.bisect(self.x, x) - 1 - - def __calc_A(self, h): - """ - calc matrix A for spline coefficient c - """ - A = np.zeros((self.nx, self.nx)) - A[0, 0] = 1.0 - for i in range(self.nx - 1): - if i != (self.nx - 2): - A[i + 1, i + 1] = 2.0 * (h[i] + h[i + 1]) - A[i + 1, i] = h[i] - A[i, i + 1] = h[i] - - A[0, 1] = 0.0 - A[self.nx - 1, self.nx - 2] = 0.0 - A[self.nx - 1, self.nx - 1] = 1.0 - # print(A) - return A - - def __calc_B(self, h): - """ - calc matrix B for spline coefficient c - """ - B = np.zeros(self.nx) - for i in range(self.nx - 2): - B[i + 1] = 3.0 * (self.a[i + 2] - self.a[i + 1]) / \ - h[i + 1] - 3.0 * (self.a[i + 1] - self.a[i]) / h[i] - # print(B) - return B - - -class Spline2D: - """ - 2D Cubic Spline class - - """ - - def __init__(self, x, y): - self.s = self.__calc_s(x, y) - self.sx = Spline(self.s, x) - self.sy = Spline(self.s, y) - - def __calc_s(self, x, y): - dx = np.diff(x) - dy = np.diff(y) - self.ds = [math.sqrt(idx ** 2 + idy ** 2) - for (idx, idy) in zip(dx, dy)] - s = [0] - s.extend(np.cumsum(self.ds)) - return s - - def calc_position(self, s): - """ - calc position - """ - x = self.sx.calc(s) - y = self.sy.calc(s) - - return x, y - - def calc_curvature(self, s): - """ - calc curvature - """ - dx = self.sx.calcd(s) - ddx = self.sx.calcdd(s) - dy = self.sy.calcd(s) - ddy = self.sy.calcdd(s) - k = (ddy * dx - ddx * dy) / (dx ** 2 + dy ** 2) - return k - - def calc_yaw(self, s): - """ - calc yaw - """ - dx = self.sx.calcd(s) - dy = self.sy.calcd(s) - yaw = math.atan2(dy, dx) - return yaw - - -def calc_spline_course(x, y, ds=0.1): - sp = Spline2D(x, y) - s = list(np.arange(0, sp.s[-1], ds)) - - rx, ry, ryaw, rk = [], [], [], [] - for i_s in s: - ix, iy = sp.calc_position(i_s) - rx.append(ix) - ry.append(iy) - ryaw.append(sp.calc_yaw(i_s)) - rk.append(sp.calc_curvature(i_s)) - - return rx, ry, ryaw, rk, s - - -def main(): # pragma: no cover - print("Spline 2D test") - import matplotlib.pyplot as plt - x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0] - y = [0.7, -6, 5, 6.5, 0.0, 5.0, -2.0] - - sp = Spline2D(x, y) - s = np.arange(0, sp.s[-1], 0.1) - - rx, ry, ryaw, rk = [], [], [], [] - for i_s in s: - ix, iy = sp.calc_position(i_s) - rx.append(ix) - ry.append(iy) - ryaw.append(sp.calc_yaw(i_s)) - rk.append(sp.calc_curvature(i_s)) - - plt.subplots(1) - plt.plot(x, y, "xb", label="input") - plt.plot(rx, ry, "-r", label="spline") - plt.grid(True) - plt.axis("equal") - plt.xlabel("x[m]") - plt.ylabel("y[m]") - plt.legend() - - plt.subplots(1) - plt.plot(s, [np.rad2deg(iyaw) for iyaw in ryaw], "-r", label="yaw") - plt.grid(True) - plt.legend() - plt.xlabel("line length[m]") - plt.ylabel("yaw angle[deg]") - - plt.subplots(1) - plt.plot(s, rk, "-r", label="curvature") - plt.grid(True) - plt.legend() - plt.xlabel("line length[m]") - plt.ylabel("curvature [1/m]") - - plt.show() - - -if __name__ == '__main__': # pragma: no cover - main() diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index b113bcbe32..ad3da20835 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -6,9 +6,11 @@ Ref: -- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame](https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf) +- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame] +(https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf) -- [Optimal trajectory generation for dynamic street scenarios in a Frenet Frame](https://www.youtube.com/watch?v=Cj6tAQe7UCY) +- [Optimal trajectory generation for dynamic street scenarios in a Frenet Frame] +(https://www.youtube.com/watch?v=Cj6tAQe7UCY) """ @@ -16,19 +18,20 @@ import matplotlib.pyplot as plt import copy import math -import cubic_spline_planner import sys import os sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../QuinticPolynomialsPlanner/") +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../CubicSpline/") try: from quintic_polynomials_planner import QuinticPolynomial + import cubic_spline_planner except ImportError: raise - SIM_LOOP = 500 # Parameter @@ -38,36 +41,35 @@ MAX_ROAD_WIDTH = 7.0 # maximum road width [m] D_ROAD_W = 1.0 # road width sampling length [m] DT = 0.2 # time tick [s] -MAXT = 5.0 # max prediction time [m] -MINT = 4.0 # min prediction time [m] +MAX_T = 5.0 # max prediction time [m] +MIN_T = 4.0 # min prediction time [m] TARGET_SPEED = 30.0 / 3.6 # target speed [m/s] D_T_S = 5.0 / 3.6 # target speed sampling length [m/s] N_S_SAMPLE = 1 # sampling number of target speed ROBOT_RADIUS = 2.0 # robot radius [m] # cost weights -KJ = 0.1 -KT = 0.1 -KD = 1.0 -KLAT = 1.0 -KLON = 1.0 +K_J = 0.1 +K_T = 0.1 +K_D = 1.0 +K_LAT = 1.0 +K_LON = 1.0 show_animation = True -class quartic_polynomial: - - def __init__(self, xs, vxs, axs, vxe, axe, T): +class QuarticPolynomial: + def __init__(self, xs, vxs, axs, vxe, axe, time): # calc coefficient of quartic polynomial self.a0 = xs self.a1 = vxs self.a2 = axs / 2.0 - A = np.array([[3 * T ** 2, 4 * T ** 3], - [6 * T, 12 * T ** 2]]) - b = np.array([vxe - self.a1 - 2 * self.a2 * T, + A = np.array([[3 * time ** 2, 4 * time ** 3], + [6 * time, 12 * time ** 2]]) + b = np.array([vxe - self.a1 - 2 * self.a2 * time, axe - 2 * self.a2]) x = np.linalg.solve(A, b) @@ -75,19 +77,19 @@ def __init__(self, xs, vxs, axs, vxe, axe, T): self.a4 = x[1] def calc_point(self, t): - xt = self.a0 + self.a1 * t + self.a2 * t**2 + \ - self.a3 * t**3 + self.a4 * t**4 + xt = self.a0 + self.a1 * t + self.a2 * t ** 2 + \ + self.a3 * t ** 3 + self.a4 * t ** 4 return xt def calc_first_derivative(self, t): xt = self.a1 + 2 * self.a2 * t + \ - 3 * self.a3 * t**2 + 4 * self.a4 * t**3 + 3 * self.a3 * t ** 2 + 4 * self.a4 * t ** 3 return xt def calc_second_derivative(self, t): - xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2 + xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t ** 2 return xt @@ -97,7 +99,7 @@ def calc_third_derivative(self, t): return xt -class Frenet_path: +class FrenetPath: def __init__(self): self.t = [] @@ -121,15 +123,14 @@ def __init__(self): def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): - frenet_paths = [] # generate path to each offset goal for di in np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W): # Lateral motion planning - for Ti in np.arange(MINT, MAXT, DT): - fp = Frenet_path() + for Ti in np.arange(MIN_T, MAX_T, DT): + fp = FrenetPath() # lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti) lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti) @@ -141,9 +142,10 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): fp.d_ddd = [lat_qp.calc_third_derivative(t) for t in fp.t] # Longitudinal motion planning (Velocity keeping) - for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S): + for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, + TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S): tfp = copy.deepcopy(fp) - lon_qp = quartic_polynomial(s0, c_speed, 0.0, tv, 0.0, Ti) + lon_qp = QuarticPolynomial(s0, c_speed, 0.0, tv, 0.0, Ti) tfp.s = [lon_qp.calc_point(t) for t in fp.t] tfp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t] @@ -154,11 +156,11 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): Js = sum(np.power(tfp.s_ddd, 2)) # square of jerk # square of diff from target speed - ds = (TARGET_SPEED - tfp.s_d[-1])**2 + ds = (TARGET_SPEED - tfp.s_d[-1]) ** 2 - tfp.cd = KJ * Jp + KT * Ti + KD * tfp.d[-1]**2 - tfp.cv = KJ * Js + KT * Ti + KD * ds - tfp.cf = KLAT * tfp.cd + KLON * tfp.cv + tfp.cd = K_J * Jp + K_T * Ti + K_D * tfp.d[-1] ** 2 + tfp.cv = K_J * Js + K_T * Ti + K_D * ds + tfp.cf = K_LAT * tfp.cd + K_LON * tfp.cv frenet_paths.append(tfp) @@ -166,7 +168,6 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): def calc_global_paths(fplist, csp): - for fp in fplist: # calc global positions @@ -174,10 +175,10 @@ def calc_global_paths(fplist, csp): ix, iy = csp.calc_position(fp.s[i]) if ix is None: break - iyaw = csp.calc_yaw(fp.s[i]) + i_yaw = csp.calc_yaw(fp.s[i]) di = fp.d[i] - fx = ix + di * math.cos(iyaw + math.pi / 2.0) - fy = iy + di * math.sin(iyaw + math.pi / 2.0) + fx = ix + di * math.cos(i_yaw + math.pi / 2.0) + fy = iy + di * math.sin(i_yaw + math.pi / 2.0) fp.x.append(fx) fp.y.append(fy) @@ -199,12 +200,11 @@ def calc_global_paths(fplist, csp): def check_collision(fp, ob): - for i in range(len(ob[:, 0])): - d = [((ix - ob[i, 0])**2 + (iy - ob[i, 1])**2) + d = [((ix - ob[i, 0]) ** 2 + (iy - ob[i, 1]) ** 2) for (ix, iy) in zip(fp.x, fp.y)] - collision = any([di <= ROBOT_RADIUS**2 for di in d]) + collision = any([di <= ROBOT_RADIUS ** 2 for di in d]) if collision: return False @@ -213,38 +213,38 @@ def check_collision(fp, ob): def check_paths(fplist, ob): - - okind = [] + ok_ind = [] for i, _ in enumerate(fplist): if any([v > MAX_SPEED for v in fplist[i].s_d]): # Max speed check continue - elif any([abs(a) > MAX_ACCEL for a in fplist[i].s_dd]): # Max accel check + elif any([abs(a) > MAX_ACCEL for a in + fplist[i].s_dd]): # Max accel check continue - elif any([abs(c) > MAX_CURVATURE for c in fplist[i].c]): # Max curvature check + elif any([abs(c) > MAX_CURVATURE for c in + fplist[i].c]): # Max curvature check continue elif not check_collision(fplist[i], ob): continue - okind.append(i) + ok_ind.append(i) - return [fplist[i] for i in okind] + return [fplist[i] for i in ok_ind] def frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob): - fplist = calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0) fplist = calc_global_paths(fplist, csp) fplist = check_paths(fplist, ob) # find minimum cost path - mincost = float("inf") - bestpath = None + min_cost = float("inf") + best_path = None for fp in fplist: - if mincost >= fp.cf: - mincost = fp.cf - bestpath = fp + if min_cost >= fp.cf: + min_cost = fp.cf + best_path = fp - return bestpath + return best_path def generate_target_course(x, y): @@ -282,7 +282,7 @@ def main(): c_speed = 10.0 / 3.6 # current speed [m/s] c_d = 2.0 # current lateral position [m] c_d_d = 0.0 # current lateral speed [m/s] - c_d_dd = 0.0 # current latral acceleration [m/s] + c_d_dd = 0.0 # current lateral acceleration [m/s] s0 = 0.0 # current course position area = 20.0 # animation area length [m] @@ -304,8 +304,9 @@ def main(): if show_animation: # pragma: no cover plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(tx, ty) plt.plot(ob[:, 0], ob[:, 1], "xk") plt.plot(path.x[1:], path.y[1:], "-or") diff --git a/PathPlanning/HybridAStar/__init__.py b/PathPlanning/HybridAStar/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star_heuristic.py similarity index 100% rename from PathPlanning/HybridAStar/a_star.py rename to PathPlanning/HybridAStar/a_star_heuristic.py diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 725976dc45..41e8b393c0 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -14,7 +14,7 @@ import sys sys.path.append("../ReedsSheppPath/") try: - from a_star import dp_planning # , calc_obstacle_map + from a_star_heuristic import dp_planning # , calc_obstacle_map import reeds_shepp_path_planning as rs from car import move, check_car_collision, MAX_STEER, WB, plot_car except: From ae2593b44425f805da85e245d925554cd8336700 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 9 Mar 2020 23:04:47 +0900 Subject: [PATCH 176/909] mypy fix test --- .github/workflows/pythonpackage.yml | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 1844e53820..b3f1aa364c 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -9,7 +9,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: [3.6, 3.7] + python-version: [3.7, 3.8] steps: - uses: actions/checkout@v1 @@ -34,7 +34,15 @@ jobs: run: pip install mypy - name: mypy check run: | - find . -name "*.py" | xargs mypy + find AerialNavigation -name "*.py" | xargs mypy + find ArmNavigation -name "*.py" | xargs mypy + find Bipedal -name "*.py" | xargs mypy + find InvertedPendulumCart -name "*.py" | xargs mypy + find Localization -name "*.py" | xargs mypy + find Mapping -name "*.py" | xargs mypy + find PathPlanning -name "*.py" | xargs mypy + find PathTracking -name "*.py" | xargs mypy + find SLAM -name "*.py" | xargs mypy - name: do all unit tests run: bash runtests.sh From 4107c424067fbbac8a855066e5cae03c9f8c4334 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 9 Mar 2020 23:06:48 +0900 Subject: [PATCH 177/909] mypy fix test --- .github/workflows/pythonpackage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index b3f1aa364c..574c7be9d5 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -9,7 +9,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: [3.7, 3.8] + python-version: [3.7] steps: - uses: actions/checkout@v1 From 19630bc0ce9419a70c3ae301fed0f70553a2e5be Mon Sep 17 00:00:00 2001 From: Chachay Date: Fri, 20 Mar 2020 10:13:41 +0100 Subject: [PATCH 178/909] Integrate update function into state class --- .../rear_wheel_feedback.py | 32 ++++--------------- 1 file changed, 6 insertions(+), 26 deletions(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 758e9ad946..af9fb0680c 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -16,7 +16,6 @@ except: raise - Kp = 1.0 # speed propotional gain # steering control parameter KTH = 1.0 @@ -26,34 +25,24 @@ L = 2.9 # [m] show_animation = True -# show_animation = False - class State: - def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0): self.x = x self.y = y self.yaw = yaw self.v = v - -def update(state, a, delta): - - state.x = state.x + state.v * math.cos(state.yaw) * dt - state.y = state.y + state.v * math.sin(state.yaw) * dt - state.yaw = state.yaw + state.v / L * math.tan(delta) * dt - state.v = state.v + a * dt - - return state - + def update(self, a, delta, dt): + self.x = self.x + self.v * math.cos(self.yaw) * dt + self.y = self.y + self.v * math.sin(self.yaw) * dt + self.yaw = self.yaw + self.v / L * math.tan(delta) * dt + self.v = self.v + a * dt def PIDControl(target, current): a = Kp * (target - current) - return a - def pi_2_pi(angle): while(angle > math.pi): angle = angle - 2.0 * math.pi @@ -63,7 +52,6 @@ def pi_2_pi(angle): return angle - def rear_wheel_feedback_control(state, cx, cy, cyaw, ck, preind): ind, e = calc_nearest_index(state, cx, cy, cyaw) @@ -78,7 +66,6 @@ def rear_wheel_feedback_control(state, cx, cy, cyaw, ck, preind): return 0.0, ind delta = math.atan2(L * omega / v, 1.0) - # print(k, v, e, th_e, omega, delta) return delta, ind @@ -104,9 +91,7 @@ def calc_nearest_index(state, cx, cy, cyaw): return ind, mind - def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): - T = 500.0 # max simulation time goal_dis = 0.3 stop_speed = 0.05 @@ -126,7 +111,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): di, target_ind = rear_wheel_feedback_control( state, cx, cy, cyaw, ck, target_ind) ai = PIDControl(speed_profile[target_ind], state.v) - state = update(state, ai, di) + state.update(ai, di, dt) if abs(state.v) <= stop_speed: target_ind += 1 @@ -163,11 +148,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): return t, x, y, yaw, v, goal_flag - def calc_speed_profile(cx, cy, cyaw, target_speed): - speed_profile = [target_speed] * len(cx) - direction = 1.0 # Set stop point @@ -190,7 +172,6 @@ def calc_speed_profile(cx, cy, cyaw, target_speed): return speed_profile - def main(): print("rear wheel feedback tracking start!!") ax = [0.0, 6.0, 12.5, 5.0, 7.5, 3.0, -1.0] @@ -237,6 +218,5 @@ def main(): plt.show() - if __name__ == '__main__': main() From 74739078fdcc18cd12a2347efec0dac4ccca6dc2 Mon Sep 17 00:00:00 2001 From: Chachay Date: Fri, 20 Mar 2020 12:08:19 +0100 Subject: [PATCH 179/909] Replace Spline module to Scipy interpolate --- .../rear_wheel_feedback.py | 139 +++++++++++------- 1 file changed, 84 insertions(+), 55 deletions(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index af9fb0680c..5e6cedb377 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -9,12 +9,9 @@ import math import numpy as np import sys -sys.path.append("../../PathPlanning/CubicSpline/") -try: - import cubic_spline_planner -except: - raise +from scipy import interpolate +from scipy import optimize Kp = 1.0 # speed propotional gain # steering control parameter @@ -39,6 +36,61 @@ def update(self, a, delta, dt): self.yaw = self.yaw + self.v / L * math.tan(delta) * dt self.v = self.v + a * dt +class TrackSpline: + def __init__(self, x, y): + x, y = map(np.asarray, (x, y)) + s = np.append([0],(np.cumsum(np.diff(x)**2) + np.cumsum(np.diff(y)**2))**0.5) + + self.X = interpolate.CubicSpline(s, x) + self.Y = interpolate.CubicSpline(s, y) + + self.dX = self.X.derivative(1) + self.ddX = self.X.derivative(2) + + self.dY = self.Y.derivative(1) + self.ddY = self.Y.derivative(2) + + self.length = s[-1] + + def yaw(self, s): + dx, dy = self.dX(s), self.dY(s) + return np.arctan2(dy, dx) + + def curvature(self, s): + dx, dy = self.dX(s), self.dY(s) + ddx, ddy = self.ddX(s), self.ddY(s) + return (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2)) + + def __findClosestPoint(self, s0, x, y): + def f(_s, *args): + _x, _y= self.X(_s), self.Y(_s) + return (_x - args[0])**2 + (_y - args[1])**2 + + def jac(_s, *args): + _x, _y = self.X(_s), self.Y(_s) + _dx, _dy = self.dX(_s), self.dY(_s) + return 2*_dx*(_x - args[0])+2*_dy*(_y-args[1]) + + minimum = optimize.fmin_cg(f, s0, jac, args=(x, y), full_output=True, disp=False) + return minimum + + def TrackError(self, x, y, s0): + ret = self.__findClosestPoint(s0, x, y) + + s = ret[0][0] + e = ret[1] + + k = self.curvature(s) + yaw = self.yaw(s) + + dxl = self.X(s) - x + dyl = self.Y(s) - y + angle = pi_2_pi(yaw - math.atan2(dyl, dxl)) + if angle < 0: + e*= -1 + + return e, k, yaw, s + def PIDControl(target, current): a = Kp * (target - current) return a @@ -52,46 +104,22 @@ def pi_2_pi(angle): return angle -def rear_wheel_feedback_control(state, cx, cy, cyaw, ck, preind): - ind, e = calc_nearest_index(state, cx, cy, cyaw) - - k = ck[ind] +def rear_wheel_feedback_control(state, e, k, yaw_r): v = state.v - th_e = pi_2_pi(state.yaw - cyaw[ind]) + th_e = pi_2_pi(state.yaw - yaw_r) omega = v * k * math.cos(th_e) / (1.0 - k * e) - \ KTH * abs(v) * th_e - KE * v * math.sin(th_e) * e / th_e if th_e == 0.0 or omega == 0.0: - return 0.0, ind + return 0.0 delta = math.atan2(L * omega / v, 1.0) - return delta, ind - - -def calc_nearest_index(state, cx, cy, cyaw): - dx = [state.x - icx for icx in cx] - dy = [state.y - icy for icy in cy] - - d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)] - - mind = min(d) - - ind = d.index(mind) + return delta - mind = math.sqrt(mind) - dxl = cx[ind] - state.x - dyl = cy[ind] - state.y - - angle = pi_2_pi(cyaw[ind] - math.atan2(dyl, dxl)) - if angle < 0: - mind *= -1 - - return ind, mind - -def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): +def closed_loop_prediction(track, speed_profile, goal): T = 500.0 # max simulation time goal_dis = 0.3 stop_speed = 0.05 @@ -105,17 +133,17 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): v = [state.v] t = [0.0] goal_flag = False - target_ind = calc_nearest_index(state, cx, cy, cyaw) + + s = np.arange(0, track.length, 0.1) + e, k, yaw_r, s0 = track.TrackError(state.x, state.y, 0.0) while T >= time: - di, target_ind = rear_wheel_feedback_control( - state, cx, cy, cyaw, ck, target_ind) - ai = PIDControl(speed_profile[target_ind], state.v) + e, k, yaw_r, s0 = track.TrackError(state.x, state.y, s0) + di = rear_wheel_feedback_control(state, e, k, yaw_r) + #ai = PIDControl(speed_profile[target_ind], state.v) + ai = PIDControl(speed_profile, state.v) state.update(ai, di, dt) - if abs(state.v) <= stop_speed: - target_ind += 1 - time = time + dt # check goal @@ -132,23 +160,22 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): v.append(state.v) t.append(time) - if target_ind % 1 == 0 and show_animation: + if show_animation: plt.cla() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) - plt.plot(cx, cy, "-r", label="course") + plt.plot(track.X(s), track.Y(s), "-r", label="course") plt.plot(x, y, "ob", label="trajectory") - plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") + plt.plot(track.X(s0), track.Y(s0), "xg", label="target") plt.axis("equal") plt.grid(True) - plt.title("speed[km/h]:" + str(round(state.v * 3.6, 2)) + - ",target index:" + str(target_ind)) + plt.title("speed[km/h]:{:.2f}, target s-param:{:.2f}".format(round(state.v * 3.6, 2), s0)) plt.pause(0.0001) return t, x, y, yaw, v, goal_flag -def calc_speed_profile(cx, cy, cyaw, target_speed): +def calc_speed_profile(track, target_speed, s): speed_profile = [target_speed] * len(cx) direction = 1.0 @@ -178,14 +205,16 @@ def main(): ay = [0.0, 0.0, 5.0, 6.5, 3.0, 5.0, -2.0] goal = [ax[-1], ay[-1]] - cx, cy, cyaw, ck, s = cubic_spline_planner.calc_spline_course( - ax, ay, ds=0.1) + track = TrackSpline(ax, ay) + s = np.arange(0, track.length, 0.1) + target_speed = 10.0 / 3.6 - sp = calc_speed_profile(cx, cy, cyaw, target_speed) + # Note: disable backward direction temporary + #sp = calc_speed_profile(track, target_speed, s) + sp = target_speed - t, x, y, yaw, v, goal_flag = closed_loop_prediction( - cx, cy, cyaw, ck, sp, goal) + t, x, y, yaw, v, goal_flag = closed_loop_prediction(track, sp, goal) # Test assert goal_flag, "Cannot goal" @@ -194,7 +223,7 @@ def main(): plt.close() plt.subplots(1) plt.plot(ax, ay, "xb", label="input") - plt.plot(cx, cy, "-r", label="spline") + plt.plot(track.X(s), track.Y(s), "-r", label="spline") plt.plot(x, y, "-g", label="tracking") plt.grid(True) plt.axis("equal") @@ -203,14 +232,14 @@ def main(): plt.legend() plt.subplots(1) - plt.plot(s, [np.rad2deg(iyaw) for iyaw in cyaw], "-r", label="yaw") + plt.plot(s, np.rad2deg(track.yaw(s)), "-r", label="yaw") plt.grid(True) plt.legend() plt.xlabel("line length[m]") plt.ylabel("yaw angle[deg]") plt.subplots(1) - plt.plot(s, ck, "-r", label="curvature") + plt.plot(s, track.curvature(s), "-r", label="curvature") plt.grid(True) plt.legend() plt.xlabel("line length[m]") From a7c82ccb9e5cc18e6aaca3b3a8ff4de6fdcf1c21 Mon Sep 17 00:00:00 2001 From: Chachay Date: Mon, 23 Mar 2020 16:00:19 +0100 Subject: [PATCH 180/909] - remove unsued import and variable - rename functions - add target speed controller (as the substitute of calc speed profile) --- .../rear_wheel_feedback.py | 83 ++++++++----------- 1 file changed, 34 insertions(+), 49 deletions(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 5e6cedb377..9b69f8eace 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -8,7 +8,6 @@ import matplotlib.pyplot as plt import math import numpy as np -import sys from scipy import interpolate from scipy import optimize @@ -24,11 +23,12 @@ show_animation = True class State: - def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0): + def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0, direction=1): self.x = x self.y = y self.yaw = yaw self.v = v + self.direction = direction def update(self, a, delta, dt): self.x = self.x + self.v * math.cos(self.yaw) * dt @@ -52,36 +52,36 @@ def __init__(self, x, y): self.length = s[-1] - def yaw(self, s): + def calc_yaw(self, s): dx, dy = self.dX(s), self.dY(s) return np.arctan2(dy, dx) - def curvature(self, s): + def calc_curvature(self, s): dx, dy = self.dX(s), self.dY(s) ddx, ddy = self.ddX(s), self.ddY(s) return (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2)) - def __findClosestPoint(self, s0, x, y): - def f(_s, *args): + def __find_nearest_point(self, s0, x, y): + def calc_distance(_s, *args): _x, _y= self.X(_s), self.Y(_s) return (_x - args[0])**2 + (_y - args[1])**2 - def jac(_s, *args): + def calc_distance_jacobian(_s, *args): _x, _y = self.X(_s), self.Y(_s) _dx, _dy = self.dX(_s), self.dY(_s) return 2*_dx*(_x - args[0])+2*_dy*(_y-args[1]) - minimum = optimize.fmin_cg(f, s0, jac, args=(x, y), full_output=True, disp=False) + minimum = optimize.fmin_cg(calc_distance, s0, calc_distance_jacobian, args=(x, y), full_output=True, disp=False) return minimum - def TrackError(self, x, y, s0): - ret = self.__findClosestPoint(s0, x, y) + def calc_track_error(self, x, y, s0): + ret = self.__find_nearest_point(s0, x, y) s = ret[0][0] e = ret[1] - k = self.curvature(s) - yaw = self.yaw(s) + k = self.calc_curvature(s) + yaw = self.calc_yaw(s) dxl = self.X(s) - x dyl = self.Y(s) - y @@ -91,7 +91,7 @@ def TrackError(self, x, y, s0): return e, k, yaw, s -def PIDControl(target, current): +def pid_control(target, current): a = Kp * (target - current) return a @@ -119,10 +119,9 @@ def rear_wheel_feedback_control(state, e, k, yaw_r): return delta -def closed_loop_prediction(track, speed_profile, goal): +def simulate(track, goal): T = 500.0 # max simulation time goal_dis = 0.3 - stop_speed = 0.05 state = State(x=-0.0, y=-0.0, yaw=0.0, v=0.0) @@ -135,13 +134,14 @@ def closed_loop_prediction(track, speed_profile, goal): goal_flag = False s = np.arange(0, track.length, 0.1) - e, k, yaw_r, s0 = track.TrackError(state.x, state.y, 0.0) + e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, 0.0) while T >= time: - e, k, yaw_r, s0 = track.TrackError(state.x, state.y, s0) + e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, s0) di = rear_wheel_feedback_control(state, e, k, yaw_r) - #ai = PIDControl(speed_profile[target_ind], state.v) - ai = PIDControl(speed_profile, state.v) + + speed_r = calc_target_speed(state, yaw_r) + ai = pid_control(speed_r, state.v) state.update(ai, di, dt) time = time + dt @@ -175,29 +175,20 @@ def closed_loop_prediction(track, speed_profile, goal): return t, x, y, yaw, v, goal_flag -def calc_speed_profile(track, target_speed, s): - speed_profile = [target_speed] * len(cx) - direction = 1.0 - - # Set stop point - for i in range(len(cx) - 1): - dyaw = cyaw[i + 1] - cyaw[i] - switch = math.pi / 4.0 <= dyaw < math.pi / 2.0 - - if switch: - direction *= -1 - - if direction != 1.0: - speed_profile[i] = - target_speed - else: - speed_profile[i] = target_speed - - if switch: - speed_profile[i] = 0.0 +def calc_target_speed(state, yaw_r): + target_speed = 10.0 / 3.6 - speed_profile[-1] = 0.0 + dyaw = yaw_r - state.yaw + switch = math.pi / 4.0 <= dyaw < math.pi / 2.0 - return speed_profile + if switch: + state.direction *= -1 + return 0.0 + + if state.direction != 1: + return -target_speed + else: + return target_speed def main(): print("rear wheel feedback tracking start!!") @@ -208,13 +199,7 @@ def main(): track = TrackSpline(ax, ay) s = np.arange(0, track.length, 0.1) - target_speed = 10.0 / 3.6 - - # Note: disable backward direction temporary - #sp = calc_speed_profile(track, target_speed, s) - sp = target_speed - - t, x, y, yaw, v, goal_flag = closed_loop_prediction(track, sp, goal) + t, x, y, yaw, v, goal_flag = simulate(track, goal) # Test assert goal_flag, "Cannot goal" @@ -232,14 +217,14 @@ def main(): plt.legend() plt.subplots(1) - plt.plot(s, np.rad2deg(track.yaw(s)), "-r", label="yaw") + plt.plot(s, np.rad2deg(track.calc_yaw(s)), "-r", label="yaw") plt.grid(True) plt.legend() plt.xlabel("line length[m]") plt.ylabel("yaw angle[deg]") plt.subplots(1) - plt.plot(s, track.curvature(s), "-r", label="curvature") + plt.plot(s, track.calc_curvature(s), "-r", label="curvature") plt.grid(True) plt.legend() plt.xlabel("line length[m]") From 7005a664e1b65d6751d1cb323833acff7c7505ca Mon Sep 17 00:00:00 2001 From: Chachay Date: Mon, 23 Mar 2020 16:10:11 +0100 Subject: [PATCH 181/909] - Changed Class name (CubicSplinePath) - remove unnecessary else case --- PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 9b69f8eace..8f1079b074 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -36,7 +36,7 @@ def update(self, a, delta, dt): self.yaw = self.yaw + self.v / L * math.tan(delta) * dt self.v = self.v + a * dt -class TrackSpline: +class CubicSplinePath: def __init__(self, x, y): x, y = map(np.asarray, (x, y)) s = np.append([0],(np.cumsum(np.diff(x)**2) + np.cumsum(np.diff(y)**2))**0.5) @@ -187,8 +187,8 @@ def calc_target_speed(state, yaw_r): if state.direction != 1: return -target_speed - else: - return target_speed + + return target_speed def main(): print("rear wheel feedback tracking start!!") @@ -196,7 +196,7 @@ def main(): ay = [0.0, 0.0, 5.0, 6.5, 3.0, 5.0, -2.0] goal = [ax[-1], ay[-1]] - track = TrackSpline(ax, ay) + track = CubicSplinePath(ax, ay) s = np.arange(0, track.length, 0.1) t, x, y, yaw, v, goal_flag = simulate(track, goal) From e880b0efe448e7024f22bd3f70c26fe8ba7ef075 Mon Sep 17 00:00:00 2001 From: Michael Dobler Date: Tue, 24 Mar 2020 12:59:49 +0100 Subject: [PATCH 182/909] Fix parameter of jacobian in EKF-SLAM --- SLAM/EKFSLAM/ekf_slam.ipynb | 2 +- SLAM/EKFSLAM/ekf_slam.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb index ccf5e5bbd6..a64c145ad4 100644 --- a/SLAM/EKFSLAM/ekf_slam.ipynb +++ b/SLAM/EKFSLAM/ekf_slam.ipynb @@ -305,8 +305,8 @@ " :returns: predicted state vector, predicted covariance, jacobian of control vector, transition fx\n", " \"\"\"\n", " S = STATE_SIZE\n", - " xEst[0:S] = motion_model(xEst[0:S], u)\n", " G, Fx = jacob_motion(xEst[0:S], u)\n", + " xEst[0:S] = motion_model(xEst[0:S], u)\n", " # Fx is an an identity matrix of size (STATE_SIZE)\n", " # sigma = G*sigma*G.T + Noise\n", " PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n", diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index 3038e40a47..6b349d858b 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -29,8 +29,8 @@ def ekf_slam(xEst, PEst, u, z): # Predict S = STATE_SIZE - xEst[0:S] = motion_model(xEst[0:S], u) G, Fx = jacob_motion(xEst[0:S], u) + xEst[0:S] = motion_model(xEst[0:S], u) PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx initP = np.eye(2) From 6d178e1feb3740c4b7dc67136b9b701f22c6e6e6 Mon Sep 17 00:00:00 2001 From: Chachay Date: Tue, 24 Mar 2020 16:33:43 +0100 Subject: [PATCH 183/909] rename yaw_r to yaw_ref --- .../rear_wheel_feedback/rear_wheel_feedback.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 8f1079b074..f6df94ac44 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -104,9 +104,9 @@ def pi_2_pi(angle): return angle -def rear_wheel_feedback_control(state, e, k, yaw_r): +def rear_wheel_feedback_control(state, e, k, yaw_ref): v = state.v - th_e = pi_2_pi(state.yaw - yaw_r) + th_e = pi_2_pi(state.yaw - yaw_ref) omega = v * k * math.cos(th_e) / (1.0 - k * e) - \ KTH * abs(v) * th_e - KE * v * math.sin(th_e) * e / th_e @@ -134,14 +134,14 @@ def simulate(track, goal): goal_flag = False s = np.arange(0, track.length, 0.1) - e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, 0.0) + e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, 0.0) while T >= time: - e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, s0) - di = rear_wheel_feedback_control(state, e, k, yaw_r) + e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, s0) + di = rear_wheel_feedback_control(state, e, k, yaw_ref) - speed_r = calc_target_speed(state, yaw_r) - ai = pid_control(speed_r, state.v) + speed_ref = calc_target_speed(state, yaw_ref) + ai = pid_control(speed_ref, state.v) state.update(ai, di, dt) time = time + dt From b61a8d8fb5d47c89c97ec8081942ce33f673538c Mon Sep 17 00:00:00 2001 From: Jeff Irion Date: Tue, 24 Mar 2020 10:56:11 -0700 Subject: [PATCH 184/909] Add Graph SLAM formulation PDF and SE(2) solver + example --- SLAM/GraphBasedSLAM/LaTeX/.gitignore | 3 + SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib | 30 + .../LaTeX/graphSLAM_formulation.tex | 175 ++ SLAM/GraphBasedSLAM/data/README.rst | 6 + SLAM/GraphBasedSLAM/data/input_INTEL.g2o | 2711 +++++++++++++++++ .../graphSLAM_SE2_example.ipynb | 332 ++ SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf | Bin 0 -> 276368 bytes SLAM/GraphBasedSLAM/graphslam/__init__.py | 9 + .../GraphBasedSLAM/graphslam/edge/__init__.py | 5 + .../graphslam/edge/edge_odometry.py | 180 ++ SLAM/GraphBasedSLAM/graphslam/graph.py | 265 ++ SLAM/GraphBasedSLAM/graphslam/load.py | 66 + .../GraphBasedSLAM/graphslam/pose/__init__.py | 5 + SLAM/GraphBasedSLAM/graphslam/pose/se2.py | 171 ++ SLAM/GraphBasedSLAM/graphslam/util.py | 78 + SLAM/GraphBasedSLAM/graphslam/vertex.py | 67 + .../images/Graph_SLAM_optimization.gif | Bin 0 -> 114966 bytes 17 files changed, 4103 insertions(+) create mode 100644 SLAM/GraphBasedSLAM/LaTeX/.gitignore create mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib create mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex create mode 100644 SLAM/GraphBasedSLAM/data/README.rst create mode 100644 SLAM/GraphBasedSLAM/data/input_INTEL.g2o create mode 100644 SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb create mode 100644 SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf create mode 100644 SLAM/GraphBasedSLAM/graphslam/__init__.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/edge/__init__.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/edge/edge_odometry.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/graph.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/load.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/pose/__init__.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/pose/se2.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/util.py create mode 100644 SLAM/GraphBasedSLAM/graphslam/vertex.py create mode 100644 SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif diff --git a/SLAM/GraphBasedSLAM/LaTeX/.gitignore b/SLAM/GraphBasedSLAM/LaTeX/.gitignore new file mode 100644 index 0000000000..b0b343402c --- /dev/null +++ b/SLAM/GraphBasedSLAM/LaTeX/.gitignore @@ -0,0 +1,3 @@ +# Ignore LaTeX generated files +graphSLAM_formulation.* +!graphSLAM_formulation.tex diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib new file mode 100644 index 0000000000..4d3b71ae50 --- /dev/null +++ b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib @@ -0,0 +1,30 @@ +@article{blanco2010tutorial, + title={A tutorial on ${SE}(3)$ transformation parameterizations and on-manifold optimization}, + author={Blanco, Jose-Luis}, + journal={University of Malaga, Tech. Rep}, + volume={3}, + year={2010}, + publisher={Citeseer} +} + +@article{grisetti2010tutorial, + title={A tutorial on graph-based {SLAM}}, + author={Grisetti, Giorgio and Kummerle, Rainer and Stachniss, Cyrill and Burgard, Wolfram}, + journal={IEEE Intelligent Transportation Systems Magazine}, + volume={2}, + number={4}, + pages={31--43}, + year={2010}, + publisher={IEEE} +} + +@article{thrun2006graph, + title={The graph {SLAM} algorithm with applications to large-scale mapping of urban structures}, + author={Thrun, Sebastian and Montemerlo, Michael}, + journal={The International Journal of Robotics Research}, + volume={25}, + number={5-6}, + pages={403--429}, + year={2006}, + publisher={SAGE Publications} +} diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex new file mode 100644 index 0000000000..25f02cd3bd --- /dev/null +++ b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex @@ -0,0 +1,175 @@ +\documentclass{article} + +\usepackage{amsfonts} +\usepackage{amsmath,amssymb,amsfonts} +\usepackage{textcomp} +\usepackage{fullpage} +\usepackage{setspace} +\usepackage{float} +\usepackage{cite} +\usepackage{graphicx} +\usepackage{caption} +\usepackage{subcaption} +\usepackage[pdfborder={0 0 0}, pdfpagemode=UseNone, pdfstartview=FitH]{hyperref} + +\DeclareMathOperator*{\argmax}{arg\,max} +\DeclareMathOperator*{\argmin}{arg\,min} + +\def\keyterm{\textit} + +\newcommand{\transp}{{\scriptstyle{\mathsf{T}}}} + + + +\begin{document} + +\title{Graph SLAM Formulation} +\author{Jeff Irion} +\date{} + +\maketitle +\vspace{3em} + + +\section{Problem Formulation} + +Let a robot's trajectory through its environment be represented by a sequence of $N$ poses: $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$. Each pose lies on a manifold: $\mathbf{p}_i \in \mathcal{M}$. Simple examples of manifolds used in Graph SLAM include 1-D, 2-D, and 3-D space, i.e., $\mathbb{R}$, $\mathbb{R}^2$, and $\mathbb{R}^3$. These environments are \keyterm{rectilinear}, meaning that there is no concept of orientation. By contrast, in $SE(2)$ problem settings a robot's pose consists of its location in $\mathbb{R}^2$ and its orientation $\theta$. Similarly, in $SE(3)$ a robot's pose consists of its location in $\mathbb{R}^3$ and its orientation, which can be represented via Euler angles, quaternions, or $SO(3)$ rotation matrices. + +As the robot explores its environment, it collects a set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_j\}$. Examples of such measurements include odometry, GPS, and IMU data. Given a set of poses $\mathbf{p}_1, \ldots, \mathbf{p}_N$, we can compute the estimated measurement $\hat{\mathbf{z}}_j(\mathbf{p}_1, \ldots, \mathbf{p}_N)$. We can then compute the \keyterm{residual} $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j)$ for measurement $j$. The formula for the residual depends on the type of measurement. As an example, let $\mathbf{z}_1$ be an odometry measurement that was collected when the robot traveled from $\mathbf{p}_1$ to $\mathbf{p}_2$. The expected measurement and the residual are computed as +% +\begin{align*} + \hat{\mathbf{z}}_1(\mathbf{p}_1, \mathbf{p}_2) &= \mathbf{p}_2 \ominus \mathbf{p}_1 \\ + \mathbf{e}_1(\mathbf{z}_1, \hat{\mathbf{z}}_1) &= \mathbf{z}_1 \ominus \hat{\mathbf{z}}_1 = \mathbf{z}_1 \ominus (\mathbf{p}_2 \ominus \mathbf{p}_1), +\end{align*} +% +where the $\ominus$ operator indicates inverse pose composition. We model measurement $\mathbf{z}_j$ as having independent Gaussian noise with zero mean and covariance matrix $\Omega_j^{-1}$; we refer to $\Omega_j$ as the \keyterm{information matrix} for measurement $j$. That is, +\begin{equation} + p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) = \eta_j \exp \left( (-\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right), \label{eq:observation_probability} +\end{equation} +where $\eta_j$ is the normalization constant. + +The objective of Graph SLAM is to find the maximum likelihood set of poses given the measurements $\mathcal{Z} = \{\mathbf{z}_j\}$; in other words, we want to find +% +\begin{equation*} + \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) +\end{equation*} +% +Using Bayes' rule, we can write this probability as +% +\begin{align} + p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \frac{p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) p(\mathbf{p}_1, \ldots, \mathbf{p}_N) }{ p(\mathcal{Z}) } \notag \\ + &\propto p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N), \label{eq:bayes} +\end{align} +% +since $p(\mathcal{Z})$ is a constant (albeit, an unknown constant) and we assume that $p(\mathbf{p}_1, \ldots, \mathbf{p}_N)$ is uniformly distributed \cite{thrun2006graph}. Therefore, we can use \eqref{eq:observation_probability} and \eqref{eq:bayes} to simplify the Graph SLAM optimization as follows: +% +\begin{align*} + \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\ + &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\ + &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M \exp \left( -(\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right) \\ + &= \argmin_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j). +\end{align*} +% +We define +% +\begin{equation*} + \chi^2 := \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j), +\end{equation*} +% +and this is what we seek to minimize. + + +\section{Dimensionality and Pose Representation} + +Before proceeding further, it is helpful to discuss the dimensionality of the problem. We have: +\begin{itemize} + \item A set of $N$ poses $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$, where each pose lies on the manifold $\mathcal{M}$ + \begin{itemize} + \item Each pose $\mathbf{p}_i$ is represented as a vector in (a subset of) $\mathbb{R}^d$. For example: + \begin{itemize} + \item[$\circ$] An $SE(2)$ pose is typically represented as $(x, y, \theta)$, and thus $d = 3$. + \item[$\circ$] An $SE(3)$ pose is typically represented as $(x, y, z, q_x, q_y, q_z, q_w)$, where $(x, y, z)$ is a point in $\mathbb{R}^3$ and $(q_x, q_y, q_z, q_w)$ is a \keyterm{quaternion}, and so $d = 7$. For more information about $SE(3)$ parameterizations and pose transformations, see \cite{blanco2010tutorial}. + \end{itemize} + \item We also need to be able to represent each pose compactly as a vector in (a subset of) $\mathbb{R}^c$. + \begin{itemize} + \item[$\circ$] Since an $SE(2)$ pose has three degrees of freedom, the $(x, y, \theta)$ representation is again sufficient and $c=3$. + \item[$\circ$] An $SE(3)$ pose only has six degrees of freedom, and we can represent it compactly as $(x, y, z, q_x, q_y, q_z)$, and thus $c=6$. + \end{itemize} + \item We use the $\boxplus$ operator to indicate pose composition when one or both of the poses are represented compactly. The output can be a pose in $\mathcal{M}$ or a vector in $\mathbb{R}^c$, as required by context. + \end{itemize} + \item A set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_1, \mathbf{z}_2, \ldots, \mathbf{z}_M\}$ + \begin{itemize} + \item Each measurement's dimensionality can be unique, and we will use $\bullet$ to denote a ``wildcard'' variable. + \item Measurement $\mathbf{z}_j \in \mathbb{R}^\bullet$ has an associated information matrix $\Omega_j \in \mathbb{R}^{\bullet \times \bullet}$ and residual function $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) = \mathbf{e}_j(\mathbf{z}_j, \mathbf{p}_1, \ldots, \mathbf{p}_N) \in \mathbb{R}^\bullet$. + \item A measurement could, in theory, constrain anywhere from 1 pose to all $N$ poses. In practice, each measurement usually constrains only 1 or 2 poses. + \end{itemize} +\end{itemize} + + +\section{Graph SLAM Algorithm} + +The ``Graph'' in Graph SLAM refers to the fact that we view the problem as a graph. The graph has a set $\mathcal{V}$ of $N$ vertices, where each vertex $v_i$ has an associated pose $\mathbf{p}_i$. Similarly, the graph has a set $\mathcal{E}$ of $M$ edges, where each edge $e_j$ has an associated measurement $\mathbf{z}_j$. In practice, the edges in this graph are either unary (i.e., a loop) or binary. (Note: $e_j$ refers to the edge in the graph associated with measurement $\mathbf{z}_j$, whereas $\mathbf{e}_j$ refers to the residual function associated with $\mathbf{z}_j$.) For more information about the Graph SLAM algorithm, see \cite{grisetti2010tutorial}. + +We want to optimize +% +\begin{equation*} + \chi^2 = \sum_{e_j \in \mathcal{E}} \mathbf{e}_j^\transp \Omega_j \mathbf{e}_j. +\end{equation*} +% +Let $\mathbf{x}_i \in \mathbb{R}^c$ be the compact representation of pose $\mathbf{p}_i \in \mathcal{M}$, and let +% +\begin{equation*} + \mathbf{x} := \begin{bmatrix} \mathbf{x}_1 \\ \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \end{bmatrix} \in \mathbb{R}^{cN} +\end{equation*} +% +We will solve this optimization problem iteratively. Let +% +\begin{equation} + \mathbf{x}^{k+1} := \mathbf{x}^k \boxplus \Delta \mathbf{x}^k = \begin{bmatrix} \mathbf{x}_1 \boxplus \Delta \mathbf{x}_1 \\ \mathbf{x}_2 \boxplus \Delta \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \boxplus \Delta \mathbf{x}_2 \end{bmatrix} \label{eq:update} +\end{equation} +% +The $\chi^2$ error at iteration $k+1$ is +\begin{equation} + \chi_{k+1}^2 = \sum_{e_j \in \mathcal{E}} \underbrace{\left[ \mathbf{e}_j(\mathbf{x}^{k+1}) \right]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^{k+1})}_{\bullet \times 1}. \label{eq:chisq_at_kplusone} +\end{equation} +% +We will linearize the residuals as: +% +\begin{align} + \mathbf{e}_j(\mathbf{x}^{k+1}) &= \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \notag \\ + &\approx \mathbf{e}_j(\mathbf{x}^{k}) + \frac{\partial}{\partial \Delta \mathbf{x}^k} \left[ \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \right] \Delta \mathbf{x}^k \notag \\ + &= \mathbf{e}_j(\mathbf{x}^{k}) + \left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right) \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \Delta \mathbf{x}^k. \label{eq:linearization} +\end{align} +% +Plugging \eqref{eq:linearization} into \eqref{eq:chisq_at_kplusone}, we get: +% +\small +\begin{align} + \chi_{k+1}^2 &\approx \ \ \ \ \ \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x}^k)]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^k)}_{\bullet \times 1} \notag \\ + &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\ + &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{(\Delta \mathbf{x}^k)^\transp}_{1 \times cN} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\ + &= \chi_k^2 + 2 \mathbf{b}^\transp \Delta \mathbf{x}^k + (\Delta \mathbf{x}^k)^\transp H \Delta \mathbf{x}^k, \notag +\end{align} +\normalsize +% +where +% +\begin{align*} + \mathbf{b}^\transp &= \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \\ + H &= \sum_{e_j \in \mathcal{E}} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN}. +\end{align*} +% +Using this notation, we obtain the optimal update as +% +\begin{equation} + \Delta \mathbf{x}^k = -H^{-1} \mathbf{b}. \label{eq:deltax} +\end{equation} +% +We apply this update to the poses via \eqref{eq:update} and repeat until convergence. + + + +\bibliographystyle{acm} +\bibliography{graphSLAM}{} + +\end{document} diff --git a/SLAM/GraphBasedSLAM/data/README.rst b/SLAM/GraphBasedSLAM/data/README.rst new file mode 100644 index 0000000000..c875cce73f --- /dev/null +++ b/SLAM/GraphBasedSLAM/data/README.rst @@ -0,0 +1,6 @@ +Acknowledgments and References +============================== + +Thanks to Luca Larlone for allowing inclusion of the `Intel dataset `_ in this repo. + +1. Carlone, L. and Censi, A., 2014. `From angular manifolds to the integer lattice: Guaranteed orientation estimation with application to pose graph optimization `_. 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6.644624 0.000000 14.655385 0.000000 163.558506 +EDGE_SE2 1075 1210 -1.662329 0.436026 -1.599840 32.701727 -4.997844 0.000000 12.268023 0.000000 369.868006 +EDGE_SE2 1084 1210 2.070374 1.896269 1.836205 12.355692 0.641825 0.000000 11.442098 0.000000 310.788312 +EDGE_SE2 1088 1210 0.609503 -1.485166 0.536770 38.204841 -4.021013 0.000000 11.707874 0.000000 1058.576530 +EDGE_SE2 1093 1210 -0.233452 -0.529583 1.494360 42.853783 90.090527 0.000000 266.801722 0.000000 401.810926 +EDGE_SE2 1098 1210 -0.378280 -0.172442 -2.937025 38.903093 -122.470929 0.000000 550.803768 0.000000 161.288572 +EDGE_SE2 1138 1188 0.583736 -1.006358 1.671805 21.764394 -23.563240 0.000000 63.228969 0.000000 350.211491 +EDGE_SE2 1147 1188 -2.232188 0.113982 2.992915 11.195782 -0.864255 0.000000 19.932721 0.000000 156.804966 +EDGE_SE2 1150 1188 -0.789302 2.834301 1.697150 11.120354 -0.063189 0.000000 11.543094 0.000000 343.660651 +EDGE_SE2 1158 1175 0.586855 -0.249767 2.829060 13.000858 20.975969 0.000000 243.941877 0.000000 170.512281 diff --git a/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb new file mode 100644 index 0000000000..cd3d9fd5db --- /dev/null +++ b/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from graphslam.graph import Graph\n", + "from graphslam.load import load_g2o_se2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction\n", + "\n", + "For a complete derivation of the Graph SLAM algorithm, please see [graphSLAM_formulation.pdf](./graphSLAM_formulation.pdf). \n", + "\n", + "This notebook illustrates the iterative optimization of a real-world $SE(2)$ dataset. The code can be found in the `graphslam` folder. For simplicity, numerical differentiation is used in lieu of analytic Jacobians. This code originated from the [python-graphslam](https://github.com/JeffLIrion/python-graphslam) repo, which is a full-featured Graph SLAM solver. The dataset in this example is used with permission from Luca Carlone and was downloaded from his [website](https://lucacarlone.mit.edu/datasets/). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of edges: 1483\n", + "Number of vertices: 1228\n" + ] + } + ], + "source": [ + "g = load_g2o_se2(\"data/input_INTEL.g2o\")\n", + "\n", + "print(\"Number of edges: {}\".format(len(g._edges)))\n", + "print(\"Number of vertices: {}\".format(len(g._vertices)))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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UhqCA2z4y3LCB6O23Wbbq358ng1lHUvIaH2zCk+qcNM3/TjiEX4ux9HIl5xTD\nRxMb59PcFrlUiuQic4iIPvnEbr0BHBNe3XjvPT5XSgrZnKrXX8+OQa+XQyxjsW0bdwwtWjAJVYVw\nmOjOO9nantHKT2tfNahHD6K+LrZ6Sdcp7OU0y1ZyD6f46NcXDfpppL0QyRNt/OSvl1zmzESEyJa+\nFo2OKYaPHjrXoAceIHr1WpWsLuT10Q/5Bj31FKefngA/zcRwWq21p+VD8+iZZygqq3TuzBO2+vcn\nmgmVGZUA2jJkOLVrp/r/SQOUbl8mYhz6fj8/N5E2hk/OiXt+SgoMeqmz7DhiOzPNlscn4OYiOZWN\nhhzULjiEX1thSPmB5Rw/8tQfTUsuMoeIs1HqOi9Sez4QETpDhqi4f5ke4fDDOXrmxhu585GhjxKh\nEE9ocruT447du4nOPpuPPWoU0aZNRL16RTJrvsfVsD7J4WRomsbr3/w/rn5V0dyBaDEXnyLmD25l\nEoztBBJZ+Fbij5V3Kk/BbD8G5eXR44+rjtLrZallE+yZUXenpNPi+wxakK2Svp2abtAMsGEQEvGF\na8JCi7TR7gNa+aylCpru4ZndFkfEjnlcFyHWmS79J2GvxQCJrc/goFbAIfxaCnOq/Y81DzkVD9Ur\nQDDIck6sYy5Rfpn9id9+Y+lGhi7KlMpLlnDUSZ06iXlA5sl58smqz7F2LRcS0TROu/zPP5w7Rtc5\nRv+339gZK6+5fXtOt5AQlRRz+fkFgw4/3J4501pxy6rhW6190rToKCy0mEsy/v6KckTLY0xNs+RK\nshA5tW9PRBwjD6jQVpmKWS4fIEdFL+k+Oq0xt2s+clR+H+vzYhhk5uRQSH6naUQ5ObTgTsOW+pl0\nnY2KSgrahFN89G0ve+jnE639tD29jb0mr0P6tQYO4ddS/HUnyzmhiJzjR170c7IVjxYutBO9XKo7\nk+LNN6u4dzmqGDyYv5NROj/+aN9n7lxef8klFWfAlFi0iKhxY5VLp7iYaMAAPudrr3ECtjp1VEc3\nevSeRyVt385ttt43WRZRRuBccAHRpncinUVenn3jvLwoWQYCnMumcWNF3CMON+jzz0kRqIUgY0ly\nVAeLFOX1se8iYj1vuE51GPbwVS1SfUvjeRzjcm2jmqBbJWsLQ1AxfHRPu3wuJFOVXGjtIC21BQJu\nH32WmhM32qGUlD27+Q6qDQ7h10YYXIdV5shRco5GAbjITCIyh4jTFbvdTITSyvZ4qrfpZWVMbO3b\nUzTKBGDdftculnnOOMO+z6pVHAffowcnL6sM+fl8zI4deRZsaSnRySdz5/LEE1xvVp63Th2VSydZ\nhEJEkybZR0WxWTOPOopDXRPhWIeZAAAgAElEQVQ2TjqSiXPoTJtGVK+e2rdnzwQZLCWB5uRQOOJU\nDaf4yMzPp08H2R2xcY56a3bTFB8t76e2DULjFMtwUyimWlroiXxbSucQNArd7d87Pd6yj5mebh/p\nADz7zEGtgEP4tRF+e4SJVc4JQKcN11Yt5wQCbATWq8d8kJFBUT29OjFrFp8nNdViGffm7+6/nz9b\ni5Ls2sVOycaNif74o/Lrueoq3v+UU9gCLytTM2D/7//4GqVjunt3ol9/3bO2z5ljzzcUu9SvT/T4\n41VX+dqxg2jiRJXhE+Asm1WWhvSrDKRhCApBYx+OFtHTK7K8K7G4P/AMs1nbO9Pb0JaMTrTa25mC\nEMoKT9ZxUgXKsu2SU3S046BWwCH8WojyxznlcQgalWlKzpHROjNzq/5jzptnJytpZQ4cWL1tP+EE\nldBMLs89x5Z4RgZXrpIwTZ55q2k8gagibNnCkg3ADudQiNMfnH46r8vOJpuvYPx4eyK1qrBqFac1\nSETyUpoaN84SWlkBNm7kOQ9SxhKC8/+sWpVkQyLOYmte/KjOnkhPr+Q40W1zc+Mcy/aC64KNiWHD\nkib8cJiT0338MdGMGZz3Z/BgonMyI9XAZNt9Pofsaxkcwq9tsFS4Cmoueq2tknPK4aLLtXzq06fq\nw4wZowgwVlquLvzwA59DOmulUbp1KxMDwGGiEvfey+seeKDiY/74I0/W8nqJXnyR1wWDKjpHxqq7\nXOygfv/95Nu7axfRuecmJnop42RlVV1EZO1anjUsZSAhmD/XrEm+LVHk26WWqJWcpIwXB8Mg8nrt\nFrdlWZfZkydqifjRw7ZtXK9g5kyiW27he3X00faRixzNdetGNK9tLktHSD6wwMGBhUP4tQ2WYb2p\n6/RlQ7ucM1H4SdMqz2leXs6ZGVu0YMvemnu9OiN0rriCbPljGjViSTsQ4ARiWVnKIbtwIVvP551X\nsZN27lxue/PmRF98wetCIc7/IolVkk92duJJXIkQDnOtXGu64FgubNaMia6yoiErVjCxy/01jeii\niyqXpqqE30+mUNWzoq+5uXt/TMMg8/JcW5EWisg4Oy7MtcmHb3b3U+/eLLFZ74eucz2CU0/lEdQT\nT/CobP36yO9nGFSuWTqWmNoNDmoHHMKvbTAMCmiqwtVTjfJs0ToyQmTWrIoPIQt4u92sOx95pPrj\n7thRPc3etYvJWVrcUkt/6ikmToBj44mIfv+dO4MuXTicMhamSXT33bzPcccpIg+HlVMW4PNpGtFd\ndyWf3nfhQtXG2MXl4uWGGyqPZPryS3tBE13n6KJkO5xKEdHgQzHlDs39oLFvuCufJ1yBHbo3pHGR\ndOsI8oZ6+dS/P0tYDzxA9O67LEnFZhCNxbJzLSGdQuxbB+Wg2uAQfm2DodIhh12uyIxNjQLCRRMa\n5RPA1vtFF1V8iJEj7QnLunZlYvR6q6/ZTzyhzqdpPMlK1zmB2pFHshRgmhy50r07d0S//BJ/nOJi\nZcEPH87bEzGh9+unCFbXOSlZskU81q6NT10gF5nN8+STOVNlIpgma9bWY7jdnARO5uDfbzAMWpOT\nGy1MHz3hXpDo778TTZ/O13aLFj/xCyAaJ9SM7nBKciG/1rYuO9dPlyGfSoUv6RngDmoGDuHXNvj9\n0UkxoUiIHgEUFhrN6sJ/0E6dOAInkVVbWsoyTseOSrKQFm11ReiYJhO61WeQkcEO4rfe4s+vvcbb\njRplt/at+N//uJqVEKzvS6ln82aKpg+Q5zj99ORK9RUXsz9Dyi5W+UYSfZs2HKGTSFoKh/m7zp3V\nfl4vzxauyom7Lwh5PPGaexKEHwox106cyCMouWvHjkSPXWRQyKOSrM1AbnRegLUjeO4IP89UrmLU\nZC7hnENB6FSq+ah0er6TYqGWwyH8WoYX++YnjKYwASq6JD9KUABXsYrFO+/wd02acPIya1bF6sqh\nY62m1aCBknNmzGBr/ogjmDz++19ef+ediY/RtCl3Vtbyg/PnqxBPj4et6unTq56cZZqcedJa/Nw6\nAnG7ufO48041irAiECB64QWitm3VfqmpXAR927Z9u19JIVbDByok0n/+4WLto0er+sC6zn6NBx9k\nrX36dE47cYKu0i7IXD/PZeXbZJ3LwM9Zy5ac7XPt2pgTGgYF7/LTp0dWkqjNQa2EQ/i1DP40f3RK\nPMdi8589BI1Kb2cLPy2N/9ATJ8bvf9FFKgSzSRN2lEq+qK4IHRkxI0mibVsm2Fdf5XXPPsuE7nJx\nrdZYR+izzzIBt2+vJJWSEqJrrrEbuEccwdWsqsLixSp/T+wiHdhnncVyRyxKSrijsIaW1q1LNGVK\n9c9QtiEtze5gTU21ff3HHzwnYNAgNVJp0IALrDz/PI+orrzSXs5QdppPt4uXdm6sb0/UlwUj2tkJ\nwXMfZs8mCi7ipHSylm5A9zgyzr8IDuHXMvx4rfrjBXVPxGHL9WzJMKLWc//+rM1bUVLC5HTcceoP\nbk0PUB1VrjZtUha9nNyVkcHt69uXO4C1a3ld+/Y8YUoiGOQYbqmfS8t52TKWraxENXJkYgevFX/9\npWLyY616WU/2yCPZcRuL7dvZUWwdETVowLlsqjpvtSEtjWQPHw5zpNItt7B8Zu0Ex4/nuQ733ssy\nWmylrZQUjiZ6//2I89UwKJyiLPq7WuXHyTqTNDYuOndmJUl2HJNT7BXY9mh+gIMah0P4tQmWGPxy\nuGiayIs6cIM6h7nJwt033cSv1hDAOXMo6qSVRS2ys5WWn4zmvacYPlwRy6mnqveSyB98kKhPHzZQ\nrflztm7lSVgAE1YwyLLPtGlMWFKKcblIVauqAKWlRNdeqzoeK9HLnDppadyW2IpTf//NuX+sseWN\nGxM98gjr/zWJ3bs5R/0ll9ijn/r2JZo8mdt46aXcqcq2y7kAqan827z/fgWT0PLzKSiURf9Ax3xb\n7eT+HiNatD0lhWh+tzz6zd2eZmJ4NIlcqeajBXcaezTJzUHNwiH82gS/ys0emyEzrLFGevzx/GvI\nzJJPPKF2P/98lR3zmGM4TUBmJg/jqyOHzoYNTEAuFxNm9+5q0tWJJ7KkNG4cf379dbXfihUcxePx\nsGVKxPJK3768rezU0tOJfvqp4vObJssX0hC2Er3Lpaz1UaPic+v//nv8vIGMDPY7VJXPpzqxbh3/\npkOGqI66Xj2e9DR5MtGECZyqQhK79GvIzm3ECA6lrJKE/X4yNfWsPeOOpFOGiNbkTU/nZ+jFFvZq\nWZsyj6Glx+XSmRkcItyoEXfaFUU4Oag9cAi/NsEwKKirP501pULQwxrphAn8a+TksFY+dCjvWlzM\nVp20mlu0YN1Vktn+jtAxTbbcJblefLEizWOO4ffnnMOvN96o9nvvPSboZs04XbJp8gzatDQmLJnL\npmPHyi3sr79WCdpiFyktHXtsvNKwfDlbvnI0IO/N009XHWteHQiH+Vpuv50jlGSb2rXj5Hd5eTw5\nTVYtE4I7UjkiqVOH/TZz5+5hRyXj/QVH7HwpekZ9R0HoNBF+0nX2ZazW2selZyjVfPTVowZ98bBB\ns7r46QSdyb9PHx6R1fToyEFiOIRfm2AYVCZ4WF0GFYNfDhd9eyVPrZc1TTMyOAbc52Pt/o03KKqF\nS71aWtfS4t6feO01ZWECnGFSnuuYY9iX4PVyDpxgkIl92jQmrO7dOQRz61aV2qBDB2Wp9ulTcYKy\nTZtUwrTYpUkTPn6jRjzhyxpW+MUXKveOHAm0a8eTwqpKhra/UVzMVvhll/EsYinV9O7Nv9nYsarT\nlL6EI45QI5k6ddg5+/bbiSOMkoZh0LYLpGUv0ykLCmkuergTR+o0bEj0aKrdwpejgmjxE6FTWNdp\nZ/2W9GTDPAJ4dHXllVy60UHtgUP4tQnWLJkQ0bwkAehUcBKHvIXDFNVqP/yQos7Yc85ha6xxYxWZ\nc8UVijT2Z4TO338rSzwtjUl94EA1HV8IliFatWKCLilhKxTgHPLFxUQffcSjEJfLbtkOGhSvsxOx\n9T1hgr1colzq1uXzaRpn1Ny6lfcxTXbQ9u+vSFWOHl55JfnZufsDf/7J6XCGDlVzCdLS2Kk+ahSP\nxmQEkcvFhH/88eo+p6ayZDd79j6SfCwsz1wYggkfGpk+H93cj6325s2JHvbm0Xotk0zdRSHB4ZzP\np1jCMi3L2gvzaMQIJUn16MHXvmvXfmy3g73CASF8AOcCWAHABNAj5ruJAFYD+BnAoGSOd7ASvrnE\n4ERWEFQGdzRCpxReykkzokU85B/p11+ZCC67jC19GR7Zvz/r36edpkI091cOHdMkOvNMe774mTP5\nc7NmTPqaxpb/11+zJt2jB3cCfj+TvXToHnYYO5elxT1kSLz2bJqs/ydKW6zrKnqkb19V0SocZmLs\n3l1tB7Az+623Ks+Ps79gmkTffMNx/sceq9rcpg2PNIYNY6s9dv3gwSqWPjWV5Zy33qpGicQwKOz2\nxFnwMq6+Vy+K6vR163JGzJ0T/PT1dINOa8yzwq3VvqzVurZuJXr0UTUBrE4ddjJ/8UXV8ygcVA8O\nFOF3AtARQKGV8AF0BvA9AC+AwwCsAaBXdbyDlfBXvyT/QCzplEUJ30O9YEStdBkfPXcuk7q0rEeO\nZLJt04ZJuUULRZSbNu2fNsp893Xr8nlbtWLHqyQuSd7PPsuZFjMyeNt33uHhvZyx2q8fdwoNGvA+\nOTnxGvQPP3CxkUTyjYyzb9GCrXXT5JHB88+zPCQtZanlv/NO9ZNMSQlHxVx+ueqIhGBrfcgQHnlJ\nCSwlha36a65hn4L0O/h8PFp74409r9K118jNjc7olvV4y+ChwGcGbd7MxoOcqJaSwqGtGzcSbXnP\noDJ44zqL2OGkafKzcMklahJd16483+GATGJzEMUBlXQSEP5EABMtnxcAyKrqOAcr4RcOqljSmRBx\nov3wg3KGjh3L9V+lfn3sscqavOsuRSD7K0Lnr7+4A7Fapvfcw9aqtarTiBHsuPN4WCf//nuOEXe7\neRQgI426d+cO6sQT7TLFli0Vpy1u1IgtRbebJZ5//mHr99FHFclKou/Vi0c21Un0Gzaww/f00xWZ\n1anD19ivn9LoAe7sxo8neugh1retNX/POov9IjUS8x8NB+aEbSGASuGlh85lj/d773E7rff3yCOJ\nHmxif163IJ1WnFa5drhzJz+z8jlNSWGH/6JFjtV/IFDThP84gBGWz88COKeCfccBWApgaevWrav5\nttQM3h7Mk65MTaMy4bFJOr1gkMvFVuJjj1FUi16xgt/LyVayM5g+3W4N7ytMk0cTKSl8rrQ0lpb+\n+IM7FekobtJESTYnnshWvUx61q8fk5zbzfltdJ2lGGnJBgI8o1VawdbF61XkOWQIJ16Tk6VkBIt0\n+vbvz4nOqoNATJOv6a677BPcMjKY5Dt3Vr6CevVYZsvPZwfr9dcr0vR6eRT26qu1Q9sueTS+5OEE\n+Omtt/j7Sy7h0coNvVVd3yxwUXYzUtB87FFGtMOvMitrXh6VtmpP87vlRY2FI4/kDJ37azTqIB77\njfABfAxgeYLlDMs2e0341uWgtPANg0oET7oil4ve65Jnmwgj0yIDrIVL6+jll/m9nHxzxhk8BJfF\nwiXx7itkimNZq1uStpzsJReZTfLqq1lekfn4zzyTibB9e7ZwZeclye7dd+MrZclFTiI7/HC2Njds\n4HZIJ6ck+pNOqqDW7D6itJQd41dcoaQkIbg9Rx+tOjuA/RW33soW6+LFXHpR7uPx8O8za9YBTtOQ\nDCxJ+0xwsr6pbfOpXj32Fe3cSXRGU1VMXabqHtXBoLI7eKZtMMg+C11nWXHx4vjTmEsMKunZzyYD\nlY/Po+ef5ygl+Xuedx479g+Ev+VQQk1b+I6kE0F4qiWfuK7T6vY5cflOdJ1JTureAEe1yHC95s3Z\nEXrmmWyNS114XyN01q/nMLsTTmApQjpBv/mGrTnZlpQU/rM+/DD/YSUBSglnxAh2QHo8bB3v2MG5\n1nv0SEz0TZvytqmpRFOn8iSsK65QIwBJ9EOGsEa8P/H33+yHGDZMpZpOSWE5y5qnp2lTliRmzWJd\n+8svOZ++7KQ8Hv4tXnqp+moR7BdEZB2Zhz8MUDjFRzlpBo08wqBfxvhtUTlhTafb3Zx+4Zhj7E5l\nw2Apr7cwaEG2n3bMM2jOHKK8vjElEOVrenp0wsTy5TxClL6ndu3YwPnrrxq6LwcZaprwj4px2v52\nqDptN01lOScsNCKfj4wx+XEzHyXJ6LoiO7dbyTgyHPPRRxX5A/uWQ8c0OXLE5+MomLQ0Xnr3ZgnG\nmhK5YUO23jMz2YIfOZLlltRU1vQ/+oiljP/8h2e6jhqVuNpUSoqSac4/n/eTk6Vkpks5mqmq/OCe\nXOf337NEdPzxql0NG7KT3JrqoV8/JqFvvuHQzq+/5lQXMoup283hly++WLtJPhxmeeqRR7gzntzS\nLusEodGrDXKjVj3PCxHRAipfPGxEUz4ceSRR2adcS9dcYtCKZ+SIVY0G7vD4E4ZxmhBxyddKS7kT\nlbmR9EjZ3Q8+OLDhtAcbDlSUzpkA1gMoB7ARwALLd7dEonN+BjA4meMddIRvyaET1l1E+fn0+HAj\nTtJJSWHylKkH5CL1elnZ6qOP+FXqxftiHT37rDrHU0+pc776qqpKJTXpSy7h9x068MQgaf2tWkX0\n6af8n+7ShXX6RPV2rW3u0oWzQZ52miJRt5uJ+NxzmZz3FWVlRAsWsPwkyVpa7bLDAfi7yy9nHX7n\nTu4cli7lHDyyU3W5eKTxwgv2BHE1jXCYaPVqbtcVV3Bn1bp1fF1agCIJ1JSsE4Kg2RhmmYFrJ+qZ\nGM4SI/LoD2RSAK4owc9ALsWW5vy/LH7Ow5pOIU2nspR6KjpI1yk0JXF65V9+4VGqlPxatWLJcp9K\nSR6icCZe1Qb4/RSy5BV/7Rh/XPbCCfDbrGH5h/V4WCeWse/p6RyuCbD843bvvfPyjz9Yg+/fn62q\nbt34mBkZbBla0xPIiJMRI9REqmuuYUvt889ZFmndWslMsUvjxmzF1a/PxCQnS3m9TKayXuyKFft2\nqzdtYvI7+2y7D6BxYxXdI0MmH3mEOyvT5GXZMo4MksVYXC7e7rnn1GSvmoBp8nyHt99mYhw0SM3M\nTTSCkgXfO3bkaJmePfm3PTOD54FYSd1aB9cWpw/Qbvjode/wCmfhyiRr1tKcsuBKLxjUK6a84ljk\nR8tytmzJIbl9+nAE1KhRLCeefbYKuwVYDpw6lUdbf/zBna1tBGAY8dk8DYOzfObm8vv8fDLljYpJ\nQ32wwSH82gDDoHKh5JsTdINuapBPAUt+cvmH8XpZZpC6va6zZd+1K3/u39/usG3Zcu+aZJqcpqFO\nHaI1a1gjl8e85pp4CzEjg2WNunW5fXPn8nG++IKPkciilOQqr2XgQJVSQGa51HUu7JGoHGKy17F8\nOYeP9u6tCDA11V4GslMnDplcsECFiMqInEmTVN4eXWdCfeaZ6sk+Wtl1/PUXRx9NmcJhnF268L22\ndrxyEYJ/i1at+Pk46igmyubNK/4t+mgGrUFbW6WtMBC1wmMt/HBkFBCbZ0c+r1ZyT3Q+gOgy2PPw\nV7Rt7LF6gQu5yKpdsev6uQ0amGrvUK6vk0+n1OeRs+rQXPHzCA5i0ncIvxbgyVFKvikTHloymisQ\nhWIsH8AuNVgXmS45J4fTFEsrdG8jdPLzef8ZM/jzyJFsCet6vBzTsiWnTADYsfu///E+n32WOMRS\nkq6cUdqunZJU6tVTPorLLuPOZk9RXs6y1rXXKslFdiLy3GlpTJpPPWWXBqSWf8star6BrnPn9/TT\n1VvW0DTZWbxoEY8uhg9nC7xJE/vMZuvi8fDopHVrvo+tWvEoL1EKCqt136UL0ePH5FOZ5qUwQJvc\nGQkdqjImX1rqHyCHdke2S9gJCEF/3ZlPy5bxyO6jjzgC6/XXOWpr0iSV6vm444jezVKj2yAErfO1\npxcy8uir1H70p96SHk3No6u9+VQOV7RTGIt8G2mXwptwHctKSqIqh5tmIDc6v8XaScXdqIMUDuHX\nAkxvbpnAInRa2To+QsdKnLIgufX5nDaNXzt2VDHhsiPYU/z+O1uHAweyBrxlCxOLrieuDdumDX++\n7TZORBYOM2FWRPQyjFGOBgBlqXq9nA9nT/XZLVvYSXruufbRjzX98bHHcrsWL7bn6zFNztV/2218\n/+Q9HjiQO779GRdumny8JUu4Axk3jkcecn5CIu7RNL5XTZuyhd64sZrkVRGpN23K0tp557GvpaAg\nxreQH19KM2ghQfm647BudE6msq5btyYaeYShcuLDQ+uRYekAdPp0kJ9+/rnie2CtZja8nSzGIios\n7Rm0jCKC0GgecmykHYKgj3T7ujAEGcfkUkhzqesRGm08K5fCHtUxyJ7RsfAdwj9guLe9GtaWwkPv\n6MOoXHiJdD1umKtpHP8d+yfv29dOAnImY6Ji4ZUhHOZRQd26qpbpfffFn89KTs2bE33yCW+7aJHS\n862LrnOn4fUy8ctRQpMmfE0+H09M+vPP5Nppmpx//d57WeeVsobVsm3ShH0KL7/MIZOxWL6c5S9Z\nXUvO+n3iicTbJwvT5JGAYfD8hfHj+bitW8d31NbF4+ERjpTsKrLS5XU2a8ZW8ogRXNxl0SI+b1I+\nm5wcm2UrJZpYnZ6GDaNgkEd60ufh9RLNutqgTzvlRjO6mhEylpZ1LxjUqRN3ot9+m7hNH37I19DX\nZdCGNHsK5th2RUccmou+vyqfQpb8P+T1cs9stYq83qg+T263esgMI6GGT46G7xD+gcBfs5XOGIAe\nIX6dyuCh8ktyaWCqYSMEK1nKV4+Hn2fp6ARUpyDllWQhC40/9RR/3rYtXvMdPdp+nk2b2CI/+eR4\nYpJtlGQhpQnpvK1Thx2NyRBsIMCW6vXX26NqJNnLmbvSiZdo0s7KlTw5SOb0EYJD/2bMYDllT7B1\nK/soXnqJRw6nnspaf0URSPJ8Ph9fd2XkL69HjtbGjOGopUWLeOLZPs8itlj40RNmZNAfaZ3txJub\nG91l504ekUhufLipGpkGodGKtJ5UCk80UifLYqhkZnJwwZIl9t9l0yaOxBqL+BFHtA1CqHjcfE4T\nHkfaFa2T650yjETkEH6N49Wj7elprflzptX3R632tm3teVlkFSv5HuA4fDlNvV49tgL3hBjWrGED\nJyeH9/vlF3v5PIBTClhHEkVFKkooEdHHdhYy7LJePSbJqjTxrVvZQj/nHOVktcpJmZkcMjlnTsUx\n76tWcbtl1kYhODzx8cfjK2HFYvt2oq++4pjwO+7gKJFOnewO34oscNkRV7adpvHv2qcPk+mMGdyp\nrVu3H2eZxhDerl1En/oNKk5rYifYnj2pzKKDhzVNEawFa9bwDGMVZcPSzvc+VUQlrOn0fh9/VCKz\nLg0asNGwcCF34qbJ+XWucOVTgTuH1vYbTqaQowadzGHD4kncwV7BIfwaxLczOKqgXLBTzPTE58+R\nf5IxY5Q2DdjJ37pIhxiwZxE64TCTYL16PCpYuDA+rO+ss/izXNe0qbLcrYvXy0Qmt9M01XE0bMil\n+iqLVV+1imWkHj3iCdPt5hQKjzzCM28r6tB+/pm1a1nwWwi2/qdPj5eNdu7kuPpXX+WO4aKLeD/r\n/a7IWu8t4iNRYiNKhCAa2sigJ9v46f6zDHr8cY4G+vMtg8J32y3P8OcGrRrlp9JPDNq+nUcdv/xC\n9NV0g9Zf5afVLxm0bBlbyrNvNGjFxX5a/rRBX33Fo5pPphq08mJOX/zuu0Qf3GZQme6jEHQq03x0\nbkuDegsm6jhLWtdtTs5wgglRVrz4IlG2l59hq7QTgkZhXVnj69bFz1q2GgaDBvEM7O++U1Lk1KEG\nLe0ZKc4i9Erb4SB5OIRfQwg9ocIuA7qHnkAu/ZpnLyR9WmNFInI2rTVaw6rxejxs7VoJck8idOTk\nrWef5dQIsfHbcuQg86NXRICS4OWr1PMbN2YjM1EOmWCQJ2ZddVXijqxtW5Zx5s+vvPjHr7+ynGOt\nFtWnD888XrWKCfH117kjGDWKO5QGDeIJOhFpJ1pntXCL4aMBKQYNbWSxeoWPLutq0LiuBpVE1pUI\nH52ZYd+uGD7q5zaojxafqybReewx7JWvs87nCEKnl4/y0wd9Ve3kihylUaknkhe/IhQXE83sZD2H\nZaSagKRDIU4/cdttHC5qfc50necDDBzIn+9Pt8zMraIdDpJDsoTvgoP9h6Ii0JVXwYUQBAAzHMJm\nX2us+nwr2iIMHQQTYRy1pRDviywQAW+9xbu63UA4zO9DIXXIQAC45hpgwgS17thjk2vO6tXAzTcD\ngwYB8+cDb77J61u1AtatU8d/8EHg1lvj99c0wDTVZyKgaVNg0yZu6wMPALm5QJ06apsdO4APPwRe\nfBFYtAgoLVXfpaQA2dnAWWdxm1q3rrjta9Zwe994A/j2W17XoQNw6qlAt9IitFxdiLm3ZeO667IA\nAL1QhGwU4mdkYymy0AtFKMBAeBBAAB4MRAEAJLUuG4XwIAAXwiAEcEKoEJ4yRNeBAvjPzkK4XIA7\nsk4ggAsyeJ13awA6whAigIlZhRAa4C3kdYQAzk4vxGk3ZKG/UQjvhwHoFIamBfDMRYV8n2YFoEXW\nPTeiEESA92W13Zu5hQj2yYa4xAMKBeDyeDD86Wy+SQM9QCAACpsACBoAgoAJQAeBwNWKNF3nH6MC\npKYCI5/NRniAB6HycuhQxzPLyqEVFgJZWdHtdR3o2ZOXu+7i52DhQvUcfPedOvbcHdm4Eh4A5RAQ\n0Bs1qvhBcLB/kUyvcKCWf72F7/fbCk6YAM2ol0fPenKpXPNSWGMLTRaGTrRIbdxqIQ0YYN/mnXeq\nbkooxFZwvXoqZt3tVhk5AZY2Lrss3uqXNWSt62SYZWYmjxqsFvmvv7Kc06lT/H7t2nGx89iQyVgU\nF3M+lREjlD8AIDpBrwpBTNUAACAASURBVNz6TsYCDkAnfz2/LUxWhsXGbjcRfhrcQOWLKXf56OWr\nDFpwp0EhL6cMNq1RIT4fm7BJrJMlBLNgUP36RPNuN/hYSe5vW0dU4WzT38f5I/HrXGUtLDQKQcXW\nhyFUtEtVMAwKnKhCI+VzvdudlnT6Z9Nkme6qq9TIcKxlYlbY68g6+wo4kk4NwDDIdLktscWIyDs6\nBXUPUW4ujepgJ3uZfTJ2kZq91Jutks5HH1XdlIceoqgkBPBszIUL7ZEmiWZmxq6T2mzr1hzWWFbG\nUs0nn/CkrNgJYz4fa7cvvhgfoVNSwnHxc+YQvXSlQbN7+GnsUYaSlaqQVhIR+ZQ6fnokw+Ig13T6\nZYyffn2R48DNBMRp6jqFvT66d5hBZ2ao2HN5DiGIBqYaNDnFT/09iaWfJk04Hv7GPgbNPd5PL1xu\n0MyZ7Jj94zWDyu+MJ+Lw3X46u4Vh+31v7mfQP5MSpAhIlDYgiYiU3bvZr9IL9jKF1lm0yUg6Nhjc\nMcVOxtqEdHryyfhi8du2sS/imWfY8X/KKfboK4BoorDPUXFknX2DQ/g1hfx84sE3h7RZk0iR308r\nV9of/OnTlY4uSV0Iew55XedtJHmPHFl5E1atsvsErrySQwwTzY6taJF+hHbt+I+7aRMXaDn++PiJ\nRO3acergb74hKv3EoL+v99Nn0wy6/36OULnyPwbdXXfPrfSJMTr17B5+mn2j3dI2l+y5BRy7bud8\ng74730839zNsEUPWe5iWxgQ/dCg7fy++mJOqHX10fNI766ioa1fOSjp2LIeN3n67+v6MM1S+n9mz\n98/jJyc+We+d1YEbBs+aJY9nz6xqw4ibrRsGCOAQ06FD2bcU66tJSWH9/sILOXXEW29xCG1wkexE\nOBJo1wgnWmdfkCzhC962dqBHjx60dOnSmm7GvqGoCGW9s+FGAJpltfB6gU8/BbKycNZZwNtv83q3\nmzX7yn6GlBSgrEx99niALVuAtLT4bXfvBjIygOJi3u+114B33wWeey7xsVNTgZKS+PUdOwKjRwO/\n/cb6f+Y61sgLkY3vfVno1g24oE0Rum4tRJE3G+9uzkL6z0V4a4ddD9cE8BHFa+RTcBtcCCMIHXdg\nCiCAu0itux1TUIhsFGAg3AggGNn3i4g+L9vylZaF1FSgn5vXfdcgG6ub8LrUVPYvJPu+Th2+t6tW\nAZ9/Dnz8MfDrr3w/GjTg77ZuVb6Wtm2B44/n5eij2b+xdSuwfn3iZePG+Pvs9fJreTnQrh1w5pnA\nEUcALVuqJT0dEKLi50Pi00+BE0/kZ+p4swgLzYHwUimsu4ahQ4cJ4fVEn8fKQMRt/+knoM+ZjZBa\nsi363Wakoxm2Rj/Xq8dugT59gM6dgU6d+B7pegUHLyrCxvtfRP23n4cLIbh8HqCgoMo2OYiHEOIb\nIupR5XYO4e9n3HMPQpNuizj8wM5bIaBdfjnwxBMA+I+fkcGb163LJN2qFbB9O79PBK+XSaFZM94/\nNzd6uCiKioABA3i7Fi2A118HLr0U+OWX+OM1acLk1NNU5PkFspCeDpxSvwjt1xdiYTA7SrBWx+ZJ\nKAAhsbPTSuTPtJ6C9IbAOd/fBh1hmJqOzddMQdpp2fCdNhAiEGAWLWDnKQ0cyF5kjwebXy3A9iOz\ngKIieL8oxMYjs7GhbRZKSrgzKylBhe8r+97qRE4Wus6EK53pQgA+H+BycXNlZ6xpfF9bt2by7tiR\nCa9uXe5M3G5ux8aNwE03cXu8XqBvX2D5cuDPPxOfPyVFkX9mpr0zkIvPB3Trxs/Pli2835vtbsbZ\nv90XPc4vaI/D8Ts7nnUdmDIFmDgRAHdia9cysa9cyctPP/Hyzz+qLZvQCI2wDbs96Xj+vq3o1Alo\n3x547z121m7fzobC3XfzM1glLP8XU9Oh3a3a5CB5OIRfUygqQrDvAGjhcmgAwtAQgBfXdi7AA0uy\n0KABb6ZpbD1JC/uIIziqxuNhwo5Fnz7AkiVswX3yCZPO0qVA9+4cSXPrrcA99/C2bdsCN9wAjB9v\nj/gBOJplAArxKbIBJB+1YiXyqZ4pqFMHGL+d14WFjhXnT4E+MBudrhkIEQxAWIgcFiKPWnBFRUBh\nIZuE0qJLtG4/wzSZoPe0oygpAXbu5Oih//0P2LABCAb5mCkp/HuEw7xub/5SXi8fp7iYf7P69blz\ntx63rEy1xxo9BfB2RNypuFzcsX3ZcBCO274QAgABWIHO6IBf4NZMhF1evDSqAB/tzsLKlcDPP9tH\nkc2bKyu9c2f1vkmTikcb27cDfj8wfTq34cYbuWOrW7eSCy8qAg0ciHBpAGHoMEddAt/lIx0rfw+R\nLOHXuG5vXQ4KDd8wKKBzzH05dCpCTxqL/KhGP2UKRy1Yo1m6dVPvBw9OrAfLyJUOHTjGPCWF481/\n/533l5r3gBSDTjiBknKCxhazmAC/zZkWhE7vZvnp3YlJRqhErn9vHY7/JoTDKu7c+vsddhj7TKZP\n50lmF17I0UtWf0CTJpyrPiND+W2OOor3GzlSJXpLSeF9u3ZlP0lGhso6mowfxo88m5O1LBIVUw53\n9Jls04afuRtuYF+NYex7oZc1a1QwQvPmfNxKq1kZBq0+WU7ysjxjDpIGHKdtDcHvp7Bl8ksIIi5R\nWkaGnfAzMhShy+pSlU3dlzNnxyKf5osc8iNvr0IVrcUsynQfBT5ziHxvsW4dpxEYOtSeZ+iss7iQ\nym+/cWjqAw9w5k9ZG9e69O7NRVx++olz6xx+OD8n48fbw2BNk1NFb9/ODtBmzXgS24ABfM7jjyca\n0tCwpVMIQVAwmv9eowngurV16nDOoUmTOCHf/kwTbRiqPGfXrjwLuUJYigWFhcZ5QJznKmk4hF9T\nMAwK6l5bREMAXBg6UZZEGfEioxusKQ2skTpy8Xi4g4hNShW05OpJFF8uLf1iqHhwazGL755wiHx/\nobiY6P33OU2MNWdRz56c4mHZMibtDRt4ZjCgIrXkUr8+E7hMSXD44TyiiMUll7Bx8OGH/Cxdfz3n\n+3/pKH/C/PBy2ezPp1df5aieHj3ss7s7dOB2Pfkk0Q8/7FutWdMkeuMNNRfklFM4NDcO0dBPLZJ/\nX3PSLuwBHMKvKRgGlQkPheSfS2hUAh/1FgYdfnh8KUCrpS+Lm8hF5r6Xi66riUhF6GkLkZNZOWMt\nfOu6Ro04bPL+dCb/nj1V5sb9ltDLgQ2myblk7r6b01fI3zszk0NW58zhDJ+ZmSzf1K/PNRByczkM\nNFa+6dyZU0d//jmHcgJEEydy6gmAOxMhiJ6+xKAyS9WnsNCiRgFpWlzce3ExF7aZNo3DRWURG4BD\nUk8+mUNK583bO8mnrIxTPTdowKe/7LIECe4Mg8r656i8P07ahaThEH5Nwe+35fk2NY2+vzo/Su6n\nnMJD90RSjczfbv2jWT9bZZpyuG0Wmx95NAF+GlTPiFprUteX6WwffphjxwG2HJs14z/f1VfX9E07\ndLBxI8s255yjfl85P6J3byb8Ll2I/vmHty8uZiloypT4yUsAy0djx/IEuF69VMnKp8YYVGZ5RmS8\ne1hLLmGZaXKR9Bdf5FrE3brZZcbOnYkuvZRzNK1cmbzBsHUrj0LcbpaT7rqLrzEKw6Cghw0VOVnR\nsfKrhkP4NYU85SgjgIso+/00erT6w9x8s/rjJJrtmpKSuEh1rEwzG8NoHnJoLPJJCHtyMYC1WTnR\nx+djy01+N3Omel9YWNM37dCELNl43XXxnfuRR/Js1Vg5Ze5c++zm446zP0Py/eOZ/rhKUUXoSdvO\n33sC3bWLZxJPmcKGg0y3AfD7wYOZwD/+mKpMu/Drr5ySGuB0C88/b+k0DIPezWQnrplkB3WowyH8\nmoKl4pCUdMjglLgtWtj/JJpWsXPWmplSWuucrtZjk2kSdRr16vEf0zSVTCTruKamckGPm27iTqVJ\nk33TaB3sH2zZws9GmzZ2aa9JE9bT33xTZSS1dta9enF66KZNubhNly4RyQT5UT1cWfkalek+mplr\n0Ny5nGp65869L7oSDrOD+bnnWKI56ih7ZtWjj2YDfeZMJvhE51m8mH0bAI8iCgp4/drLlXGzR2kg\nDlE4hF9TiK0pKkQ0f/gHH1DUIQfwsPb++/l9ZVWS7AUpVKm5RNt26MCWIxFbWtbvTj2VX5csYVLR\nNFvhIwc1DEnkTz6pwhr791dGgtvN5F63Lst/L72knL2nnMIE3Lcv0WVdDVvh77AlQkc68K3PhRAs\nr7RowaPEwYP5ubjvPpZ05s9n38D69erZqgjbt/P2d9zBgTZWZ3TjxlwF6557eFQppRzT5JoFUrI6\n9VSi31/hfP9BaGS6XAkLtjhQcAi/JjFsmL0AhcsVHZJaywgCXNXJ7VYWuJXkZWRNooibijqHI45Q\nQ+Nhw9T622/nP3R2NkdJyPUff1yD98mBDabJv0+DBlx3uEcPlnp++IEt4ZtvthNox452Ga9/f97+\nla6xcg5b9yFoVO7y0cXtDVteJbe78jq7sUudOhwG2rs3yzJXXMEE/9//8kjks8/Y8t+6lROr/fgj\n8/Xo0WSrlKXr7Eu6+mquPLZqFTuk69fn7x7twhk1w3AidqqCQ/g1idi6ohEdn4grMFn/PCkpHH63\nfbuy8mNj6MciPy7ixnqM1FS2+tq25c85OUwY8vvzzuPhPsCa8V13UVR3jc106KBm8dNPTMAjRnBs\nf7Nm3Ilv28bhjQBnoJw+XRUUkc+AjOi5q1UiOUfYqlUFg+zgnTyZ02jLfVNTWSa64AKuxjZ0KMfQ\np6cn9iu5XFUXZW/enDumk0/m67riCib/U0/lY1szuGZk8PrevYkmWSYBOrH5lSNZwncKoFQHtm6F\nCREtOAEhICJFHubPV5tpGk9n37YN+PKRIowv55w2sQU4GmNrNMWBzHkDAJMnA3fcwdPtr78emDYN\nOOwwLjzRtSufo25dYOZM4KijgOOO4ywHN97I5z77bJ4C76D24MgjudjNlCnAmDHA7NmcH+nss4Hv\nvwd69ADuvZd/twYNOFPFnXdyuofXXwe6FhfhpnXXRAqWIFr8xAVC2DSx5N2tWJPCCdkaNgTOPRcY\nN46fB8PgZ2fBAuCLL7g9bdoAOTm89O3LuXr++IPz7sS+rl+vEstJpKTwus2buXDOsmV8jETpQwDO\nA7RgAaeXMJGNWyKFUnQyEV74MbRFiyE+cRKs7TWS6RUO1HLQWPiGwQVPoGY5yiFp167x8fV7atHL\n5cIL1XshuCi4Va4BeJbmrFn8/u23ORWD/K7SmY8OagwlJTzRqkMHjl/Pz6eoBLJihdouK4tn7E6a\npGr8TkBiOScIrdJnSVr3mZns+D3uOD5m69YqbFQIovbtOT10fj63ZcsW5fQPBnlkWVjI/ojJk3li\n2Ikn8vXEptWWo8wjjmD5qm9fHu32789ST+vWRANSDJoHFZsfhE7hqY4DNxZwLPyaBZkUfa+DQIEA\nNr9RiB9/zMKUKYD4UmWpTNaiBzhxlaax1fTqqyp7JgCMGAEMHmxvx5NPcsKro44CTj+dE1sBnJxr\nwIDqvgsO9gY+HzBjBpeBvPdezkYJ8G/+1VecZO+FFzjXHMBJ8zIz+X097IAmR5ZAJDUywYQLN3se\nwcqULGCXOld6Ou/buDGn2/Z6Oc/djh08+jRNlRWUiM+9ejXwyiv2NtetCzRqxMeTo4f0dH72OnTg\n9/Xq8bYyEd22bZyE7n//4xHCjz/GW/6707PwWus7kb16McjkNNmPPdcI5/1xDxqfnY20HMfS3xM4\nhF8dKCyEjnCknigQhoCAhi/XNIIQwOlNi/B/loyU1+ERBOABRfK+fxYheSvRSxBx+t0//uA/ozXH\nev36wLx5KuVyWhqnYf71V+CMM3ib2bO5wzjrLM6s6KB2IicHuOAC/D971x0eRfW1z8xsyYaENEJv\nkoCAIEWICTUKREDAKIpoEBQpsfdQRKTIWj8VC7IIomJXbIgoGAkiA2IBFBQsqCAg8iP0lG3n++Pd\nu3dmdlNQxJbzPPMkOzs75c697z33lPfQrFkA4VNOAUhecQW+t9vxHkeNQg3hX34BE+ot9CARUZgh\nkwnmnKAapHYN9tPhn/G7Ll1A3+zzEX35JdGqVZKBMykJVMv9+hF16oT/W7QATXJxMfreRx/B7LNp\nE/YJWm9mAPrOnbjfAwcwWVQkmgbTVHIyagrExUkzYyCASeeLI1k0vE4htS8uol/9KTT7hxvJ8YOX\nvPMc1De2kPalZ1GzZmCJFX/F/ykp1aslUKWMGIHBNWAA0XPPnYAT/jVSA/h/ghy2p1AMqRRUmBSb\nRkFfkJRAgHKWXEcvJm2gT68laluBRr9azabNtbKIjlR8/h9/xF+HA3+F9nXoEDQ1wYd+5AhAPz6e\n6K23oDGuWYNjL7zwT22CGvkDwgwgbdwY79brxbts0gT/O52gSE5KIlq4UBaRz6YiUikYBnsiCfyq\nTaPxL2bT2XWwOnjmGawWUlKI8vKI5s/HRLBhA7aNG7HKEJTJMTHwC3XqhO2880DJ7XKBMnr5cmwf\nfgiNXVVRGKZfP6Levc0TxoED8q/xf+u+gwflJPQVZdFblEUT6W7Tari7r4ge3J5F33+PtrHSgTud\nWAU3aoRJID0dE116OiYEQUEdjZr78Ptr6cCbRZSwsYgS1i3HCZ9/HqumfyroV8fuc7K2f4UNX9fZ\nZ3Oyn1BflXNzTaXm/KRwKTmjJlCJAs/V2Yxsi0beE+MmiLtmzEDInEjiio2FbbhG/j5SVgaemquu\nYm7SRNrMxbu85x6EbRYWyvcYH48wzdhYcPWMIZTXNEbnhE9kSbjw+3G9YcOkjb5jR+bZs2GXZ4ZN\nfvNmxPvffDPs69bEwTZtYNO//36E+O7ZA6bPKVOQUCWeISEBzKFz54I5tDoSCDAfPIjjP/8cEWYf\n3KWzzy79XXfU9/Bzp7n52jN07tABfT421uwfM1KEW/dlks5PhBIafaRxqeLim+I87FHlPmtpR46P\nP8Fv/48L1YRl/kWSn2/OtO3Vi8vJFiZTY5LUxKLTGQmyRE1V8beizeHAIDTuM6bnG7Nvzz4bYCGc\nxTYb89tv/9UNVSO//QZKgQsukO87Nhb5E/ffj1DbHj3gQE1NxT4j4CoKEui+/BLF0I+Ri/2kWOrO\nKlXGsO/fz/zYY8ydO8u+deGFSBS0hu0Gg3DMvvEGcjsGDzYzghJhwhoyBLH5ixYxz5kD3h2jkpKe\nDv7/N9+UGcTVFl3n4Cw3z+vqCT0z6BdKZnv4t5vc/NmjOj/1FPO9uTqXKHJyGKd4wtnqgluolJym\nNhP1Aoz7jGM3SIQY07+Z1AD+XyUWwPeTGu48YislZ4XREkVF0fl1om2JiebPdepUfOy8eTJeOi2N\nw5p/DUvmyZNgEJEtd9+NOHMjc2Z+PgC2tBTH5eQA/L//HgVExHs0FlshAiHe1q0iZl2ViobYNO24\nslQ3bQK5mehLDRsyT5wIGobKZN8+aOCi6Evr1uYVSnIyVghXXIEiL717y0lO0zCxzZjBvG5dJNXH\n0aPM336LhK4XXwTr5i23IMEsHKdPIhpJRrqZo3tU9pKdAyYgV8IRTWJy9Ks2xPwbZ1WHQy6rNO1v\nmQtQA/h/leg6sxa5rDZ2sjmUXyWYiyQqsXQ2ftehg0yUEYPGataxfjaac4qLkQBDBO2yKqKrGvn9\n4vXCDHPjjWaOnM6dmadNg6nCyjEjkuTuugvmEuPqTdByXH898znnYBLv0IF5vGqh9BAX+p08NOXl\noF8eNEj2tW7dMPlUt78cPYrkrjlzwLXTpYuZQsTphPLRsqVZWbHb0X/r148klRNbTAzzhY10LjdQ\nQBu1dC/Z2R9KPoPWbjNNiAFS2GdzcMDhxKTodGLW9XhkASCxT1R4+xvXiKgB/L9KPB6TFiE6VzC0\nNKxMu69os3b6Xr3YZLYRdlzr70aOxNLZuK9nT9xmMMj84IOYCE47DZpkjZwYKS5G7sPw4RKgnU4w\nTM6dC06aiuTHHzGJp6Xhr9MJe/h778lJ+5xz5HXExD7FFlnwpDrmnOrI7t2gPGjdWioNI0cyr1wZ\nfYVYVobnWLMGVAuzZ2OVMHIksoPT0qL3V6NyYlRyUlKY+/bFCmD9emSli0nS16SZ2YRKMJkKcPeT\nypsb5fDqyzzsd7jM4F4RkP/NwT2a1AD+XyC/vqGjJqehA/pI5a+oLZeTFnLYOioEfGOKeWWbomBA\njBxpXgkQwYkm6HObNWPesEFSKYhtwAAMSGYsw5OTYR56772/svX+2fLddzA1ZGdLjbhuXSQevfEG\ntN2qJBBAspMwhQwZgvqwzz6LviFAcuJEHL9zJyYFTavAYXuc5pyqpKwMz3LeebKvJiaiUEuvXkjY\nSk6O3mftdtjwMzOxqrz2WhRtWbgQ/W7ZMtQJmDIFqwprAIORhfO008D2un2SJ0K7DxDx1ra57BPg\nXlWZzn+J1AD+XyDPxedbNHviMrJZnEKS/Kxhw0h+kmh8JdG2tDQ49TZvNtv8GzTAoHC5sNx3OFBZ\nKdoAnD4dNuMffgCniarCBvt76XL/S+L3g9CsoEBqvkRox8mTYco4Hv/I998DMInAn7NsGRymN92E\nfb17w9Zfpw5A/qef4DB1uZgfGCoytRWzOSdKZato4vVi8li/Hk7UOXMAvKNHg4WzQ4eKI8GM/TU1\nFZr41KkojLJsGXwC+/b9Pl/R3r3wa8yYgUnGOgkso5wI7Z6JMMn9i8E9mlQX8Gvi8E+gdOlCRCvl\n52+oLZ1K28KcOgFSiBSVDttSiHyIYWZGsklpKRJNmKt3rR9+wN/MTGQybtqEz3v2IHFl5kxw5nTt\nSjRvHr7r3BlcJkRIuLnzTqKnniJ69FGEIV9xBVFBAeKw588nio09Ea3y75EjR8DzsmQJ0dKlRPv3\nIwGqd2+iq68mGjwYsd7HI8eOIVP2/vsRR96qFRKhjhxB3sSHHxJdfz2uMXQoMqdvvhkcOJ9+SvTA\nA0Sdl4tMbfSzcBy+qtL+dtn08+dEu3ebtz178HfXLnDcWEVRkNMhsmczMpAgVbs2tlq1sDmdONe6\ndUSffUb0wQdIymrVClvt2uhr5eV4PvHX+H9l31U2HhbTUDqHlptzDlSVlP37EUtfw7cTIX8I8BVF\nuZ+IBhORl4h+IKIrmPlg6LtJRHQlEQWI6Hpmfv8P3uvfXmLGjSTvygVkJx/5yE4P0w30KF1PCgUp\nSBqpFCCNffSw7xryElHT3fvpXcqmO1/NoquvJmrw01rqxWY6hbp1iVr8tjaCZqGnbS119xdR0dFs\nWrcJ+zIpdJw/m7ZuzaL0dGQ9ulyYULZuBehv2YLJxe9HRuSQIUQDBxLNno3vJ08m+uYbojffRHLK\nf1l+/hkAv2QJ0cqVAK/kZLTXkCEAZUEZcDzCTPTqq5iUd+4EBUFZGcjQtm4lys0FkC5cSHT55UT9\n+xM1bEh05ZXYP306UVoa0Q03EM19NoW6k0J+ItKIwgC4yH8xjRoSCXoi87QyMGUG4dm+fcf3XIL2\nY/NmbHY72iclBQqE04mEwZgYZIY7HHKf9W9l3+HvONpSSNT4/QVU69sNRBykIGtk+2kHaWvX1gB+\nFFG4uipltB8rSg4RfcjMfkVR7iUiYuYJiqK0JaIXiSiDiBoS0QdE1IqZAxWfjahLly782Wef/e77\n+avll1fXUp1hZ5E9RJEwLfkRmlZ8HdnJR0REKnFY+wqQQkQqeclBAx2FVLcu0dO/SLqFPlRI6yiL\nMmktFZJ5PxFVa98PqVmUtg+TwMdaNpV1yqLPPiPqrq6lC1OLaMnhbPo4kEVeL1Lcs2gtTe1VRPZ+\n2ZR7bxZl0Vp6/KIiajE6+z8zeIJBaKpLlhC9/Ta0bSJkZw4eDJDPyvpjLKNffQWtvagItAU9ehA9\n9hjA3eXCSis5mej116FZ//ADMkOnTcOqbNQoomefRZbo9ufXEvXtQw4qD/HmBMOg7yM7ZdMq+rJW\nFiUl4fgmTZB1GhtbHUA9HvDFpml4xkOHwN65cCG0f5uN6Nxz8WwDB/4JtB5r19LXE5+lFh8tJBv5\nSXM5SCn877BqKoryOTN3qfLA6th9qrMR0flE9Hzo/0lENMnw3ftElFXVOf7pNvyNFxviglWNtzbP\nMWXZWu37TLKgSUVFTqLtr+4+KwtnJuncQ4vcRxTJ2Hlvuif8uUxz8Qd36fz++4iT/uJxnXdd6+aj\nK3RTHdJqRTscT1REde2w1T2nx4MAd4uN99gx5rfeQjHw+vWl+btXL+YHHqg6Br26cuAA83XXwZea\nnAxb+ZYtcICeey78AUQIf9yzR/7u1lvxm127mJcvxzGXXYa/73SX790XiisPOzAVld/t5ea+fc3O\nVE2D4/OyyxDHv2rV70h+Og75+ms4WUXb1q2LpMGvvjrBF3K7kYRFxH7lv1UWkf4CG/5oIno59H8j\nIlpn+O6X0L4IURRlHBGNIyJq2rTpCbydky8dbsimspc1UihIqk2jhYeG0jR1NSnBMlJDC22xnvKT\nRgoR+chBRZRNRGQiUBP7iig7gliNKzjWuG9LnWzqc6CIHAHJO3IWFREHyMRFkk1FtI6yIhg7T/9+\nsfwc8NIHU4roHuuK4zEHdadCinURLSnFPp/ioNFNC0lViZ78Ue4bewpWIU9u70N28pJfcdAN7QpJ\n04ge2ChXJpekFtL2elnUxbeW5nzbhxzsJb/qoGk9C+mnBlmUvm8ttd9fRFvrZ9PWpCxqsXctTSnq\nQ/YgjpvYtZD8AaL7Pu9DdsZ1xqUVUvOjX9H0X8ej8ZcvJx/ZSCUmn+KgHKWQ1gSzqKdtLRUkF9Fv\nvbPJ1jOL4uJw+HvvEa1YAe3VZovc7PbIz5qGv4Lk7O23iR56CJrviBEwm6WkgIvL6YTN/r77iMaP\nB6Op4EkqLYWfJTcXJpDx42EbnzcPv3t7QQr1I4X8xvoLoX6mOuw04J5sGpAFqN+5Ez4csX3wAdGi\nRbL/tmwJnpzOvBSGpwAAIABJREFUnbF16gQ7/h+VNm3wbG432vKpp/CMDz4IH9MVVxBdcgl8BH9I\nsrNJdTnIX+olP2v07fs7qE12jWnHJFXNCARzzOYo23mGY24nojdImogeI6IRhu8XENGFVV3rn67h\ns65zmeLkACkcsCPefuuZeabQscO2BF7VrSDM57Ho6ugcH8ZohKr4QCraN/ksPYJXX2jyVh4f6/5o\nnPx2O/OjDaVG6Vc0XtbbbdIy/YrGz7dz86LTjJqnxs+2dfOituZ9s+LcPEmp/som2oqluqsda0SH\ndYUV7dxVvZe/alMUaOndVXHPaqiEoTFrlDjocFS5Otqzh/ndd5HkdcEFkWG+TZuC6mH6dOYlS7DK\nOBFRXL/9htVF+/a4jtOJDN3lyyMzbY9LdJ29V+ZzGTnZRxr7nf+N0oh0ojR8Zu5b2feKolxORIOI\nqE/owkREu4ioieGwxqF9/24pKiKN/aQSU8Dvpz5aEaXv+4SIZOSEPyaeSmxQZf7PPolGlkMDDAap\nQkrkaPuN+xQFw9PWI4vuXZNF4i24V2bRhxZe/cREoj4HI7n211FWBAf/ZmpvPs5H9PzubBotVhLs\noOmrsqleXaI+qoMU9lLQ5qBTx2dTs2ZE2sUOIq+XbA4HXTY/m4qLifh8B/l90OY/DGZTvyFEynsO\nCvq85AtWvLJZrWZTbnwROQ5h1aEoXnpqRBGVZWYT3eSgoN9LmsNBk97Mhi35XAdx6Nq3v5VNyuYU\noptlREdYww+tkKwrHLHy6a6upeVBrED8ioNuOr2QdjTCSiOjpIh2t8qmXU2zKBgkarprLaXtLKJv\n6mXTl7XgL0n9YS31tRXR7pbZdKA1jgsEoOl//DHem92OegVxcegH4phgEM7zQAB29+3boQWfU3st\ndTlaRJmH3qMYKiWViPxExKRSgOArUonI5w3QnPOLSO+dRZ07E7VrB0dv8+ZwmhKBPnvAAHMdheJi\nyZopVgNvvSWdvPXqyVWAWAk0b358NMSpqajSdsMNOP/CheDYf/FF+BlGjYKzOi2t+uckIqKsLLIX\nFRGrflKCAfKXl5N/yjSy3TWtRtMn+mM2fCLqT0RfE1GqZf9pRLSJiJxEdAoRbScirarz/Rs0fK/m\nYD8pXEYOLuiphw2zQsMvJy2sRfaLg8bYtu2J0fwuuQQx0BWlo4uEoA4dkLxjJb0ybrVqQbuLlhcQ\nTePt7dB5smre1ydW58caufnaLjq3bQu7eCbpPKepm9+5XZfJSCF7uu8jnXNyIq9ztkvnAQOYr+9q\nXoX0dujcrRvzQ8N03jDMzTte1qX2WYENf2vzHB5DHn4mX+d3erg5i3Ru0yZyhTO2vQ5+mx7mVckk\nJXI10F3VeVCKziWhfSWKi3toOndTQN7lJ419dhcvn67zkiXM7sE6Twq1X6tWsKFv28a8Zb7O+252\n8+7FOm/fDoK7TNL5zTPdPLShzgkJzNP7i3Oa+ZmMWablZA+TgI0hT9T3m5SEhKkRI6C9L1qEzNhf\nf42uwR8+jLyD2bOZR41CgpiR9C8pCSR9t97K/MIL4Pc53tj70lLml19GJrHod717IyGrOolrxnHI\nLhcHFDXMZxX8lxdBp5OReEVE3xPRTiLaGNrmGr67nRCquY2IBlTnfP8KwFedYQrk96ehg/luLeDv\nlHT+pl6vsFPJSxo/oeTzrDgM/E6dOLxc/7NNAqqKlP9VqzCAKzs2MREDWVDoVrQ5HGaeFCIkBVl5\ngMTE06oVMkkLCpifegpjsbgYzbhkSXQCOaeT+f4L4DB+f5rON92EAtzGYxMTkfwzaRLz66+baQzE\ns159NUBt1y7c9/jx4LsZXAcTTBbp4UzSgUk6l9tk1qbvI50PFLg5oEjn/PKz3PxaF7MZaYrm5qn2\n6pmlxORm3V+VCStoaJywuUrTeHWd3DB3TFVlDaNtMTFw6g4ZAg6gRx5hfucdOF9LSmR7lpQgWWvu\nXCT3Wbly4uLwfq67Dhm1mzYhyas6snMnMnEFNUhcHBg3P/64miYlXWfOyWG/oFj4lztxTwrgn+jt\nHw/4bjc6VmiAl92JDrZyJVq66G5oHqBmdUqaVhUaod0OG2Y0kDyRm7G26NCh0KLi4yunZLbbmU89\nVQJ2tO+jUUMYj9U0rBo6doR2mZYWOZHUrYvomNGjJatntK1PHxnl4fMBTObPB3h37iyZQYmQfdyl\nC/7PyoL9WMj48bgHMTE8+qikMHA4YGPuoQFoR7bU+aGHmPe/o0uCLZeLdy/Ww/4SH2ngbFkDnpZA\njIv9CrT+TNJDrJa/PxpLTAKCGCxCw7e7eNvZ+aZVibhGtM3lQgZrcrJZ2VBVvM9o77pRI3AyjRpl\nXh3s2QPStU2bAPDXXQfAN/YrpxN0z+PGYaJYvx6afUUSDIJf/4or5HlatQJ2V8ZJxMygUQ6PNwfv\nODf/X6vl1wD+XyGhAQ62PluYx+TWWwGIhw/jmFlxbp5D+abBPIfyQff6kW4C/GiFn6vajJzplW1G\nfhJVBQ9MvXpV/04Ae2XcP7VqgdRNfK5dGxQErVubaZ01DSatnBxMPuedh7BEwQcU7X6NW4sWoMy1\nan2lpaA3eOQRNpmJxJaWBnKzyZPx7NddJ39bXo5CJEZOnPx8OWnYbMy39dB548VunneFzi4XgPOp\nsTp7Z+AdLl3KfNFFzD1tmCxGpJkni4AKrf3CRqiHEM2Zbt2XFdLU+yfo/EQzNz/bqIDLyGEwF8KE\nM4Y8YbPOMXJxN0XnWrUq7kvGdo2Lw4R51lmYHK10BjExaI8GDSLpuYkwWVpXB2+9BYqEp5/GWDj7\n7Mg+cPrpzJdfjuNXr2Y+ciRyeB05gtVgz56y3w4YwPzKK5UU9NF1Lrk8n0tDTtxgzL/TtFNdwP9D\niVcnWv7piVdERNtunUen/N+1pFGANJeTqLCQThuTRQ0aIAxu/36EuonwRjt5KUA2ImI4I50O6lFe\nSNuSsqh2bZSL0zQMjUClaWsVi3AKV0dat0aq/eHDkeXiqpK4OCQM7dyJ+xVSvz4yhvfulTV4k5IQ\nXhgfj3qo27ahtB0RnJjt28PJWK8envvZZ2XpxmiiaUhiGjGCqEMHPEf9+sgq7tGDqEEDlCTdvh2U\nBGLbsUOeo00b+PW6dsVWvz6ch4WIKKXTTwdlxerVRAsWyPtNT0fIZYsWKB24aBGyYUX5wCuuwP2s\nW4fasfveXktJXxbRB344w1u2xLViNqylM44W0Rfx2bTiKJzv4expSzF7IRPpbppJd5CNAhRQNNra\nYyylffwM2bicFFWlogsfp6JW48JZsz/+iO3gweq9U5sN77ROHWTMKgr68C+/IItbSHIy3nFsLN79\n4cNoA+MxRMgWbtECW3Iy+uWhQ+gzmzdLmgdFQf+wOoeTkvC9KOT+9NOgh0hOlm3dqZPlIe6+m4K3\n30Eqo420WTOJJk2qXgP8Q+SkJ16diO0fr+Ez85tnymU4axoX34bl9IMP4vvzzpOaTS+7ztNj3Pxp\nF7O2v5J68Q5nOh++poBbtWLOIjj5uim/L1xzcB2dn2yBfcKEUlWooQj9s+7XtOj7xeZwIFnpySdh\nmrEem5gIDe3ss80VkHLidV6Q7uYZA3SeMUDn+WnusFNbnLdxY6mNNm3KfMopFd8HEcxUdju079tu\ng03/66+hwQv59VckQAmt38jL7nSiTN+wYeaVjyASa9YMhTuM5iNFwYrihRcQ7jhlCp5XtLuiwJx1\n440gGIuLg3kqGMSq5NFH5T0oCrRpq2/E+K5FBSexCjCuHH2k8csd3XzffSAy++UXuRIqLQWFc/fu\nlbeh3V75+xabIOqzrsKSkmCCOfNMPGe/fmDMbNQo8liXC8dmZOCY9u0jSduaN0f46F13oX137QLb\n5sUXy3bq0AH+mn37Qi855MT1KzDtfNf332faoRqTzskXvx9OvjJCpA47HPzaLQCtrVtBLWsFx88+\nY37kEizdA6rGXktBh+e1vD/k5LPu66HpPKpVxY7D6sb735Pg5kua6+FBaz2uXj3UHy27080fzNT5\nvPMwII3H2Wwwk9x4pixFZ/RtlKtOXtsxn5/J18OmAGsEUpMmAMVoICRMVdbvozmNe/eG2engQVBH\nv/IKzA+9ewOUo52/fn1pJqlf33wd0S6aBpv1rbfCGX3ggLnPPP44jnvuOblv6NBIkC0owCTSpw/M\nJsb3WkpOnkP54fctyvaVkpN7O8wTelISJqCrr2Z+4gk4Qb/6CoyUzZpJ8BZmReNklpICk5xx8nM6\n0ZY9eoBZMycHJp06dcy/jaZQuFwA9BYtAO4dO4Leu2HD6JNcfDzOa+0D9esjU/nWW1FoRcT22+1o\ny3feYfZ9pHOp0bTzL4vaqQH8v0AKCzEQy5VQnUynk2/O0jktDY5Cq2196FD8LjubeXQbhBHudTYy\nJQgdiUk2RfacCMqF6iY1VWffA0N1vq1H5ceVKC6+qqPOI9KiTzTG+7GWnfOTEg7B7N6decEYnb+8\n1M23dJNAVpUGmpyMcNWPPoKD8fbb0fannRbpNI6Lw8pk3DisypYuRS1ZoXVbnZtiYjH6Qxo0kKBU\nuzYcw7oePbrE74f2W7cuopR275bPk5IC0BLXzspifv99hDv+ck105293VefSkMJRSo6w3V8AYIMG\n0K6t/pemTVGg5ZJLoPWLdomPl+Av/NREmHQyMtB3W7aU56lVC1r8rFmYTIqLQeX88cdYvYwbh9+0\naFF13WbrBCFqAlQVMUaEY1NT5cSRmsr8bi8ZVBGIUtj9nyzVBfwaeuQTKK+8QpRjLyLN7yeNmII+\nP8V+WkTnXp1F48ZJm69IlGrcGDbMzz8najMii2hSFr0y6yBdU35fmOY2bugAsGh5vRRkBxUFs4ko\nMjGpMnoG475v6mbT3t/M+7xZ2TSrVhE5C72ksUw8IoqkYbDu+9/iItKqOo69lLCxiOrHyH2K4qXb\ns4rokVpZ9PnabPIexf0IfwaRj1Ri0ojJTl7K8hZR0Rqi4WuQBDWDHLSrWSEt+V8WtT+G5Ka6F2XT\np7YseuEFs7+juJhoxgyiu+6Cbf7OO8E2qWnwU/z0Exgqb7kFNn2fj+i11/A7qxQXg9KgvFzuCwbh\nJxH/79lD1LQpbMlHjiCpyOOBTXrkSKLLLsP3RLgHj4fojDOIJk6E3T8QgO08EIDNv3NnnMPtBjtn\nZibR1fYUupgUCioq2ZwO6jAumzp/TNR7YxHZKID+RwE6x1lEmx1ZdOQInuu333CPzLh+Sgr8B3Y7\nkrx27JBtp6pon2AQfdb43JpG9PXX8L8Q4R5btcJ5N28muv127He54Bfp3ZsoO5tozBiZ9EUE+/2W\nLSCU27wZNN9ffWX2McTF4T7j4tAuwSB8A//7H34fTcrKsAnZt49oxr5sOivEWqswU2DBQtJGjvxv\nJWRVZ1Y4Wds/WcP3+aCJ3dFXZ78TkTp+1cZjyMOTJkVqU0RYgm7bhv/nz4ddVVGY3VTA/hbpWMcz\nM+s6l051c3f1xFAuiKiQKTY3D64Du34m6VyqhsxK9oppGKqzL6+Fzn1iKz+uVHXx0jt0PnQoVNx7\nvs7Lerv5wkZ6VNv0uPY6v9yxspWJyl7S+A01l8eQh1/qgHOJNuiumttAVZkvaa7z21luXnqHzlu2\nIHw2k3T+sJ+br+sSvf2M2r3Y30OTxw5I1HlRW6xABE1BZsgHYzSvXNFa508vcHNJIcwKt9yC427X\ncJ26dVHcxphAVlaGUMYPnTmhot3EAc3OwVA0WDDIfOwD0HsIk46xH8TEwA9iND2pqjl6R1FgUmnb\nFpEzLVuao62iraqEn0R8jo2FBt+mDfwsxt8rCj43bIgY+9NOQ7hvWhrGRYMG0MYTEnC/NtuJzU2Z\nQ/nhFaSP/j2x+VQTpXNyZcUKopwcojfeIGrzsYzUKScnDbAX0ke+rHC0TEYG0fr1CBRo357o0kuJ\nNm7EeTp2RCSCVbu8+24QbsXGQrtJT0ekwh8RcT9LlxJ9+y3R+tlrqdlPRbTOmU1fJ2TR0aNEXf2I\nEvkwmE2r/RbefUPkiNi3OSWbvquTRdu2VX6c2Od0Eg0aBPKsc8+FFvnNN2jH755dSw2+xbEbnFnU\nqVwStwVtDnr7+kJy6EV07roppFGQjD05QCp5yXlcdNJV7fORgy6tV0ifqFnU7shaevPoHz+nlxzU\n31ZI8fFErx4wH6cqRCtY7hsSW0ijyx+nSwLPExFWgH4imkpuuledRMEg2nclZYdrMpwVoog4mWKM\nClMURGIlJODdlpVBKz9yRH4vVhkNGoBCIiZGEs8JMjpBSHfkCDT7vXuJfv0Vq6lff5XXU1WsnJs3\nBy1DejpI4Zo2xfXjN6+l5mP6ULDcS0HSyDF+NCmj/mItf+1acGVnZ//u+6hulE6NSecEySuvoGP3\n70/kXb+fVAqSRkGyk5e6+YroYzWLevWC6UBwqdvt4F53OonatsU5iMC9bhRmoocfxv8izC0tLRLw\n4+PlQKqOiEEyYUKI9/2GLPrkkyzaN5/okxfA1Ph5XBZtsmVRWRlR7iBMbOuOZdEniuTsITJw++wn\nov0YaA1Pz6Kv/Vn05QdEVGI5LiTl5QD3xYuxZB86FOA/YQKR7fYs+umnLKr7BpH9DaLVq8H3089W\nRIWBbNIfzKKceKKBikbMQRJULkxENgoel2kqm4pIqeI4Ii9dkFJEKVlZ1H9jETk+l+apGdlFpChE\njg/Nv3fFEDnKLNdRiBws93X3FxEdiHJtNu/rWlJE/WgZEUluJpWIVmvZFAwgbPLypCKy/RwIMWcG\n6MHBRbRlSBbZ7XifGzagItV335nNXomJ6HcJCQDTLVtkWG5SEoC0uFiagho1AlDv2oUwTZsNxx09\niusIYUZVr8OH8blFC4S65uaiL69eDbPV55/DpGOzwezWuze27t3RrysTrxdhvZs3S9PQ5s14TiGx\nseArat8+i/qNL6QGK56lM79ZSDzvSVKefQaxt38F6M+bR8Hx+aSE1BWlWTPYGP8sqc4y4GRt/1ST\njtcLZ96IEaEdeqQ5Y/FiLGOHD4eTjoh55kxEgWRk4GcTJ2L/NdeYz//hhxx2oCUlYZk7aBD2OZ0V\nR5Ecz3bmmeZwxcOHwWliXIoTIZlo0qTju6bInq1OeJ9wENapg0iSjz+WnCy//so8bx6iQUQEiMvF\nfJUtsoC3P5R0dFFjnZ+7BvQI0ZzKXpJZsEZHc5nm4gGJ0U1YNhvzsCY6l2swgQViXBz4WA+H/wU1\n/D7LcM6ACubGZVN1fmKkzmVaxfdT2b5nyMy+upRyIsx8wsQlErGSk2Feyc5GiOm118KJfd11Msva\neA67HclvN9yA8FERGSS+T02FqcaYHKco5neXmYnoHfF9tIgdux3HjB6NqKgXX8QYyMqSx2saxsdt\ntyHa5uDB6o/Lw4dRv+HJJ/EsffpIc5YxUMBHGr+V6ea5c5ExfDzXOG7xeNjXJ4fXj/Pw1H56uA4x\nk4Eqo02b4z4t1UTpnDxZtgwt+fbbct+NtTy8jEDUNW8e87ff4pi5c5HJSIRIhvh4ABszOqQ4xijd\nuslBMm0aoigE905iIvqHcSC9++7vA/3YWESBCDl6FKF6LVogoEFER8TGwuZ8ww2wtRrPES2crqKY\n/uqCf9OmcGds2CAjXQ4cQCjj0KEA/UzS2aPl8wJHPo8hD08k+Cfi4nD9x0fo7Jvh5sPvg8Bs+HAQ\nvhnt+ikpkkYhk3Ru0ID5+uuZb8qUtvq6dWWEi9G273LhvTx6qc6bhrt55ys6b92KdyeOu6CBzh98\nEGpcA2Hct9+i7zx3jc4vtHdzbj2ZbR3NJ/MM5fFvlMzPUF7UdkOmrZlLR1Xxblyu48/ejovDc+Tl\nMY8ciec0ThKpqbDFt2oVGUGTnIy+Kgq0EyGsMy0tesRTfDzOf801KJJyxRWYAMQ9qyrGz803I4NX\n8C8dj+zdy/zZo/BZiaz4a51mkjkRtVRQgMiuDRsqp4CIRtYXmOvhw91y+INhHh4+nHlCssc0WS+m\n3DBNd0THP06pAfyTKJdfDuALp3frUlMstyHed948tPbWrXCGEUFrIUICDrOsCPTRR/Lce/diUNjt\nGHj79yOZR9OwT6TMN2gg+8sppzBfemn1AdW6DR4MJzQztCoxOZWUyEpLAsj79kXSizXMr7LQuYqu\nq2myDawThvi/TRvEjH/3nWyjY8eY33wTYCRS9h0OAItRs0xIQMLO//4nf/vOO5H0AdbNbme+8EJM\nEk4n7uess6Ap9+5tdlga7zUhAbkDF11krjjVowcqXVUmgQBCQocMkeev7mrOrL2qvIxyqiRQczrR\nXunpAO6GDaMT2Fk3h4MjaBtiYvAek5Ii33Viotlp3KYN+k+/fuakN2M7KgomiB49kEPQtq28nqIg\n0er665FYZ3y3VcnPU0BBEQixae5eDGXg7rsxuZ1+uvm5NA15CJPP0vmDPm5e6dZ52zbmfW/r7NMc\nHCCFvaqDR7XS+VqnGdzHkCeiJsP+lhkc0DQTCV64UY5TagD/JEl5OQb2qFFyX+lUA6NhKN73kksw\nCIJBaENEMAERgWzq6FH5vo2d9tprZce+7TbsKyzEvjp15IBq0cIMpqecImOXqxq0FQGA0PaHDsV5\nfvgBn5ctgyYWGyuX9XXqyOeqbKtOxEVcHDS8gQMjQcf4+y5dmP/v/8wkWl4vCOiuukpOHjYb7lP8\nVlGgsd5zDyJhgkGYzYzx5HXryjY0Xr9RI4C9APCuXZEQtWYNFLyzzpKTnaoCDCua4Hr1Yv7kk6rZ\nH0tKmF96Cbwx4lynn44ko5tvxqTSoAGbkuCEWScYAn2h6ScmIjGpQwc8i/XeNC36/VaUcRsXhwmi\nSxdE2yQk/P6ompQUmOuuvBLvR5wnNlZG8BjvzeHAvvr1zRP7aaeh/7zyChSmCsVtzoqPFrHj+0jn\nX29E8uCUKcw3ZJjzHEREmRHI51A+L1fM4H7gzBwOzPWYH1iU2jTaO38H2DNzDeCfLBEa8NKlct8H\nd6FThGd4p5MHpeg8fDi+F6yT2dkAUp+P+dNPsS8hQZ7H75candOJpBxmAIDDYaYmMJpSEhIwMATw\n5Ob+vgEoQPW773Af/ftLcNq6FQPdboctODc3OiBUpCVWZzJyOrGkf/ppaMkVJeooCuy8c+eaJ8tA\nAOPp1lvlhGgEMPH/KafgGd5/H+/ReGyHDsx33BFpNiOCBitWFE2bMj/8MOzGpaWYlCdPhm9EgJTd\nDs3Z+hwuF8x5kyYxL16MRKWKJoFdu5jvu09Ork4nNOR334XysWMHbOF3D9F5TVwO+wQ9MCk8h/Ij\nnqFWLTzjRRehpu/IkZi0oq20BElcRROYogCA+/aFLX7mTExKlZHyVcUMGxuL/iwAXVEwUWVk4Dpd\nu5pXBqKdjedt1gx+ghdflGOImZl14dtROWizRdQ6LinU2WdHPYMy1cUDk6KDu3Xfobx8hMpawZ3Z\nXFf5BEoN4J8kuewyDHyjw/Pyy5nn22W8b1BDzLiwzQva3/R0OLeYQSdLJB24zOA6EZ38qqvM1+3V\ny6zBCiVFUTB4iSSgulzRqYYrMrtYM4IVRTpwX35Z3kNxsWSivO46cJjfc49M0a/sWiFm4Urvw7jF\nx8OsMn06JpeK+GUUBSn6jz5qZlwMBpk3bmS+807zRJmYiAlYtFVcHLhabr7ZfFyXLqgfMGGC2YEZ\nbZK64AJzge6DB2Fvvv568yrIOEEKGgjxuU4dtPntt8NUsWOHeRIIBqEkXHutXG00aIBV4ObNoYN0\nnYMOp1Q87HZ+Jl8P89NURH2QkoJrT58OXHrqKfyflweAtcblV0ejT0rCyiIjAzZ9K92FoN/u2RMT\nsNXPYLNVTNds7GNJSWi7xMSKj01MhCnu7ruZ1472IJ+BsBr3aU72h/JE3qDc8BgOEPFXto7s0czg\nvqlbPu94WZe2PqdT2vH/JHCPJjWAfxKktBSdf/RouS8QQGee0AtafoAU9mlY+m3dimNEQo7TKSNy\nbrkF+4zAbuSf//FH87WnTpUdWIBVly44tnlz3JOiyEEtIoOsGrcxXd46QK3RG6qKwWLkg/H5mG+6\nCd/37QsfQzDI/OqrZmA0mlOM5zzlFHPlrSZNIjU2IygYAfiSS/BcFQ1sRcGkOnGiRbNjRG8YHYlE\nuA8jGCmKNFOIYzIy4MBbsCByEo2PNz9fUhJ8KVbb8p49mMxHj5aar5sKeBul8/1aQdgv0qGD+dlS\nU2HWmTIFPoudO9HWZWVYGQwZIt/3GWdg0isbmGuOAsnPZ78fDm/Rv5o1wyTapUvFq64mTXBPjzwC\nPPv5Z/ia5s+HY3PwYEyQlWn/cXFok4oc+9b+16sXHLbRVgc2m0zSEr91OND3Tz8dbde4cdXKxOfU\n0dQ+4n8fqexXzJFfQSLe1CiH/TYnTLVGcI9WYe0kSg3gnwR580204HvvyX3r12Pfa7eAxAqOHJh0\nhIZmBLiFC7EvOxufH38cn7/7Tg6EkSMjry2KqhgHyzPPyH0ffYTrWNkfjeBvBbtog7BTp+iDMRxt\nEpKnnsLgSk8HIyUzwiiNoXkVbZoGH0iTJvLzwIHSEW0FdKfT7GhNTQUwGDXyaCCSkiLNH8eOASyf\nfx6ALkIExbXq18fqy2jKMd5HRgbzN9+gHUR0ldjq1mWeUxsA/gzlhaNs2rXDBL9kss7rL3Dz94t0\n/vhj5o97FphAxU0FTATwGjcOmui998K81b69+T7q1UNbTZ2KVcSmTSgO3rEjvp+r5EcAvhC/H/6H\n1q1x7Gmnwezx1VcA8ry8yDYVm6qibcaORdjjxo2Y/H0+5u+/BwPpwIGRbJfGLTYWE/6ZZyIyp127\nistzqiqONyosIhPX+NnoP+nWDe32ySeIkluzBgSGjz0GxSozk/mgUjsC8P2hyB1hDjO1X3L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RpstvfdV/V9PP00ricqbH1fJyPiOuzxcKkiC4aLDrx8uh4uDpJJeniy8JHG8+35PCRVD2fMJiaa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\nnQRuCt4U4/PCC8pjvv/+9I97+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g.plot(title=r\"Original ($\\chi^2 = {:.0f}$)\".format(g.calc_chi2()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each edge in this dataset is a constraint that compares the measured $SE(2)$ transformation between two poses to the expected $SE(2)$ transformation between them, as computed using the current pose estimates. These edges can be classified into two categories:\n", + "\n", + "1. Odometry edges constrain two consecutive vertices, and the measurement for the $SE(2)$ transformation comes directly from odometry data.\n", + "2. Scan-matching edges constrain two non-consecutive vertices. These scan matches can be computed using, for example, 2-D LiDAR data or landmarks; the details of how these contstraints are determined is beyond the scope of this example. This is often referred to as *loop closure* in the Graph SLAM literature.\n", + "\n", + "We can easily parse out the two different types of edges present in this dataset and plot them." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_edges(g):\n", + " \"\"\"Split the graph `g` into two graphs, one with only odometry edges and the other with only scan-matching edges.\n", + "\n", + " Parameters\n", + " ----------\n", + " g : graphslam.graph.Graph\n", + " The input graph\n", + "\n", + " Returns\n", + " -------\n", + " g_odom : graphslam.graph.Graph\n", + " A graph consisting of the vertices and odometry edges from `g`\n", + " g_scan : graphslam.graph.Graph\n", + " A graph consisting of the vertices and scan-matching edges from `g`\n", + "\n", + " \"\"\"\n", + " edges_odom = []\n", + " edges_scan = []\n", + " \n", + " for e in g._edges:\n", + " if abs(e.vertex_ids[1] - e.vertex_ids[0]) == 1:\n", + " edges_odom.append(e)\n", + " else:\n", + " edges_scan.append(e)\n", + "\n", + " g_odom = Graph(edges_odom, g._vertices)\n", + " g_scan = Graph(edges_scan, g._vertices)\n", + "\n", + " return g_odom, g_scan" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of odometry edges: 1227\n", + "Number of scan-matching edges: 256\n", + "\n", + "χ^2 error from odometry edges: 0.232\n", + "χ^2 error from scan-matching edges: 7191686.151\n" + ] + } + ], + "source": [ + "g_odom, g_scan = parse_edges(g)\n", + "\n", + "print(\"Number of odometry edges: {:4d}\".format(len(g_odom._edges)))\n", + "print(\"Number of scan-matching edges: {:4d}\".format(len(g_scan._edges)))\n", + "\n", + "print(\"\\nχ^2 error from odometry edges: {:11.3f}\".format(g_odom.calc_chi2()))\n", + "print(\"χ^2 error from scan-matching edges: {:11.3f}\".format(g_scan.calc_chi2()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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yZfp370hhD5BKYf1hEiidfD04ZxyJwZUESmcPaMmkcsGJF7CoYjtevettcsoh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Jzn1WrGAcfP/+FC+rDMEgj9mtG7NgS0tFjjqKnHXffaw3q89br56lpVNdVFSI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\nFTs2RGNhsDdQwXa9ICp46ZiyZb7fH++ePWdmZ1MQUK/5nme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ILDMMDI1YvWlrqUU4hRsj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\nxzSUft/UxKy4xGIYd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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g_scan.plot(title=\"Scan-matching edges\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimization\n", + "\n", + "Initially, the pose estimates are consistent with the collected odometry measurements, and the odometry edges contribute almost zero towards the $\\chi^2$ error. However, there are large discrepancies between the scan-matching constraints and the initial pose estimates. This is not surprising, since small errors in odometry readings that are propagated over time can lead to large errors in the robot's trajectory. What makes Graph SLAM effective is that it allows incorporation of multiple different data sources into a single optimization problem." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Iteration chi^2 rel. change\n", + "--------- ----- -----------\n", + " 0 7191686.3825\n", + " 1 320031728.8624 43.500234\n", + " 2 125083004.3299 -0.609154\n", + " 3 338155.9074 -0.997297\n", + " 4 735.1344 -0.997826\n", + " 5 215.8405 -0.706393\n", + " 6 215.8405 -0.000000\n" + ] + } + ], + "source": [ + "g.optimize()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Graph_SLAM_optimization.gif](images/Graph_SLAM_optimization.gif)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+QwlrklvTY+kKDiCGkUoPMfvUmSSIMJAgXgdO5GmSgAF8bOzLPc553MVY0mm4\n+/EOEYbwmnM0AjgAyuDRb4OM2g/GBaOcu2eE7qcFNyqd9fffw3PP6fTRb78NjqPX+3zQqZOuDras\nZxC+syAZQyWzKcBjhp+xzwZJDITp50TpUxHZYtJp14v69ApNtRSMhr8x1KWVpQNU0qOClFnmt/Gh\nvIyUGX/p/v0zhS0cv1+iZ4XXGyyVfMuWBceFZLDXlmM6t0x7fV282LM0L4L2xUMKe7K54NjU9M41\nJpnj92WfvSrly7iU1vTRn6pK6xw5DMSWKP3rdBPNnQg+fvvsyCJRDzPPZ5/pmI7998+PNjdNkdJ9\nbXlg15AM8tjSvr3I7bfr9B/rym35+dKQvH+fLS//w5bZg0Ny2cG27LSTTiedvn6yTct+d0Rck05h\nUIdXRdy0ar0AX+2XE8hjmvLrgOK8z+lJ24oX8k04FS8UnsvlpvKPHcNSncpnU2W2/BewRZH7DOWY\n1xzTlE+DpXKDPz8ddW7AVoz8dNMgEjbzPXVq/nVAXrBK8s43e0i+mSWZFFmwQGTcOD0XUXPe4U9/\nEvn730X+UZyfsfTsvW3p2TOTomm9y1Vmvpnp8X1DsmRJs/0Cm40r8AuUD8O2zKJEfu7YSz5mbxlF\nOE/bEL9ffrsjmz4g4dPC7+23RW7bJlRnR9DSWTkrff960tZpjLgHl3rhzLcz6bmrlM4NFAiILH0o\nfyTw6XRbJp1sy6TtQzLEyp8IDmQCyrLzWDWDw941+8s6ld9hFHewZXpvrYVvvXVtIe316rmD3HU1\ncw/lzlEYhk7ud/DBIuecIzJ1qnbtXbtW8ibJqz1+ObSNLUqJjB9qy8cjWp4C5Qr8AuX3V9KRi1qY\nD/Zmk23NO1I/aI4jUtRWe07897BSeeR87Q5Xst2WY8IREfn+e5ELLxSZqvLNOc8OCMnvvzd361on\n1feGM5k5q/HKf67Ubp4issHR5LJlOoProEE6K2tusNk4QvJiymEhvYQok4OUTuo3inAqm6qVN3mc\nFtzt2ol07y5yYjedvTSQsy130rnK9MuDo7S76pIl2mtng+Tcz08/iUwZmRMlbfjl+2dq3OeIESKd\nOum/BYYr8AuVGsUqlo/R/s3j0DbI5cv1biN7Z8PuK7Fk3BBbFwffAkw4q1Zp3/N27bRLYJXKmrli\npk8C6KCgFnyLLRM761ufMcOUblrg1LJlOlX2UUeJbLONftxDlEkiVSozLdRHEZZqvHn+/+kI5Q4d\npE7Bvha/XH+kLc89J/LFFyKJNxvonQiFxDGyo4WrPSF5+siwxIcWS3yPvfL9+QtM6LsCv1Cxs37R\n6YCr3Ae5qK0tq1aJPLtDvh2ElrDMAAAgAElEQVTUGdP8EYuby7p1uuxep076yTv5ZJEfL8mmTU6C\nSEmJvP66SM+eekg+fnw9NDWXhiFURxrpTRT4NfnqK5E3j6qd+rm6Rh2DtIIzVZXKP/cMy+QeWhHK\nS/HQWObMnAnsZBu/PLdXWa35tkxH2KlTw19/M3AFfgFz/RG2TNhKaySrx2Uf5DhKPmc3Oc8bzgtR\nb8gXrzmIxbT9dMcd9a0ccUROmgRbFxHJvFA+n/5eVutSgaAjTRcvbtZbaB3YtiQ93nxNtiHnU1Kx\nBGnlJmyU5iWIi2FK1DdYEql0z+kkb9WmX1ZcE66VsqJRyB1B7733eiefW6qGbzSjR2irZdttdfm5\nZBI+2z5IDAsHhYnQmy+4Nz6G342OVOPDQWkH45Ejm7vZG43jwGOPwd57Q2kp9OoFb7wBs2dD3756\nn292CjCds3BQOr4hkYBIhI4d4cEHtb/1ypVwwAEwcaL+zlwaiUCAqlPPzv4WSsGiRQ16flVejnHT\njdw4pJwZzkgSqYKVCUxmbHMpB1TPx8DJCCYPDl5i7OSvgPJyuPFG/bex/OYDAQgGWfN8hO+Xr621\nWYF29n/kkca5fmNTn16hqZZWoeFn0gjoSdv/N0ZnLEzsslueFjGbYhlFWD7aobhxsnU2Io6j0yHs\nu69Whv78Z5Hnn5c6c/o/cHa2/GFC1a29/fijLucIelLwq6+a6EZaId8/oyt1ZbT8Rsx4OXF4TlUw\n5ZOwmV/MRpt4kHXKL6tfbqIcPG9lTa65+f/zzFwF+D7iavgFSkQXt/aQRMVjdJ09kxM7RzBPPAEg\nE4W7iP24i7Hs9V25juaNRpuvzRvBW2/B4MFw9NHw2286WHnRIjjmGK0w5hGN8rcHiziH+1Ek+W7H\nA2DSpFraW9euMGuWjqz88EP485+19i+CSwMTj8MX7JIpKk8yqaN7G4FLD4jgIYGJoCTBUft/nyla\nL4AyDH48rpRhZjlXXQXJm25p1Pdg8WKYVBLB4+j302s6qJISVHExjBgBxcUQDsPo0Y3WhkanPr1C\nUy2tRcOPe7X/sWP5Mq5o4vfLh/uOkM/ZTWKXlMn1bVpWmuMPPhA5+mjd3B12EJkyRdvuN8TvV+fn\nO0mi/tA+u3y5yKGH6uscc4zId9818I20Zmxb4h5f/iRlak6lsa6X9oVfhz+Vs1/b9BMg5XuUytq1\nIi9e04hlOm1bqq8LyeTTbTEM7Q20TqUq0LUg12fcSdvC5alLtdfBL6dkoxfFMCSuvJniGOmc+Jn0\nyQX04C0ZG5bqjp0kaZoS37qTTDswLKDd7yZMSAW21IPnxtVRTakenVsyKTJpkkibNiKdO4s89VQD\n3JSLSGlpbfNFYzsLpCZJv3nS1jUBUkFfMdOSALoAe8VJ2eItSaPhlJ/YG1nz6lr8cohpy+6760SG\nLc31ub4C302e1gysXq3/frdDX3xYGEYMw1AYiSQGDvFEjC5UUEQ55eMjtD0qWDDJnZJTp7HXpDGZ\nz+aqXxj17hjMbb+k/2Fb07tdZ3x3VWQTUkWjmYRfefcQjfLjExHGMokD1CLOUjPwqgRYlt53AxgG\nXHyxHmGffjqceKL+e/fdsPXWjXHXrYBoFJk6FciaFRU0vrNAKmFcNyDcNoo6WqWur7jzTrjhhiht\nP5mOQhAg7nhwBgTxb+LlROCDD2DGDOj6YIQrE9nEcce0jzCiPMBW3QNwRGG8bw1OfXqFplpahYaf\n44cf9/h0moW/loqEwxLzZkPNzyEsV5mFp2UkDiuupQXmpsZNu9KtxS/XbB/OVnHy+GXB2WFZemZI\nlo0PS8KXTqVgyixK5Ksrw3UnofsDTSsWE/nHP/TAoHt3kVdeqd9xLjUIhWrlttcGgCZug5n103/0\nTyH56ZL81NmTKZV7R2z87/vVo7puxGm76ihdn0/k6iJbYt6shr/8sZb7vOCadAqUUEh7o+QEmjh+\nncv+hB20x0q68PXGljhsEsLhTNtzl9yatJmEWBTnxBgYqeRoZqpoDHnnqVI+Kd3XlpIS7X9/z6m2\nVJupFBSWXz6+35Zly3TwVoYcwf7OOyJ77KFtsFX4dEFupXRtA5c/xrbzfg9pjuCiHJt+zKsLuYzY\nxZakpc08lVhynrd+pk7H0ekVJkzQRWByUybMukxHrf/yi8iIXWy51huSJQ8U0Du2CbgCv1CxbYl5\n8u3WjmnKv/qEMknUNtam3eSEw1oYmKb+W1aWLbidmo8Qv19+vzMsSZ9+ORNGtmxjzWRakuowwjuH\npE8fkW7dRK72rD8pVocOIsN3zL7E1R7t3jplikjUyi+xV6hudAVJOtK0OSNJczrxOXNEijto180k\nSpJenzzcPjvv5SiVN8ewrlynDr/zJDuvpu/dO2SjudPv09q1OqmaZaVGhS0cV+AXMM8cFZYF9M9o\nvDGvzivyyD41vFbUH3utFAzpFzVcwzSTuz5dz9UwapsPTLOWOSdd4Snh88ubt9vy4IP6VBdfXHcN\n2FGE6/ab3oKLV2/pfDU6NxLdlBVHl2ZMhw5I3GPJzHNtubh/fq6dSwJaAfj6a6md83+eLUcfrfPq\nP/lkc99hw+AK/ELFtqXKzBaYeLJLqRykbDnhBJEzds9NE2zK78UlLUPY15e08C+rkaNEqbq18I0o\n3FH1ui1rBxXX1u5dDb9lk+r4k4Z23cwtq5h235xMqdy5bVaLrzPXTupZSr5ly+mn68di6tTmuaXG\nwBX4hUoo+2DGMWSuKpZTetryxBMp2bRNmcQxJJ6y7W9RAj+HBWeHZTF7yWfm3psukGt2COH8IvHS\nq5cr7LcEUr/zunJbft4mPyJdp3E25Z4+YXEsX7b+dB3vjePooikgcuONzXAfjYgr8AsV25a45c/z\naEm20ZV6jt/e1gVA0pqpYRSe/b6BuOOv+UVfGqRjs+1Uql3E8Xq32M6yVZMzOsw1CT6jSqQKj07N\n4PHU+dvfcos+5MIL607z0ZKpr8B3Uys0NYEAK6aX8yqHkcTAgwOxGF0/iXDFgAgGyUx4OYbxhz7p\nLRXLjmChfaCJxRokfP+HJyKZxFvKcRotJYBLM3LrragRI4BsvABAb/kciwQGIIkE8dBtcEs2FcMD\nD8CVV8Kpp+rsHbXSfLQSXIHfDHT/a4Bn1HAcDBIYxLBY0iVI378HiRs+Ehg4hhfuu69gAq4akupq\nePz7VJZQw6xXsFV9+Ne3+pxiNtw5XQqQRx5BhcOIYZIEqvEhnbvm7WK88BwyfjwMGULklihjxsAR\nR+iAK6M1S736DAOaamkVJh0RETs7OVuNV0YRlkmT9Kartg3LbIrlP8duubbnBQv00Hogtnx3ccME\nSK1aJdK+va414AZdtRJsW57cX3tnfWbuXcu2n2vuCQRkiy6bSXOnVlBKXQecA/yUWjVeRF5qrOu1\nKCIRfFRj4pAAuvsqOOccIBpl/I9jsaiGF1+DabTszHzr4e23YSBRhhoRugwPNsgoZva1US74PcJJ\nJwXhzCs3+3wuLYBAgGG3wDHDDsWXrAbIZPnMNffs7FvJCy9Au3bN0cjCorFz6dwpIhMb+Rotj86d\nMXAQwMShTbfOtG0L8noEi2o8OIjjwAUXQJ8+W5xZp+KFKOUUYTkxPMOszS5oEZ8X5S93F3EiMTzn\nW7BnIxbIcCkoOrwXwSFGXSb5tNDf8eqz6dSpKVtVuLRma1bzUVFBMlVVKImifXUFAL/3C+JgNEku\n8uak3bvZCVuprMQZPBiGDcvfKRrNm3TbEIvubPgJYJcCJvVsVEeivO0P4igjk0M/vTzddgSvmcVM\noIzP7YoWU0+isWlsgX+BUuojpdR0pdQ2jXytlkPnzpip7H8mwuLvOlNVBcuXwwscQxJT+xv4fFvc\nxONPP8Ezq1KTq6l1KpFA5s7lwx2Hcfnl8PB5UeJDikhedQ3JQ4v46T9R4vGck+R0BiIw+ZMgceVO\n1m7pVFfDwnuiVB1SRGK8fjbG/h3e4wBAK0lpTf/XWAd2CF/HxdzDwJeugaIiV+izmSYdpdSrwPZ1\nbLoKmALciO5wbwT+CZxVxzlGA6MBevTosTnNaTlUVOBgYOLgoNjXWcTSmVH2PK+IvYiRVCbT5SxG\n/mck7QrBNHHFFfD003DCCXDrrZt8mi8fifLhXRH+RGce4gzO5n68OW6ovb97k3vvhbFVERQxTJLE\nq2PccVyECQTo0gWGdYxy/7IivBJDvBbv9jiB8V++zYoBJ7DHcfvUTsPs0jKJRlnzfIQPtw7yQkWA\n+fNh4UL4e3WE/dKjOWJcekCEz9qcTf/57+TZ7c+UBzAXQlLFMCWJxGKoSKTVPxubJfBF5LD67KeU\nuh94YT3nmIaenqRfv36to2hdMEhSeTGkGoVwJtP5bAaYSS3klAh9eZ/V96QmmprzIb3iCuS22/T/\nt93G9w/NYfG5U/AODrDttrDVJ1F2LJ+JodC50wMBnPlRnIdmIg58uVVfVrxfQfS/nbn827H0pJrj\ncRAUKucVVUDb4kGsexmqXg9iHGXhxGMoj8WAsUGuawvffw8HvhrBK/qFT8arCHz5qD7B21/AkLJW\n/0K3KHJqJcT7BVi8WK/65sko17xRRFtiHIDFFZTzyVYB2raFtxJBYkkLIUYci3++F+TzbQIs7giX\nrbmK7fgZBRjJBHz/PUYbi3hlDMHCckd+jeeWCeyQ8//fgcf/6JhW45YpIk91LZVEKqVwHFNe3rk0\nlTveyE8P0IhFpOvD2m61Q9kr8clAdPH1Sqy89aMI561LRxPrlMhG5jy1UvHutVf+hW1bkjeH5MtH\nbJk6VWTECJEePbQr51p03YBkbsZRENltt+b5klw2mt9f0emv4+gcOQcpO5P6aBz5ifFuaheS/v1F\nTjpJ5PLLdcW4T0eG5IuHbXn3XZEDD9THzd21NO95qDqzVLtuHhCS87xhWXfNluuuS3O7ZQK3KaX2\nQ4/WlwNjNrx762JBrC9/wURwiCuL+34byRud+vJ/P99Ob77Ieh3E41oLagbNNRaDx6pP4Cxuy04k\nAxYxDiWCAF7imfVeYvzNOwtvPLtOAA8OjgJRBglHeyblng+AnXYiFoP339eF0N98M8BbbwX45Re9\nebvtYNAgGHRpgK87lrPbtxGMT5foKunpc51wQmN+HS6bQkqLrw4EsSVAebl2yhr6doTrJVtt6uj2\nEdSfA+y7LwxoH0TdZSGJGB7L4qpXglyV9/gHSCYDTJoEV42C9u3h3/+Gw3caSfVBM/AQI4mH8tfg\nqHOg96ggR51bhHVjDCZuvldYi6Y+vUJTLa1Fw4+9kc17n8CQiZ6yjOaaq+FLM2v411+vm/Ae++Vp\nTnFMCWyEhu+k8uN/fU1YxhGSFymuVUBl4u7hTCr2tLJ+5pki06eLLF26gdwnZWV6Z7fQSdMQDku1\nqQvMVHfrKd98I7JypUjFC7b8dlVIVr9sS8WEsFQOKZYfzy7LZIZdi04Bbpq6IMmC/Usl7rF0ptS6\nEt1tIFPqF1+IHHKIPuwvf8kvZH9hP1tmUSIxTIljSMLnFynNqR1diPUlGgAKQMNvWayv9moj8M0j\nEXaiOuWpI1yYuIO2rEm5FjokUVT12pN2RwzJ2MWblGiUHybOpOvTMJCRPDxgMn0XBSEWQ5kmnsmT\nKT89wGefwRvPRdjqPzP59ReYVjWS534M8LH0YSQzAXifvmxvVLBshyCrJ8OfiPC1sQuOozKeSs9Q\nwqPtRnPOOVqLP+QQ2L4uV4C6uPXWzZpIdtkIpk1DxozBm/ro/fZ/VHbvxek8puMqiJHEoAPapcr3\nxlySKDxIpmbsTlvDXUv0vhr9DLB8OWpMyggwenSm1m0ujgNTp8Lll4PXCw89pGsZ5+bF6dYNjln4\nIp6UM0CiuhrHAfFaxOMxTI+F0Zpt+fXpFZpqaS4NP/63EakyfYiznkx7Dcnsa3VWx7TWHMeQyZTm\na8brSfHa6Ni2OD5fNvWsYUl8nl3vOrHr1ol88IHII4+IjBolsuuuepCSHcGYUolPKrEkoXRxk99f\n2TLtqlscxfn1jNOlLW/bJmtzT9aYo0koQxLKlGrTL9cNs2XG7nXv+0fFav73P5GiouwuX39ddxPL\nDwvlzRVV45En/m7LLy/aMkWVir1v6RZpx8dNj1xPaqRbdUCkpKRRL3nRRSLnecMSUx497LT8Moqw\nROmfqQ3bXEPP368KZdtAqjhJPdvhOCKffCIyaZLI0UeLtGuXvZUpPfIn4sp3L3Vz3rQ0UvWM85ae\nPfOL0Xi9+UK8rKx2BbT0vh5PvoMC1Kpf4DgiDz6oy1q2b683byi18Wcz0uZSQxKGzlMV9Nny2+ml\nUpWqE70l1plwBX592W23WlrLsu36SyLReJc8p48tU3qGZNI+Ybm3W0iSU8P59vuUzbupH8rFi0WO\n7aJriNZ3DuGnn0Qee0zb27t3z77nvXuLnH++yHPPiaxeLbUSxq28bstNDrdFEw7rAiNpYZ8mdwQY\nDms1fH3FZ3L2fe/wMvmOrhLv3ksfk56PsW1ZPS4klwS0986QISJffZU9PnlTSBJv2lJVpZOirVol\n8vPPIsuXi4wiLPM7FMvqiWEZ2zZca14saWx5dnxX4NeXmuX2QEKUybn72fL71Q2vgVa9ntZATKlW\nPpnuK5WH2pZmhqFxlER8xTJuiC2hkMicOfpBblRsW5acHpKJnjJ5mWK5q22ZdmkrrT38raoSee01\nkXHjRPbfXxcYApFtthE58USRadNEli2rfYnVL+dPprWYWr0ujUdOuU/HNPPewViq3vNa/HLk1rbs\nvLPIjjuKHN4+v3btQOy8AUfakSCBkiq8unJcrokJpNqz5T179RX47qTtrbeiFizAmTcPA53b5vB+\na9h7YRHWBzGciRbGaw3jxhWLwbwbIhyaiiI1JMnI6jAJPBlXRRNhaZ/hPPVtgC/GZ4/deWc44ADo\n108v++8P2zRAsgpnfpTY4CL2cKrYKxUIVbxuLmpgGEaPRgQ+WQKvvAJz58Ibb8C6deDxwEEHwQ03\nQHGxbptpruci0Sht/1LEcVRhINqFMp3zprW6x7lAJIKZTEXNJsnLdOkhkfo/xr6rIsyLB9hpJzi1\nfQTf0lSAoopxy+ER3j0sgMejn8lB/5qJb4FOpmYQr+X+W2F14+6tr+WmdL6lVvb8uQIfYMIEjKIi\nklUxqsVil13A954O7YlXxfjfjAg7b+KD8eOPMHs2PPsszJkD+1YGKcdCpYSfiWCoBCIKA0EMg3NO\nqOCcK2HVKu2XvnAhvPee/vvUU9lz77pr7U5gq63q2bBolPgrEV6etoIjnVjGYyb9wq28dxbj7dG8\n8gqsXKkP2WMPOPtsOPxw7czUoUM9rxWJoGLZa6CUm/PGRUecmxYkY3hMIJnMCmiPBxHB9FoMuCjI\n5/+FV1+F8G9BTsLCIoZ4LLY/JcilZ+QUNfkEZEH2EjVTJb+6/QjGrxiLc3UMw9cKffLrMwxoqqVZ\n/fBt7Uc8rKMtoZ3D4ni94hiGrFN+OcS05clLbHFu/mMTj+OIvP++yA03iAwYkDV55P4dsYstq08t\nlbhpSQxTqg1LqvFKAiVJa8PeORUVIq+8outznniidl/OHdL27i3yt7+JTJwoEomk7Oe5hMMS279/\nasLYlEosqcQnyRoRvqMIS6dOOrrx/vu1bXRTSZaV6Uk0DO19VIepyKV1Mn6oLXd01e/VMx1GyCpv\nJx1WXYdXWCwmMm+eyJSRttyzYyhjzunaVR/y8MM6HiBm+CSBkjimxFMOCA7IUu/eMlVtmT75uDb8\nTWP2tdlZfvF4ZO2ksFx6cNZu6LSpbf/7/XeRZ58VOeccbWfMne8EkbZtRc46SyT8f7aMIyTzJ+rj\nlz5ky2RKJaIGa28dkIR34wOtfvpJ2/pvvlnk+ON1CoJ0G5QS2WMP/UK8dHy4lldEHFPCZqmMIyQT\nVJm8vU2xvHhcWN59Vxpm4jpc45pugJRLDmMH2HJf95DE7wtvdFH7H37QQv6007TQTz/zl3QIy8sU\nS4gyWaeyzhBxjIxL8MZcpyXgCvxNxLk56z7ooET695dkSUnGVTGGKZ8NLZWKy0Ly5CW2DBuWdVpo\n00ZkWEct1Adiy+GHa3/0tWtFxLZTD1/WLSw+Lz9SVdIeOg2gdfzwg8hLL4nceKPIccdpD5oo/Wvl\nsanCkjv+assLL4j89tvmf3+1yPHddqB2zhyX1oudVaSSpifjuLApmncyKfLeeyL/b0z+pO4owjJX\nFWfOHcOUyZTK1WZIx5dsIdRX4Ls2/BqoQ4MYlgeJJQFB3nknLy+MYNDztRl4XktwFBavqEmUmRWU\nqyBOFTxdpaMIHY/Fc0yi+78r+PjnIL2evI2uUqltitXVJGfMxPO/r1A51XoEtDGyAWzb224LRx6p\nlzRVR+0Is1P3mbqer/Qs/j6lEW2Y++0Hc+dm7aiffgrTpm2RpRtdNpJItnCN4xg4mIipUJswv2MY\neg5r/56RTEpkpWLsL4v4UnZhEB6EJHEsZjISknBgWYS/3IFrw2+upRA0fBERKS2VZA3ffAFJoCRK\n/8wIQPuUZ93HJlNa57YqPLVcP6vwZLI9ppcExvp9lxsC2xbxePJtTo09pA2F6h1N6dK6WP1yynyq\nTIkZlvzHUyLOmM2c37FtiVupbKpW1nwTN33y+DalMopwJqo9jikxr1/WvtryNX3qqeG7JQ7rYuRI\nlGVlyqUBiDJQvjbscNXZKJ+FGCaGaWLi4CFJGyPGUUeCeC0cZaKM7DYvCSBbkUdnmUymihlm3cZ+\nNHfQNWwbi0AA5s2D0lK9NIVbZDBIHG/ed8nw4Y17TZcWwUftAhRRzjdHnIPjKI5MPI+a+dDmnTQQ\nYP715TzAOXzVYV88JHRGzmSCX36FuxjLaML40kVU4jH+eWyE668nk5l1S8Y16dRFIKCr48zUCcDo\n2xcqKlDBID0DATi6j3Y17NwZxo6FWAzDsuh5zUi4ZqQWpLnblIJEInN6wzQR0yQZS2SKmQN0Ta7U\npdga01WsjqRUjcnPkcV8QV/aGtX06efTfp2uOccF+OLhKEEi+P1gpgTzpsRniMAnn+jDIhFYNRue\n4yF8Fdr1OYEijkWfP4H1cdY9OInC8Fms2T/IxOvg9tthzBi45BKdhG2LpD7DgKZaCsakszFsKKlY\nzXDz/v11nh7bloX36MjTWrlEtiBXsVoeOo1prnJpWdi2VKacGOKelGuwUT/PmWRSpwG55x7tmpzr\nodO9u8jUnvkJ1Byl5Nk9yyRslkos7QqNV5tnJ+tn8qOPtCebaWpL56hROi13SwHXS6ewOfVUkTmq\nuJbXTHNXuGpQanrouLZ7lzShkCRS810JZcr/85dKcj1xLrkCfvhwkS5dsgJ+p51ERo7UdRO+/FLH\nwXw2w5YYZq0aDnF0hs7ldM/my7f8WhFJKWZffily7rna884wRE4+WWTRomb4fjaS+gp816TTDPz6\nq67bWanaguSHlHPWWVuM18Caw4fTIddDx7Xdu6QJBokbFuJU44hCHdAXY7w29TkOLFmSNdG88QZU\nVOjDevSAo4+GIUO0I0+vXvn58AH8QwM8z7Ecz7OZdQZJDPQ71oNvAP3OJWJVxMech4GDY3p576II\nw4cHGD0a5k+M8tOTEc79d5Btjgxw5ZW6XkOLpj69QlMtrUXDf/KSbObImp46W5LZ45ZbdObCxd02\nkDnRpdUyuWM6AltJtccv/x5ry/HHi3TunNXge/USOeMMkRkz6k7KVxc//aSfu4RKRdSmPNPyRpsZ\nzT8/udpkSjNJ2NLvaAyPXGCFBUQCAZEXXhBx5tevPkRTgavhFyYi8O2/IvioziRMg5SGbxhZVaaF\nIwKrbp/GcGax9dnD3YlalzzWzIly9po7MHD0s5+oZtGkCG/vGCAYhKFDYdgwnS9qYzHejnIXY/VD\n6PXCvffCrFl58SAJwMHLIl+A/tXz8o5XCo72R7DWVePBQXD4Z+wCFtKHaDTATcdEOTRV4Uv5LBIv\nl+MLtoxRuSvwG5uc0onOgACvvw6Pfx/kAvJNOQKoBgq6KgS+uGIat/yiS9apG+ZCN1yh75Kh4ukI\nO6WFPeBgEiHIypVaNs+apfdr314nBNyYZbfZEToS0wqVo1AVFciQIDL3FQwEB8VCDmQlO7JTV1A/\neJFEAsdj4TltJMf8DB9/GMRZkd5bm4SCRFhAgCDZgLFkdRUzhs7k/r4BBgyA/v2hfzLKHt9FMIuC\nBWeedQX+ZrLm4GG0f/9N4oFBfPLPOXz9NcTnRWm3MMLXazszYuFYLGLEsThMlWNLAAjwKXuyD58A\nOfb7/fcvuAdkU1GpNzZjXp01yxX4LhnW9A2SwIMiBobJt1fcy41DA6xeTWZZs4a8z6tX6wHwV19l\nP1dV1T73QHRGWiFGPGlxzA1BLAuepg1eYjjKQ19ZxADegW+gGg/TGcPM+EgWzNDvn2kG2Lnzfdzw\nywUYkiRp+Kg6MMif1kLk4yAJTJ3iHOH/ZAbPLxvJ9CUBFk2JchJFCNUkrjWYf+p99LhxNDvv3LTf\n7/pwBf5mkDh8GB3suQBYr8/lu/2HcQvXZQo6OyhMHEwcIMY/dp7J2mW3sXvblbQ/oC/M+yQvdStn\nn90ct9EoPBYfztXMzaa7dSdsXXLoOOk6fOlC5iLsfGwfdt4EXScWq90prF4d4OqRk/irmsXPweH0\n7R3g6yeiPLTmDBSwzdZw4q/hjDLiIckKerCAbAOSSZhQMZp32/ah2IrwybZBvmkf4JvPYZ0VILrL\nWQz5LIyB4CHBwfEIL1Wntf+UKUgcAo9ewJBH+/DjLgGGDYPD20cp+nYmHTsCI0c2vYJXH0N/Uy0t\nbdI26ffnTfisU36ZtF2oRnoFr8RShbvTrmLp5TN6SyLlKhY3G794elNRVSVyiGnL894SHXvgTti6\n5DJiRH7sCejShg1FTqJC8fslPjmbibPS8MvYtuFMGU8HxLEsWTnLloULRebO1SU777tPpzi/+GKR\n008XOeqorL9/hw4igWhfd/sAACAASURBVNSkbqxG5a2B2FKdSqWSlgHjCOVV40pfN276ZOlDtp4A\n9vv1yTt12qRbxp20bXyMQYN0GSi0Fus/fBAXXxeEIgtiMUzLIj5hElXfVBD/cgVdn56alyitN//N\npFtQTlJH9m4BJp1XT55GefICzGQSFvsaN12ES8tjts7gl+dNuWJFw50/EsErsUzk7q8PzGKbdCoF\nJ0abdRWc3u11/vbtbeykVnLgPWezwwkBdtjAKe+/H156Ca65Rld5c5wAv79STuUrEb7fM8g1Owb4\n5ReoqAgw+5X7OGb2BeAkSRg+vtghyDbrYOjqCF4nnrlvlYzx6hkz2ZmppIvFqV9+0VH6jeW8UZ9e\noamWlqbhi4gOJvL784OK6oq+tW0Rr7dWZG3eZ9+Gi5+0COx8DUcaKN2zyxbEiBFZzb4xEurZtlQZ\nWQ3/gQHhPG38yK1tmX5Otvat8wfBju+8o+Mhi4s3okbEemSAY2U1/FgqoVuy5ncBG33LuJG2BYht\ny8IeJbKYvTKZNFOJXGVLSavw3cX5Ye3i9bb8Tsyl4UnnMTDNRonAvmqoLRO7hKTyNVvatRM52NB1\nKgLY8tFHIh8eXJrvl19aWud5fvpJFxTq2VPk558boGG2ra+Vrvpm27XNW0pt9GnrK/Bdk05TEgjw\n9V3PcOvxUUYyk622ghWrO3IJd+JRSYwtoM7r8peW0BUhAXg8Hu0DvQWYqVwamEce0UsjYfkgVg1v\nvgl91kYZRIQIQQ69MkCfPvBJPSRfMgmnngrffw/z52tLy2ZTR/JCNWIEPPpodsXllzfAhdZDfXqF\nplq2eA1fRN683ZZKdM3NarypvNxKkspo8eX//ju8LN9ENWJEczfJpTWSM2lbbWRz4q8lm5jt5X9k\n30NnPXWkr75aK9z3398EbS4r0xPXmygDaIp8+EqpvyqlliilHKVUvxrbrlRKfaGU+lwpNWyzeqUt\niF3enJmKshW8xFMBHKIDvO+8UwdqtVA6v/E0kDMZ9/bbzdYWl1ZMJIKVmrQ1nRhe4nhIYpFKvQys\n2zfAhdzNuxzIqoOOrHWKF16Am27Sqa1GjWqCNt96K/z3v/pvI7K5BVA+Bk4A8mKTlVJ7A6cA+wBH\nAJOVUmbtw1sf69blf04HXSnQY8jUA9kSsU4+Aci5nxNOaM7muLRWgkEcr0UckzgWcbzEMYlh8eVO\nQQB2WB7lHi5kAO+wdeRZOPTQjLL15Zdw2mm6DMa99zbjfTQCmyXwReRTEfm8jk3HAY+LSLWILAO+\nAPpvzrW2FKK9R+KgMkIxr56tz9eibfirx9/KBMr4ps1uUFbW6NqKi0udBALYJ02inCIu5G5u6HIP\n8zxFXMwkxj2n7efbfRbBSzz7DqYKr6xbp2MEDUMHh/v9zXkjDU9jTdp2AxbkfP4mta4WSqnRwGiA\nHj16NFJzCodVby3Olk3MWV+1U2/8/36oRU9wvnZzlK1Zw6c7HsZOJSXN3RyX1ko0ysDHx2ISI8gb\n8LPgIUmANyl6qg9LlgQwBwaJT/NipKJ9lWUhQ4Kcey589BG8+CIFkw6hIflDDV8p9apS6uM6luMa\nogEiMk1E+olIv65duzbEKQuXaJTSxedjItngi9TfNt8tb542NRTRKP+/vTOPj6K8H//7mdkji4BA\nREQQEMWKiqIFZFFxNYqCWlG01lKhKmJsPahWQKvVigSPbxWLUhZvqtafimcVUSPrNVsREQ8EBRG8\nDxAKQrLXfH5/PHsmAQJJdrPJvF+vfSW7szv7zOzM5/k8n/PMmcdQziyOXzVLr1SK2B/hUMSEQpiJ\naLKfdMaG7yZKmRliyhSQwX4uYQZvM4gPeo+EBQsIfuBnzhy47joYXtus3yLYroYvIsftxH6/BvbK\net49+VqrRhaEUFkVAiGrYmYinp+m4k1FKISbaMZhG4sV9/E4FC+BAMrrIRaJksAFCEKCuPKwYs8A\njz8GVwzRJZQ9ROBzg1XPDufSv/sZPlxn07ZUmsqk8yzwiFLqNmBPoA+wsIm+q2hY1iVAL7woqjCo\nYb83zaK23xMIYJseVCICgHK7i/t4HIoXv5/Ft1by5KU69h4gQIhlnQO8tsmPzwefzA5xaFaRs71u\nupiT9+jHPQ/5MRoaytKMaWhY5mlKqa8AP/C8Umo+gIgsBR4DPgZeBP4oIomGDrbYefVVeJET0gad\nVB38BCbqrruKWxv2+3lh+D9YygH8tEdfmDGjuI/HoajZdKCfEAEu6zCHMcyhw6kBnlvrZ8MGKCuD\nmUsD2BhZwRMJZowK0alToUfexNQnWD9fj5aceFX1qiVb8OlEj6yU7s/oJQv2Ly/+8gOWJRHlaVl1\ngRyKlieusHIqYkYMjyyosNIVTDwe3QYxgltiGBJz+4r6eiUfiVcO9efT2drGbeZWwKcnazhq+Wyt\ndhSzkzMUwpTaYW4ODoXgkPUZn5ICTDvGwM0hevbUJRKiUfiIfvyHk/i4ZACuO6e3ihWpI/DzRNXh\nAaJ4iGeZcwAMRDdIiUSKW0AGAsSVO22mogXUBXIoXtqMCBDDk74eY7h5cE2AY4+FeBymnhxmAQFO\n42n6VS+ESy8tboWrnjgCP0+s7OznMqbzTTIdITssU0BnehSzgPT7OaNTiKcYydreg+Af/2gVGpND\n88Qb0KUTvmp/AEvpy72HzODKJ/0ceij89BOUflh34lVLx6mWmScSb+pU7lRbt5SGbwPKNKHInbbr\nXwgzYt0cRjAPz+dxmPChbnxSxMfkULy0+yjMDC7FuzFCd6Dv0kt4JNGP99/X1+ODawKMJTfxqqgV\nrnriaPh5ovu/b8GTZVPU0TkapVRxd4UKh2k7sozxBPESwZBEq9GYHJonHivXhq8SMSYcGuLhh6Ft\nW33/3c95PMVIvju1HBYsaBXKiaPh54HvpszmmP89DWQ0ewWYyb/E48Xd3jAUQsW0Q1rbTFWr0Zgc\nmimBAHE8GGTyQoZcFSD2Gzi0OswrlOEhgo3BhoHFvbreERwNPw90evJeILeHp6r7rUXJorbaIZ1Q\nJhE8rD7hQqisbDU3kUMzxO9nbI8FzPGV80RpOSoUotsZfg47DI4mhCeZdOUmTufrL24VDltwBH5e\n8PTas9ZrGRu+0lUyx4zJ76AaicSbYd65NcT1HabzzYgLuJ/z+OnkMY6wdyg4u+wCkSh06KCfR6Ow\nfDmEqJF0ZRd3WfIdoj7B+vl6tNTEq8SblkQwJZHVuzKOkqX0lcdKizjpyrIk4tKdhWKmR2KmV//v\nKe4kFocWgJWbeCUejzw1USdeDWtnyVxGShRT4hgivuK/XnF62jYfVqyAvTFIuWkT6Pj7X7CcPj+t\ngA8PLU6NOFmV0CSB2DYi+rgkEXUKpzkUllAoHSQBILEYS2eGGAw8s7kMF1ESKFZ1+CW/uPn8VnOt\nOiadPOD69xzcxNInO/XXRHBJHC4uUhtiIEDC1J2FcLsz/zsOW4dCE8hNvEqYbv7zc4CT24Zw2dFk\ny8M4fTa8AxMmFOf9txM4Aj8PdK/R+iW7SmZRtzb0+3lk0HTe8pShZszgr0cuYEbnKSjHYetQYBKD\n/Fxm/IN3zUHIqSM5q3OI/+Kn19hkgEHyLjSQVhVC7Aj8POAZN4YInnTcfcphm8DAVkbxtjYMhzkr\nPIEjo5UwYQLuTz5k1/aFHpSDAyy9bDb/sC/m0MQi7Hnz+eZbvfDcsgUeZCzPcCoRvIjZulakjg0/\nD6ghfq7aZQZ/2nwDe/F1OvHqWX7FXiMHMfDKQHFqxKEQbtHLY4lEuOa7izGwoczjhGU6FI5wmL4z\n/4grWbkqEY0QIMQB+8Do+8vwECWOScg3ghPH7qEj5FrJtepo+PkgHGbq5gl0q9H0a0++ITokULwX\nW9KGH8cEw8AkgQsny9ahwIRCmGJnZbSbhAiw+zLtyHWRwEuUYVXPwIMPFnq0ecUR+PkgFMJLNWby\nacqkM4BFHP6XIi6L7PcTPLOSKe4pqLvuIqq8JFTrWiI7NEMCASjxEkeRwOA2/sR/8bO4Xeu234Mj\n8PNDUvilnbRJXNiYieK+4HbdFaIx2LJPP27rMZ0lpWUwvXXUFndopvj92LdNRzAwECZwB6P2DBPY\n9DRrKWVlm0OI4MU2Wp9y4tjw84Hfzw9GF/awv8t5OYGBWcwXXDjM2feUYRBFDTf5c0xhEocJbziV\nMh0KilryHmZSl/cS4epvLuJQ3tcbt3zFvxjNWdceiPeEQKu6Th0NPx+Ew3Sy1wIZLd8Gvth9QHE7\nN0OZmGYjHsOdtI+2tmWyQ/Ojqir3+d6sAjIr7JOY1+qEPTgCPz+EQmltA7TQN4Bua98v4KAagUCA\nhEsnW4nbnS6g1tqWyQ7Njy+OToVCKyJ4WLj7r4CM/6wj65Fibyu6EzgCPw/Edy1Nlw7O7nRl2rHi\n1oT9fl4/fTqVlPHllTM4hgW8e+qU4l61OLQIVq/W9e6f4VTeaj+CNWvb8S9Gs5ZOCAoDwa6OUv1i\nqNBDzSuODT8PbP5iHbugcCWFPqS0fBs2bCjk0BpGOMxRcyforkE3hRjDeZSc2Hpimh2aKeEwgRt1\nvXsTGzbCsUBUebhEZjCdCXhVlIh4+N3sAH8+AYYMKfSg84Oj4eeB72Kl2JjEUbocMhlNv+q/Swo3\nsIYSCmHGtd3eTEQZT5CDJrS+ZbJDMyOUire3AdJdr9wS43Tmcm276ZhTp/BZsJIlPj9HHQV/+5vu\nQ9TScQR+UxMO0/uOS3ERx0CxlP2BjC1xQadRhRtbQwkEsN2ZuGYTwYg5DluHAhPQ8fbxpHhLFVBT\nCMfxCtOqJkAgQL/xfpYsgd/+Fq6/XrudVq8u3LDzgSPwm5o5c3AlIhiAwqYfy9Kb5jGMj19fh1hF\nqhH7/cy9qJLZXEgEJ+nKoZng9zOB6Vglx/FE74nMopxlbQeRwMCFjcvOKCXt28O//gUPPQQffACH\nHAKPPlrY4TcljsDfFuEw7LcflJTACSfU3jZt2g6ZL1SNvyfwMhN+uhY5tnjNIFu26L8vMJw1wy5w\nHLYOBefbJ8NMZwJDqisZuep2DmUxz/4cIIqXOCaGt7ZSMno0LFkCBxwAZ58NY8fCpk2FGX9T4jht\nt0Y4THzIEZgp48tLL/FW+xOYeNB8fvX9bK5YdTEGCRIuL0/9sRLjCD/dusHe34Xp/HEI1+6lsG4d\nHHooCUzMdK3MjDnHQFAkSESLtGFIOMzv7gvgJqqfh7xwXXG2anRoOax+IMTAZE6IkOBwFnI4C3mN\nofQ97QC6XFl3YEHv3vDGG3DDDTB1Krz1FjzyCAwaVICDaCIcgb81QqGkQNYI0H/TG0g4zOVkKvFJ\nPMKSO0LcdIefwYSppAyIINjYGMSVu85lVKqwkw1ExIP3qEC61k7REArhsmOZchHFOnE5tChe2BLg\nEDyY6Oyr1L02lNcxXnwHrty6UuJyaYF//PHwu9/BEUdoh+6kSWAW3Q1amwaZdJRSZyqlliqlbKXU\ngKzXeymlqpRSS5KPWQ0fap4JBFBKpR0+ALHDj+Jf54VwkanEpwwT49gAhx8Op3XIjQ4wsdPlg2ua\ncyTr+ROczqr7Qk1r1tkJE9R2CQSwXe7MOXLs9w7NgBUr4EVOYK27K5C9ogaqq2HOnO3u46ij4P33\n4fTT4S9/gbIy+PLLJhty/qhP49utPYC+wC+AEDAg6/VewEc7ur9m18TcskT69BHxekWGDcu85vOJ\nGIaIyyUSDOa+3+cT2zB082TDENs0043La/5N/R/HkBhmg5spf37UaEm0ay+xfv3lq8cteecdkfnz\nRV76myUR0yfxZIPxD2dbsmaNSCRSx/FWVOzQGBZdGJQwg2Seb2TRN4J2aAHUaF5e1yPm8spHlwXl\n64sr5PunLdmyRcR+q+5r37ZF7r9fZJddRDp2FHn88cIc1vagnk3MGyTw0ztpqQJ/a2xLMKa2BYPp\nvzHTI/FtXIBxlBb+pqk/sxPEzh6ds88ohgzGEhCZTIWeUJLfNZPy1HwjpaUi/fqJXDbIkipDTwpx\nr082vVQP4W1ZEnP7JAaSgMyk6OBQINZeUSGJ5P0kNRSrlKIVw5AIbolhymZ8Mo6gbMYnMUypNnzy\nzMlB+eC3FbL635bEYiJiWfLj5RVy7v76fjr/fJGffy70kebSHAT+ZuA94DXgqG18djywCFjUo0eP\npj4vBWFLpSWzVLkspr98y+6ynD7yGb3kc9VT4kcNlYjhkSimRN07r+HbnTrlrBwSIDe2rRDDEBlM\nrtZTjUd+09OSAQNEBg8WGThQ5KH25emJJ4opV1EhBx6oL+577hH5/OqgJI4bJhIMim2LrFolsvjM\nConVXLWMHt3IZ8/Bof48f40lMYyc1XT6YZiSMEyJGy6JY6Sv9RcZllaIak4G41VmMoi6ffLgEUG5\nigr59V6WLFpU6KPN0GgCH3gF+KiOx6lZ76kp8L1AafL/XwJfAu23911Fo+HvINHXLKnCI3GUXlJi\nSBUeqcIrCWWK7fHKTMrl9K4NMImMHp2r1RiG1sBjImvWiHx9anla84krU+7qXiGlpfqtekLwpG+M\niOGVq4+1JBAQuaxNUD6kb86Nc7E3mP5cooaJSjp1arwT5+Cwg/zlWCt9vdY0n0p5eWb17fOJmNqM\nGpsZlESJr87JYN42JoPBWHJGN0u+uCi52t8Jk2hjUVANf0e3px4tVeC/N7i81gWYgMzS0zTltt0r\nZDCWfP+nBlwwo0eLtG8v0r9/7X2kfA9mrq/gp59EVpxXIXGVMfkETW3yGUcwR9Cnxj+PYWlz0PJO\ng3K2S8+eIvvuKzJxYsNOmoPDjmJZSW1ca/gpZcRWRm3/WE3hnG2KzbpPttwRlIRXTwYxlTsZzKQ8\nrf1X4ZFq5dUmUY9Poq/ldwKor8BvkrBMpVRn4CcRSSilegN9IFmQuhWydm3u80zVTCGebIKy+/6l\nVP5Qhuf2KMzaySbgDz209W1+v95nKKQjaZL77tgROo4LwL89EI1iejycN38Mg9pB6ei58HEmrC01\n7gEVo7i3C7z6Kjz+9EiuZiEGOsRUrVkDgLrlFv29N9+8Y8fg4LCTrH0iRIdklJyNYiEDeaff+Vxy\n9rqcax7Q/2/teb9+2AtCfN4jwCub/Xx/TD98b4f4dH0pdzABN1FieCjxgieSisKzQXR5kVg0yj1H\nz2EsD+Ihiu3y8P650+m7YCYl363Cdeqvtn2vNiX1mRW29gBOA74CIsD3wPzk66OApcASYDFwSn32\n1xI1/KoqkbI2lkQNr9hJk04iqQ3HMWQew2Tuny35qzvjWJUGOG93mrq0kWAw10x0wAG5UUkikpiV\nuwrIWULvu29+j8GhVfPidbm+qhiGLJ0Q3P4HRTthKytFbrhB5MQTRXbdNXMZt22ro3RA38vP+ivk\n80es3FWzxyO21ysJw5Rq0yf/7lieYwpKJO/99D3SyL4u8qHhi8hTwFN1vD4XmNuQfRcl4TCyIIQ6\nJgB+P5s2wby/hhm4JcQbAy/j6HduTdbUSWrMhmKuPYrOfw9R0q2U6FcehCimy4OR73j2mhoPwPjx\n+u/cuTBqVOZ5NuvWpWuUZJd+VqCDmB0c8sTDq/xsZjin8bTuN4FN3xl/gF/Xbrf51Vc6k/att8Cy\ndFmFRAKU0uUVhg6FH36AxYvh5591AtaFF8IZZ/jx+bL2lbVqVoAKhfAGAvwGoOxBJBpFiULZ8dwc\nnHnz8nBGatNyMm0nTYInn9RCZntmhHC4lmmjvtvtt8JseSHE2oMCrOri59tv4dtvwfNumAseLcNN\nlCgehrsricagkjJOJ4r9joGqkblb7WrPHdEJeCSKuc7DVZ2mY6xfR8cRAa5sLtmq48fXLeiTbDgk\nQAleDBVBiRb6SildgtAx5zjkicSbYfZ7MpTzmgKwbRKvhvigxJ8j4L/4Qr+nTRs4/HC46io46CBY\nuVIXU3vuOejQAS66SF/+Bx64lS+uyzSUorISFQphlpbCH/6AJBKZcQ0f3jgHvoO0CIG/4aJJ7Dor\naTO+5RY++wwSJ4+ky7IQ9tAAkQgYr4dY1y9AdTX0+1MZRjyKuD3Mu6KSL7v7iUR0Et4uH4QZ/1gZ\nbjtK3PQweUAllvjZsAH2/THM4+vLKCHK7ng4m0r+i/6Br3WF0j1dhShDYiEGspASqjCAODaCymqB\nAiXRDSgUJjYSjdK94zqmdb2KX8wLc+FV02j/q0CzL1OwZk8/f6CSuQddz+4fvqKzjA1jG3eIg0Mj\nEw4jZWVMjkaS1akytauiuBkxNcCr1+i3duumtfUrrtB/+/WDt9+GYBBuvRUiEX3LPfAAnHmmnhB2\nmhp+AXXRRbBqFfyqcDb8FiHwjTn3ARlTSce5QXxzZ+AhSvwWF20QXCRoh4cHGcvBRDFJEItG+Wra\nHL4kRIgA/8XPZEK4ktslEaXP1yE+PsDP3nvDWZ+H8C7U2wwjykPnhYhd4adrV2i/NIA6zoMdiRKz\nPfTpvIHTfnwakmMyEf7ZbiLDN/0/erImq1K3Io6BGB4+6RpgyHdhHqwuw3tzFO7YSedtHvnx2TAB\nQoT3HMXwD9/AMKIYTokFh3wSCmFEIxhJs6IA79GftxnMf/cdQ99hfsYdoQX8Xntps81PP2lN/pxz\nYNkyXSZ53DhttunXrwnG6Pdru1GBaRECv6RjW9iSCYXxuhWemNa2UxeB7imrqzpG0bbyBC7O5T5c\nJIgpD1ccUklJlwB2pQfbjoLLw8ARpZy9q9a2TTMAZTqaxfB42Oe8AKwPw1MhLeAqKzFCIRa7AnSf\ndD2QWzvny00dqOBqZnNhckwgCAlc/D12CZ2Xhvil+gIPUUxJaHXj+uv1ozkK/XCYo6eUcSwRZL7J\nA53+xLgrOkDSh+HgkBcCARLKRImdvt8OZQl977iIiy7NXIci2pwTDMLjj+sV/aBBcO+9cNZZsMsu\nhRl+XqmPZzdfj52O0qkZTTJxYsZ77vWKeDyZJIvXLfnhGZ0ssfL48nT8eQxT/tmjQvbaS+QIw5LJ\nVOSkXG/GJyd1smRMH0vu61MhNwy35M7RlkRdPokrXaPmg6Aly5aJfPutyHt/qBG94nLJN3Mt+fTc\ninQsbyqiJY4hEVzJeF6vVOGRWNKrH0dJtemTv59hyU03iTz0kMi7d1ryzanlsmVseSaqZtAgSZim\nSPfumbIOTRz/+/M1FZLIOpaYcjn1dBzyztrnLJnLyHT5krQcSJb6WL9e5B//EDnoIP1yu3Y6B+u9\n9wo88EaEfCZeNdajQWGZwaD+gVNhg9lhhltLgNhKMlIsJvLVVyKf/D4rIUmZMndAhZx8si5FsNde\nIn8xMqGUUUyZTEXOvHOTmigxlMST5QzO6mHJb/e2ZAu6/kxOQadkElYC5F0OSWflpsK6Zqny9CSU\nnRVbhVc+o2cd9XkMiXl8svzyoHx7WYWsfc6SaFRqn5t6EI+LLF8u8uijIlddJTJihEi3bjrTNoI7\nk0ymjPyHkzq0bixLIi6dbBVPJlylHisnBeX3v9e3NogMGCBy990imzYVetCNT30FvtLvbR4MGDBA\nFi1alN8v3VbETjis66JGo7r0bw17uljaWURUO4DfmVbJ6q5+1q+HDRug33+mcaJ1rTYZYfLI/lN4\n7sCrGPjebP68qjxdbz8OGCgUub9FyieRwCCOCxcJbAxM4unGLHbW57LNR3q/BjYGBkIUD2VU4nbB\ni/EyvESwMbm11528/ovxdOigk7D2+irML96eQ8+e8HLXMTz9vZ8PP4QqXVqcC43ZnNNmLssPHMX6\nM8bT7+FJlC35P0AwfSWoZu5zcGhhTJtG4uprdOADOvlvfZe+3OWawPVfj6dtWx0wduGFcNhhhR5s\n06GUeldEBmz3jfWZFfL1aJaJV9vThrdXOdPnE9vUJqHT9rDks89EqoaPzClVkF1Js2ZlPxvkQ/rm\nJHHEsjSZKGatz6WSTnTdj0wq+GQqkpUzM2aYCK50Vc2aRdaq8MgRhiW77CKy554iN+yVa6Z6/6SJ\naa0qgaqVlOXg0NRsesmqdQ9EMWXsfpbMmiWycWOhR5gfKGRphRZFXQlJ9d2eLGegQiG+7BrgtSv8\nDB0Kn3T8Judt2c1RUjp+DBMTG9went1zAr3XTMAwoqBcJBI6ldvG5JVDruD4ZTMwo1oFr27fmU/P\nuRG1bh3rjVL8j00gkYgipofdTg1oTX2eiSQdXAY2o/cMsb6dn+FrQriro+nxuIkRUCGsLX42b4aB\nyVy61Dh7Pz8Dg5SjTHRc2zZi9h0cGpsHPvGzJ6dwGjoiTgEubO4fG0Jd6Kw0a1GfWSFfj2ap4Tci\nH3wg0rmzyOXtgjlafE17/lsMksFYcmunCnnjVq1933amJXf3rpCZZFK20/Xz61Ofv2bJBJdLV9RM\n+i7eeUdkqNuSiMoqo6w8EglZYtu6RMT/bs3V8GOqRnMXjyd/J9PBQUSuaK8b8GSvesXrbXXBA7Q6\np22R8PHHIieXWhJJLkNTjtfnGSbraS/v0l8GY8lgLPmnKpcHfOVyrE8L/c6dRR69zBK7DkfzDpM1\nEfz4o0iPHvqx/gVLpLxcVh5fLoOxZPz4Gp/Ldo4PG5ZbH8RpgOKQR94Zn6uAfOjur8NvWpmwF3EE\nfrPmx8trd6AajJXpuoMnHf2SsqXfO87K2CMbsexqPK7ltMcjsnBh7rbJk/UVsi3T/BcHDpNN+OTn\nIx1h75Bf3usyLGeFmVBGqxT2IvUX+A1qYu6wc+x2RoC44SGGSYQS5jCGAKkG6AlcxHAR08WYAK+K\ncV7vEO3aJXfg9+viH40QDXP99fDSS3DnnTBwYO62G2+EE06Aiy/eeu/zGSPmU+rZgnfB/AaPxcFh\nRwj9rz+QiUpTIjrizmGrOAK/EPj93BioZHrHKYztVsm7bj9rKcVOllmI4SaOO50mHhU3Gw8LNPow\nnntOC/XzztNp5TUxTXjkEZ2OPmoUfPNN7fcsWwZ9+oDLcf875JFvnwwzuDqUFvYCKLfLKemxHRyB\nXyCqqsDn04J262LBcQAAHyVJREFUiApzBxMwkpE3lzCDP3InSzmAT119uZgZDLpMl1tuLFau1HVE\nDjtMa/dK1f2+Tp3g6adh40Y44wxd7SGbZcugb9/GG5eDw3YJh9ntzACHszAt7G3D1BeykwOybepj\n98nXo1XY8C1Lqn5fno4djmHIfzwjc+LswwyqZcMfjG46XlXV8CFs3izSr59uP/v55/X7zGOPaXt+\nthO3qkoH+lx7bcPH5OBQX+ypFRJPtQdNRYydPLLQwyooODb85sfCO8JUHVGG54FZybZouknDCdFn\nsA2XbneIzUDewZ1lw3cTo8wI4VoU5uGDphF/ow6DejgM06Zt3dieRERnHX70ETz8MPTqVb+xn3km\nTJ4Ms2frB8CKFWDbjobvkF8+3j2QzjVP5a245j+/3WvfoYVUy2zOxGLwzDPJ1eZrIQ4jikHmQtXJ\nT8Jjbc9l982rCCReSXePyiRhufneLqWSMjyfVcNQxfSSP3Nfn5vp3RuO9YUpf6IMM6FLQKy+p5KS\nY/x06KDreav/ZspHzFzs56GH4IYb4MQTk1+wvYYwSW68Ed57Tztx+/WDL7/UrzsC3yGfPPYYDOcw\nBiV7KQMQj+tr2DHpbJv6LAPy9WhJJp2V/7Lk6cMr5JTddAx9r14iD/3RkkSJT9tBsqusuVzpIm8R\nl0+imBLFkI3sIsvpI3MZmUwuySxhbZBxBAUkWS4hN8wztWt/jXDPmZTLKbtZMmGCyI03ivz7Ukti\nyiUJkITLJfE36igwlxUCum6dSO/eIl27ilx+uYhSIlu2FOAEO7RKoq9ZUoUnXVwwXR2zFSZbZYMT\nh18YNm4Uufu8jJCtMnzyxi2WxOPJN6QE6MSJIoMGiYwcWSsL9n+ltatf1mwSboP8QKlswidvMSin\ngmYEt8xlpMykPDczNzkhVOGVmZRLBRNlHR1y9rmAodK2rcgZ3Sx5Zs9yqcYjcQyJGy757/lBCYVE\n/vMf3dR5WDtLbu6wAxm+Dg4NZOWw8pzrtbprz1abbJWNI/DziWVJbEqFPPYnSzp31hp3PClkJVX+\nYCufSwnFn34SWfgPSyKmTxI1BHu2c2prj9V0Tzuysl+P4M7RiNJJKluZROIY6T4ANT8TwZ3OAp5J\nuVTjSk4gusha+/YiffuKXO63pMrwSRzdJ2DJPy354gtddrrmcTs41JeqKpF5vtzCg2K03mSrbOor\n8B0bfgOJvqZLKBuJKCfh4bXDKjlnWgDzEt0ZC5dLd0wOh8Hvx7Zh9Wr4+okwg67WdveY8jBCKgkQ\n4tAsG3/Khp9dNFlMky92+yV7fv8u7qTjV4AufEeEErxUp8suA5jEuZcLATife3ETA0h/R82Sygqb\n87lXd91KduBNbTNJcDsT6M/7uIlgkPJBRLncvoV3Ng5i7cZSjmcuLqoxEeLRCI9eFOIm/PgJM75k\nDr+p1l3GbJeHZy6pxD3UT/fu0L077L47GG/Xz6fg0PKJx2HBAh1g8OijcHtkj/S2VJNyx3ZffxyB\nv5Ns2KCjVRI3hrgyEcGFjakizBgVQp1/FVV7V7Jl1hx2ffI+1Ky7id/9IOX7VvLYl362bIHJhPAn\nM2uRKNOGhWgzIoA52QMxXbEy0aUrT/10NPvYKzg4+k66Bn6vy0ay7raf6LR2ZXo88T17sXzyHLq+\nPIc9XrgHIxHXG1weSi8ew4rd/Cx6HAa/H8TIaqWeM5mgb6JDWUwcV7K+uJGsqi8Y2AxMOspUjc+e\nwnP8imcxsbHJTCgmNu3ZwEwu4lzux10dzfQBiEdYe/scvrg9xPOUshu6wudt9gQ8REmYHmadUUli\nkJ9u3XQD6t1WhOn5eYiSEwOoIVk3eTgMc+bo/8eMSQsA24YN88K4H51DGx+Y546pu++BM8E0G0Rg\n4UKd9Pfoo/DDDzpP5HAJ04XvSKAwU0qN1+skW+0I9VkG5OvR7E06waBsOWqYPFoWlLZttaVlaq/c\nAk7TegelSxep5UyNYsq9+1bIhAm6684HQUvskjqKoNUwd7z1lsiRpjaRRDEl5tbvDf7eSteir7Ws\ntXQBtFq2zVSHL8MQcblk8UGj5UMOkG869ZWqw4dKQiW7bhmmLDumXKxTKmTORZbMGmvJ2x2H5dTR\nT6T/Kolh5myrq31jTfNQuiJnsmZ/Kichipk2TcVB1tA97ZweRzDZCtKQKjwSNMulrI0lw9rl1vGv\nxiVBs1yONK06a/z/0ROUKW0q5FedLRnZRW9PoCRmemXxsImyqV1XiXtKRPr3z1u7SAeRpUtF/vIX\nHRQAIm63SMeO+v+ze1lSjStz7RhGbf9XKwbHht94bNki8uKoXMF+sTcoSklOQ5EYhgT3rpDzzhOZ\nOlXkpb9ZEvfqBih2XZUt62nL/vvfdXOSae0rZFKnoESur5CZ/YPyyK51CPXtkfzOxJuW3NInKBHc\neuKo0fs3tc8NG0TOOSfZHEXpYxGPR39vShgGg7rRi5EsUatUuphV3HDXOVFIcrLI9jtsy09RwcSc\ndoqpyWYzPplJea1EnK1tSyQn31Sf4rmM3O53p47lf2Uja0+gzmTQIFavFrnpJpGDD9Y/kWGI+P0i\nhxyin3frJnLvvSI/jMo4awV0eJjTTjONI/B3ksSblnwzslxWHFcufz3ekq5d9VmaR25lvve6DJO/\n/lXk5RsyQr3OcsWNIBRsWyszRxhW0pma0Yh3tkRy1auWRJIakyQFmpSX54w1FNIlk01T5K9/FYm9\nXo+6+9mTQNZkIKmJYuRIHUKXbDBvezySUJmuWVtzVn/CvjkTR2p7DENmUp6jxW9vW2p7FFPCDKol\n8KXGd2dvq8IrQ5QlAW/mt0hgyIaSzvLakIkyfbpuNH/fBZYsP6Zc1v26XD5/xJKPPhJ5/32Rd98V\nef1mSz4+ulzW/rpcvn7Cku+/F/n5Z5FEQp/H2JChWtJNnJh7futatSXZOH8HeyI0dFsD+OEHkbvu\nEjnyyMypHjxY5G9/Ezn7bC30d91VZNo0nRUuIvK813HWbgtH4NeTdeMnyk+77SvW0IkyaahVq0H4\nEGXJfvuJPFqW27Qkp2ZwHjS99etFbulYkRNiud0ooG2QmJrb6jCKWxJv6vFXV4tceaVWovbdVyQc\nbuDga56fuhrMB4NamHm96TyFbAG9+PiJEnH5ak0KUdzy+19Ycu7+lnywyyBJZG2LKbfcdKolM35r\nyZfdcrfZKIl5fLLowqDEjbrbRNY1AcRRdbaKzF6J1GVG2lobyShm2mQ1GEui2WYLkOm+iXJ5u6DE\nsvonVOORER0t6dhRpE0bkfEqY+rajE+O28WS3XcX6dVLm0KyQ4THHWjJkCEiRxwhcuHBlmxRyRwN\n0ye3nm7JNdeI3HabyPPXWhJ1+yRhNGx1mmLjRpF//Utk+HB9yYLIAQfoXJD33hOZNEmkpETrBJdf\nLrJ2beazsdetWhOzjGzdpRRqkheBD9wKLAc+AJ4COmRtuwpYCXwCnFCf/eVb4Ecvn5hzc31D5xqm\nASWR67OEaXbzjwKw/P7UzWukzQw73QTFsqTa1PuKmy4ZR1BevsGSby+rkHP21cJp/HiRTZsa/zi2\nN670BDBypM5VSJ3vlJbr8aT9ELUm3iwfRZ3bsk1SqfMWDKZNT1GMnJDVmDJyrpEqvOnQ1OxeqilB\n9IV3X5mxZ0V6ckmZkVLJcDpkN9f8lAp3nUxFrf2toXvOSiw7uW4yFUm/RsbUFcOQq6hI7y87DyOK\nKbd2qpCDD06e1l4ZBSKGKTe0qUjnBNb0P01tWyFHHily7rkiD1xoScTwiq3UNhOeqqtFnn5a5Ne/\n1qce9Ipx0iS92qmq0ubKTp20cvG732VqO9m2yIoVIg+WpxKtakzATv/kHPIl8IcBruT/NwM3J/8/\nAHgf8AJ7A58B5vb2l3cNf999a2l2sSwNsjlm7z1xhY6BX8BQWf+LQQ268B++WO9r85hyuaZLMK0J\nbsEnb9zavI47hyYwUWycb8k97vLchtiGkTGjjBwp0XHlsvguS6ZNEznuOJFnzNr2/9tLJsqT++Uq\nEtkThb+Ghp8S0pOTQrqmhr+AobVWEtXJ3IoYZtohnpk8XGl/h3Zue6Uaj0QxpQpPOiEvNZaUhh9x\n+eSxP1nywgsiS5aIrHnUkpjHJwllStTtk6knWzJ0qM6wnkkNe3p5efo8xuMilZUi558v0qGD3rzb\nbiJ/+IPIm29qs1UiITJnjkjPnnr7sGH6J3ntNW3PP/54vXIBqeVjSZkfq69z7PfZ1FfgK/3ehqOU\nOg04Q0RGK6WuSkYATUtumw9cLyLbrG40YMAAWbRoUaOMp15MmoTccguQVVO7fXv47W/19qzwvuaC\nWGGiRwTwEAVAeb06UHknxll5Yxj/tWWUGFFsUSC2ruNjmqgpU3STlVbEc0OmMTx8TbqWURwXXz/y\nOr3OrvvcihVGjj4aFde5DXEMhvIm13E9J/BS5poCBEW0/0A27H0YH5ccSrs359H/y+dQCAlM/sid\nPFU6noA3zOU/TqZ77DNCBNhMO87lPlzEAcV/1Cl8K3twAbNxYRNHIbhQJLAx+Tt/4s/8PV2cL47B\n3ejG8tl5GFFcBHgdBQQI0Z4N9GcJ79GfjXTgdRWgbVs4oSSEq0sp+3RYR7tepfRuv46OKxfS5qWn\nM6G5ffuyeuQElr62juAnAf6zzk/btnDlkWHO6hJin/MDuFwgC0K87QtQ/qAf3/thTusQ4qeDA7xa\n5WfxYkgk9HkdTJgAIdZRyj8pT4fxSvI8VlPCyLaVDLjEzyWXQNeuTXM9FBNKqXdFZMB231ifWaE+\nD+A54HfJ/+9M/Z98fi96Mqjrc+OBRcCiHj16NN0UuDX69s1oKiAyenT+x7AjVFRIIssk0JBohdXl\nWT4Bw9ARO6qBvXKLmOlnZUxmEdxyAUHp2FHk7be38aHy8szvYZqy5doKWVye6++pre1rm352mOlm\nfGk7f8qen7a9J0thpLaPIzdirIKJ6RVCtvad8hGkzDuJGqaklJmp5v5iyQinlOkqO1M7FRIbzfIp\nZD6njyPgtWRER0u2ZNVwqsKbjoy6gMxqcjM++YM7mDZDlbXJHHe8jnO4jg7y0d2WnHGGtty53drM\n9NFHebtMmiU0VnlkpdQrSqmP6nicmvWevwBx4OEdnJgQkdkiMkBEBnTu3HlHP95wPv4YRo/WnT5G\nj4aHHsr/GHaEQIC4kemGhWvnu/z0OCeAuHWrxYTLy/2/vJObd5mC/XJls1vZNDWRUJhNz4WY2nk6\ndzOe5/c4nxXefmzeDMccA6+8spUPjhlDzCwhjgkeD77hAXqc1I/XGJqTpQypTGZd7vqM0hB9OqzD\nQHBh4ybKMYT0+xQc78pueRmnqnMPPu/iRynYjXUkMFDoxLi9D+7Al6OvYr8xfvbrkzu8RXucQulJ\nfqoPDyDKyEmW69gBevSAs91z0+MDcCG4iRIgxBjm4E1mXettNiYJ3uWXWdnZpLe5iTI4EuLg9SHc\nWS073URwkcBDhNOZmz42DxFuj13MDVxLJWWM2jInvS1bOKW+Y1f+R8+e8Pjj8OmnutT3//t/cNBB\nMGIEvPqqniEctkJ9ZoVtPYDfA2GgTdZrVwFXZT2fD/i3t6/mEJZZDNy7+0SJo7Rz0eNpkDYee92S\nGXtWyIiOlgSDWrP84qJWFluerFIaw5S425u2j8e9PjnCsKRtW+0Dfvzxuj8+e2BQXvMlnflZ+0q4\n3NpJrLJWZKCdxskIJdvnk7gy0xr1r3+drDmU3JYwdHTNYCxp00bk0ktFvn4iywFdczVmWdr3VJdD\nNRjMhMhkbwsGc8ZnKyUJr0+ev9aStw7OXTHEk9r/OIJSndWkp66VQWq1lAoyyF6RZG+LZ/kygka5\nVOHJydeouUqKe3KPee1aHe2TSng89FCRhx8WiUYb/1JprpAnp+2JwMdA5xqvH0iu03YVzdFpW4TE\n38iNn7cNo8EJKIsX6xj/53uWp5fercmsk7gxy7SlMslgYpqyaFSFgEjnzvqlWbN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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g.plot(title=\"Optimized\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "χ^2 error from odometry edges: 142.189\n", + "χ^2 error from scan-matching edges: 73.652\n" + ] + } + ], + "source": [ + "print(\"\\nχ^2 error from odometry edges: {:7.3f}\".format(g_odom.calc_chi2()))\n", + "print(\"χ^2 error from scan-matching edges: {:7.3f}\".format(g_scan.calc_chi2()))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Wm4l+8IHIUUfpR+rEAbaeTZhm0Qyk3tCDryFDRP4T62Az4fXgG/y2Yl36ZnV1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\nPPQDW1yzVMP/7OjRkm00kmgI5lZkMPMO8ULnEcr7OzIYklYRcUxLXMO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kWku1JLBlLdUyAkd69hQ5q0dUPqFPXl1oqalpu0pZZQbtHWkLbO97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\nXvgUCWjQIb10hNcDHOIvddM6Jvn0CsVauqSGn8Gbb5K/qjT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z^A4^1Gkzc@7m9=)c4Rk@3ypT2D$RG=27Zj;fvjbcyLa1n+O@Z4PaY5ZEHIs!fuD&- zNBQaL)Ir1S;n+cpu$W6HfrcloTuMQgJ3^{rjVnTiZ0)N~(vMN@6~*GmUA8PXzD>5G z>wUjzxx9ut#+JNVt}_R%gMP9C`n-+}2MIAP%hTG>t^#>hhJ54n(_$B{QwN3KFa-JA z?<4c7fRat;lZ7 0: + rel_diff = (chi2_prev - self._chi2) / (chi2_prev + np.finfo(float).eps) + print("{:9d} {:20.4f} {:18.6f}".format(i, self._chi2, -rel_diff)) + if self._chi2 < chi2_prev and rel_diff < tol: + return + else: + print("{:9d} {:20.4f}".format(i, self._chi2)) + + # Update the previous iteration's chi^2 error + chi2_prev = self._chi2 + + # Hold the first pose fixed + if fix_first_pose: + self._hessian[:dim, :] = 0. + self._hessian[:, :dim] = 0. + self._hessian[:dim, :dim] += np.eye(dim) + self._gradient[:dim] = 0. + + # Solve for the updates + dx = spsolve(self._hessian, -self._gradient) # pylint: disable=invalid-unary-operand-type + + # Apply the updates + for v, dx_i in zip(self._vertices, np.split(dx, n)): + v.pose += dx_i + + # If we reached the maximum number of iterations, print out the final iteration's results + self.calc_chi2() + rel_diff = (chi2_prev - self._chi2) / (chi2_prev + np.finfo(float).eps) + print("{:9d} {:20.4f} {:18.6f}".format(max_iter, self._chi2, -rel_diff)) + + def to_g2o(self, outfile): + """Save the graph in .g2o format. + + Parameters + ---------- + outfile : str + The path where the graph will be saved + + """ + with open(outfile, 'w') as f: + for v in self._vertices: + f.write(v.to_g2o()) + + for e in self._edges: + f.write(e.to_g2o()) + + def plot(self, vertex_color='r', vertex_marker='o', vertex_markersize=3, edge_color='b', title=None): + """Plot the graph. + + Parameters + ---------- + vertex_color : str + The color that will be used to plot the vertices + vertex_marker : str + The marker that will be used to plot the vertices + vertex_markersize : int + The size of the plotted vertices + edge_color : str + The color that will be used to plot the edges + title : str, None + The title that will be used for the plot + + """ + plt.figure() + + for e in self._edges: + e.plot(edge_color) + + for v in self._vertices: + v.plot(vertex_color, vertex_marker, vertex_markersize) + + if title: + plt.title(title) + + plt.show() diff --git a/SLAM/GraphBasedSLAM/graphslam/load.py b/SLAM/GraphBasedSLAM/graphslam/load.py new file mode 100644 index 0000000000..b0dd023d93 --- /dev/null +++ b/SLAM/GraphBasedSLAM/graphslam/load.py @@ -0,0 +1,66 @@ +# Copyright (c) 2020 Jeff Irion and contributors +# +# This file originated from the `graphslam` package: +# +# https://github.com/JeffLIrion/python-graphslam + +"""Functions for loading graphs. + +""" + + +import logging + +import numpy as np + +from .edge.edge_odometry import EdgeOdometry +from .graph import Graph +from .pose.se2 import PoseSE2 +from .util import upper_triangular_matrix_to_full_matrix +from .vertex import Vertex + + +_LOGGER = logging.getLogger(__name__) + + +def load_g2o_se2(infile): + """Load an :math:`SE(2)` graph from a .g2o file. + + Parameters + ---------- + infile : str + The path to the .g2o file + + Returns + ------- + Graph + The loaded graph + + """ + edges = [] + vertices = [] + + with open(infile) as f: + for line in f.readlines(): + if line.startswith("VERTEX_SE2"): + numbers = line[10:].split() + arr = np.array([float(number) for number in numbers[1:]], dtype=np.float64) + p = PoseSE2(arr[:2], arr[2]) + v = Vertex(int(numbers[0]), p) + vertices.append(v) + continue + + if line.startswith("EDGE_SE2"): + numbers = line[9:].split() + arr = np.array([float(number) for number in numbers[2:]], dtype=np.float64) + vertex_ids = [int(numbers[0]), int(numbers[1])] + estimate = PoseSE2(arr[:2], arr[2]) + information = upper_triangular_matrix_to_full_matrix(arr[3:], 3) + e = EdgeOdometry(vertex_ids, information, estimate) + edges.append(e) + continue + + if line.strip(): + _LOGGER.warning("Line not supported -- '%s'", line.rstrip()) + + return Graph(edges, vertices) diff --git a/SLAM/GraphBasedSLAM/graphslam/pose/__init__.py b/SLAM/GraphBasedSLAM/graphslam/pose/__init__.py new file mode 100644 index 0000000000..afb8549087 --- /dev/null +++ b/SLAM/GraphBasedSLAM/graphslam/pose/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) 2020 Jeff Irion and contributors +# +# This file originated from the `graphslam` package: +# +# https://github.com/JeffLIrion/python-graphslam diff --git a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py new file mode 100644 index 0000000000..88518510e7 --- /dev/null +++ b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py @@ -0,0 +1,171 @@ +# Copyright (c) 2020 Jeff Irion and contributors +# +# This file originated from the `graphslam` package: +# +# https://github.com/JeffLIrion/python-graphslam + +r"""Representation of a pose in :math:`SE(2)`. + +""" + +import math + +import numpy as np + +from ..util import neg_pi_to_pi + + +class PoseSE2(np.ndarray): + r"""A representation of a pose in :math:`SE(2)`. + + Parameters + ---------- + position : np.ndarray, list + The position in :math:`\mathbb{R}^2` + orientation : float + The angle of the pose (in radians) + + """ + def __new__(cls, position, orientation): + obj = np.array([position[0], position[1], neg_pi_to_pi(orientation)], dtype=np.float64).view(cls) + return obj + + # pylint: disable=arguments-differ + def copy(self): + """Return a copy of the pose. + + Returns + ------- + PoseSE2 + A copy of the pose + + """ + return PoseSE2(self[:2], self[2]) + + def to_array(self): + """Return the pose as a numpy array. + + Returns + ------- + np.ndarray + The pose as a numpy array + + """ + return np.array(self) + + def to_compact(self): + """Return the pose as a compact numpy array. + + Returns + ------- + np.ndarray + The pose as a compact numpy array + + """ + return np.array(self) + + def to_matrix(self): + """Return the pose as an :math:`SE(2)` matrix. + + Returns + ------- + np.ndarray + The pose as an :math:`SE(2)` matrix + + """ + return np.array([[np.cos(self[2]), -np.sin(self[2]), self[0]], [np.sin(self[2]), np.cos(self[2]), self[1]], [0., 0., 1.]], dtype=np.float64) + + @classmethod + def from_matrix(cls, matrix): + """Return the pose as an :math:`SE(2)` matrix. + + Parameters + ---------- + matrix : np.ndarray + The :math:`SE(2)` matrix that will be converted to a `PoseSE2` instance + + Returns + ------- + PoseSE2 + The matrix as a `PoseSE2` object + + """ + return cls([matrix[0, 2], matrix[1, 2]], math.atan2(matrix[1, 0], matrix[0, 0])) + + # ======================================================================= # + # # + # Properties # + # # + # ======================================================================= # + @property + def position(self): + """Return the pose's position. + + Returns + ------- + np.ndarray + The position portion of the pose + + """ + return np.array(self[:2]) + + @property + def orientation(self): + """Return the pose's orientation. + + Returns + ------- + float + The angle of the pose + + """ + return self[2] + + @property + def inverse(self): + """Return the pose's inverse. + + Returns + ------- + PoseSE2 + The pose's inverse + + """ + return PoseSE2([-self[0] * np.cos(self[2]) - self[1] * np.sin(self[2]), self[0] * np.sin(self[2]) - self[1] * np.cos([self[2]])], -self[2]) + + # ======================================================================= # + # # + # Magic Methods # + # # + # ======================================================================= # + def __add__(self, other): + r"""Add poses (i.e., pose composition): :math:`p_1 \oplus p_2`. + + Parameters + ---------- + other : PoseSE2 + The other pose + + Returns + ------- + PoseSE2 + The result of pose composition + + """ + return PoseSE2([self[0] + other[0] * np.cos(self[2]) - other[1] * np.sin(self[2]), self[1] + other[0] * np.sin(self[2]) + other[1] * np.cos(self[2])], neg_pi_to_pi(self[2] + other[2])) + + def __sub__(self, other): + r"""Subtract poses (i.e., inverse pose composition): :math:`p_1 \ominus p_2`. + + Parameters + ---------- + other : PoseSE2 + The other pose + + Returns + ------- + PoseSE2 + The result of inverse pose composition + + """ + return PoseSE2([(self[0] - other[0]) * np.cos(other[2]) + (self[1] - other[1]) * np.sin(other[2]), (other[0] - self[0]) * np.sin(other[2]) + (self[1] - other[1]) * np.cos(other[2])], neg_pi_to_pi(self[2] - other[2])) diff --git a/SLAM/GraphBasedSLAM/graphslam/util.py b/SLAM/GraphBasedSLAM/graphslam/util.py new file mode 100644 index 0000000000..070dc1aa06 --- /dev/null +++ b/SLAM/GraphBasedSLAM/graphslam/util.py @@ -0,0 +1,78 @@ +# Copyright (c) 2020 Jeff Irion and contributors +# +# This file originated from the `graphslam` package: +# +# https://github.com/JeffLIrion/python-graphslam + +"""Utility functions used throughout the package. + +""" + +import numpy as np + + +TWO_PI = 2 * np.pi + + +def neg_pi_to_pi(angle): + r"""Normalize ``angle`` to be in :math:`[-\pi, \pi)`. + + Parameters + ---------- + angle : float + An angle (in radians) + + Returns + ------- + float + The angle normalized to :math:`[-\pi, \pi)` + + """ + return (angle + np.pi) % (TWO_PI) - np.pi + + +def solve_for_edge_dimensionality(n): + r"""Solve for the dimensionality of an edge. + + In a .g2o file, an edge is specified as `` ``, where only the upper triangular portion of the matrix is provided. + + This solves the problem: + + .. math:: + + d + \frac{d (d + 1)}{2} = n + + Returns + ------- + int + The dimensionality of the edge + + """ + return int(round(np.sqrt(2 * n + 2.25) - 1.5)) + + +def upper_triangular_matrix_to_full_matrix(arr, n): + """Given an upper triangular matrix, return the full matrix. + + Parameters + ---------- + arr : np.ndarray + The upper triangular portion of the matrix + n : int + The size of the matrix + + Returns + ------- + mat : np.ndarray + The full matrix + + """ + triu0 = np.triu_indices(n, 0) + triu1 = np.triu_indices(n, 1) + tril1 = np.tril_indices(n, -1) + + mat = np.zeros((n, n), dtype=np.float64) + mat[triu0] = arr + mat[tril1] = mat[triu1] + + return mat diff --git a/SLAM/GraphBasedSLAM/graphslam/vertex.py b/SLAM/GraphBasedSLAM/graphslam/vertex.py new file mode 100644 index 0000000000..6fb1f0d098 --- /dev/null +++ b/SLAM/GraphBasedSLAM/graphslam/vertex.py @@ -0,0 +1,67 @@ +# Copyright (c) 2020 Jeff Irion and contributors +# +# This file originated from the `graphslam` package: +# +# https://github.com/JeffLIrion/python-graphslam + +"""A ``Vertex`` class. + +""" + +import matplotlib.pyplot as plt + + +# pylint: disable=too-few-public-methods +class Vertex: + """A class for representing a vertex in Graph SLAM. + + Parameters + ---------- + vertex_id : int + The vertex's unique ID + pose : graphslam.pose.se2.PoseSE2 + The pose associated with the vertex + vertex_index : int, None + The vertex's index in the graph's ``vertices`` list + + Attributes + ---------- + id : int + The vertex's unique ID + index : int, None + The vertex's index in the graph's ``vertices`` list + pose : graphslam.pose.se2.PoseSE2 + The pose associated with the vertex + + """ + def __init__(self, vertex_id, pose, vertex_index=None): + self.id = vertex_id + self.pose = pose + self.index = vertex_index + + def to_g2o(self): + """Export the vertex to the .g2o format. + + Returns + ------- + str + The vertex in .g2o format + + """ + return "VERTEX_SE2 {} {} {} {}\n".format(self.id, self.pose[0], self.pose[1], self.pose[2]) + + def plot(self, color='r', marker='o', markersize=3): + """Plot the vertex. + + Parameters + ---------- + color : str + The color that will be used to plot the vertex + marker : str + The marker that will be used to plot the vertex + markersize : int + The size of the plotted vertex + + """ + x, y = self.pose.position + plt.plot(x, y, color=color, marker=marker, markersize=markersize) diff --git a/SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif b/SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif new file mode 100644 index 0000000000000000000000000000000000000000..2fabeaafc9cd4ad4b71bbf451637bf2560f1be5e 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9b22d3fb88cde649a0f7b0d20b7d4a5f08cc3a99 Mon Sep 17 00:00:00 2001 From: Chachay Date: Tue, 24 Mar 2020 20:12:26 +0100 Subject: [PATCH 185/909] rename yaw_r to yaw_ref --- PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index f6df94ac44..4141cfbff3 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -175,10 +175,10 @@ def simulate(track, goal): return t, x, y, yaw, v, goal_flag -def calc_target_speed(state, yaw_r): +def calc_target_speed(state, yaw_ref): target_speed = 10.0 / 3.6 - dyaw = yaw_r - state.yaw + dyaw = yaw_ref - state.yaw switch = math.pi / 4.0 <= dyaw < math.pi / 2.0 if switch: From b6e89c6d8b15a4907b47ec8bfee145e36a54c783 Mon Sep 17 00:00:00 2001 From: Chachay Date: Wed, 25 Mar 2020 16:42:00 +0100 Subject: [PATCH 186/909] rename a variable --- .../rear_wheel_feedback.py | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 4141cfbff3..52b4a11a0b 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -119,7 +119,7 @@ def rear_wheel_feedback_control(state, e, k, yaw_ref): return delta -def simulate(track, goal): +def simulate(path_ref, goal): T = 500.0 # max simulation time goal_dis = 0.3 @@ -133,11 +133,11 @@ def simulate(track, goal): t = [0.0] goal_flag = False - s = np.arange(0, track.length, 0.1) - e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, 0.0) + s = np.arange(0, path_ref.length, 0.1) + e, k, yaw_ref, s0 = path_ref.calc_track_error(state.x, state.y, 0.0) while T >= time: - e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, s0) + e, k, yaw_ref, s0 = path_ref.calc_track_error(state.x, state.y, s0) di = rear_wheel_feedback_control(state, e, k, yaw_ref) speed_ref = calc_target_speed(state, yaw_ref) @@ -165,9 +165,9 @@ def simulate(track, goal): # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) - plt.plot(track.X(s), track.Y(s), "-r", label="course") + plt.plot(path_ref.X(s), path_ref.Y(s), "-r", label="course") plt.plot(x, y, "ob", label="trajectory") - plt.plot(track.X(s0), track.Y(s0), "xg", label="target") + plt.plot(path_ref.X(s0), path_ref.Y(s0), "xg", label="target") plt.axis("equal") plt.grid(True) plt.title("speed[km/h]:{:.2f}, target s-param:{:.2f}".format(round(state.v * 3.6, 2), s0)) @@ -196,10 +196,10 @@ def main(): ay = [0.0, 0.0, 5.0, 6.5, 3.0, 5.0, -2.0] goal = [ax[-1], ay[-1]] - track = CubicSplinePath(ax, ay) - s = np.arange(0, track.length, 0.1) + reference_path = CubicSplinePath(ax, ay) + s = np.arange(0, reference_path.length, 0.1) - t, x, y, yaw, v, goal_flag = simulate(track, goal) + t, x, y, yaw, v, goal_flag = simulate(reference_path, goal) # Test assert goal_flag, "Cannot goal" @@ -208,7 +208,7 @@ def main(): plt.close() plt.subplots(1) plt.plot(ax, ay, "xb", label="input") - plt.plot(track.X(s), track.Y(s), "-r", label="spline") + plt.plot(reference_path.X(s), reference_path.Y(s), "-r", label="spline") plt.plot(x, y, "-g", label="tracking") plt.grid(True) plt.axis("equal") @@ -217,14 +217,14 @@ def main(): plt.legend() plt.subplots(1) - plt.plot(s, np.rad2deg(track.calc_yaw(s)), "-r", label="yaw") + plt.plot(s, np.rad2deg(reference_path.calc_yaw(s)), "-r", label="yaw") plt.grid(True) plt.legend() plt.xlabel("line length[m]") plt.ylabel("yaw angle[deg]") plt.subplots(1) - plt.plot(s, track.calc_curvature(s), "-r", label="curvature") + plt.plot(s, reference_path.calc_curvature(s), "-r", label="curvature") plt.grid(True) plt.legend() plt.xlabel("line length[m]") From e3190328128bd51c4b25665d2268e2c9ac18ea4a Mon Sep 17 00:00:00 2001 From: Nguyen Dinh Tuan Date: Thu, 9 Apr 2020 15:12:02 +0700 Subject: [PATCH 187/909] fix compute jacobian of F in ekf_estimation --- Localization/extended_kalman_filter/extended_kalman_filter.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py index 2b057e21de..2d0d86bf77 100644 --- a/Localization/extended_kalman_filter/extended_kalman_filter.py +++ b/Localization/extended_kalman_filter/extended_kalman_filter.py @@ -117,7 +117,7 @@ def jacob_h(): def ekf_estimation(xEst, PEst, z, u): # Predict xPred = motion_model(xEst, u) - jF = jacob_f(xPred, u) + jF = jacob_f(xEst, u) PPred = jF @ PEst @ jF.T + Q # Update @@ -128,7 +128,6 @@ def ekf_estimation(xEst, PEst, z, u): K = PPred @ jH.T @ np.linalg.inv(S) xEst = xPred + K @ y PEst = (np.eye(len(xEst)) - K @ jH) @ PPred - return xEst, PEst From d0d4f1116e70987b8ab251a137ae3188bd464c9d Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Fri, 10 Apr 2020 15:07:21 +0200 Subject: [PATCH 188/909] add Depth-First Search --- PathPlanning/DepthFirstSearch/dfs.py | 273 +++++++++++++++++++++++++++ 1 file changed, 273 insertions(+) create mode 100644 PathPlanning/DepthFirstSearch/dfs.py diff --git a/PathPlanning/DepthFirstSearch/dfs.py b/PathPlanning/DepthFirstSearch/dfs.py new file mode 100644 index 0000000000..4ceb0e2bec --- /dev/null +++ b/PathPlanning/DepthFirstSearch/dfs.py @@ -0,0 +1,273 @@ +""" + +Depth-First grid planning + +author: Erwin Lejeune (@spida_rwin) + +See Wikipedia article (https://en.wikipedia.org/wiki/depth-First_search) + +""" + +import math + +import matplotlib.pyplot as plt + +import random + +show_animation = True + + +class DFSPlanner: + + def __init__(self, ox, oy, reso, rr): + """ + Initialize grid map for DFS planning + + ox: x position list of Obstacles [m] + oy: y position list of Obstacles [m] + reso: grid resolution [m] + rr: robot radius[m] + """ + + self.reso = reso + self.rr = rr + self.calc_obstacle_map(ox, oy) + self.motion = self.get_motion_model() + + class Node: + def __init__(self, x, y, cost, pind, parent): + self.x = x # index of grid + self.y = y # index of grid + self.cost = cost + self.pind = pind + self.parent = parent + + def __str__(self): + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.pind) + + def planning(self, sx, sy, gx, gy): + """ + Depth First search + + input: + sx: start x position [m] + sy: start y position [m] + gx: goal x position [m] + gy: goal y position [m] + + output: + rx: x position list of the final path + ry: y position list of the final path + """ + + nstart = self.Node(self.calc_xyindex(sx, self.minx), + self.calc_xyindex(sy, self.miny), 0.0, -1, None) + ngoal = self.Node(self.calc_xyindex(gx, self.minx), + self.calc_xyindex(gy, self.miny), 0.0, -1, None) + + open_set, closed_set = dict(), dict() + open_set[self.calc_grid_index(nstart)] = nstart + + while 1: + if len(open_set) == 0: + print("Open set is empty..") + break + + c_id = max( + open_set, + key=lambda o: open_set[o].pind) + + current = open_set[c_id] + + # show graph + if show_animation: # pragma: no cover + plt.plot(self.calc_grid_position(current.x, self.minx), + self.calc_grid_position(current.y, self.miny), "xc") + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit( + 0) if event.key == 'escape' else None]) + if len(closed_set.keys()) % 10 == 0: + plt.pause(0.001) + + if current.x == ngoal.x and current.y == ngoal.y: + print("Find goal") + ngoal.pind = current.pind + ngoal.cost = current.cost + closed_set[current.pind] = current + break + + # Remove the item from the open set + del open_set[c_id] + + # Add it to the closed set + closed_set[c_id] = current + + # expand_grid search grid based on motion model + successors = [self.Node(current.x + self.motion[i][0], + current.y + self.motion[i][1], + current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)] + + random.shuffle(successors) + for node in successors: + n_id = self.calc_grid_index(node) + + # If the node is not safe, do nothing + if not self.verify_node(node): + continue + + if n_id not in closed_set: + open_set[n_id] = node + + rx, ry = self.calc_final_path(ngoal, closed_set) + return rx, ry + + def calc_final_path(self, ngoal, closedset): + # generate final course + rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ + self.calc_grid_position(ngoal.y, self.miny)] + n = closedset[ngoal.pind] + while n.parent is not None: + rx.append(self.calc_grid_position(n.x, self.minx)) + ry.append(self.calc_grid_position(n.y, self.miny)) + n = n.parent + + return rx, ry + + @staticmethod + def calc_heuristic(n1, n2): + w = 1.0 # weight of heuristic + d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) + return d + + def calc_grid_position(self, index, minp): + """ + calc grid position + + :param index: + :param minp: + :return: + """ + pos = index * self.reso + minp + return pos + + def calc_xyindex(self, position, min_pos): + return round((position - min_pos) / self.reso) + + def calc_grid_index(self, node): + return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + + def verify_node(self, node): + px = self.calc_grid_position(node.x, self.minx) + py = self.calc_grid_position(node.y, self.miny) + + if px < self.minx: + return False + elif py < self.miny: + return False + elif px >= self.maxx: + return False + elif py >= self.maxy: + return False + + # collision check + if self.obmap[node.x][node.y]: + return False + + return True + + def calc_obstacle_map(self, ox, oy): + + self.minx = round(min(ox)) + self.miny = round(min(oy)) + self.maxx = round(max(ox)) + self.maxy = round(max(oy)) + print("minx:", self.minx) + print("miny:", self.miny) + print("maxx:", self.maxx) + print("maxy:", self.maxy) + + self.xwidth = round((self.maxx - self.minx) / self.reso) + self.ywidth = round((self.maxy - self.miny) / self.reso) + print("xwidth:", self.xwidth) + print("ywidth:", self.ywidth) + + # obstacle map generation + self.obmap = [[False for i in range(self.ywidth)] + for i in range(self.xwidth)] + for ix in range(self.xwidth): + x = self.calc_grid_position(ix, self.minx) + for iy in range(self.ywidth): + y = self.calc_grid_position(iy, self.miny) + for iox, ioy in zip(ox, oy): + d = math.hypot(iox - x, ioy - y) + if d <= self.rr: + self.obmap[ix][iy] = True + break + + @staticmethod + def get_motion_model(): + # dx, dy, cost + motion = [[1, 0, 1], + [0, 1, 1], + [-1, 0, 1], + [0, -1, 1], + [-1, -1, math.sqrt(2)], + [-1, 1, math.sqrt(2)], + [1, -1, math.sqrt(2)], + [1, 1, math.sqrt(2)]] + + return motion + + +def main(): + print(__file__ + " start!!") + + # start and goal position + sx = 10.0 # [m] + sy = 10.0 # [m] + gx = 50.0 # [m] + gy = 50.0 # [m] + grid_size = 2.0 # [m] + robot_radius = 1.0 # [m] + + # set obstable positions + ox, oy = [], [] + for i in range(-10, 60): + ox.append(i) + oy.append(-10.0) + for i in range(-10, 60): + ox.append(60.0) + oy.append(i) + for i in range(-10, 61): + ox.append(i) + oy.append(60.0) + for i in range(-10, 61): + ox.append(-10.0) + oy.append(i) + for i in range(-10, 40): + ox.append(20.0) + oy.append(i) + for i in range(0, 40): + ox.append(40.0) + oy.append(60.0 - i) + + if show_animation: # pragma: no cover + plt.plot(ox, oy, ".k") + plt.plot(sx, sy, "og") + plt.plot(gx, gy, "xb") + plt.grid(True) + plt.axis("equal") + + DFS = DFSPlanner(ox, oy, grid_size, robot_radius) + rx, ry = DFS.planning(sx, sy, gx, gy) + + if show_animation: # pragma: no cover + plt.plot(rx, ry, "-r") + plt.pause(0.01) + plt.show() + + +if __name__ == '__main__': + main() From 39d12558e99b14e79bb5333b9ad54b24d2a8fca5 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Fri, 10 Apr 2020 15:07:54 +0200 Subject: [PATCH 189/909] add Breadth-First Search --- PathPlanning/BreadthFirstSearch/bfs.py | 270 +++++++++++++++++++++++++ 1 file changed, 270 insertions(+) create mode 100644 PathPlanning/BreadthFirstSearch/bfs.py diff --git a/PathPlanning/BreadthFirstSearch/bfs.py b/PathPlanning/BreadthFirstSearch/bfs.py new file mode 100644 index 0000000000..d20c608006 --- /dev/null +++ b/PathPlanning/BreadthFirstSearch/bfs.py @@ -0,0 +1,270 @@ +""" + +Breadth-First grid planning + +author: Erwin Lejeune (@spida_rwin) + +See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-First_search) + +""" + +import math + +import matplotlib.pyplot as plt + +import random + +show_animation = True + + +class BFSPlanner: + + def __init__(self, ox, oy, reso, rr): + """ + Initialize grid map for bfs planning + + ox: x position list of Obstacles [m] + oy: y position list of Obstacles [m] + reso: grid resolution [m] + rr: robot radius[m] + """ + + self.reso = reso + self.rr = rr + self.calc_obstacle_map(ox, oy) + self.motion = self.get_motion_model() + + class Node: + def __init__(self, x, y, cost, pind, parent): + self.x = x # index of grid + self.y = y # index of grid + self.cost = cost + self.pind = pind + self.parent = parent + + def __str__(self): + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.pind) + + def planning(self, sx, sy, gx, gy): + """ + Breadth First search + + input: + sx: start x position [m] + sy: start y position [m] + gx: goal x position [m] + gy: goal y position [m] + + output: + rx: x position list of the final path + ry: y position list of the final path + """ + + nstart = self.Node(self.calc_xyindex(sx, self.minx), + self.calc_xyindex(sy, self.miny), 0.0, -1, None) + ngoal = self.Node(self.calc_xyindex(gx, self.minx), + self.calc_xyindex(gy, self.miny), 0.0, -1, None) + + open_set, closed_set = dict(), dict() + open_set[self.calc_grid_index(nstart)] = nstart + + while 1: + if len(open_set) == 0: + print("Open set is empty..") + break + + c_id = min( + open_set, + key=lambda o: open_set[o].pind) + + current = open_set[c_id] + + # show graph + if show_animation: # pragma: no cover + plt.plot(self.calc_grid_position(current.x, self.minx), + self.calc_grid_position(current.y, self.miny), "xc") + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit( + 0) if event.key == 'escape' else None]) + if len(closed_set.keys()) % 10 == 0: + plt.pause(0.001) + + if current.x == ngoal.x and current.y == ngoal.y: + print("Find goal") + ngoal.pind = current.pind + ngoal.cost = current.cost + break + + # Remove the item from the open set + del open_set[c_id] + + # expand_grid search grid based on motion model + successors = [self.Node(current.x + self.motion[i][0], + current.y + self.motion[i][1], + current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)] + + random.shuffle(successors) + for node in successors: + n_id = self.calc_grid_index(node) + + # If the node is not safe, do nothing + if not self.verify_node(node): + continue + + if n_id not in closed_set: + closed_set[n_id] = node + open_set[n_id] = node + + rx, ry = self.calc_final_path(ngoal, closed_set) + return rx, ry + + def calc_final_path(self, ngoal, closedset): + # generate final course + rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ + self.calc_grid_position(ngoal.y, self.miny)] + n = closedset[ngoal.pind] + while n.parent is not None: + rx.append(self.calc_grid_position(n.x, self.minx)) + ry.append(self.calc_grid_position(n.y, self.miny)) + n = n.parent + + return rx, ry + + @staticmethod + def calc_heuristic(n1, n2): + w = 1.0 # weight of heuristic + d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) + return d + + def calc_grid_position(self, index, minp): + """ + calc grid position + + :param index: + :param minp: + :return: + """ + pos = index * self.reso + minp + return pos + + def calc_xyindex(self, position, min_pos): + return round((position - min_pos) / self.reso) + + def calc_grid_index(self, node): + return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + + def verify_node(self, node): + px = self.calc_grid_position(node.x, self.minx) + py = self.calc_grid_position(node.y, self.miny) + + if px < self.minx: + return False + elif py < self.miny: + return False + elif px >= self.maxx: + return False + elif py >= self.maxy: + return False + + # collision check + if self.obmap[node.x][node.y]: + return False + + return True + + def calc_obstacle_map(self, ox, oy): + + self.minx = round(min(ox)) + self.miny = round(min(oy)) + self.maxx = round(max(ox)) + self.maxy = round(max(oy)) + print("minx:", self.minx) + print("miny:", self.miny) + print("maxx:", self.maxx) + print("maxy:", self.maxy) + + self.xwidth = round((self.maxx - self.minx) / self.reso) + self.ywidth = round((self.maxy - self.miny) / self.reso) + print("xwidth:", self.xwidth) + print("ywidth:", self.ywidth) + + # obstacle map generation + self.obmap = [[False for i in range(self.ywidth)] + for i in range(self.xwidth)] + for ix in range(self.xwidth): + x = self.calc_grid_position(ix, self.minx) + for iy in range(self.ywidth): + y = self.calc_grid_position(iy, self.miny) + for iox, ioy in zip(ox, oy): + d = math.hypot(iox - x, ioy - y) + if d <= self.rr: + self.obmap[ix][iy] = True + break + + @staticmethod + def get_motion_model(): + # dx, dy, cost + motion = [[1, 0, 1], + [0, 1, 1], + [-1, 0, 1], + [0, -1, 1], + [-1, -1, math.sqrt(2)], + [-1, 1, math.sqrt(2)], + [1, -1, math.sqrt(2)], + [1, 1, math.sqrt(2)]] + + return motion + + +def main(): + print(__file__ + " start!!") + + # start and goal position + sx = 10.0 # [m] + sy = 10.0 # [m] + gx = 50.0 # [m] + gy = 50.0 # [m] + grid_size = 2.0 # [m] + robot_radius = 1.0 # [m] + + # set obstable positions + ox, oy = [], [] + for i in range(-10, 60): + ox.append(i) + oy.append(-10.0) + for i in range(-10, 60): + ox.append(60.0) + oy.append(i) + for i in range(-10, 61): + ox.append(i) + oy.append(60.0) + for i in range(-10, 61): + ox.append(-10.0) + oy.append(i) + for i in range(-10, 40): + ox.append(20.0) + oy.append(i) + for i in range(0, 40): + ox.append(40.0) + oy.append(60.0 - i) + + if show_animation: # pragma: no cover + plt.plot(ox, oy, ".k") + plt.plot(sx, sy, "og") + plt.plot(gx, gy, "xb") + plt.grid(True) + plt.axis("equal") + + bfs = BFSPlanner(ox, oy, grid_size, robot_radius) + rx, ry = bfs.planning(sx, sy, gx, gy) + + if show_animation: # pragma: no cover + plt.plot(rx, ry, "-r") + plt.pause(0.01) + plt.show() + + +if __name__ == '__main__': + main() From 89ef0a9a5287695c928db24a0fa7e15c51c02206 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Fri, 10 Apr 2020 15:08:27 +0200 Subject: [PATCH 190/909] add bidirectional A* --- .../BidirectionalAStar/bidir_a_star.py | 331 ++++++++++++++++++ 1 file changed, 331 insertions(+) create mode 100644 PathPlanning/BidirectionalAStar/bidir_a_star.py diff --git a/PathPlanning/BidirectionalAStar/bidir_a_star.py b/PathPlanning/BidirectionalAStar/bidir_a_star.py new file mode 100644 index 0000000000..3c709c64c1 --- /dev/null +++ b/PathPlanning/BidirectionalAStar/bidir_a_star.py @@ -0,0 +1,331 @@ +""" + +Bidirectional A* grid planning + +author: Erwin Lejeune (@spida_rwin) + +See Wikipedia article (https://en.wikipedia.org/wiki/Bidirectional_search) + +""" + +import math + +import matplotlib.pyplot as plt + +show_animation = True + + +class BidirAStarPlanner: + + def __init__(self, ox, oy, reso, rr): + """ + Initialize grid map for a star planning + + ox: x position list of Obstacles [m] + oy: y position list of Obstacles [m] + reso: grid resolution [m] + rr: robot radius[m] + """ + + self.reso = reso + self.rr = rr + self.calc_obstacle_map(ox, oy) + self.motion = self.get_motion_model() + + class Node: + def __init__(self, x, y, cost, pind): + self.x = x # index of grid + self.y = y # index of grid + self.cost = cost + self.pind = pind + + def __str__(self): + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.pind) + + def planning(self, sx, sy, gx, gy): + """ + Bidirectional A star path search + + input: + sx: start x position [m] + sy: start y position [m] + gx: goal x position [m] + gy: goal y position [m] + + output: + rx: x position list of the final path + ry: y position list of the final path + """ + + nstart = self.Node(self.calc_xyindex(sx, self.minx), + self.calc_xyindex(sy, self.miny), 0.0, -1) + ngoal = self.Node(self.calc_xyindex(gx, self.minx), + self.calc_xyindex(gy, self.miny), 0.0, -1) + + open_set_A, closed_set_A = dict(), dict() + open_set_B, closed_set_B = dict(), dict() + open_set_A[self.calc_grid_index(nstart)] = nstart + open_set_B[self.calc_grid_index(ngoal)] = ngoal + + current_A = nstart + current_B = ngoal + + while 1: + if len(open_set_A) == 0: + print("Open set A is empty..") + break + + if len(open_set_B) == 0: + print("Open set B is empty..") + break + + c_id_A = min( + open_set_A, + key=lambda o: open_set_A[o].cost + self.calc_heuristic(current_B, + open_set_A[ + o])) + + current_A = open_set_A[c_id_A] + + c_id_B = min( + open_set_B, + key=lambda o: open_set_B[o].cost + self.calc_heuristic(current_A, + open_set_B[ + o])) + + current_B = open_set_B[c_id_B] + + # show graph + if show_animation: # pragma: no cover + plt.plot(self.calc_grid_position(current_A.x, self.minx), + self.calc_grid_position(current_A.y, self.miny), "xc") + plt.plot(self.calc_grid_position(current_B.x, self.minx), + self.calc_grid_position(current_B.y, self.miny), "xc") + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit( + 0) if event.key == 'escape' else None]) + if len(closed_set_A.keys()) % 10 == 0: + plt.pause(0.001) + + if current_A.x == current_B.x and current_A.y == current_B.y: + print("Found goal") + meetpointA = current_A + meetpointB = current_B + break + + # Remove the item from the open set + del open_set_A[c_id_A] + del open_set_B[c_id_B] + + # Add it to the closed set + closed_set_A[c_id_A] = current_A + closed_set_B[c_id_B] = current_B + + # expand_grid search grid based on motion model + for i, _ in enumerate(self.motion): + node = self.Node(current_A.x + self.motion[i][0], + current_A.y + self.motion[i][1], + current_A.cost + self.motion[i][2], c_id_A) + n_id = self.calc_grid_index(node) + + # If the node is not safe, do nothing + if not self.verify_node(node): + continue + + if n_id in closed_set_A: + continue + + if n_id not in open_set_A: + open_set_A[n_id] = node # discovered a new node + else: + if open_set_A[n_id].cost > node.cost: + # This path is the best until now. record it + open_set_A[n_id] = node + + for i, _ in enumerate(self.motion): + node = self.Node(current_B.x + self.motion[i][0], + current_B.y + self.motion[i][1], + current_B.cost + self.motion[i][2], c_id_B) + n_id = self.calc_grid_index(node) + + # If the node is not safe, do nothing + if not self.verify_node(node): + continue + + if n_id in closed_set_B: + continue + + if n_id not in open_set_B: + open_set_B[n_id] = node # discovered a new node + else: + if open_set_B[n_id].cost > node.cost: + # This path is the best until now. record it + open_set_B[n_id] = node + + rx, ry = self.calc_final_path_bidir(meetpointA, meetpointB, closed_set_A, closed_set_B) + + return rx, ry + + def calc_final_path_bidir(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B): + rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A) + rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B) + + rx_A.reverse() + ry_A.reverse() + + rx = rx_A + rx_B + ry = ry_A + ry_B + + return rx, ry + + def calc_final_path(self, ngoal, closedset): + # generate final course + rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ + self.calc_grid_position(ngoal.y, self.miny)] + pind = ngoal.pind + while pind != -1: + n = closedset[pind] + rx.append(self.calc_grid_position(n.x, self.minx)) + ry.append(self.calc_grid_position(n.y, self.miny)) + pind = n.pind + + return rx, ry + + @staticmethod + def calc_heuristic(n1, n2): + w = 1.0 # weight of heuristic + d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) + return d + + def calc_grid_position(self, index, minp): + """ + calc grid position + + :param index: + :param minp: + :return: + """ + pos = index * self.reso + minp + return pos + + def calc_xyindex(self, position, min_pos): + return round((position - min_pos) / self.reso) + + def calc_grid_index(self, node): + return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + + def verify_node(self, node): + px = self.calc_grid_position(node.x, self.minx) + py = self.calc_grid_position(node.y, self.miny) + + if px < self.minx: + return False + elif py < self.miny: + return False + elif px >= self.maxx: + return False + elif py >= self.maxy: + return False + + # collision check + if self.obmap[node.x][node.y]: + return False + + return True + + def calc_obstacle_map(self, ox, oy): + + self.minx = round(min(ox)) + self.miny = round(min(oy)) + self.maxx = round(max(ox)) + self.maxy = round(max(oy)) + print("minx:", self.minx) + print("miny:", self.miny) + print("maxx:", self.maxx) + print("maxy:", self.maxy) + + self.xwidth = round((self.maxx - self.minx) / self.reso) + self.ywidth = round((self.maxy - self.miny) / self.reso) + print("xwidth:", self.xwidth) + print("ywidth:", self.ywidth) + + # obstacle map generation + self.obmap = [[False for i in range(self.ywidth)] + for i in range(self.xwidth)] + for ix in range(self.xwidth): + x = self.calc_grid_position(ix, self.minx) + for iy in range(self.ywidth): + y = self.calc_grid_position(iy, self.miny) + for iox, ioy in zip(ox, oy): + d = math.hypot(iox - x, ioy - y) + if d <= self.rr: + self.obmap[ix][iy] = True + break + + @staticmethod + def get_motion_model(): + # dx, dy, cost + motion = [[1, 0, 1], + [0, 1, 1], + [-1, 0, 1], + [0, -1, 1], + [-1, -1, math.sqrt(2)], + [-1, 1, math.sqrt(2)], + [1, -1, math.sqrt(2)], + [1, 1, math.sqrt(2)]] + + return motion + + +def main(): + print(__file__ + " start!!") + + # start and goal position + sx = 10.0 # [m] + sy = 10.0 # [m] + gx = 50.0 # [m] + gy = 50.0 # [m] + grid_size = 2.0 # [m] + robot_radius = 1.0 # [m] + + # set obstable positions + ox, oy = [], [] + for i in range(-10, 60): + ox.append(i) + oy.append(-10.0) + for i in range(-10, 60): + ox.append(60.0) + oy.append(i) + for i in range(-10, 61): + ox.append(i) + oy.append(60.0) + for i in range(-10, 61): + ox.append(-10.0) + oy.append(i) + for i in range(-10, 40): + ox.append(20.0) + oy.append(i) + for i in range(0, 40): + ox.append(40.0) + oy.append(60.0 - i) + + if show_animation: # pragma: no cover + plt.plot(ox, oy, ".k") + plt.plot(sx, sy, "og") + plt.plot(gx, gy, "ob") + plt.grid(True) + plt.axis("equal") + + bidir_a_star = BidirAStarPlanner(ox, oy, grid_size, robot_radius) + rx, ry = bidir_a_star.planning(sx, sy, gx, gy) + + if show_animation: # pragma: no cover + plt.plot(rx, ry, "-r") + plt.pause(.0001) + plt.show() + + +if __name__ == '__main__': + main() From 01596296c41b0a76775b64378734fa0bd25c5662 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 13:45:22 +0200 Subject: [PATCH 191/909] change breadth first search file name --- .../BreadthFirstSearch/{bfs.py => breadth_first_search.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename PathPlanning/BreadthFirstSearch/{bfs.py => breadth_first_search.py} (100%) diff --git a/PathPlanning/BreadthFirstSearch/bfs.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py similarity index 100% rename from PathPlanning/BreadthFirstSearch/bfs.py rename to PathPlanning/BreadthFirstSearch/breadth_first_search.py From f2c651ff5514109e5d26bce8dff234b798048d8e Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 13:56:57 +0200 Subject: [PATCH 192/909] Changes following review --- .../breadth_first_search.py | 20 ++++++------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index d20c608006..3f5c95de02 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -17,7 +17,7 @@ show_animation = True -class BFSPlanner: +class BreadthFirstSearchPlanner: def __init__(self, ox, oy, reso, rr): """ @@ -48,7 +48,7 @@ def __str__(self): def planning(self, sx, sy, gx, gy): """ - Breadth First search + Breadth First search based planning input: sx: start x position [m] @@ -101,12 +101,10 @@ def planning(self, sx, sy, gx, gy): del open_set[c_id] # expand_grid search grid based on motion model - successors = [self.Node(current.x + self.motion[i][0], + for i, _ in enumerate(self.motion): + node = self.Node(current.x + self.motion[i][0], current.y + self.motion[i][1], - current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)] - - random.shuffle(successors) - for node in successors: + current.cost + self.motion[i][2], c_id+1, current) n_id = self.calc_grid_index(node) # If the node is not safe, do nothing @@ -132,12 +130,6 @@ def calc_final_path(self, ngoal, closedset): return rx, ry - @staticmethod - def calc_heuristic(n1, n2): - w = 1.0 # weight of heuristic - d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) - return d - def calc_grid_position(self, index, minp): """ calc grid position @@ -257,7 +249,7 @@ def main(): plt.grid(True) plt.axis("equal") - bfs = BFSPlanner(ox, oy, grid_size, robot_radius) + bfs = BreadthFirstSearchPlanner(ox, oy, grid_size, robot_radius) rx, ry = bfs.planning(sx, sy, gx, gy) if show_animation: # pragma: no cover From 68c4c78e089d9a41eeeb50dad50fc9481649138c Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 14:02:07 +0200 Subject: [PATCH 193/909] Fix path not connecting to start node --- PathPlanning/BreadthFirstSearch/breadth_first_search.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index 3f5c95de02..e67a478848 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -128,6 +128,9 @@ def calc_final_path(self, ngoal, closedset): ry.append(self.calc_grid_position(n.y, self.miny)) n = n.parent + rx.append(self.calc_grid_position(n.x, self.minx)) + ry.append(self.calc_grid_position(n.y, self.miny)) + return rx, ry def calc_grid_position(self, index, minp): From 6f358adc5ea9c3a80ef6448e69006540f7f726e3 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 14:23:13 +0200 Subject: [PATCH 194/909] shorten motion expansion loop --- ...idir_a_star.py => bidirectional_a_star.py} | 70 ++++++++++--------- 1 file changed, 37 insertions(+), 33 deletions(-) rename PathPlanning/BidirectionalAStar/{bidir_a_star.py => bidirectional_a_star.py} (84%) diff --git a/PathPlanning/BidirectionalAStar/bidir_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py similarity index 84% rename from PathPlanning/BidirectionalAStar/bidir_a_star.py rename to PathPlanning/BidirectionalAStar/bidirectional_a_star.py index 3c709c64c1..37d14eab68 100644 --- a/PathPlanning/BidirectionalAStar/bidir_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -15,7 +15,7 @@ show_animation = True -class BidirAStarPlanner: +class BidirectionalAStarPlanner: def __init__(self, ox, oy, reso, rr): """ @@ -125,44 +125,48 @@ def planning(self, sx, sy, gx, gy): # expand_grid search grid based on motion model for i, _ in enumerate(self.motion): - node = self.Node(current_A.x + self.motion[i][0], + continue_A = False + continue_B = False + + child_node_A = self.Node(current_A.x + self.motion[i][0], current_A.y + self.motion[i][1], current_A.cost + self.motion[i][2], c_id_A) - n_id = self.calc_grid_index(node) - # If the node is not safe, do nothing - if not self.verify_node(node): - continue - - if n_id in closed_set_A: - continue - - if n_id not in open_set_A: - open_set_A[n_id] = node # discovered a new node - else: - if open_set_A[n_id].cost > node.cost: - # This path is the best until now. record it - open_set_A[n_id] = node - - for i, _ in enumerate(self.motion): - node = self.Node(current_B.x + self.motion[i][0], + child_node_B = self.Node(current_B.x + self.motion[i][0], current_B.y + self.motion[i][1], current_B.cost + self.motion[i][2], c_id_B) - n_id = self.calc_grid_index(node) - # If the node is not safe, do nothing - if not self.verify_node(node): - continue - - if n_id in closed_set_B: - continue + n_id_A = self.calc_grid_index(child_node_A) + n_id_B = self.calc_grid_index(child_node_B) - if n_id not in open_set_B: - open_set_B[n_id] = node # discovered a new node - else: - if open_set_B[n_id].cost > node.cost: - # This path is the best until now. record it - open_set_B[n_id] = node + # If the node is not safe, do nothing + if not self.verify_node(child_node_A): + continue_A = True + + if not self.verify_node(child_node_B): + continue_B = True + + if n_id_A in closed_set_A: + continue_A = True + + if n_id_B in closed_set_B: + continue_B = True + + if not(continue_A): + if n_id_A not in open_set_A: + open_set_A[n_id_A] = child_node_A # discovered a new node + else: + if open_set_A[n_id_A].cost > child_node_A.cost: + # This path is the best until now. record it + open_set_A[n_id_A] = child_node_A + + if not(continue_B): + if n_id_B not in open_set_B: + open_set_B[n_id_B] = child_node_B # discovered a new node + else: + if open_set_B[n_id_B].cost > child_node_B.cost: + # This path is the best until now. record it + open_set_B[n_id_B] = child_node_B rx, ry = self.calc_final_path_bidir(meetpointA, meetpointB, closed_set_A, closed_set_B) @@ -318,7 +322,7 @@ def main(): plt.grid(True) plt.axis("equal") - bidir_a_star = BidirAStarPlanner(ox, oy, grid_size, robot_radius) + bidir_a_star = BidirectionalAStarPlanner(ox, oy, grid_size, robot_radius) rx, ry = bidir_a_star.planning(sx, sy, gx, gy) if show_animation: # pragma: no cover From 6d4ea8bd1da771ea1315a73f0759c5f732fe1b99 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 14:24:16 +0200 Subject: [PATCH 195/909] change file name --- PathPlanning/DepthFirstSearch/{dfs.py => depth_first_search.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename PathPlanning/DepthFirstSearch/{dfs.py => depth_first_search.py} (100%) diff --git a/PathPlanning/DepthFirstSearch/dfs.py b/PathPlanning/DepthFirstSearch/depth_first_search.py similarity index 100% rename from PathPlanning/DepthFirstSearch/dfs.py rename to PathPlanning/DepthFirstSearch/depth_first_search.py From 18b73652f6767aef8a32da970f4f82387182d99b Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 14:25:35 +0200 Subject: [PATCH 196/909] remove heuristic method --- PathPlanning/DepthFirstSearch/depth_first_search.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index 4ceb0e2bec..466aa1646a 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -17,7 +17,7 @@ show_animation = True -class DFSPlanner: +class DepthFirstSearchPlanner: def __init__(self, ox, oy, reso, rr): """ @@ -135,12 +135,6 @@ def calc_final_path(self, ngoal, closedset): return rx, ry - @staticmethod - def calc_heuristic(n1, n2): - w = 1.0 # weight of heuristic - d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) - return d - def calc_grid_position(self, index, minp): """ calc grid position @@ -260,8 +254,8 @@ def main(): plt.grid(True) plt.axis("equal") - DFS = DFSPlanner(ox, oy, grid_size, robot_radius) - rx, ry = DFS.planning(sx, sy, gx, gy) + dfs = DepthFirstSearchPlanner(ox, oy, grid_size, robot_radius) + rx, ry = dfs.planning(sx, sy, gx, gy) if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") From 952cff6b34e6abf9c52469952c20373c8d842f40 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 14:29:12 +0200 Subject: [PATCH 197/909] fix path not connecting to start node --- PathPlanning/DepthFirstSearch/depth_first_search.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index 466aa1646a..bb08080734 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -104,13 +104,13 @@ def planning(self, sx, sy, gx, gy): # Add it to the closed set closed_set[c_id] = current + random.shuffle(self.motion) + # expand_grid search grid based on motion model - successors = [self.Node(current.x + self.motion[i][0], + for i, _ in enumerate(self.motion): + node = self.Node(current.x + self.motion[i][0], current.y + self.motion[i][1], - current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)] - - random.shuffle(successors) - for node in successors: + current.cost + self.motion[i][2], c_id+1, current) n_id = self.calc_grid_index(node) # If the node is not safe, do nothing @@ -128,7 +128,7 @@ def calc_final_path(self, ngoal, closedset): rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ self.calc_grid_position(ngoal.y, self.miny)] n = closedset[ngoal.pind] - while n.parent is not None: + while n is not None: rx.append(self.calc_grid_position(n.x, self.minx)) ry.append(self.calc_grid_position(n.y, self.miny)) n = n.parent From 71daa7898d88f590ee4e65935e239115cb431fb5 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Mon, 13 Apr 2020 14:34:47 +0200 Subject: [PATCH 198/909] easier path retrieving and shuffle children node --- PathPlanning/BreadthFirstSearch/breadth_first_search.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index e67a478848..f4c0fcd0bb 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -100,6 +100,8 @@ def planning(self, sx, sy, gx, gy): # Remove the item from the open set del open_set[c_id] + random.shuffle(self.motion) + # expand_grid search grid based on motion model for i, _ in enumerate(self.motion): node = self.Node(current.x + self.motion[i][0], @@ -123,14 +125,11 @@ def calc_final_path(self, ngoal, closedset): rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ self.calc_grid_position(ngoal.y, self.miny)] n = closedset[ngoal.pind] - while n.parent is not None: + while n is not None: rx.append(self.calc_grid_position(n.x, self.minx)) ry.append(self.calc_grid_position(n.y, self.miny)) n = n.parent - rx.append(self.calc_grid_position(n.x, self.minx)) - ry.append(self.calc_grid_position(n.y, self.miny)) - return rx, ry def calc_grid_position(self, index, minp): From 81622428ef03cf27123cf9c79dede051cf8d1b89 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Tue, 14 Apr 2020 15:27:33 +0200 Subject: [PATCH 199/909] not adding parent if node in closed set --- PathPlanning/BreadthFirstSearch/breadth_first_search.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index f4c0fcd0bb..f25802434a 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -106,7 +106,7 @@ def planning(self, sx, sy, gx, gy): for i, _ in enumerate(self.motion): node = self.Node(current.x + self.motion[i][0], current.y + self.motion[i][1], - current.cost + self.motion[i][2], c_id+1, current) + current.cost + self.motion[i][2], c_id+1, None) n_id = self.calc_grid_index(node) # If the node is not safe, do nothing @@ -114,6 +114,7 @@ def planning(self, sx, sy, gx, gy): continue if n_id not in closed_set: + node.parent = current closed_set[n_id] = node open_set[n_id] = node From ab05ac600aeb9bc5d80947e5a2ba5694a07e0a66 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 16:00:11 +0200 Subject: [PATCH 200/909] Fix typo in url and variable names --- PathPlanning/DepthFirstSearch/depth_first_search.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index bb08080734..d2f41347c3 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -4,7 +4,7 @@ author: Erwin Lejeune (@spida_rwin) -See Wikipedia article (https://en.wikipedia.org/wiki/depth-First_search) +See Wikipedia article (https://en.wikipedia.org/wiki/Depth-first_search) """ @@ -188,8 +188,8 @@ def calc_obstacle_map(self, ox, oy): print("ywidth:", self.ywidth) # obstacle map generation - self.obmap = [[False for i in range(self.ywidth)] - for i in range(self.xwidth)] + self.obmap = [[False for _ in range(self.ywidth)] + for _ in range(self.xwidth)] for ix in range(self.xwidth): x = self.calc_grid_position(ix, self.minx) for iy in range(self.ywidth): From fa5ca3cd7c88736d28ec96868328637c3772b803 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 16:01:19 +0200 Subject: [PATCH 201/909] Fix typo in url, variable name --- PathPlanning/BreadthFirstSearch/breadth_first_search.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index f25802434a..5a38035507 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -4,7 +4,7 @@ author: Erwin Lejeune (@spida_rwin) -See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-First_search) +See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-first_search) """ @@ -186,8 +186,8 @@ def calc_obstacle_map(self, ox, oy): print("ywidth:", self.ywidth) # obstacle map generation - self.obmap = [[False for i in range(self.ywidth)] - for i in range(self.xwidth)] + self.obmap = [[False for _ in range(self.ywidth)] + for _ in range(self.xwidth)] for ix in range(self.xwidth): x = self.calc_grid_position(ix, self.minx) for iy in range(self.ywidth): From 0400ce3ad962f9f3ec7fa27455ba106abd580bec Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 16:11:18 +0200 Subject: [PATCH 202/909] Replace meaningless variable name --- PathPlanning/BidirectionalAStar/bidirectional_a_star.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index 37d14eab68..4ffd34718b 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -256,8 +256,8 @@ def calc_obstacle_map(self, ox, oy): print("ywidth:", self.ywidth) # obstacle map generation - self.obmap = [[False for i in range(self.ywidth)] - for i in range(self.xwidth)] + self.obmap = [[False for _ in range(self.ywidth)] + for _ in range(self.xwidth)] for ix in range(self.xwidth): x = self.calc_grid_position(ix, self.minx) for iy in range(self.ywidth): From 7993d7ca8b594fdaa9e8b8071f540cab079670e2 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 18:11:47 +0200 Subject: [PATCH 203/909] use pop instead of min+del --- .../breadth_first_search.py | 22 +++++++------------ 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index 5a38035507..d9e2aa5bc3 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -74,11 +74,11 @@ def planning(self, sx, sy, gx, gy): print("Open set is empty..") break - c_id = min( - open_set, - key=lambda o: open_set[o].pind) + current = open_set.pop(list(open_set.keys())[0]) - current = open_set[c_id] + c_id = self.calc_grid_index(current) + + closed_set[c_id] = current # show graph if show_animation: # pragma: no cover @@ -97,25 +97,19 @@ def planning(self, sx, sy, gx, gy): ngoal.cost = current.cost break - # Remove the item from the open set - del open_set[c_id] - - random.shuffle(self.motion) - # expand_grid search grid based on motion model for i, _ in enumerate(self.motion): node = self.Node(current.x + self.motion[i][0], current.y + self.motion[i][1], - current.cost + self.motion[i][2], c_id+1, None) + current.cost + self.motion[i][2], c_id, None) n_id = self.calc_grid_index(node) # If the node is not safe, do nothing if not self.verify_node(node): continue - if n_id not in closed_set: + if (n_id not in closed_set) and (n_id not in open_set): node.parent = current - closed_set[n_id] = node open_set[n_id] = node rx, ry = self.calc_final_path(ngoal, closed_set) @@ -186,8 +180,8 @@ def calc_obstacle_map(self, ox, oy): print("ywidth:", self.ywidth) # obstacle map generation - self.obmap = [[False for _ in range(self.ywidth)] - for _ in range(self.xwidth)] + self.obmap = [[False for i in range(self.ywidth)] + for i in range(self.xwidth)] for ix in range(self.xwidth): x = self.calc_grid_position(ix, self.minx) for iy in range(self.ywidth): From 0dca0e328ddda954f149459073a944fc80a3452d Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 18:25:00 +0200 Subject: [PATCH 204/909] Use pop instead of min/del --- .../DepthFirstSearch/depth_first_search.py | 23 +++++-------------- 1 file changed, 6 insertions(+), 17 deletions(-) diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index d2f41347c3..54bbc48fdd 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -74,11 +74,8 @@ def planning(self, sx, sy, gx, gy): print("Open set is empty..") break - c_id = max( - open_set, - key=lambda o: open_set[o].pind) - - current = open_set[c_id] + current = open_set.pop(list(open_set.keys())[-1]) + c_id = self.calc_grid_index(current) # show graph if show_animation: # pragma: no cover @@ -88,29 +85,19 @@ def planning(self, sx, sy, gx, gy): plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit( 0) if event.key == 'escape' else None]) - if len(closed_set.keys()) % 10 == 0: - plt.pause(0.001) + plt.pause(0.01) if current.x == ngoal.x and current.y == ngoal.y: print("Find goal") ngoal.pind = current.pind ngoal.cost = current.cost - closed_set[current.pind] = current break - # Remove the item from the open set - del open_set[c_id] - - # Add it to the closed set - closed_set[c_id] = current - - random.shuffle(self.motion) - # expand_grid search grid based on motion model for i, _ in enumerate(self.motion): node = self.Node(current.x + self.motion[i][0], current.y + self.motion[i][1], - current.cost + self.motion[i][2], c_id+1, current) + current.cost + self.motion[i][2], c_id, None) n_id = self.calc_grid_index(node) # If the node is not safe, do nothing @@ -119,6 +106,8 @@ def planning(self, sx, sy, gx, gy): if n_id not in closed_set: open_set[n_id] = node + closed_set[n_id] = node + node.parent = current rx, ry = self.calc_final_path(ngoal, closed_set) return rx, ry From b827b3e2eef46a393cacd1fa31f2cc1acafb84fe Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 18:38:09 +0200 Subject: [PATCH 205/909] add plt pause in astar path display --- PathPlanning/AStar/a_star.py | 1 + 1 file changed, 1 insertion(+) diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index 55881f4e49..cd5a365c90 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -271,6 +271,7 @@ def main(): if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") plt.show() + plt.pause(0.001) if __name__ == '__main__': From 6c977850c6e8e31fd136b265a515d84709bfe610 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 18:39:01 +0200 Subject: [PATCH 206/909] remove useless import --- PathPlanning/DepthFirstSearch/depth_first_search.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index 54bbc48fdd..d42aa5f100 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -12,8 +12,6 @@ import matplotlib.pyplot as plt -import random - show_animation = True @@ -21,7 +19,7 @@ class DepthFirstSearchPlanner: def __init__(self, ox, oy, reso, rr): """ - Initialize grid map for DFS planning + Initialize grid map for Depth-First planning ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] From 97978ce51fcffeffd141bc4fae48e653b5339fc3 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 16 Apr 2020 18:39:35 +0200 Subject: [PATCH 207/909] remove useless import --- PathPlanning/BreadthFirstSearch/breadth_first_search.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index d9e2aa5bc3..b4a5c38a8b 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -12,8 +12,6 @@ import matplotlib.pyplot as plt -import random - show_animation = True From 5ba82c6ced66004d1bf8a8dc0b91672394c5af9b Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Sat, 18 Apr 2020 15:27:59 +0200 Subject: [PATCH 208/909] replace meaningless variable name with _ --- PathPlanning/BreadthFirstSearch/breadth_first_search.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index b4a5c38a8b..dbdeff47c8 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -178,8 +178,8 @@ def calc_obstacle_map(self, ox, oy): print("ywidth:", self.ywidth) # obstacle map generation - self.obmap = [[False for i in range(self.ywidth)] - for i in range(self.xwidth)] + self.obmap = [[False for _ in range(self.ywidth)] + for _ in range(self.xwidth)] for ix in range(self.xwidth): x = self.calc_grid_position(ix, self.minx) for iy in range(self.ywidth): From b7f18b8416603237793effb70ed519d61c38b557 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Sun, 19 Apr 2020 15:02:08 +0200 Subject: [PATCH 209/909] Improve readability to fetch lowest cost node --- .../bidirectional_a_star.py | 40 ++++++++++--------- 1 file changed, 22 insertions(+), 18 deletions(-) diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index 4ffd34718b..1924cf59b1 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -75,24 +75,20 @@ def planning(self, sx, sy, gx, gy): if len(open_set_A) == 0: print("Open set A is empty..") break - + if len(open_set_B) == 0: print("Open set B is empty..") break c_id_A = min( open_set_A, - key=lambda o: open_set_A[o].cost + self.calc_heuristic(current_B, - open_set_A[ - o])) - + key=lambda o: self.find_lowest_cost(open_set_A, o, current_B)) + current_A = open_set_A[c_id_A] c_id_B = min( open_set_B, - key=lambda o: open_set_B[o].cost + self.calc_heuristic(current_A, - open_set_B[ - o])) + key=lambda o: self.find_lowest_cost(open_set_B, o, current_A)) current_B = open_set_B[c_id_B] @@ -129,12 +125,12 @@ def planning(self, sx, sy, gx, gy): continue_B = False child_node_A = self.Node(current_A.x + self.motion[i][0], - current_A.y + self.motion[i][1], - current_A.cost + self.motion[i][2], c_id_A) + current_A.y + self.motion[i][1], + current_A.cost + self.motion[i][2], c_id_A) child_node_B = self.Node(current_B.x + self.motion[i][0], - current_B.y + self.motion[i][1], - current_B.cost + self.motion[i][2], c_id_B) + current_B.y + self.motion[i][1], + current_B.cost + self.motion[i][2], c_id_B) n_id_A = self.calc_grid_index(child_node_A) n_id_B = self.calc_grid_index(child_node_B) @@ -142,33 +138,36 @@ def planning(self, sx, sy, gx, gy): # If the node is not safe, do nothing if not self.verify_node(child_node_A): continue_A = True - + if not self.verify_node(child_node_B): continue_B = True if n_id_A in closed_set_A: continue_A = True - + if n_id_B in closed_set_B: continue_B = True if not(continue_A): if n_id_A not in open_set_A: - open_set_A[n_id_A] = child_node_A # discovered a new node + # discovered a new node + open_set_A[n_id_A] = child_node_A else: if open_set_A[n_id_A].cost > child_node_A.cost: # This path is the best until now. record it open_set_A[n_id_A] = child_node_A - + if not(continue_B): if n_id_B not in open_set_B: - open_set_B[n_id_B] = child_node_B # discovered a new node + # discovered a new node + open_set_B[n_id_B] = child_node_B else: if open_set_B[n_id_B].cost > child_node_B.cost: # This path is the best until now. record it open_set_B[n_id_B] = child_node_B - rx, ry = self.calc_final_path_bidir(meetpointA, meetpointB, closed_set_A, closed_set_B) + rx, ry = self.calc_final_path_bidir( + meetpointA, meetpointB, closed_set_A, closed_set_B) return rx, ry @@ -203,6 +202,11 @@ def calc_heuristic(n1, n2): d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) return d + def find_lowest_cost(self, open_set, lambda_, n1): + cost = open_set[lambda_].cost + \ + self.calc_heuristic(n1, open_set[lambda_]) + return cost + def calc_grid_position(self, index, minp): """ calc grid position From 63a920223b034296edc3c4eae75ac4c0e80bb120 Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Sun, 19 Apr 2020 15:04:38 +0200 Subject: [PATCH 210/909] Fix pep8 indents --- .../BidirectionalAStar/bidirectional_a_star.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index 1924cf59b1..3839e4dff3 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -82,13 +82,13 @@ def planning(self, sx, sy, gx, gy): c_id_A = min( open_set_A, - key=lambda o: self.find_lowest_cost(open_set_A, o, current_B)) + key=lambda o: self.find_total_cost(open_set_A, o, current_B)) current_A = open_set_A[c_id_A] c_id_B = min( open_set_B, - key=lambda o: self.find_lowest_cost(open_set_B, o, current_A)) + key=lambda o: self.find_total_cost(open_set_B, o, current_A)) current_B = open_set_B[c_id_B] @@ -148,7 +148,7 @@ def planning(self, sx, sy, gx, gy): if n_id_B in closed_set_B: continue_B = True - if not(continue_A): + if not continue_A: if n_id_A not in open_set_A: # discovered a new node open_set_A[n_id_A] = child_node_A @@ -157,7 +157,7 @@ def planning(self, sx, sy, gx, gy): # This path is the best until now. record it open_set_A[n_id_A] = child_node_A - if not(continue_B): + if not continue_B: if n_id_B not in open_set_B: # discovered a new node open_set_B[n_id_B] = child_node_B @@ -202,10 +202,11 @@ def calc_heuristic(n1, n2): d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) return d - def find_lowest_cost(self, open_set, lambda_, n1): - cost = open_set[lambda_].cost + \ - self.calc_heuristic(n1, open_set[lambda_]) - return cost + def find_total_cost(self, open_set, lambda_, n1): + g_cost = open_set[lambda_].cost + h_cost = self.calc_heuristic(n1, open_set[lambda_]) + f_cost = g_cost + h_cost + return f_cost def calc_grid_position(self, index, minp): """ From 38519b5df83141f6fc6bf452593fa748de2eae0f Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Fri, 24 Apr 2020 15:16:05 +0200 Subject: [PATCH 211/909] fix requests --- .../BidirectionalAStar/bidirectional_a_star.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index 3839e4dff3..ed1e58699f 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -126,11 +126,13 @@ def planning(self, sx, sy, gx, gy): child_node_A = self.Node(current_A.x + self.motion[i][0], current_A.y + self.motion[i][1], - current_A.cost + self.motion[i][2], c_id_A) + current_A.cost + self.motion[i][2], + c_id_A) child_node_B = self.Node(current_B.x + self.motion[i][0], current_B.y + self.motion[i][1], - current_B.cost + self.motion[i][2], c_id_B) + current_B.cost + self.motion[i][2], + c_id_B) n_id_A = self.calc_grid_index(child_node_A) n_id_B = self.calc_grid_index(child_node_B) @@ -166,12 +168,12 @@ def planning(self, sx, sy, gx, gy): # This path is the best until now. record it open_set_B[n_id_B] = child_node_B - rx, ry = self.calc_final_path_bidir( + rx, ry = self.calc_final_bidirectional_path( meetpointA, meetpointB, closed_set_A, closed_set_B) return rx, ry - def calc_final_path_bidir(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B): + def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B): rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A) rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B) @@ -299,7 +301,7 @@ def main(): grid_size = 2.0 # [m] robot_radius = 1.0 # [m] - # set obstable positions + # set obstacle positions ox, oy = [], [] for i in range(-10, 60): ox.append(i) From 18fe1960fb65ecfe1cee299b29ab4221cf03cc1c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 28 Apr 2020 21:00:04 +0900 Subject: [PATCH 212/909] remove say thanks link --- README.md | 1 - users_comments.md | 2 -- 2 files changed, 3 deletions(-) diff --git a/README.md b/README.md index ab62c9af3c..9a9bc9502a 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python) [![CodeFactor](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/badge/master)](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master) [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics) -[![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai) Python codes for robotics algorithm. diff --git a/users_comments.md b/users_comments.md index 44e9355ec1..43ffc7cc36 100644 --- a/users_comments.md +++ b/users_comments.md @@ -40,8 +40,6 @@ This is a list of related projects. # Individual users comments -These are comments from user's using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai) - --- Dear AtsushiSakai,
thank you for building this cool repo for robotics.
I am still learning robotics and this will give me a good starting point.
I hope this note gets to you.

From e8a006f747f9fbf4821a9e14033d21aa8c5e53b7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 3 May 2020 23:02:59 +0900 Subject: [PATCH 213/909] Add code style checker CI (#321) Add code style checker CI --- .github/workflows/pythonpackage.yml | 12 +++++------- environment.yml | 2 +- rundiffstylecheck.sh | 15 +++++++++++++++ 3 files changed, 21 insertions(+), 8 deletions(-) create mode 100644 rundiffstylecheck.sh diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 574c7be9d5..defc159687 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -21,17 +21,12 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt - - name: Lint with flake8 - run: | - pip install flake8 - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: install coverage run: pip install coverage - name: install mypy run: pip install mypy + - name: install pycodestyle + run: pip install pycodestyle - name: mypy check run: | find AerialNavigation -name "*.py" | xargs mypy @@ -43,7 +38,10 @@ jobs: find PathPlanning -name "*.py" | xargs mypy find PathTracking -name "*.py" | xargs mypy find SLAM -name "*.py" | xargs mypy + - name: do diff style check + run: bash rundiffstylecheck.sh - name: do all unit tests run: bash runtests.sh + diff --git a/environment.yml b/environment.yml index 6866ed93ca..da1c3c0a6f 100644 --- a/environment.yml +++ b/environment.yml @@ -4,7 +4,7 @@ dependencies: - pip - matplotlib - scipy -- numpy==1.15 +- numpy - pandas - coverage - pip: diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh new file mode 100644 index 0000000000..627e4a1b63 --- /dev/null +++ b/rundiffstylecheck.sh @@ -0,0 +1,15 @@ +#!/bin/bash +echo "$(basename $0) start!" +VERSION=v0.1.3 +wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip +unzip ${VERSION}.zip +./DiffSentinel*/starter.sh HEAD origin/master +check_result=$? +rm -rf ${VERSION}.zip DiffSentinel* +if [[ ${check_result} -ne 0 ]]; +then + echo "Error: Your changes contain pycodestyle errors." + exit 1 +fi +echo "$(basename $0) done!" +exit 0 From 614e18d246109566f3e071b63a57c5c87cb5164a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 4 May 2020 10:12:41 +0900 Subject: [PATCH 214/909] add test for PR --- .github/workflows/pythonpackage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 574c7be9d5..76196b6ed0 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -1,6 +1,6 @@ name: Python package -on: [push] +on: [push, pull_request] jobs: build: From ae19d07bc3fd2a79d5b6c45ff614ddb1ef84a1d3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 4 May 2020 14:22:25 +0900 Subject: [PATCH 215/909] bump DiffSentinelVersion --- rundiffstylecheck.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh index 627e4a1b63..d170973751 100644 --- a/rundiffstylecheck.sh +++ b/rundiffstylecheck.sh @@ -1,6 +1,6 @@ #!/bin/bash echo "$(basename $0) start!" -VERSION=v0.1.3 +VERSION=v0.1.5 wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip unzip ${VERSION}.zip ./DiffSentinel*/starter.sh HEAD origin/master From ab49acb1acb2f60334febf6dc44dafa42ff6a836 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 4 May 2020 20:34:03 +0900 Subject: [PATCH 216/909] bump DiffSentinelVersion --- rundiffstylecheck.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh index d170973751..8a19842c6e 100644 --- a/rundiffstylecheck.sh +++ b/rundiffstylecheck.sh @@ -1,6 +1,6 @@ #!/bin/bash echo "$(basename $0) start!" -VERSION=v0.1.5 +VERSION=v0.1.6 wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip unzip ${VERSION}.zip ./DiffSentinel*/starter.sh HEAD origin/master From 734f3aed20cfbbd634e0fe8f342655458ac3b2ff Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Tue, 5 May 2020 07:06:01 +0200 Subject: [PATCH 217/909] Greedy Best-First Search (#315) --- PathPlanning/AStar/a_star.py | 2 +- .../bidirectional_a_star.py | 78 ++--- .../breadth_first_search.py | 7 +- .../DepthFirstSearch/depth_first_search.py | 7 +- .../greedy_best_first_search.py | 278 ++++++++++++++++++ 5 files changed, 326 insertions(+), 46 deletions(-) create mode 100644 PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index cd5a365c90..14c51ad9a1 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -237,7 +237,7 @@ def main(): grid_size = 2.0 # [m] robot_radius = 1.0 # [m] - # set obstable positions + # set obstacle positions ox, oy = [], [] for i in range(-10, 60): ox.append(i) diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index ed1e58699f..65d1fc31a7 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -100,8 +100,9 @@ def planning(self, sx, sy, gx, gy): self.calc_grid_position(current_B.y, self.miny), "xc") # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit( - 0) if event.key == 'escape' else None]) + lambda event: + [exit(0) if event.key == 'escape' + else None]) if len(closed_set_A.keys()) % 10 == 0: plt.pause(0.001) @@ -121,61 +122,50 @@ def planning(self, sx, sy, gx, gy): # expand_grid search grid based on motion model for i, _ in enumerate(self.motion): - continue_A = False - continue_B = False - child_node_A = self.Node(current_A.x + self.motion[i][0], - current_A.y + self.motion[i][1], - current_A.cost + self.motion[i][2], - c_id_A) + c_nodes = [self.Node(current_A.x + self.motion[i][0], + current_A.y + self.motion[i][1], + current_A.cost + self.motion[i][2], + c_id_A), + self.Node(current_B.x + self.motion[i][0], + current_B.y + self.motion[i][1], + current_B.cost + self.motion[i][2], + c_id_B)] - child_node_B = self.Node(current_B.x + self.motion[i][0], - current_B.y + self.motion[i][1], - current_B.cost + self.motion[i][2], - c_id_B) - - n_id_A = self.calc_grid_index(child_node_A) - n_id_B = self.calc_grid_index(child_node_B) + n_ids = [self.calc_grid_index(c_nodes[0]), + self.calc_grid_index(c_nodes[1])] # If the node is not safe, do nothing - if not self.verify_node(child_node_A): - continue_A = True - - if not self.verify_node(child_node_B): - continue_B = True - - if n_id_A in closed_set_A: - continue_A = True - - if n_id_B in closed_set_B: - continue_B = True + continue_ = self.check_nodes_and_sets(c_nodes, closed_set_A, + closed_set_B, n_ids) - if not continue_A: - if n_id_A not in open_set_A: + if not continue_[0]: + if n_ids[0] not in open_set_A: # discovered a new node - open_set_A[n_id_A] = child_node_A + open_set_A[n_ids[0]] = c_nodes[0] else: - if open_set_A[n_id_A].cost > child_node_A.cost: + if open_set_A[n_ids[0]].cost > c_nodes[0].cost: # This path is the best until now. record it - open_set_A[n_id_A] = child_node_A + open_set_A[n_ids[0]] = c_nodes[0] - if not continue_B: - if n_id_B not in open_set_B: + if not continue_[1]: + if n_ids[1] not in open_set_B: # discovered a new node - open_set_B[n_id_B] = child_node_B + open_set_B[n_ids[1]] = c_nodes[1] else: - if open_set_B[n_id_B].cost > child_node_B.cost: + if open_set_B[n_ids[1]].cost > c_nodes[1].cost: # This path is the best until now. record it - open_set_B[n_id_B] = child_node_B + open_set_B[n_ids[1]] = c_nodes[1] rx, ry = self.calc_final_bidirectional_path( meetpointA, meetpointB, closed_set_A, closed_set_B) return rx, ry - def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B): - rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A) - rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B) + # takes two sets and two meeting nodes and return the optimal path + def calc_final_bidirectional_path(self, n1, n2, setA, setB): + rx_A, ry_A = self.calc_final_path(n1, setA) + rx_B, ry_B = self.calc_final_path(n2, setB) rx_A.reverse() ry_A.reverse() @@ -198,6 +188,16 @@ def calc_final_path(self, ngoal, closedset): return rx, ry + def check_nodes_and_sets(self, c_nodes, closedSet_A, closedSet_B, n_ids): + continue_ = [False, False] + if not self.verify_node(c_nodes[0]) or n_ids[0] in closedSet_A: + continue_[0] = True + + if not self.verify_node(c_nodes[1]) or n_ids[1] in closedSet_B: + continue_[1] = True + + return continue_ + @staticmethod def calc_heuristic(n1, n2): w = 1.0 # weight of heuristic diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index dbdeff47c8..198ddd2e3d 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -84,8 +84,9 @@ def planning(self, sx, sy, gx, gy): self.calc_grid_position(current.y, self.miny), "xc") # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit( - 0) if event.key == 'escape' else None]) + lambda event: + [exit(0) if event.key == 'escape' + else None]) if len(closed_set.keys()) % 10 == 0: plt.pause(0.001) @@ -216,7 +217,7 @@ def main(): grid_size = 2.0 # [m] robot_radius = 1.0 # [m] - # set obstable positions + # set obstacle positions ox, oy = [], [] for i in range(-10, 60): ox.append(i) diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index d42aa5f100..229eb70c2d 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -81,8 +81,9 @@ def planning(self, sx, sy, gx, gy): self.calc_grid_position(current.y, self.miny), "xc") # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit( - 0) if event.key == 'escape' else None]) + lambda event: + [exit(0) if event.key == 'escape' + else None]) plt.pause(0.01) if current.x == ngoal.x and current.y == ngoal.y: @@ -213,7 +214,7 @@ def main(): grid_size = 2.0 # [m] robot_radius = 1.0 # [m] - # set obstable positions + # set obstacle positions ox, oy = [], [] for i in range(-10, 60): ox.append(i) diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py new file mode 100644 index 0000000000..c29e37e896 --- /dev/null +++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py @@ -0,0 +1,278 @@ +""" + +Greedy Best-First grid planning + +author: Erwin Lejeune (@spida_rwin) + +See Wikipedia article (https://en.wikipedia.org/wiki/Best-first_search) + +""" + +import math + +import matplotlib.pyplot as plt + +show_animation = True + + +class BestFirstSearchPlanner: + + def __init__(self, ox, oy, reso, rr): + """ + Initialize grid map for greedy best-first planning + + ox: x position list of Obstacles [m] + oy: y position list of Obstacles [m] + reso: grid resolution [m] + rr: robot radius[m] + """ + + self.reso = reso + self.rr = rr + self.calc_obstacle_map(ox, oy) + self.motion = self.get_motion_model() + + class Node: + def __init__(self, x, y, cost, pind, parent): + self.x = x # index of grid + self.y = y # index of grid + self.cost = cost + self.pind = pind + self.parent = parent + + def __str__(self): + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.pind) + + def planning(self, sx, sy, gx, gy): + """ + Greedy Best-First search + + input: + sx: start x position [m] + sy: start y position [m] + gx: goal x position [m] + gy: goal y position [m] + + output: + rx: x position list of the final path + ry: y position list of the final path + """ + + nstart = self.Node(self.calc_xyindex(sx, self.minx), + self.calc_xyindex(sy, self.miny), 0.0, -1, None) + ngoal = self.Node(self.calc_xyindex(gx, self.minx), + self.calc_xyindex(gy, self.miny), 0.0, -1, None) + + open_set, closed_set = dict(), dict() + open_set[self.calc_grid_index(nstart)] = nstart + + while 1: + if len(open_set) == 0: + print("Open set is empty..") + break + + c_id = min( + open_set, + key=lambda o: self.calc_heuristic(ngoal, open_set[o])) + + current = open_set[c_id] + + # show graph + if show_animation: # pragma: no cover + plt.plot(self.calc_grid_position(current.x, self.minx), + self.calc_grid_position(current.y, self.miny), "xc") + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: + [exit(0) + if event.key == 'escape' + else None]) + if len(closed_set.keys()) % 10 == 0: + plt.pause(0.001) + + # Remove the item from the open set + del open_set[c_id] + + # Add it to the closed set + closed_set[c_id] = current + + if current.x == ngoal.x and current.y == ngoal.y: + print("Found goal") + ngoal.pind = current.pind + ngoal.cost = current.cost + break + + # expand_grid search grid based on motion model + for i, _ in enumerate(self.motion): + node = self.Node(current.x + self.motion[i][0], + current.y + self.motion[i][1], + current.cost + self.motion[i][2], + c_id, current) + + n_id = self.calc_grid_index(node) + + # If the node is not safe, do nothing + if not self.verify_node(node): + continue + + if n_id in closed_set: + continue + + if n_id not in open_set: + open_set[n_id] = node + else: + if open_set[n_id].cost > node.cost: + open_set[n_id] = node + closed_set[ngoal.pind] = current + rx, ry = self.calc_final_path(ngoal, closed_set) + return rx, ry + + def calc_final_path(self, ngoal, closedset): + # generate final course + rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ + self.calc_grid_position(ngoal.y, self.miny)] + n = closedset[ngoal.pind] + while n is not None: + rx.append(self.calc_grid_position(n.x, self.minx)) + ry.append(self.calc_grid_position(n.y, self.miny)) + n = n.parent + + return rx, ry + + @staticmethod + def calc_heuristic(n1, n2): + w = 1.0 # weight of heuristic + d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) + return d + + def calc_grid_position(self, index, minp): + """ + calc grid position + + :param index: + :param minp: + :return: + """ + pos = index * self.reso + minp + return pos + + def calc_xyindex(self, position, min_pos): + return round((position - min_pos) / self.reso) + + def calc_grid_index(self, node): + return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + + def verify_node(self, node): + px = self.calc_grid_position(node.x, self.minx) + py = self.calc_grid_position(node.y, self.miny) + + if px < self.minx: + return False + elif py < self.miny: + return False + elif px >= self.maxx: + return False + elif py >= self.maxy: + return False + + # collision check + if self.obmap[node.x][node.y]: + return False + + return True + + def calc_obstacle_map(self, ox, oy): + + self.minx = round(min(ox)) + self.miny = round(min(oy)) + self.maxx = round(max(ox)) + self.maxy = round(max(oy)) + print("minx:", self.minx) + print("miny:", self.miny) + print("maxx:", self.maxx) + print("maxy:", self.maxy) + + self.xwidth = round((self.maxx - self.minx) / self.reso) + self.ywidth = round((self.maxy - self.miny) / self.reso) + print("xwidth:", self.xwidth) + print("ywidth:", self.ywidth) + + # obstacle map generation + self.obmap = [[False for _ in range(self.ywidth)] + for _ in range(self.xwidth)] + for ix in range(self.xwidth): + x = self.calc_grid_position(ix, self.minx) + for iy in range(self.ywidth): + y = self.calc_grid_position(iy, self.miny) + for iox, ioy in zip(ox, oy): + d = math.hypot(iox - x, ioy - y) + if d <= self.rr: + self.obmap[ix][iy] = True + break + + @staticmethod + def get_motion_model(): + # dx, dy, cost + motion = [[1, 0, 1], + [0, 1, 1], + [-1, 0, 1], + [0, -1, 1], + [-1, -1, math.sqrt(2)], + [-1, 1, math.sqrt(2)], + [1, -1, math.sqrt(2)], + [1, 1, math.sqrt(2)]] + + return motion + + +def main(): + print(__file__ + " start!!") + + # start and goal position + sx = 10.0 # [m] + sy = 10.0 # [m] + gx = 50.0 # [m] + gy = 50.0 # [m] + grid_size = 2.0 # [m] + robot_radius = 1.0 # [m] + + # set obstacle positions + ox, oy = [], [] + for i in range(-10, 60): + ox.append(i) + oy.append(-10.0) + for i in range(-10, 60): + ox.append(60.0) + oy.append(i) + for i in range(-10, 61): + ox.append(i) + oy.append(60.0) + for i in range(-10, 61): + ox.append(-10.0) + oy.append(i) + for i in range(-10, 40): + ox.append(20.0) + oy.append(i) + for i in range(0, 40): + ox.append(40.0) + oy.append(60.0 - i) + + if show_animation: # pragma: no cover + plt.plot(ox, oy, ".k") + plt.plot(sx, sy, "og") + plt.plot(gx, gy, "xb") + plt.grid(True) + plt.axis("equal") + + greedybestfirst = BestFirstSearchPlanner(ox, oy, grid_size, robot_radius) + rx, ry = greedybestfirst.planning(sx, sy, gx, gy) + + if show_animation: # pragma: no cover + plt.plot(rx, ry, "-r") + plt.pause(0.01) + plt.show() + + +if __name__ == '__main__': + main() From 86c511efc022c613979c949bacab5dd079aaf36a Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Tue, 5 May 2020 07:15:03 +0200 Subject: [PATCH 218/909] Bidirectional Breadth-First Search (#316) --- .../bidirectional_breadth_first_search.py | 311 ++++++++++++++++++ 1 file changed, 311 insertions(+) create mode 100644 PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py new file mode 100644 index 0000000000..d29112eb65 --- /dev/null +++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py @@ -0,0 +1,311 @@ +""" + +Bidirectional Breadth-First grid planning + +author: Erwin Lejeune (@spida_rwin) + +See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-first_search) + +""" + +import math + +import matplotlib.pyplot as plt + +show_animation = True + + +class BidirectionalBreadthFirstSearchPlanner: + + def __init__(self, ox, oy, reso, rr): + """ + Initialize grid map for bfs planning + + ox: x position list of Obstacles [m] + oy: y position list of Obstacles [m] + reso: grid resolution [m] + rr: robot radius[m] + """ + + self.reso = reso + self.rr = rr + self.calc_obstacle_map(ox, oy) + self.motion = self.get_motion_model() + + class Node: + def __init__(self, x, y, cost, pind, parent): + self.x = x # index of grid + self.y = y # index of grid + self.cost = cost + self.pind = pind + self.parent = parent + + def __str__(self): + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.pind) + + def planning(self, sx, sy, gx, gy): + """ + Bidirectional Breadth First search based planning + + input: + sx: start x position [m] + sy: start y position [m] + gx: goal x position [m] + gy: goal y position [m] + + output: + rx: x position list of the final path + ry: y position list of the final path + """ + + nstart = self.Node(self.calc_xyindex(sx, self.minx), + self.calc_xyindex(sy, self.miny), 0.0, -1, None) + ngoal = self.Node(self.calc_xyindex(gx, self.minx), + self.calc_xyindex(gy, self.miny), 0.0, -1, None) + + open_set_A, closed_set_A = dict(), dict() + open_set_B, closed_set_B = dict(), dict() + open_set_B[self.calc_grid_index(ngoal)] = ngoal + open_set_A[self.calc_grid_index(nstart)] = nstart + + while 1: + if len(open_set_A) == 0: + print("Open set A is empty..") + break + + if len(open_set_B) == 0: + print("Open set B is empty") + break + + current_A = open_set_A.pop(list(open_set_A.keys())[0]) + current_B = open_set_B.pop(list(open_set_B.keys())[0]) + + c_id_A = self.calc_grid_index(current_A) + c_id_B = self.calc_grid_index(current_B) + + closed_set_A[c_id_A] = current_A + closed_set_B[c_id_B] = current_B + + # show graph + if show_animation: # pragma: no cover + plt.plot(self.calc_grid_position(current_A.x, self.minx), + self.calc_grid_position(current_A.y, self.miny), "xc") + plt.plot(self.calc_grid_position(current_B.x, self.minx), + self.calc_grid_position(current_B.y, self.miny), "xc") + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: + [exit(0) if + event.key == 'escape' else None]) + if len(closed_set_A.keys()) % 10 == 0: + plt.pause(0.001) + + if c_id_A in closed_set_B: + print("Find goal") + meetpointA = closed_set_A[c_id_A] + meetpointB = closed_set_B[c_id_A] + break + + elif c_id_B in closed_set_A: + print("Find goal") + meetpointA = closed_set_A[c_id_B] + meetpointB = closed_set_B[c_id_B] + break + + # expand_grid search grid based on motion model + for i, _ in enumerate(self.motion): + breakA = False + breakB = False + + node_A = self.Node(current_A.x + self.motion[i][0], + current_A.y + self.motion[i][1], + current_A.cost + self.motion[i][2], + c_id_A, None) + node_B = self.Node(current_B.x + self.motion[i][0], + current_B.y + self.motion[i][1], + current_B.cost + self.motion[i][2], + c_id_B, None) + + n_id_A = self.calc_grid_index(node_A) + n_id_B = self.calc_grid_index(node_B) + + # If the node is not safe, do nothing + if not self.verify_node(node_A): + breakA = True + + if not self.verify_node(node_B): + breakB = True + + if (n_id_A not in closed_set_A) and (n_id_A not in + open_set_A) and (not + breakA): + node_A.parent = current_A + open_set_A[n_id_A] = node_A + + if (n_id_B not in closed_set_B) and (n_id_B not in + open_set_B) and (not + breakB): + node_B.parent = current_B + open_set_B[n_id_B] = node_B + + rx, ry = self.calc_final_path_bidir( + meetpointA, meetpointB, closed_set_A, closed_set_B) + return rx, ry + + # takes both set and meeting nodes and calculate optimal path + def calc_final_path_bidir(self, n1, n2, setA, setB): + rxA, ryA = self.calc_final_path(n1, setA) + rxB, ryB = self.calc_final_path(n2, setB) + + rxA.reverse() + ryA.reverse() + + rx = rxA + rxB + ry = ryA + ryB + + return rx, ry + + def calc_final_path(self, ngoal, closedset): + # generate final course + rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ + self.calc_grid_position(ngoal.y, self.miny)] + n = closedset[ngoal.pind] + while n is not None: + rx.append(self.calc_grid_position(n.x, self.minx)) + ry.append(self.calc_grid_position(n.y, self.miny)) + n = n.parent + + return rx, ry + + def calc_grid_position(self, index, minp): + """ + calc grid position + + :param index: + :param minp: + :return: + """ + pos = index * self.reso + minp + return pos + + def calc_xyindex(self, position, min_pos): + return round((position - min_pos) / self.reso) + + def calc_grid_index(self, node): + return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + + def verify_node(self, node): + px = self.calc_grid_position(node.x, self.minx) + py = self.calc_grid_position(node.y, self.miny) + + if px < self.minx: + return False + elif py < self.miny: + return False + elif px >= self.maxx: + return False + elif py >= self.maxy: + return False + + # collision check + if self.obmap[node.x][node.y]: + return False + + return True + + def calc_obstacle_map(self, ox, oy): + + self.minx = round(min(ox)) + self.miny = round(min(oy)) + self.maxx = round(max(ox)) + self.maxy = round(max(oy)) + print("minx:", self.minx) + print("miny:", self.miny) + print("maxx:", self.maxx) + print("maxy:", self.maxy) + + self.xwidth = round((self.maxx - self.minx) / self.reso) + self.ywidth = round((self.maxy - self.miny) / self.reso) + print("xwidth:", self.xwidth) + print("ywidth:", self.ywidth) + + # obstacle map generation + self.obmap = [[False for _ in range(self.ywidth)] + for _ in range(self.xwidth)] + for ix in range(self.xwidth): + x = self.calc_grid_position(ix, self.minx) + for iy in range(self.ywidth): + y = self.calc_grid_position(iy, self.miny) + for iox, ioy in zip(ox, oy): + d = math.hypot(iox - x, ioy - y) + if d <= self.rr: + self.obmap[ix][iy] = True + break + + @staticmethod + def get_motion_model(): + # dx, dy, cost + motion = [[1, 0, 1], + [0, 1, 1], + [-1, 0, 1], + [0, -1, 1], + [-1, -1, math.sqrt(2)], + [-1, 1, math.sqrt(2)], + [1, -1, math.sqrt(2)], + [1, 1, math.sqrt(2)]] + + return motion + + +def main(): + print(__file__ + " start!!") + + # start and goal position + sx = 10.0 # [m] + sy = 10.0 # [m] + gx = 50.0 # [m] + gy = 50.0 # [m] + grid_size = 2.0 # [m] + robot_radius = 1.0 # [m] + + # set obstacle positions + ox, oy = [], [] + for i in range(-10, 60): + ox.append(i) + oy.append(-10.0) + for i in range(-10, 60): + ox.append(60.0) + oy.append(i) + for i in range(-10, 61): + ox.append(i) + oy.append(60.0) + for i in range(-10, 61): + ox.append(-10.0) + oy.append(i) + for i in range(-10, 40): + ox.append(20.0) + oy.append(i) + for i in range(0, 40): + ox.append(40.0) + oy.append(60.0 - i) + + if show_animation: # pragma: no cover + plt.plot(ox, oy, ".k") + plt.plot(sx, sy, "og") + plt.plot(gx, gy, "ob") + plt.grid(True) + plt.axis("equal") + + bi_bfs = BidirectionalBreadthFirstSearchPlanner( + ox, oy, grid_size, robot_radius) + rx, ry = bi_bfs.planning(sx, sy, gx, gy) + + if show_animation: # pragma: no cover + plt.plot(rx, ry, "-r") + plt.pause(0.01) + plt.show() + + +if __name__ == '__main__': + main() From ada734f6622bf6a380266344c01601f97aedece3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 5 May 2020 14:17:28 +0900 Subject: [PATCH 219/909] add an author --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 9a9bc9502a..aa9a47ca77 100644 --- a/README.md +++ b/README.md @@ -598,4 +598,6 @@ If you or your company would like to support this project, please consider: - [Guillaume Jacquenot](https://github.com/Gjacquenot) +- [Erwin Lejeune](https://github.com/guilyx) + From 2b0020764b63b105ce90fc880d7e200203f44b2f Mon Sep 17 00:00:00 2001 From: Erwin Lejeune Date: Thu, 7 May 2020 13:06:08 +0200 Subject: [PATCH 220/909] Fix Hybrid A* (#327) --- PathPlanning/HybridAStar/hybrid_a_star.py | 47 ++++++++++++++--------- tests/test_hybrid_a_star.py | 22 +++++++++++ 2 files changed, 51 insertions(+), 18 deletions(-) create mode 100644 tests/test_hybrid_a_star.py diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 41e8b393c0..14e4ac5aec 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -12,19 +12,22 @@ import math import matplotlib.pyplot as plt import sys -sys.path.append("../ReedsSheppPath/") +import os + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../ReedsSheppPath") try: from a_star_heuristic import dp_planning # , calc_obstacle_map import reeds_shepp_path_planning as rs from car import move, check_car_collision, MAX_STEER, WB, plot_car -except: +except Exception: raise XY_GRID_RESOLUTION = 2.0 # [m] YAW_GRID_RESOLUTION = np.deg2rad(15.0) # [rad] MOTION_RESOLUTION = 0.1 # [m] path interporate resolution -N_STEER = 20.0 # number of steer command +N_STEER = 20 # number of steer command H_COST = 1.0 VR = 1.0 # robot radius @@ -131,7 +134,8 @@ def __init__(self, ox, oy, xyreso, yawreso): def calc_motion_inputs(): - for steer in np.concatenate((np.linspace(-MAX_STEER, MAX_STEER, N_STEER),[0.0])): + for steer in np.concatenate((np.linspace(-MAX_STEER, MAX_STEER, + N_STEER), [0.0])): for d in [1, -1]: yield [steer, d] @@ -225,7 +229,8 @@ def update_node_with_analystic_expantion(current, goal, apath = analytic_expantion(current, goal, c, ox, oy, kdtree) if apath: - plt.plot(apath.x, apath.y) + if show_animation: + plt.plot(apath.x, apath.y) fx = apath.x[1:] fy = apath.y[1:] fyaw = apath.yaw[1:] @@ -337,6 +342,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso): current, ngoal, config, ox, oy, obkdtree) if isupdated: + print("path found") break for neighbor in get_neighbors(current, config, ox, oy, obkdtree): @@ -437,12 +443,16 @@ def main(): start = [10.0, 10.0, np.deg2rad(90.0)] goal = [50.0, 50.0, np.deg2rad(-90.0)] - plt.plot(ox, oy, ".k") - rs.plot_arrow(start[0], start[1], start[2], fc='g') - rs.plot_arrow(goal[0], goal[1], goal[2]) + print("start : ", start) + print("goal : ", goal) + + if show_animation: + plt.plot(ox, oy, ".k") + rs.plot_arrow(start[0], start[1], start[2], fc='g') + rs.plot_arrow(goal[0], goal[1], goal[2]) - plt.grid(True) - plt.axis("equal") + plt.grid(True) + plt.axis("equal") path = hybrid_a_star_planning( start, goal, ox, oy, XY_GRID_RESOLUTION, YAW_GRID_RESOLUTION) @@ -451,14 +461,15 @@ def main(): y = path.ylist yaw = path.yawlist - for ix, iy, iyaw in zip(x, y, yaw): - plt.cla() - plt.plot(ox, oy, ".k") - plt.plot(x, y, "-r", label="Hybrid A* path") - plt.grid(True) - plt.axis("equal") - plot_car(ix, iy, iyaw) - plt.pause(0.0001) + if show_animation: + for ix, iy, iyaw in zip(x, y, yaw): + plt.cla() + plt.plot(ox, oy, ".k") + plt.plot(x, y, "-r", label="Hybrid A* path") + plt.grid(True) + plt.axis("equal") + plot_car(ix, iy, iyaw) + plt.pause(0.0001) print(__file__ + " done!!") diff --git a/tests/test_hybrid_a_star.py b/tests/test_hybrid_a_star.py new file mode 100644 index 0000000000..8cf5c30ad6 --- /dev/null +++ b/tests/test_hybrid_a_star.py @@ -0,0 +1,22 @@ +from unittest import TestCase +import sys +import os +sys.path.append(os.path.dirname(__file__) + "/../") +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../PathPlanning/HybridAStar") +try: + from PathPlanning.HybridAStar import hybrid_a_star as m +except Exception: + raise + + +class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main() + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test1() From 3607d72b60cd500806e0f026ac8beb82850a01f9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 8 May 2020 22:15:43 +0900 Subject: [PATCH 221/909] Update official Python version to 3.8 (#329) --- .../{pythonpackage.yml => Linux_CI.yml} | 15 +++-- .github/workflows/MacOS_CI.yml | 58 +++++++++++++++++++ .travis.yml | 2 +- README.md | 4 +- appveyor.yml | 12 ++-- 5 files changed, 78 insertions(+), 13 deletions(-) rename .github/workflows/{pythonpackage.yml => Linux_CI.yml} (82%) create mode 100644 .github/workflows/MacOS_CI.yml diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/Linux_CI.yml similarity index 82% rename from .github/workflows/pythonpackage.yml rename to .github/workflows/Linux_CI.yml index 7d1014ca57..293c5621e2 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/Linux_CI.yml @@ -1,4 +1,4 @@ -name: Python package +name: Linux_CI on: [push, pull_request] @@ -7,14 +7,17 @@ jobs: runs-on: ubuntu-latest strategy: - max-parallel: 4 matrix: - python-version: [3.7] + python-version: ['3.8'] + + name: Python ${{ matrix.python-version }} CI steps: - - uses: actions/checkout@v1 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v1 + - uses: actions/checkout@v2 + - run: git fetch --prune --unshallow + + - name: Setup python + uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml new file mode 100644 index 0000000000..3a28347b18 --- /dev/null +++ b/.github/workflows/MacOS_CI.yml @@ -0,0 +1,58 @@ +# This is a basic workflow to help you get started with Actions + +name: MacOS_CI + +# Controls when the action will run. Triggers the workflow on push or pull request +# events but only for the master branch +on: [push, pull_request] + +jobs: + build: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [ '3.8' ] + name: Python ${{ matrix.python-version }} CI + steps: + - uses: actions/checkout@v2 + - run: git fetch --prune --unshallow + + - name: Setup python + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + - name: install coverage + run: pip install coverage + + - name: install mypy + run: pip install mypy + + - name: install pycodestyle + run: pip install pycodestyle + + - name: mypy check + run: | + find AerialNavigation -name "*.py" | xargs mypy + find ArmNavigation -name "*.py" | xargs mypy + find Bipedal -name "*.py" | xargs mypy + find InvertedPendulumCart -name "*.py" | xargs mypy + find Localization -name "*.py" | xargs mypy + find Mapping -name "*.py" | xargs mypy + find PathPlanning -name "*.py" | xargs mypy + find PathTracking -name "*.py" | xargs mypy + find SLAM -name "*.py" | xargs mypy + + - name: do diff style check + run: bash rundiffstylecheck.sh + + - name: do all unit tests + run: bash runtests.sh + + + + diff --git a/.travis.yml b/.travis.yml index 042c69ee42..da82e6062d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,7 +5,7 @@ matrix: - os: linux python: - - 3.7 + - 3.8 before_install: - sudo apt-get update diff --git a/README.md b/README.md index aa9a47ca77..4a64f53642 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ header pic # PythonRobotics +![GitHub_Action_Linux_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Linux_CI/badge.svg) +![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg) [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics) [![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest) [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) @@ -90,7 +92,7 @@ See this paper for more details: # Requirements -- Python 3.7.x (2.7 is not supported) +- Python 3.8.x - numpy diff --git a/appveyor.yml b/appveyor.yml index 33d4d02970..f34bcb2b28 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -1,3 +1,5 @@ +os: Visual Studio 2019 + environment: global: # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the @@ -6,7 +8,8 @@ environment: CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd" matrix: - - MINICONDA: C:\Miniconda37-x64 + - MINICONDA: C:\Miniconda38-x64 + PYTHON_VERSION: "3.8" init: - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%" @@ -24,13 +27,12 @@ install: - ECHO "Filesystem root:" - ps: "ls \"C:/\"" - - ECHO "Installed SDKs:" - - ps: "ls \"C:/Program Files/Microsoft SDKs/Windows\"" - # Prepend newly installed Python to the PATH of this build (this cannot be # done from inside the powershell script as it would require to restart # the parent CMD process). - - "SET PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%" + - SET PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH% + - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH% + - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin - conda config --set always_yes yes --set changeps1 no - conda update -q conda - conda info -a From 03a92fc23eb97b0d58bea37c4f8cf22c3ef02fb3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 28 May 2020 22:03:17 +0900 Subject: [PATCH 222/909] fix bipedal_planner and add its test (#332) --- Bipedal/bipedal_planner/__init__.py | 0 Bipedal/bipedal_planner/bipedal_planner.py | 103 +++++++++++------- .../reeds_shepp_path_planning.py | 22 ++-- tests/test_bipedal_planner.py | 24 ++++ 4 files changed, 104 insertions(+), 45 deletions(-) create mode 100644 Bipedal/bipedal_planner/__init__.py create mode 100644 tests/test_bipedal_planner.py diff --git a/Bipedal/bipedal_planner/__init__.py b/Bipedal/bipedal_planner/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py index 6502ce5bed..60c03024bf 100644 --- a/Bipedal/bipedal_planner/bipedal_planner.py +++ b/Bipedal/bipedal_planner/bipedal_planner.py @@ -12,13 +12,17 @@ class BipedalPlanner(object): def __init__(self): + self.act_p = [] # actual footstep positions + self.ref_p = [] # reference footstep positions + self.com_trajectory = [] self.ref_footsteps = None self.g = 9.8 def set_ref_footsteps(self, ref_footsteps): self.ref_footsteps = ref_footsteps - def inverted_pendulum(self, x, x_dot, px_star, y, y_dot, py_star, z_c, time_width): + def inverted_pendulum(self, x, x_dot, px_star, y, y_dot, py_star, z_c, + time_width): time_split = 100 for i in range(time_split): @@ -37,23 +41,21 @@ def inverted_pendulum(self, x, x_dot, px_star, y, y_dot, py_star, z_c, time_widt return x, x_dot, y, y_dot - def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): + def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False): if self.ref_footsteps is None: print("No footsteps") return + com_trajectory_for_plot, ax = None, None + # set up plotter if plot: fig = plt.figure() ax = Axes3D(fig) com_trajectory_for_plot = [] - self.com_trajectory = [] - self.ref_p = [] # reference footstep positions - self.act_p = [] # actual footstep positions - - px, py = 0.0, 0.0 # reference footstep position - px_star, py_star = px, py # modified footstep position + px, py = 0.0, 0.0 # reference footstep position + px_star, py_star = px, py # modified footstep position xi, xi_dot, yi, yi_dot = 0.0, 0.0, 0.01, 0.0 time = 0.0 n = 0 @@ -62,10 +64,10 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): for i in range(len(self.ref_footsteps)): # simulate x, y and those of dot of inverted pendulum xi, xi_dot, yi, yi_dot = self.inverted_pendulum( - xi, xi_dot, px_star, yi, yi_dot, py_star, z_c, T_sup) + xi, xi_dot, px_star, yi, yi_dot, py_star, z_c, t_sup) # update time - time += T_sup + time += t_sup n += 1 # calculate px, py, x_, y_, vx_, vy_ @@ -77,19 +79,22 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): f_x_next, f_y_next, f_theta_next = 0., 0., 0. else: f_x_next, f_y_next, f_theta_next = self.ref_footsteps[n] - rotate_mat_next = np.array([[math.cos(f_theta_next), -math.sin(f_theta_next)], - [math.sin(f_theta_next), math.cos(f_theta_next)]]) - - T_c = math.sqrt(z_c / self.g) - C = math.cosh(T_sup / T_c) - S = math.sinh(T_sup / T_c) - - px, py = list(np.array( - [px, py]) + np.dot(rotate_mat, np.array([f_x, -1 * math.pow(-1, n) * f_y]))) + rotate_mat_next = np.array( + [[math.cos(f_theta_next), -math.sin(f_theta_next)], + [math.sin(f_theta_next), math.cos(f_theta_next)]]) + + Tc = math.sqrt(z_c / self.g) + C = math.cosh(t_sup / Tc) + S = math.sinh(t_sup / Tc) + + px, py = list(np.array([px, py]) + + np.dot(rotate_mat, + np.array([f_x, -1 * math.pow(-1, n) * f_y]) + )) x_, y_ = list(np.dot(rotate_mat_next, np.array( [f_x_next / 2., math.pow(-1, n) * f_y_next / 2.]))) vx_, vy_ = list(np.dot(rotate_mat_next, np.array( - [(1 + C) / (T_c * S) * x_, (C - 1) / (T_c * S) * y_]))) + [(1 + C) / (Tc * S) * x_, (C - 1) / (Tc * S) * y_]))) self.ref_p.append([px, py, f_theta]) # calculate reference COM @@ -97,11 +102,11 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): yd, yd_dot = py + y_, vy_ # calculate modified footsteps - D = a * math.pow(C - 1, 2) + b * math.pow(S / T_c, 2) - px_star = -a * (C - 1) / D * (xd - C * xi - T_c * S * xi_dot) - \ - b * S / (T_c * D) * (xd_dot - S / T_c * xi - C * xi_dot) - py_star = -a * (C - 1) / D * (yd - C * yi - T_c * S * yi_dot) - \ - b * S / (T_c * D) * (yd_dot - S / T_c * yi - C * yi_dot) + D = a * math.pow(C - 1, 2) + b * math.pow(S / Tc, 2) + px_star = -a * (C - 1) / D * (xd - C * xi - Tc * S * xi_dot) \ + - b * S / (Tc * D) * (xd_dot - S / Tc * xi - C * xi_dot) + py_star = -a * (C - 1) / D * (yd - C * yi - Tc * S * yi_dot) \ + - b * S / (Tc * D) * (yd_dot - S / Tc * yi - C * yi_dot) self.act_p.append([px_star, py_star, f_theta]) # plot @@ -112,17 +117,22 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): # set up plotter plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: + [exit(0) if event.key == 'escape' else None]) ax.set_zlim(0, z_c * 2) - ax.set_aspect('equal', 'datalim') + ax.set_xlim(0, 1) + ax.set_ylim(-0.5, 0.5) # update com_trajectory_for_plot com_trajectory_for_plot.append(self.com_trajectory[c]) # plot com - ax.plot([p[0] for p in com_trajectory_for_plot], [p[1] for p in com_trajectory_for_plot], [ - 0 for p in com_trajectory_for_plot], color="red") + ax.plot([p[0] for p in com_trajectory_for_plot], + [p[1] for p in com_trajectory_for_plot], [ + 0 for _ in com_trajectory_for_plot], + color="red") # plot inverted pendulum ax.plot([px_star, com_trajectory_for_plot[-1][0]], @@ -137,22 +147,39 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False): foot_height = 0.04 for j in range(len(self.ref_p)): angle = self.ref_p[j][2] + \ - math.atan2(foot_height, foot_width) - math.pi + math.atan2(foot_height, + foot_width) - math.pi r = math.sqrt( - math.pow(foot_width / 3., 2) + math.pow(foot_height / 2., 2)) - rec = pat.Rectangle(xy=(self.ref_p[j][0] + r * math.cos(angle), self.ref_p[j][1] + r * math.sin(angle)), - width=foot_width, height=foot_height, angle=self.ref_p[j][2] * 180 / math.pi, color="blue", fill=False, ls=":") + math.pow(foot_width / 3., 2) + math.pow( + foot_height / 2., 2)) + rec = pat.Rectangle(xy=( + self.ref_p[j][0] + r * math.cos(angle), + self.ref_p[j][1] + r * math.sin(angle)), + width=foot_width, + height=foot_height, + angle=self.ref_p[j][ + 2] * 180 / math.pi, + color="blue", fill=False, + ls=":") ax.add_patch(rec) art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z") # foot rectangle for self.act_p for j in range(len(self.act_p)): angle = self.act_p[j][2] + \ - math.atan2(foot_height, foot_width) - math.pi + math.atan2(foot_height, + foot_width) - math.pi r = math.sqrt( - math.pow(foot_width / 3., 2) + math.pow(foot_height / 2., 2)) - rec = pat.Rectangle(xy=(self.act_p[j][0] + r * math.cos(angle), self.act_p[j][1] + r * math.sin(angle)), - width=foot_width, height=foot_height, angle=self.act_p[j][2] * 180 / math.pi, color="blue", fill=False) + math.pow(foot_width / 3., 2) + math.pow( + foot_height / 2., 2)) + rec = pat.Rectangle(xy=( + self.act_p[j][0] + r * math.cos(angle), + self.act_p[j][1] + r * math.sin(angle)), + width=foot_width, + height=foot_height, + angle=self.act_p[j][ + 2] * 180 / math.pi, + color="blue", fill=False) ax.add_patch(rec) art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z") diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index 9c72a0c9b2..3bed9e2997 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -369,13 +369,21 @@ def reeds_shepp_path_planning(sx, sy, syaw, def main(): print("Reeds Shepp path planner sample start!!") - start_x = -1.0 # [m] - start_y = -4.0 # [m] - start_yaw = np.deg2rad(-20.0) # [rad] - - end_x = 5.0 # [m] - end_y = 5.0 # [m] - end_yaw = np.deg2rad(25.0) # [rad] + # start_x = -1.0 # [m] + # start_y = -4.0 # [m] + # start_yaw = np.deg2rad(-20.0) # [rad] + # + # end_x = 5.0 # [m] + # end_y = 5.0 # [m] + # end_yaw = np.deg2rad(25.0) # [rad] + + start_x = 0.0 # [m] + start_y = 0.0 # [m] + start_yaw = np.deg2rad(0.0) # [rad] + + end_x = 0.0 # [m] + end_y = 0.0 # [m] + end_yaw = np.deg2rad(0.0) # [rad] curvature = 1.0 step_size = 0.1 diff --git a/tests/test_bipedal_planner.py b/tests/test_bipedal_planner.py new file mode 100644 index 0000000000..66049c68ec --- /dev/null +++ b/tests/test_bipedal_planner.py @@ -0,0 +1,24 @@ +from unittest import TestCase + +import sys +sys.path.append("./Bipedal/bipedal_planner/") +try: + from Bipedal.bipedal_planner import bipedal_planner as m +except Exception: + raise + +print(__file__) + + +class Test(TestCase): + + def test(self): + bipedal_planner = m.BipedalPlanner() + + footsteps = [[0.0, 0.2, 0.0], + [0.3, 0.2, 0.0], + [0.3, 0.2, 0.2], + [0.3, 0.2, 0.2], + [0.0, 0.2, 0.2]] + bipedal_planner.set_ref_footsteps(footsteps) + bipedal_planner.walk(plot=False) From 26b2c552efe7eec8f8f1505aabbf5591627878d8 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 5 Jun 2020 21:21:35 +0900 Subject: [PATCH 223/909] fix inaccurate covariance calculation to add a new landmark in FastSLAM1,2 (#334) --- SLAM/FastSLAM1/fast_slam1.py | 108 +++++++++++++++++++---------------- SLAM/FastSLAM2/fast_slam2.py | 68 ++++++++++++---------- 2 files changed, 96 insertions(+), 80 deletions(-) diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index 2d7c003785..c4beaba257 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -16,16 +16,16 @@ R = np.diag([1.0, np.deg2rad(20.0)]) ** 2 # Simulation parameter -Qsim = np.diag([0.3, np.deg2rad(2.0)]) ** 2 -Rsim = np.diag([0.5, np.deg2rad(10.0)]) ** 2 -OFFSET_YAWRATE_NOISE = 0.01 +Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2 +R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2 +OFFSET_YAW_RATE_NOISE = 0.01 DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] MAX_RANGE = 20.0 # maximum observation range M_DIST_TH = 2.0 # Threshold of Mahalanobis distance for data association. STATE_SIZE = 3 # State size [x,y,yaw] -LM_SIZE = 2 # LM srate size [x,y] +LM_SIZE = 2 # LM state size [x,y] N_PARTICLE = 100 # number of particle NTH = N_PARTICLE / 1.5 # Number of particle for re-sampling @@ -34,15 +34,15 @@ class Particle: - def __init__(self, N_LM): + def __init__(self, n_landmark): self.w = 1.0 / N_PARTICLE self.x = 0.0 self.y = 0.0 self.yaw = 0.0 # landmark x-y positions - self.lm = np.zeros((N_LM, LM_SIZE)) + self.lm = np.zeros((n_landmark, LM_SIZE)) # landmark position covariance - self.lmP = np.zeros((N_LM * LM_SIZE, LM_SIZE)) + self.lmP = np.zeros((n_landmark * LM_SIZE, LM_SIZE)) def fast_slam1(particles, u, z): @@ -56,11 +56,11 @@ def fast_slam1(particles, u, z): def normalize_weight(particles): - sumw = sum([p.w for p in particles]) + sum_w = sum([p.w for p in particles]) try: for i in range(N_PARTICLE): - particles[i].w /= sumw + particles[i].w /= sum_w except ZeroDivisionError: for i in range(N_PARTICLE): particles[i].w = 1.0 / N_PARTICLE @@ -101,7 +101,7 @@ def predict_particles(particles, u): return particles -def add_new_lm(particle, z, Q_cov): +def add_new_landmark(particle, z, Q_cov): r = z[0] b = z[1] lm_id = int(z[2]) @@ -113,10 +113,14 @@ def add_new_lm(particle, z, Q_cov): particle.lm[lm_id, 1] = particle.y + r * s # covariance - Gz = np.array([[c, -r * s], - [s, r * c]]) - - particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T + dx = r * c + dy = r * s + d2 = dx**2 + dy**2 + d = math.sqrt(d2) + Gz = np.array([[dx / d, dy / d], + [-dy / d2, dx / d2]]) + particle.lmP[2 * lm_id:2 * lm_id + 2] = np.linalg.inv( + Gz) @ Q_cov @ np.linalg.inv(Gz.T) return particle @@ -146,10 +150,10 @@ def update_kf_with_cholesky(xf, Pf, v, Q_cov, Hf): S = Hf @ PHt + Q_cov S = (S + S.T) * 0.5 - SChol = np.linalg.cholesky(S).T - SCholInv = np.linalg.inv(SChol) - W1 = PHt @ SCholInv - W = W1 @ SCholInv.T + s_chol = np.linalg.cholesky(S).T + s_chol_inv = np.linalg.inv(s_chol) + W1 = PHt @ s_chol_inv + W = W1 @ s_chol_inv.T x = xf + W @ v P = Pf - W1 @ W1.T @@ -187,7 +191,7 @@ def compute_weight(particle, z, Q_cov): try: invS = np.linalg.inv(Sf) except np.linalg.linalg.LinAlgError: - print("singuler") + print("singular") return 1.0 num = math.exp(-0.5 * dx.T @ invS @ dx) @@ -201,12 +205,12 @@ def compute_weight(particle, z, Q_cov): def update_with_observation(particles, z): for iz in range(len(z[0, :])): - lmid = int(z[2, iz]) + landmark_id = int(z[2, iz]) for ip in range(N_PARTICLE): # new landmark - if abs(particles[ip].lm[lmid, 0]) <= 0.01: - particles[ip] = add_new_lm(particles[ip], z[:, iz], Q) + if abs(particles[ip].lm[landmark_id, 0]) <= 0.01: + particles[ip] = add_new_landmark(particles[ip], z[:, iz], Q) # known landmark else: w = compute_weight(particles[ip], z[:, iz], Q) @@ -229,28 +233,29 @@ def resampling(particles): pw = np.array(pw) - Neff = 1.0 / (pw @ pw.T) # Effective particle number - # print(Neff) + n_eff = 1.0 / (pw @ pw.T) # Effective particle number + # print(n_eff) - if Neff < NTH: # resampling - wcum = np.cumsum(pw) + if n_eff < NTH: # resampling + w_cum = np.cumsum(pw) base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE - resampleid = base + np.random.rand(base.shape[0]) / N_PARTICLE + resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE inds = [] ind = 0 for ip in range(N_PARTICLE): - while (ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind]): + while (ind < w_cum.shape[0] - 1) \ + and (resample_id[ip] > w_cum[ind]): ind += 1 inds.append(ind) - tparticles = particles[:] + tmp_particles = particles[:] for i in range(len(inds)): - particles[i].x = tparticles[inds[i]].x - particles[i].y = tparticles[inds[i]].y - particles[i].yaw = tparticles[inds[i]].yaw - particles[i].lm = tparticles[inds[i]].lm[:, :] - particles[i].lmP = tparticles[inds[i]].lmP[:, :] + particles[i].x = tmp_particles[inds[i]].x + particles[i].y = tmp_particles[inds[i]].y + particles[i].yaw = tmp_particles[inds[i]].yaw + particles[i].lm = tmp_particles[inds[i]].lm[:, :] + particles[i].lmP = tmp_particles[inds[i]].lmP[:, :] particles[i].w = 1.0 / N_PARTICLE return particles @@ -259,37 +264,39 @@ def resampling(particles): def calc_input(time): if time <= 3.0: # wait at first v = 0.0 - yawrate = 0.0 + yaw_rate = 0.0 else: v = 1.0 # [m/s] - yawrate = 0.1 # [rad/s] + yaw_rate = 0.1 # [rad/s] - u = np.array([v, yawrate]).reshape(2, 1) + u = np.array([v, yaw_rate]).reshape(2, 1) return u -def observation(xTrue, xd, u, RFID): +def observation(xTrue, xd, u, rfid): # calc true state xTrue = motion_model(xTrue, u) # add noise to range observation z = np.zeros((3, 0)) - for i in range(len(RFID[:, 0])): + for i in range(len(rfid[:, 0])): - dx = RFID[i, 0] - xTrue[0, 0] - dy = RFID[i, 1] - xTrue[1, 0] + dx = rfid[i, 0] - xTrue[0, 0] + dy = rfid[i, 1] - xTrue[1, 0] d = math.hypot(dx, dy) angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: - dn = d + np.random.randn() * Qsim[0, 0] ** 0.5 # add noise - anglen = angle + np.random.randn() * Qsim[1, 1] ** 0.5 # add noise - zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1) + dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise + angle_with_noize = angle + np.random.randn() * Q_sim[ + 1, 1] ** 0.5 # add noise + zi = np.array([dn, pi_2_pi(angle_with_noize), i]).reshape(3, 1) z = np.hstack((z, zi)) # add noise to input - ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] ** 0.5 - ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] ** 0.5 + OFFSET_YAWRATE_NOISE + ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * R_sim[ + 1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE ud = np.array([ud1, ud2]).reshape(2, 1) xd = motion_model(xd, ud) @@ -332,7 +339,7 @@ def main(): [-5.0, 5.0], [-10.0, 15.0] ]) - N_LM = RFID.shape[0] + n_landmark = RFID.shape[0] # State Vector [x y yaw v]' xEst = np.zeros((STATE_SIZE, 1)) # SLAM estimation @@ -344,7 +351,7 @@ def main(): hxTrue = xTrue hxDR = xTrue - particles = [Particle(N_LM) for _ in range(N_PARTICLE)] + particles = [Particle(n_landmark) for _ in range(N_PARTICLE)] while SIM_TIME >= time: time += DT @@ -366,8 +373,9 @@ def main(): if show_animation: # pragma: no cover plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', lambda event: + [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") for i in range(N_PARTICLE): diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 4780b57982..7cd708df81 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -113,10 +113,14 @@ def add_new_lm(particle, z, Q_cov): particle.lm[lm_id, 1] = particle.y + r * s # covariance - Gz = np.array([[c, -r * s], - [s, r * c]]) - - particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T + dx = r * c + dy = r * s + d2 = dx ** 2 + dy ** 2 + d = math.sqrt(d2) + Gz = np.array([[dx / d, dy / d], + [-dy / d2, dx / d2]]) + particle.lmP[2 * lm_id:2 * lm_id + 2] = np.linalg.inv( + Gz) @ Q_cov @ np.linalg.inv(Gz.T) return particle @@ -224,11 +228,11 @@ def proposal_sampling(particle, z, Q_cov): def update_with_observation(particles, z): for iz in range(len(z[0, :])): - lmid = int(z[2, iz]) + landmark_id = int(z[2, iz]) for ip in range(N_PARTICLE): # new landmark - if abs(particles[ip].lm[lmid, 0]) <= 0.01: + if abs(particles[ip].lm[landmark_id, 0]) <= 0.01: particles[ip] = add_new_lm(particles[ip], z[:, iz], Q) # known landmark else: @@ -254,27 +258,28 @@ def resampling(particles): pw = np.array(pw) - Neff = 1.0 / (pw @ pw.T) # Effective particle number + n_eff = 1.0 / (pw @ pw.T) # Effective particle number - if Neff < NTH: # resampling - wcum = np.cumsum(pw) + if n_eff < NTH: # resampling + w_cum = np.cumsum(pw) base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE - resamplei_id = base + np.random.rand(base.shape[0]) / N_PARTICLE + resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE inds = [] ind = 0 for ip in range(N_PARTICLE): - while (ind < wcum.shape[0] - 1) and (resamplei_id[ip] > wcum[ind]): + while (ind < w_cum.shape[0] - 1) \ + and (resample_id[ip] > w_cum[ind]): ind += 1 inds.append(ind) - tparticles = particles[:] + tmp_particles = particles[:] for i in range(len(inds)): - particles[i].x = tparticles[inds[i]].x - particles[i].y = tparticles[inds[i]].y - particles[i].yaw = tparticles[inds[i]].yaw - particles[i].lm = tparticles[inds[i]].lm[:, :] - particles[i].lmP = tparticles[inds[i]].lmP[:, :] + particles[i].x = tmp_particles[inds[i]].x + particles[i].y = tmp_particles[inds[i]].y + particles[i].yaw = tmp_particles[inds[i]].yaw + particles[i].lm = tmp_particles[inds[i]].lm[:, :] + particles[i].lmP = tmp_particles[inds[i]].lmP[:, :] particles[i].w = 1.0 / N_PARTICLE return particles @@ -283,12 +288,12 @@ def resampling(particles): def calc_input(time): if time <= 3.0: # wait at first v = 0.0 - yawrate = 0.0 + yaw_rate = 0.0 else: v = 1.0 # [m/s] - yawrate = 0.1 # [rad/s] + yaw_rate = 0.1 # [rad/s] - u = np.array([v, yawrate]).reshape(2, 1) + u = np.array([v, yaw_rate]).reshape(2, 1) return u @@ -308,13 +313,15 @@ def observation(xTrue, xd, u, RFID): angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise - anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise - zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1) + angle_noise = np.random.randn() * Q_sim[1, 1] ** 0.5 + angle_with_noise = angle + angle_noise # add noise + zi = np.array([dn, pi_2_pi(angle_with_noise), i]).reshape(3, 1) z = np.hstack((z, zi)) # add noise to input ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5 - ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE + ud2 = u[1, 0] + np.random.randn() * R_sim[ + 1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE ud = np.array([ud1, ud2]).reshape(2, 1) xd = motion_model(xd, ud) @@ -357,7 +364,7 @@ def main(): [-5.0, 5.0], [-10.0, 15.0] ]) - N_LM = RFID.shape[0] + n_landmark = RFID.shape[0] # State Vector [x y yaw v]' xEst = np.zeros((STATE_SIZE, 1)) # SLAM estimation @@ -369,7 +376,7 @@ def main(): hxTrue = xTrue hxDR = xTrue - particles = [Particle(N_LM) for _ in range(N_PARTICLE)] + particles = [Particle(n_landmark) for _ in range(N_PARTICLE)] while SIM_TIME >= time: time += DT @@ -391,14 +398,15 @@ def main(): if show_animation: # pragma: no cover plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") for iz in range(len(z[:, 0])): - lmid = int(z[2, iz]) - plt.plot([xEst[0], RFID[lmid, 0]], [ - xEst[1], RFID[lmid, 1]], "-k") + landmark_id = int(z[2, iz]) + plt.plot([xEst[0], RFID[landmark_id, 0]], [ + xEst[1], RFID[landmark_id, 1]], "-k") for i in range(N_PARTICLE): plt.plot(particles[i].x, particles[i].y, ".r") From d1bde5835f7f6063bcff5675e138d57644bc6d4a Mon Sep 17 00:00:00 2001 From: Justin Hutchings Date: Fri, 5 Jun 2020 16:21:00 -0700 Subject: [PATCH 224/909] Add CodeQL security scanning (#333) * Add CodeQL security scanning * Add configuration for LGTM queries * Remove excess whitespace --- .github/codeql/codeql-config.yml | 6 ++++ .github/workflows/codeql.yml | 54 ++++++++++++++++++++++++++++++++ 2 files changed, 60 insertions(+) create mode 100644 .github/codeql/codeql-config.yml create mode 100644 .github/workflows/codeql.yml diff --git a/.github/codeql/codeql-config.yml b/.github/codeql/codeql-config.yml new file mode 100644 index 0000000000..4542231f10 --- /dev/null +++ b/.github/codeql/codeql-config.yml @@ -0,0 +1,6 @@ +name: "Extended CodeQL Config" + +# This file adds additional queries to the default configuration to make it equivalent to what LGTM checks. +queries: + - name: Security and quality queries + uses: security-and-quality diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 0000000000..526b465eec --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,54 @@ +name: "Code scanning - action" + +on: + push: + pull_request: + schedule: + - cron: '0 19 * * 0' + +jobs: + CodeQL-Build: + + # CodeQL runs on ubuntu-latest and windows-latest + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + with: + # We must fetch at least the immediate parents so that if this is + # a pull request then we can checkout the head. + fetch-depth: 2 + + # If this run was triggered by a pull request event, then checkout + # the head of the pull request instead of the merge commit. + - run: git checkout HEAD^2 + if: ${{ github.event_name == 'pull_request' }} + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v1 + with: + config-file: ./.github/codeql/codeql-config.yml + # Override language selection by uncommenting this and choosing your languages + # with: + # languages: go, javascript, csharp, python, cpp, java + + # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). + # If this step fails, then you should remove it and run the build manually (see below) + - name: Autobuild + uses: github/codeql-action/autobuild@v1 + + # ℹ️ Command-line programs to run using the OS shell. + # 📚 https://git.io/JvXDl + + # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines + # and modify them (or add more) to build your code if your project + # uses a compiled language + + #- run: | + # make bootstrap + # make release + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v1 From b8afdb10d8d8f57f5c6bde9b41ab64911d01ef8e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 7 Jun 2020 20:28:29 +0900 Subject: [PATCH 225/909] Using scipy.spatial.rotation matrix (#335) --- .travis.yml | 3 +- .../ensemble_kalman_filter.py | 40 ++- .../histogram_filter/histogram_filter.py | 121 ++++--- .../particle_filter/particle_filter.py | 16 +- .../lidar_to_grid_map/lidar_to_grid_map.py | 187 +++++----- .../rectangle_fitting/rectangle_fitting.py | 74 ++-- Mapping/rectangle_fitting/simulator.py | 81 ++--- PathPlanning/AStar/a_star.py | 130 +++---- .../bidirectional_a_star.py | 22 +- .../bidirectional_breadth_first_search.py | 20 +- .../breadth_first_search.py | 20 +- .../ClosedLoopRRTStar/pure_pursuit.py | 6 +- .../DepthFirstSearch/depth_first_search.py | 20 +- PathPlanning/Dijkstra/dijkstra.py | 4 +- .../DubinsPath/dubins_path_planning.py | 96 +++--- .../dynamic_window_approach.py | 36 +- .../greedy_best_first_search.py | 20 +- .../grid_based_sweep_coverage_path_planner.py | 187 +++++----- PathPlanning/HybridAStar/a_star_heuristic.py | 147 ++++---- PathPlanning/HybridAStar/car.py | 50 +-- PathPlanning/HybridAStar/hybrid_a_star.py | 322 +++++++++--------- .../InformedRRTStar/informed_rrt_star.py | 91 ++--- .../probabilistic_road_map.py | 128 +++---- .../quintic_polynomials_planner.py | 6 +- .../VoronoiRoadMap/dijkstra_search.py | 4 +- SLAM/GraphBasedSLAM/graph_based_slam.py | 104 +++--- 26 files changed, 1003 insertions(+), 932 deletions(-) diff --git a/.travis.yml b/.travis.yml index da82e6062d..99d239217b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,10 +18,9 @@ before_install: - conda update -q conda # Useful for debugging any issues with conda - conda info -a -# - conda install python==3.6.8 install: - - conda install numpy==1.15 + - conda install numpy - conda install scipy - conda install matplotlib - conda install pandas diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index 58a998e2f3..1307b1ebd1 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -5,7 +5,8 @@ author: Ryohei Sasaki(rsasaki0109) Ref: -- [Ensemble Kalman filtering](https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135) +Ensemble Kalman filtering +(https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135) """ @@ -13,6 +14,7 @@ import matplotlib.pyplot as plt import numpy as np +from scipy.spatial.transform import Rotation as Rot # Simulation parameter Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2 @@ -48,8 +50,8 @@ def observation(xTrue, xd, u, RFID): angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) if d <= MAX_RANGE: dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise - anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 # add noise - zi = np.array([dn, anglen, RFID[i, 0], RFID[i, 1]]) + angle_with_noise = angle + np.random.randn() * Q_sim[1, 1] ** 0.5 + zi = np.array([dn, angle_with_noise, RFID[i, 0], RFID[i, 1]]) z = np.vstack((z, zi)) # add noise to input @@ -79,10 +81,12 @@ def motion_model(x, u): def observe_landmark_position(x, landmarks): landmarks_pos = np.zeros((2 * landmarks.shape[0], 1)) for (i, lm) in enumerate(landmarks): - landmarks_pos[2 * i] = x[0, 0] + lm[0] * math.cos(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[ - 0, 0] ** 0.5 / np.sqrt(2) - landmarks_pos[2 * i + 1] = x[1, 0] + lm[0] * math.sin(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[ - 0, 0] ** 0.5 / np.sqrt(2) + index = 2 * i + q = Q_sim[0, 0] ** 0.5 + landmarks_pos[index] = x[0, 0] + lm[0] * math.cos( + x[2, 0] + lm[1]) + np.random.randn() * q / np.sqrt(2) + landmarks_pos[index + 1] = x[1, 0] + lm[0] * math.sin( + x[2, 0] + lm[1]) + np.random.randn() * q / np.sqrt(2) return landmarks_pos @@ -148,8 +152,9 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover t = np.arange(0, 2 * math.pi + 0.1, 0.1) - # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely - # close to 0 (~10^-20), catch these cases and set the respective variable to 0 + # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative + # numbers extremely close to 0 (~10^-20), catch these cases and set + # the respective variable to 0 try: a = math.sqrt(eig_val[big_ind]) except ValueError: @@ -163,11 +168,11 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0]) - R = np.array([[math.cos(angle), math.sin(angle)], - [-math.sin(angle), math.cos(angle)]]) - fx = R.dot(np.array([[x, y]])) - px = np.array(fx[0, :] + xEst[0, 0]).flatten() - py = np.array(fx[1, :] + xEst[1, 0]).flatten() + rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2] + fx = np.stack([x, y]).T @ rot + + px = np.array(fx[:, 0] + xEst[0, 0]).flatten() + py = np.array(fx[:, 1] + xEst[1, 0]).flatten() plt.plot(px, py, "--r") @@ -214,8 +219,9 @@ def main(): if show_animation: plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for i in range(len(z[:, 0])): plt.plot([xTrue[0, 0], z[i, 2]], [xTrue[1, 0], z[i, 3]], "-k") @@ -227,7 +233,7 @@ def main(): np.array(hxDR[1, :]).flatten(), "-k") plt.plot(np.array(hxEst[0, :]).flatten(), np.array(hxEst[1, :]).flatten(), "-r") - # plot_covariance_ellipse(xEst, PEst) + plot_covariance_ellipse(xEst, PEst) plt.axis("equal") plt.grid(True) plt.pause(0.001) diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py index 135b476cc4..7f453d69dd 100644 --- a/Localization/histogram_filter/histogram_filter.py +++ b/Localization/histogram_filter/histogram_filter.py @@ -28,11 +28,11 @@ RANGE_STD = 3.0 # standard deviation for observation gaussian distribution # grid map param -XY_RESO = 0.5 # xy grid resolution -MINX = -15.0 -MINY = -5.0 -MAXX = 15.0 -MAXY = 25.0 +XY_RESOLUTION = 0.5 # xy grid resolution +MIN_X = -15.0 +MIN_Y = -5.0 +MAX_X = 15.0 +MAX_Y = 25.0 # simulation parameters NOISE_RANGE = 2.0 # [m] 1σ range noise parameter @@ -41,17 +41,17 @@ show_animation = True -class GridMap(): +class GridMap: def __init__(self): self.data = None - self.xy_reso = None - self.minx = None - self.miny = None - self.maxx = None - self.maxx = None - self.xw = None - self.yw = None + self.xy_resolution = None + self.min_x = None + self.min_y = None + self.max_x = None + self.max_y = None + self.x_w = None + self.y_w = None self.dx = 0.0 # movement distance self.dy = 0.0 # movement distance @@ -64,10 +64,10 @@ def histogram_filter_localization(grid_map, u, z, yaw): return grid_map -def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std): +def calc_gaussian_observation_pdf(grid_map, z, iz, ix, iy, std): # predicted range - x = ix * gmap.xy_reso + gmap.minx - y = iy * gmap.xy_reso + gmap.miny + x = ix * grid_map.xy_resolution + grid_map.min_x + y = iy * grid_map.xy_resolution + grid_map.min_y d = math.hypot(x - z[iz, 1], y - z[iz, 2]) # likelihood @@ -76,16 +76,16 @@ def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std): return pdf -def observation_update(gmap, z, std): +def observation_update(grid_map, z, std): for iz in range(z.shape[0]): - for ix in range(gmap.xw): - for iy in range(gmap.yw): - gmap.data[ix][iy] *= calc_gaussian_observation_pdf( - gmap, z, iz, ix, iy, std) + for ix in range(grid_map.x_w): + for iy in range(grid_map.y_w): + grid_map.data[ix][iy] *= calc_gaussian_observation_pdf( + grid_map, z, iz, ix, iy, std) - gmap = normalize_probability(gmap) + grid_map = normalize_probability(grid_map) - return gmap + return grid_map def calc_input(): @@ -112,8 +112,8 @@ def motion_model(x, u): def draw_heat_map(data, mx, my): - maxp = max([max(igmap) for igmap in data]) - plt.pcolor(mx, my, data, vmax=maxp, cmap=plt.cm.get_cmap("Blues")) + max_value = max([max(i_data) for i_data in data]) + plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues")) plt.axis("equal") @@ -140,43 +140,47 @@ def observation(xTrue, u, RFID): return xTrue, z, ud -def normalize_probability(gmap): - sump = sum([sum(igmap) for igmap in gmap.data]) +def normalize_probability(grid_map): + sump = sum([sum(i_data) for i_data in grid_map.data]) - for ix in range(gmap.xw): - for iy in range(gmap.yw): - gmap.data[ix][iy] /= sump + for ix in range(grid_map.x_w): + for iy in range(grid_map.y_w): + grid_map.data[ix][iy] /= sump - return gmap + return grid_map -def init_gmap(xy_reso, minx, miny, maxx, maxy): +def init_grid_map(xy_resolution, min_x, min_y, max_x, max_y): grid_map = GridMap() - grid_map.xy_reso = xy_reso - grid_map.minx = minx - grid_map.miny = miny - grid_map.maxx = maxx - grid_map.maxy = maxy - grid_map.xw = int(round((grid_map.maxx - grid_map.minx) / grid_map.xy_reso)) - grid_map.yw = int(round((grid_map.maxy - grid_map.miny) / grid_map.xy_reso)) - - grid_map.data = [[1.0 for _ in range(grid_map.yw)] for _ in range(grid_map.xw)] + grid_map.xy_resolution = xy_resolution + grid_map.min_x = min_x + grid_map.min_y = min_y + grid_map.max_x = max_x + grid_map.max_y = max_y + grid_map.x_w = int(round((grid_map.max_x - grid_map.min_x) + / grid_map.xy_resolution)) + grid_map.y_w = int(round((grid_map.max_y - grid_map.min_y) + / grid_map.xy_resolution)) + + grid_map.data = [[1.0 for _ in range(grid_map.y_w)] + for _ in range(grid_map.x_w)] grid_map = normalize_probability(grid_map) return grid_map def map_shift(grid_map, x_shift, y_shift): - tgmap = copy.deepcopy(grid_map.data) + tmp_grid_map = copy.deepcopy(grid_map.data) - for ix in range(grid_map.xw): - for iy in range(grid_map.yw): + for ix in range(grid_map.x_w): + for iy in range(grid_map.y_w): nix = ix + x_shift niy = iy + y_shift - if 0 <= nix < grid_map.xw and 0 <= niy < grid_map.yw: - grid_map.data[ix + x_shift][iy + y_shift] = tgmap[ix][iy] + if 0 <= nix < grid_map.x_w and 0 <= niy < grid_map.y_w: + grid_map.data[ix + x_shift][iy + y_shift] =\ + tmp_grid_map[ix][iy] return grid_map @@ -185,22 +189,26 @@ def motion_update(grid_map, u, yaw): grid_map.dx += DT * math.cos(yaw) * u[0] grid_map.dy += DT * math.sin(yaw) * u[0] - x_shift = grid_map.dx // grid_map.xy_reso - y_shift = grid_map.dy // grid_map.xy_reso + x_shift = grid_map.dx // grid_map.xy_resolution + y_shift = grid_map.dy // grid_map.xy_resolution if abs(x_shift) >= 1.0 or abs(y_shift) >= 1.0: # map should be shifted grid_map = map_shift(grid_map, int(x_shift), int(y_shift)) - grid_map.dx -= x_shift * grid_map.xy_reso - grid_map.dy -= y_shift * grid_map.xy_reso + grid_map.dx -= x_shift * grid_map.xy_resolution + grid_map.dy -= y_shift * grid_map.xy_resolution grid_map.data = gaussian_filter(grid_map.data, sigma=MOTION_STD) return grid_map -def calc_grid_index(gmap): - mx, my = np.mgrid[slice(gmap.minx - gmap.xy_reso / 2.0, gmap.maxx + gmap.xy_reso / 2.0, gmap.xy_reso), - slice(gmap.miny - gmap.xy_reso / 2.0, gmap.maxy + gmap.xy_reso / 2.0, gmap.xy_reso)] +def calc_grid_index(grid_map): + mx, my = np.mgrid[slice(grid_map.min_x - grid_map.xy_resolution / 2.0, + grid_map.max_x + grid_map.xy_resolution / 2.0, + grid_map.xy_resolution), + slice(grid_map.min_y - grid_map.xy_resolution / 2.0, + grid_map.max_y + grid_map.xy_resolution / 2.0, + grid_map.xy_resolution)] return mx, my @@ -217,7 +225,7 @@ def main(): time = 0.0 xTrue = np.zeros((4, 1)) - grid_map = init_gmap(XY_RESO, MINX, MINY, MAXX, MAXY) + grid_map = init_grid_map(XY_RESOLUTION, MIN_X, MIN_Y, MAX_X, MAX_Y) mx, my = calc_grid_index(grid_map) # for grid map visualization while SIM_TIME >= time: @@ -234,8 +242,9 @@ def main(): if show_animation: plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) draw_heat_map(grid_map.data, mx, my) plt.plot(xTrue[0, :], xTrue[1, :], "xr") plt.plot(RF_ID[:, 0], RF_ID[:, 1], ".k") diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 6eca4eb53b..89a43ea600 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -10,6 +10,7 @@ import matplotlib.pyplot as plt import numpy as np +from scipy.spatial.transform import Rotation as Rot # Estimation parameter of PF Q = np.diag([0.2]) ** 2 # range error @@ -172,8 +173,9 @@ def plot_covariance_ellipse(x_est, p_est): # pragma: no cover t = np.arange(0, 2 * math.pi + 0.1, 0.1) - # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely - # close to 0 (~10^-20), catch these cases and set the respective variable to 0 + # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative + # numbers extremely close to 0 (~10^-20), catch these cases and set the + # respective variable to 0 try: a = math.sqrt(eig_val[big_ind]) except ValueError: @@ -187,9 +189,8 @@ def plot_covariance_ellipse(x_est, p_est): # pragma: no cover x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0]) - Rot = np.array([[math.cos(angle), -math.sin(angle)], - [math.sin(angle), math.cos(angle)]]) - fx = Rot.dot(np.array([[x, y]])) + rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2] + fx = rot.dot(np.array([[x, y]])) px = np.array(fx[0, :] + x_est[0, 0]).flatten() py = np.array(fx[1, :] + x_est[1, 0]).flatten() plt.plot(px, py, "--r") @@ -235,8 +236,9 @@ def main(): if show_animation: plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for i in range(len(z[:, 0])): plt.plot([x_true[0, 0], z[i, 1]], [x_true[1, 0], z[i, 2]], "-k") diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py index 60bf4db69c..911a0bf0ea 100644 --- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py +++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py @@ -2,15 +2,16 @@ LIDAR to 2D grid map example -author: Erno Horvath, Csaba Hajdu based on Atsushi Sakai's scripts (@Atsushi_twi) +author: Erno Horvath, Csaba Hajdu based on Atsushi Sakai's scripts """ import math -import numpy as np -import matplotlib.pyplot as plt from collections import deque +import matplotlib.pyplot as plt +import numpy as np + EXTEND_AREA = 1.0 @@ -44,47 +45,49 @@ def bresenham(start, end): x2, y2 = end dx = x2 - x1 dy = y2 - y1 - is_steep = abs(dy) > abs(dx) # determine how steep the line is - if is_steep: # rotate line + is_steep = abs(dy) > abs(dx) # determine how steep the line is + if is_steep: # rotate line x1, y1 = y1, x1 x2, y2 = y2, x2 - swapped = False # swap start and end points if necessary and store swap state + # swap start and end points if necessary and store swap state + swapped = False if x1 > x2: x1, x2 = x2, x1 y1, y2 = y2, y1 swapped = True - dx = x2 - x1 # recalculate differentials - dy = y2 - y1 # recalculate differentials - error = int(dx / 2.0) # calculate error - ystep = 1 if y1 < y2 else -1 + dx = x2 - x1 # recalculate differentials + dy = y2 - y1 # recalculate differentials + error = int(dx / 2.0) # calculate error + y_step = 1 if y1 < y2 else -1 # iterate over bounding box generating points between start and end y = y1 points = [] for x in range(x1, x2 + 1): coord = [y, x] if is_steep else (x, y) - points.append(coord) + points.append(coord) error -= abs(dy) if error < 0: - y += ystep + y += y_step error += dx - if swapped: # reverse the list if the coordinates were swapped + if swapped: # reverse the list if the coordinates were swapped points.reverse() points = np.array(points) return points -def calc_grid_map_config(ox, oy, xyreso): +def calc_grid_map_config(ox, oy, xy_resolution): """ - Calculates the size, and the maximum distances according to the the measurement center + Calculates the size, and the maximum distances according to the the + measurement center """ - minx = round(min(ox) - EXTEND_AREA / 2.0) - miny = round(min(oy) - EXTEND_AREA / 2.0) - maxx = round(max(ox) + EXTEND_AREA / 2.0) - maxy = round(max(oy) + EXTEND_AREA / 2.0) - xw = int(round((maxx - minx) / xyreso)) - yw = int(round((maxy - miny) / xyreso)) + min_x = round(min(ox) - EXTEND_AREA / 2.0) + min_y = round(min(oy) - EXTEND_AREA / 2.0) + max_x = round(max(ox) + EXTEND_AREA / 2.0) + max_y = round(max(oy) + EXTEND_AREA / 2.0) + xw = int(round((max_x - min_x) / xy_resolution)) + yw = int(round((max_y - min_y) / xy_resolution)) print("The grid map is ", xw, "x", yw, ".") - return minx, miny, maxx, maxy, xw, yw + return min_x, min_y, max_x, max_y, xw, yw def atan_zero_to_twopi(y, x): @@ -94,96 +97,110 @@ def atan_zero_to_twopi(y, x): return angle -def init_floodfill(cpoint, opoints, xypoints, mincoord, xyreso): +def init_flood_fill(center_point, obstacle_points, xy_points, min_coord, + xy_resolution): """ - cpoint: center point - opoints: detected obstacles points (x,y) - xypoints: (x,y) point pairs + center_point: center point + obstacle_points: detected obstacles points (x,y) + xy_points: (x,y) point pairs """ - centix, centiy = cpoint - prev_ix, prev_iy = centix - 1, centiy - ox, oy = opoints - xw, yw = xypoints - minx, miny = mincoord - pmap = (np.ones((xw, yw))) * 0.5 + center_x, center_y = center_point + prev_ix, prev_iy = center_x - 1, center_y + ox, oy = obstacle_points + xw, yw = xy_points + min_x, min_y = min_coord + occupancy_map = (np.ones((xw, yw))) * 0.5 for (x, y) in zip(ox, oy): - ix = int(round((x - minx) / xyreso)) # x coordinate of the the occupied area - iy = int(round((y - miny) / xyreso)) # y coordinate of the the occupied area + # x coordinate of the the occupied area + ix = int(round((x - min_x) / xy_resolution)) + # y coordinate of the the occupied area + iy = int(round((y - min_y) / xy_resolution)) free_area = bresenham((prev_ix, prev_iy), (ix, iy)) for fa in free_area: - pmap[fa[0]][fa[1]] = 0 # free area 0.0 + occupancy_map[fa[0]][fa[1]] = 0 # free area 0.0 prev_ix = ix prev_iy = iy - return pmap + return occupancy_map -def flood_fill(cpoint, pmap): +def flood_fill(center_point, occupancy_map): """ - cpoint: starting point (x,y) of fill - pmap: occupancy map generated from Bresenham ray-tracing + center_point: starting point (x,y) of fill + occupancy_map: occupancy map generated from Bresenham ray-tracing """ # Fill empty areas with queue method - sx, sy = pmap.shape + sx, sy = occupancy_map.shape fringe = deque() - fringe.appendleft(cpoint) + fringe.appendleft(center_point) while fringe: n = fringe.pop() nx, ny = n # West if nx > 0: - if pmap[nx - 1, ny] == 0.5: - pmap[nx - 1, ny] = 0.0 + if occupancy_map[nx - 1, ny] == 0.5: + occupancy_map[nx - 1, ny] = 0.0 fringe.appendleft((nx - 1, ny)) # East if nx < sx - 1: - if pmap[nx + 1, ny] == 0.5: - pmap[nx + 1, ny] = 0.0 + if occupancy_map[nx + 1, ny] == 0.5: + occupancy_map[nx + 1, ny] = 0.0 fringe.appendleft((nx + 1, ny)) # North if ny > 0: - if pmap[nx, ny - 1] == 0.5: - pmap[nx, ny - 1] = 0.0 + if occupancy_map[nx, ny - 1] == 0.5: + occupancy_map[nx, ny - 1] = 0.0 fringe.appendleft((nx, ny - 1)) # South if ny < sy - 1: - if pmap[nx, ny + 1] == 0.5: - pmap[nx, ny + 1] = 0.0 + if occupancy_map[nx, ny + 1] == 0.5: + occupancy_map[nx, ny + 1] = 0.0 fringe.appendleft((nx, ny + 1)) -def generate_ray_casting_grid_map(ox, oy, xyreso, breshen=True): +def generate_ray_casting_grid_map(ox, oy, xy_resolution, breshen=True): """ - The breshen boolean tells if it's computed with bresenham ray casting (True) or with flood fill (False) + The breshen boolean tells if it's computed with bresenham ray casting + (True) or with flood fill (False) """ - minx, miny, maxx, maxy, xw, yw = calc_grid_map_config(ox, oy, xyreso) - pmap = np.ones((xw, yw))/2 # default 0.5 -- [[0.5 for i in range(yw)] for i in range(xw)] - centix = int(round(-minx / xyreso)) # center x coordinate of the grid map - centiy = int(round(-miny / xyreso)) # center y coordinate of the grid map + min_x, min_y, max_x, max_y, x_w, y_w = calc_grid_map_config( + ox, oy, xy_resolution) + # default 0.5 -- [[0.5 for i in range(y_w)] for i in range(x_w)] + occupancy_map = np.ones((x_w, y_w)) / 2 + center_x = int( + round(-min_x / xy_resolution)) # center x coordinate of the grid map + center_y = int( + round(-min_y / xy_resolution)) # center y coordinate of the grid map # occupancy grid computed with bresenham ray casting if breshen: for (x, y) in zip(ox, oy): - ix = int(round((x - minx) / xyreso)) # x coordinate of the the occupied area - iy = int(round((y - miny) / xyreso)) # y coordinate of the the occupied area - laser_beams = bresenham((centix, centiy), (ix, iy)) # line form the lidar to the cooupied point + # x coordinate of the the occupied area + ix = int(round((x - min_x) / xy_resolution)) + # y coordinate of the the occupied area + iy = int(round((y - min_y) / xy_resolution)) + laser_beams = bresenham((center_x, center_y), ( + ix, iy)) # line form the lidar to the occupied point for laser_beam in laser_beams: - pmap[laser_beam[0]][laser_beam[1]] = 0.0 # free area 0.0 - pmap[ix][iy] = 1.0 # occupied area 1.0 - pmap[ix+1][iy] = 1.0 # extend the occupied area - pmap[ix][iy+1] = 1.0 # extend the occupied area - pmap[ix+1][iy+1] = 1.0 # extend the occupied area + occupancy_map[laser_beam[0]][ + laser_beam[1]] = 0.0 # free area 0.0 + occupancy_map[ix][iy] = 1.0 # occupied area 1.0 + occupancy_map[ix + 1][iy] = 1.0 # extend the occupied area + occupancy_map[ix][iy + 1] = 1.0 # extend the occupied area + occupancy_map[ix + 1][iy + 1] = 1.0 # extend the occupied area # occupancy grid computed with with flood fill else: - pmap = init_floodfill((centix, centiy), (ox, oy), (xw, yw), (minx, miny), xyreso) - flood_fill((centix, centiy), pmap) - pmap = np.array(pmap, dtype=np.float) + occupancy_map = init_flood_fill((center_x, center_y), (ox, oy), + (x_w, y_w), + (min_x, min_y), xy_resolution) + flood_fill((center_x, center_y), occupancy_map) + occupancy_map = np.array(occupancy_map, dtype=np.float) for (x, y) in zip(ox, oy): - ix = int(round((x - minx) / xyreso)) - iy = int(round((y - miny) / xyreso)) - pmap[ix][iy] = 1.0 # occupied area 1.0 - pmap[ix+1][iy] = 1.0 # extend the occupied area - pmap[ix][iy+1] = 1.0 # extend the occupied area - pmap[ix+1][iy+1] = 1.0 # extend the occupied area - return pmap, minx, maxx, miny, maxy, xyreso + ix = int(round((x - min_x) / xy_resolution)) + iy = int(round((y - min_y) / xy_resolution)) + occupancy_map[ix][iy] = 1.0 # occupied area 1.0 + occupancy_map[ix + 1][iy] = 1.0 # extend the occupied area + occupancy_map[ix][iy + 1] = 1.0 # extend the occupied area + occupancy_map[ix + 1][iy + 1] = 1.0 # extend the occupied area + return occupancy_map, min_x, max_x, min_y, max_y, xy_resolution def main(): @@ -191,18 +208,20 @@ def main(): Example usage """ print(__file__, "start") - xyreso = 0.02 # x-y grid resolution + xy_resolution = 0.02 # x-y grid resolution ang, dist = file_read("lidar01.csv") ox = np.sin(ang) * dist oy = np.cos(ang) * dist - pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(ox, oy, xyreso, True) - xyres = np.array(pmap).shape - plt.figure(1, figsize=(10,4)) + occupancy_map, min_x, max_x, min_y, max_y, xy_resolution = \ + generate_ray_casting_grid_map(ox, oy, xy_resolution, True) + xy_res = np.array(occupancy_map).shape + plt.figure(1, figsize=(10, 4)) plt.subplot(122) - plt.imshow(pmap, cmap="PiYG_r") # cmap = "binary" "PiYG_r" "PiYG_r" "bone" "bone_r" "RdYlGn_r" + plt.imshow(occupancy_map, cmap="PiYG_r") + # cmap = "binary" "PiYG_r" "PiYG_r" "bone" "bone_r" "RdYlGn_r" plt.clim(-0.4, 1.4) - plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor=True) - plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor=True) + plt.gca().set_xticks(np.arange(-.5, xy_res[1], 1), minor=True) + plt.gca().set_yticks(np.arange(-.5, xy_res[0], 1), minor=True) plt.grid(True, which="minor", color="w", linewidth=0.6, alpha=0.5) plt.colorbar() plt.subplot(121) @@ -210,11 +229,11 @@ def main(): plt.axis("equal") plt.plot(0.0, 0.0, "ob") plt.gca().set_aspect("equal", "box") - bottom, top = plt.ylim() # return the current ylim - plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation + bottom, top = plt.ylim() # return the current y-lim + plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation plt.grid(True) plt.show() - + if __name__ == '__main__': - main() + main() diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index 1a2a534093..ae41b3c53e 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -5,7 +5,10 @@ author: Atsushi Sakai (@Atsushi_twi) Ref: -- [Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University](https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners/) +- Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners - +The Robotics Institute Carnegie Mellon University +https://www.ri.cmu.edu/publications/ +efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners/ """ @@ -13,13 +16,14 @@ import numpy as np import itertools from enum import Enum +from scipy.spatial.transform import Rotation as Rot from simulator import VehicleSimulator, LidarSimulator show_animation = True -class LShapeFitting(): +class LShapeFitting: class Criteria(Enum): AREA = 1 @@ -29,26 +33,27 @@ class Criteria(Enum): def __init__(self): # Parameters self.criteria = self.Criteria.VARIANCE - self.min_dist_of_closeness_crit = 0.01 # [m] - self.dtheta_deg_for_serarch = 1.0 # [deg] + self.min_dist_of_closeness_criteria = 0.01 # [m] + self.d_theta_deg_for_search = 1.0 # [deg] self.R0 = 3.0 # [m] range segmentation param self.Rd = 0.001 # [m] range segmentation param def fitting(self, ox, oy): # step1: Adaptive Range Segmentation - idsets = self._adoptive_range_segmentation(ox, oy) + id_sets = self._adoptive_range_segmentation(ox, oy) # step2 Rectangle search rects = [] - for ids in idsets: # for each cluster + for ids in id_sets: # for each cluster cx = [ox[i] for i in range(len(ox)) if i in ids] cy = [oy[i] for i in range(len(oy)) if i in ids] rects.append(self._rectangle_search(cx, cy)) - return rects, idsets + return rects, id_sets - def _calc_area_criterion(self, c1, c2): + @staticmethod + def _calc_area_criterion(c1, c2): c1_max = max(c1) c2_max = max(c2) c1_min = min(c1) @@ -71,12 +76,13 @@ def _calc_closeness_criterion(self, c1, c2): beta = 0 for i, _ in enumerate(D1): - d = max(min([D1[i], D2[i]]), self.min_dist_of_closeness_crit) + d = max(min([D1[i], D2[i]]), self.min_dist_of_closeness_criteria) beta += (1.0 / d) return beta - def _calc_variance_criterion(self, c1, c2): + @staticmethod + def _calc_variance_criterion(c1, c2): c1_max = max(c1) c2_max = max(c2) c1_min = min(c1) @@ -110,17 +116,17 @@ def _rectangle_search(self, x, y): X = np.array([x, y]).T - dtheta = np.deg2rad(self.dtheta_deg_for_serarch) - minp = (-float('inf'), None) - for theta in np.arange(0.0, np.pi / 2.0 - dtheta, dtheta): + d_theta = np.deg2rad(self.d_theta_deg_for_search) + min_cost = (-float('inf'), None) + for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta): - e1 = np.array([np.cos(theta), np.sin(theta)]) - e2 = np.array([-np.sin(theta), np.cos(theta)]) - - c1 = X @ e1.T - c2 = X @ e2.T + rot = Rot.from_euler('z', theta).as_matrix()[0:2, 0:2] + c = X @ rot + c1 = c[:, 0] + c2 = c[:, 1] # Select criteria + cost = 0.0 if self.criteria == self.Criteria.AREA: cost = self._calc_area_criterion(c1, c2) elif self.criteria == self.Criteria.CLOSENESS: @@ -128,12 +134,12 @@ def _rectangle_search(self, x, y): elif self.criteria == self.Criteria.VARIANCE: cost = self._calc_variance_criterion(c1, c2) - if minp[0] < cost: - minp = (cost, theta) + if min_cost[0] < cost: + min_cost = (cost, theta) # calc best rectangle - sin_s = np.sin(minp[1]) - cos_s = np.cos(minp[1]) + sin_s = np.sin(min_cost[1]) + cos_s = np.cos(min_cost[1]) c1_s = X @ np.array([cos_s, sin_s]).T c2_s = X @ np.array([-sin_s, cos_s]).T @@ -181,7 +187,7 @@ def _adoptive_range_segmentation(self, ox, oy): return S -class RectangleData(): +class RectangleData: def __init__(self): self.a = [None] * 4 @@ -207,7 +213,8 @@ def calc_rect_contour(self): [self.a[3], self.a[0]], [self.b[3], self.b[0]], [self.c[3], self.c[0]]) self.rect_c_x[4], self.rect_c_y[4] = self.rect_c_x[0], self.rect_c_y[0] - def calc_cross_point(self, a, b, c): + @staticmethod + def calc_cross_point(a, b, c): x = (b[0] * -c[1] - b[1] * -c[0]) / (a[0] * b[1] - a[1] * b[0]) y = (a[1] * -c[0] - a[0] * -c[1]) / (a[0] * b[1] - a[1] * b[0]) return x, y @@ -216,42 +223,43 @@ def calc_cross_point(self, a, b, c): def main(): # simulation parameters - simtime = 30.0 # simulation time + sim_time = 30.0 # simulation time dt = 0.2 # time tick - angle_reso = np.deg2rad(3.0) # sensor angle resolution + angle_resolution = np.deg2rad(3.0) # sensor angle resolution v1 = VehicleSimulator(-10.0, 0.0, np.deg2rad(90.0), 0.0, 50.0 / 3.6, 3.0, 5.0) v2 = VehicleSimulator(20.0, 10.0, np.deg2rad(180.0), 0.0, 50.0 / 3.6, 4.0, 10.0) - lshapefitting = LShapeFitting() + l_shape_fitting = LShapeFitting() lidar_sim = LidarSimulator() time = 0.0 - while time <= simtime: + while time <= sim_time: time += dt v1.update(dt, 0.1, 0.0) v2.update(dt, 0.1, -0.05) - ox, oy = lidar_sim.get_observation_points([v1, v2], angle_reso) + ox, oy = lidar_sim.get_observation_points([v1, v2], angle_resolution) - rects, idsets = lshapefitting.fitting(ox, oy) + rects, id_sets = l_shape_fitting.fitting(ox, oy) if show_animation: # pragma: no cover plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.axis("equal") plt.plot(0.0, 0.0, "*r") v1.plot() v2.plot() # Plot range observation - for ids in idsets: + for ids in id_sets: x = [ox[i] for i in range(len(ox)) if i in ids] y = [oy[i] for i in range(len(ox)) if i in ids] diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py index b21d4003a8..c7c3141fa9 100644 --- a/Mapping/rectangle_fitting/simulator.py +++ b/Mapping/rectangle_fitting/simulator.py @@ -10,15 +10,16 @@ import matplotlib.pyplot as plt import math import random +from scipy.spatial.transform import Rotation as Rot -class VehicleSimulator(): +class VehicleSimulator: - def __init__(self, ix, iy, iyaw, iv, max_v, w, L): - self.x = ix - self.y = iy - self.yaw = iyaw - self.v = iv + def __init__(self, i_x, i_y, i_yaw, i_v, max_v, w, L): + self.x = i_x + self.y = i_y + self.yaw = i_yaw + self.v = i_v self.max_v = max_v self.W = w self.L = L @@ -40,10 +41,10 @@ def plot(self): plt.plot(gx, gy, "--b") def calc_global_contour(self): - gx = [(ix * np.cos(self.yaw) + iy * np.sin(self.yaw)) + - self.x for (ix, iy) in zip(self.vc_x, self.vc_y)] - gy = [(ix * np.sin(self.yaw) - iy * np.cos(self.yaw)) + - self.y for (ix, iy) in zip(self.vc_x, self.vc_y)] + rot = Rot.from_euler('z', self.yaw).as_matrix()[0:2, 0:2] + gxy = np.stack([self.vc_x, self.vc_y]).T @ rot + gx = gxy[:, 0] + self.x + gy = gxy[:, 1] + self.y return gx, gy @@ -67,69 +68,71 @@ def _calc_vehicle_contour(self): self.vc_x.append(self.L / 2.0) self.vc_y.append(self.W / 2.0) - self.vc_x, self.vc_y = self._interporate(self.vc_x, self.vc_y) + self.vc_x, self.vc_y = self._interpolate(self.vc_x, self.vc_y) - def _interporate(self, x, y): + @staticmethod + def _interpolate(x, y): rx, ry = [], [] - dtheta = 0.05 + d_theta = 0.05 for i in range(len(x) - 1): - rx.extend([(1.0 - θ) * x[i] + θ * x[i + 1] - for θ in np.arange(0.0, 1.0, dtheta)]) - ry.extend([(1.0 - θ) * y[i] + θ * y[i + 1] - for θ in np.arange(0.0, 1.0, dtheta)]) + rx.extend([(1.0 - theta) * x[i] + theta * x[i + 1] + for theta in np.arange(0.0, 1.0, d_theta)]) + ry.extend([(1.0 - theta) * y[i] + theta * y[i + 1] + for theta in np.arange(0.0, 1.0, d_theta)]) - rx.extend([(1.0 - θ) * x[len(x) - 1] + θ * x[1] - for θ in np.arange(0.0, 1.0, dtheta)]) - ry.extend([(1.0 - θ) * y[len(y) - 1] + θ * y[1] - for θ in np.arange(0.0, 1.0, dtheta)]) + rx.extend([(1.0 - theta) * x[len(x) - 1] + theta * x[1] + for theta in np.arange(0.0, 1.0, d_theta)]) + ry.extend([(1.0 - theta) * y[len(y) - 1] + theta * y[1] + for theta in np.arange(0.0, 1.0, d_theta)]) return rx, ry -class LidarSimulator(): +class LidarSimulator: def __init__(self): self.range_noise = 0.01 - def get_observation_points(self, vlist, angle_reso): + def get_observation_points(self, v_list, angle_resolution): x, y, angle, r = [], [], [], [] # store all points - for v in vlist: + for v in v_list: gx, gy = v.calc_global_contour() for vx, vy in zip(gx, gy): - vangle = math.atan2(vy, vx) + v_angle = math.atan2(vy, vx) vr = np.hypot(vx, vy) * random.uniform(1.0 - self.range_noise, 1.0 + self.range_noise) x.append(vx) y.append(vy) - angle.append(vangle) + angle.append(v_angle) r.append(vr) # ray casting filter - rx, ry = self.ray_casting_filter(x, y, angle, r, angle_reso) + rx, ry = self.ray_casting_filter(angle, r, angle_resolution) return rx, ry - def ray_casting_filter(self, xl, yl, thetal, rangel, angle_reso): + @staticmethod + def ray_casting_filter(theta_l, range_l, angle_resolution): rx, ry = [], [] - rangedb = [float("inf") for _ in range( - int(np.floor((np.pi * 2.0) / angle_reso)) + 1)] + range_db = [float("inf") for _ in range( + int(np.floor((np.pi * 2.0) / angle_resolution)) + 1)] - for i in range(len(thetal)): - angleid = int(round(thetal[i] / angle_reso)) + for i in range(len(theta_l)): + angle_id = int(round(theta_l[i] / angle_resolution)) - if rangedb[angleid] > rangel[i]: - rangedb[angleid] = rangel[i] + if range_db[angle_id] > range_l[i]: + range_db[angle_id] = range_l[i] - for i in range(len(rangedb)): - t = i * angle_reso - if rangedb[i] != float("inf"): - rx.append(rangedb[i] * np.cos(t)) - ry.append(rangedb[i] * np.sin(t)) + for i in range(len(range_db)): + t = i * angle_resolution + if range_db[i] != float("inf"): + rx.append(range_db[i] * np.cos(t)) + ry.append(range_db[i] * np.sin(t)) return rx, ry diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index 14c51ad9a1..eb2672a9ce 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -18,39 +18,43 @@ class AStarPlanner: - def __init__(self, ox, oy, reso, rr): + def __init__(self, ox, oy, resolution, rr): """ Initialize grid map for a star planning ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ - self.reso = reso + self.resolution = resolution self.rr = rr - self.calc_obstacle_map(ox, oy) + self.min_x, self.min_y = 0, 0 + self.max_x, self.max_y = 0, 0 + self.obstacle_map = None + self.x_width, self.y_width = 0, 0 self.motion = self.get_motion_model() + self.calc_obstacle_map(ox, oy) class Node: - def __init__(self, x, y, cost, pind): + def __init__(self, x, y, cost, parent_index): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.pind = pind + self.parent_index = parent_index def __str__(self): return str(self.x) + "," + str(self.y) + "," + str( - self.cost) + "," + str(self.pind) + self.cost) + "," + str(self.parent_index) def planning(self, sx, sy, gx, gy): """ A star path search input: - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gy: goal y position [m] @@ -59,13 +63,13 @@ def planning(self, sx, sy, gx, gy): ry: y position list of the final path """ - nstart = self.Node(self.calc_xyindex(sx, self.minx), - self.calc_xyindex(sy, self.miny), 0.0, -1) - ngoal = self.Node(self.calc_xyindex(gx, self.minx), - self.calc_xyindex(gy, self.miny), 0.0, -1) + start_node = self.Node(self.calc_xy_index(sx, self.min_x), + self.calc_xy_index(sy, self.min_y), 0.0, -1) + goal_node = self.Node(self.calc_xy_index(gx, self.min_x), + self.calc_xy_index(gy, self.min_y), 0.0, -1) open_set, closed_set = dict(), dict() - open_set[self.calc_grid_index(nstart)] = nstart + open_set[self.calc_grid_index(start_node)] = start_node while 1: if len(open_set) == 0: @@ -74,15 +78,15 @@ def planning(self, sx, sy, gx, gy): c_id = min( open_set, - key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal, + key=lambda o: open_set[o].cost + self.calc_heuristic(goal_node, open_set[ o])) current = open_set[c_id] # show graph if show_animation: # pragma: no cover - plt.plot(self.calc_grid_position(current.x, self.minx), - self.calc_grid_position(current.y, self.miny), "xc") + plt.plot(self.calc_grid_position(current.x, self.min_x), + self.calc_grid_position(current.y, self.min_y), "xc") # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit( @@ -90,10 +94,10 @@ def planning(self, sx, sy, gx, gy): if len(closed_set.keys()) % 10 == 0: plt.pause(0.001) - if current.x == ngoal.x and current.y == ngoal.y: + if current.x == goal_node.x and current.y == goal_node.y: print("Find goal") - ngoal.pind = current.pind - ngoal.cost = current.cost + goal_node.parent_index = current.parent_index + goal_node.cost = current.cost break # Remove the item from the open set @@ -123,20 +127,20 @@ def planning(self, sx, sy, gx, gy): # This path is the best until now. record it open_set[n_id] = node - rx, ry = self.calc_final_path(ngoal, closed_set) + rx, ry = self.calc_final_path(goal_node, closed_set) return rx, ry - def calc_final_path(self, ngoal, closedset): + def calc_final_path(self, goal_node, closed_set): # generate final course - rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ - self.calc_grid_position(ngoal.y, self.miny)] - pind = ngoal.pind - while pind != -1: - n = closedset[pind] - rx.append(self.calc_grid_position(n.x, self.minx)) - ry.append(self.calc_grid_position(n.y, self.miny)) - pind = n.pind + rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], [ + self.calc_grid_position(goal_node.y, self.min_y)] + parent_index = goal_node.parent_index + while parent_index != -1: + n = closed_set[parent_index] + rx.append(self.calc_grid_position(n.x, self.min_x)) + ry.append(self.calc_grid_position(n.y, self.min_y)) + parent_index = n.parent_index return rx, ry @@ -146,69 +150,69 @@ def calc_heuristic(n1, n2): d = w * math.hypot(n1.x - n2.x, n1.y - n2.y) return d - def calc_grid_position(self, index, minp): + def calc_grid_position(self, index, min_position): """ calc grid position :param index: - :param minp: + :param min_position: :return: """ - pos = index * self.reso + minp + pos = index * self.resolution + min_position return pos - def calc_xyindex(self, position, min_pos): - return round((position - min_pos) / self.reso) + def calc_xy_index(self, position, min_pos): + return round((position - min_pos) / self.resolution) def calc_grid_index(self, node): - return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + return (node.y - self.min_y) * self.x_width + (node.x - self.min_x) def verify_node(self, node): - px = self.calc_grid_position(node.x, self.minx) - py = self.calc_grid_position(node.y, self.miny) + px = self.calc_grid_position(node.x, self.min_x) + py = self.calc_grid_position(node.y, self.min_y) - if px < self.minx: + if px < self.min_x: return False - elif py < self.miny: + elif py < self.min_y: return False - elif px >= self.maxx: + elif px >= self.max_x: return False - elif py >= self.maxy: + elif py >= self.max_y: return False # collision check - if self.obmap[node.x][node.y]: + if self.obstacle_map[node.x][node.y]: return False return True def calc_obstacle_map(self, ox, oy): - self.minx = round(min(ox)) - self.miny = round(min(oy)) - self.maxx = round(max(ox)) - self.maxy = round(max(oy)) - print("minx:", self.minx) - print("miny:", self.miny) - print("maxx:", self.maxx) - print("maxy:", self.maxy) + self.min_x = round(min(ox)) + self.min_y = round(min(oy)) + self.max_x = round(max(ox)) + self.max_y = round(max(oy)) + print("min_x:", self.min_x) + print("min_y:", self.min_y) + print("max_x:", self.max_x) + print("max_y:", self.max_y) - self.xwidth = round((self.maxx - self.minx) / self.reso) - self.ywidth = round((self.maxy - self.miny) / self.reso) - print("xwidth:", self.xwidth) - print("ywidth:", self.ywidth) + self.x_width = round((self.max_x - self.min_x) / self.resolution) + self.y_width = round((self.max_y - self.min_y) / self.resolution) + print("x_width:", self.x_width) + print("y_width:", self.y_width) # obstacle map generation - self.obmap = [[False for i in range(self.ywidth)] - for i in range(self.xwidth)] - for ix in range(self.xwidth): - x = self.calc_grid_position(ix, self.minx) - for iy in range(self.ywidth): - y = self.calc_grid_position(iy, self.miny) + self.obstacle_map = [[False for _ in range(self.y_width)] + for _ in range(self.x_width)] + for ix in range(self.x_width): + x = self.calc_grid_position(ix, self.min_x) + for iy in range(self.y_width): + y = self.calc_grid_position(iy, self.min_y) for iox, ioy in zip(ox, oy): d = math.hypot(iox - x, ioy - y) if d <= self.rr: - self.obmap[ix][iy] = True + self.obstacle_map[ix][iy] = True break @staticmethod @@ -270,8 +274,8 @@ def main(): if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") - plt.show() plt.pause(0.001) + plt.show() if __name__ == '__main__': diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index 65d1fc31a7..cf76ab67cd 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr): ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ @@ -48,8 +48,8 @@ def planning(self, sx, sy, gx, gy): Bidirectional A star path search input: - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gy: goal y position [m] @@ -179,12 +179,12 @@ def calc_final_path(self, ngoal, closedset): # generate final course rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ self.calc_grid_position(ngoal.y, self.miny)] - pind = ngoal.pind + pind = ngoal.parent_index while pind != -1: n = closedset[pind] rx.append(self.calc_grid_position(n.x, self.minx)) ry.append(self.calc_grid_position(n.y, self.miny)) - pind = n.pind + pind = n.parent_index return rx, ry @@ -252,15 +252,15 @@ def calc_obstacle_map(self, ox, oy): self.miny = round(min(oy)) self.maxx = round(max(ox)) self.maxy = round(max(oy)) - print("minx:", self.minx) - print("miny:", self.miny) - print("maxx:", self.maxx) - print("maxy:", self.maxy) + print("min_x:", self.minx) + print("min_y:", self.miny) + print("max_x:", self.maxx) + print("max_y:", self.maxy) self.xwidth = round((self.maxx - self.minx) / self.reso) self.ywidth = round((self.maxy - self.miny) / self.reso) - print("xwidth:", self.xwidth) - print("ywidth:", self.ywidth) + print("x_width:", self.xwidth) + print("y_width:", self.ywidth) # obstacle map generation self.obmap = [[False for _ in range(self.ywidth)] diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py index d29112eb65..328898db85 100644 --- a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py +++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py @@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr): ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ @@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy): Bidirectional Breadth First search based planning input: - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gy: goal y position [m] @@ -170,7 +170,7 @@ def calc_final_path(self, ngoal, closedset): # generate final course rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ self.calc_grid_position(ngoal.y, self.miny)] - n = closedset[ngoal.pind] + n = closedset[ngoal.parent_index] while n is not None: rx.append(self.calc_grid_position(n.x, self.minx)) ry.append(self.calc_grid_position(n.y, self.miny)) @@ -220,15 +220,15 @@ def calc_obstacle_map(self, ox, oy): self.miny = round(min(oy)) self.maxx = round(max(ox)) self.maxy = round(max(oy)) - print("minx:", self.minx) - print("miny:", self.miny) - print("maxx:", self.maxx) - print("maxy:", self.maxy) + print("min_x:", self.minx) + print("min_y:", self.miny) + print("max_x:", self.maxx) + print("max_y:", self.maxy) self.xwidth = round((self.maxx - self.minx) / self.reso) self.ywidth = round((self.maxy - self.miny) / self.reso) - print("xwidth:", self.xwidth) - print("ywidth:", self.ywidth) + print("x_width:", self.xwidth) + print("y_width:", self.ywidth) # obstacle map generation self.obmap = [[False for _ in range(self.ywidth)] diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index 198ddd2e3d..01f1a31d64 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr): ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ @@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy): Breadth First search based planning input: - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gy: goal y position [m] @@ -118,7 +118,7 @@ def calc_final_path(self, ngoal, closedset): # generate final course rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ self.calc_grid_position(ngoal.y, self.miny)] - n = closedset[ngoal.pind] + n = closedset[ngoal.parent_index] while n is not None: rx.append(self.calc_grid_position(n.x, self.minx)) ry.append(self.calc_grid_position(n.y, self.miny)) @@ -168,15 +168,15 @@ def calc_obstacle_map(self, ox, oy): self.miny = round(min(oy)) self.maxx = round(max(ox)) self.maxy = round(max(oy)) - print("minx:", self.minx) - print("miny:", self.miny) - print("maxx:", self.maxx) - print("maxy:", self.maxy) + print("min_x:", self.minx) + print("min_y:", self.miny) + print("max_x:", self.maxx) + print("max_y:", self.maxy) self.xwidth = round((self.maxx - self.minx) / self.reso) self.ywidth = round((self.maxy - self.miny) / self.reso) - print("xwidth:", self.xwidth) - print("ywidth:", self.ywidth) + print("x_width:", self.xwidth) + print("y_width:", self.ywidth) # obstacle map generation self.obmap = [[False for _ in range(self.ywidth)] diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py index a413a05d7f..f3984f87b0 100644 --- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py +++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py @@ -40,7 +40,7 @@ def pure_pursuit_control(state, cx, cy, pind): if pind >= ind: ind = pind - # print(pind, ind) + # print(parent_index, ind) if ind < len(cx): tx = cx[ind] ty = cy[ind] @@ -181,12 +181,12 @@ def set_stop_point(target_speed, cx, cy, cyaw): speed_profile[i] = 0.0 forward = False # plt.plot(cx[i], cy[i], "xb") - # print(iyaw, move_direction, dx, dy) + # print(i_yaw, move_direction, dx, dy) elif not is_back and not forward: speed_profile[i] = 0.0 forward = True # plt.plot(cx[i], cy[i], "xb") - # print(iyaw, move_direction, dx, dy) + # print(i_yaw, move_direction, dx, dy) speed_profile[0] = 0.0 if is_back: speed_profile[-1] = -stop_speed diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index 229eb70c2d..c8d2d5eaba 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr): ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ @@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy): Depth First search input: - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gy: goal y position [m] @@ -115,7 +115,7 @@ def calc_final_path(self, ngoal, closedset): # generate final course rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ self.calc_grid_position(ngoal.y, self.miny)] - n = closedset[ngoal.pind] + n = closedset[ngoal.parent_index] while n is not None: rx.append(self.calc_grid_position(n.x, self.minx)) ry.append(self.calc_grid_position(n.y, self.miny)) @@ -165,15 +165,15 @@ def calc_obstacle_map(self, ox, oy): self.miny = round(min(oy)) self.maxx = round(max(ox)) self.maxy = round(max(oy)) - print("minx:", self.minx) - print("miny:", self.miny) - print("maxx:", self.maxx) - print("maxy:", self.maxy) + print("min_x:", self.minx) + print("min_y:", self.miny) + print("max_x:", self.maxx) + print("max_y:", self.maxy) self.xwidth = round((self.maxx - self.minx) / self.reso) self.ywidth = round((self.maxy - self.miny) / self.reso) - print("xwidth:", self.xwidth) - print("ywidth:", self.ywidth) + print("x_width:", self.xwidth) + print("y_width:", self.ywidth) # obstacle map generation self.obmap = [[False for _ in range(self.ywidth)] diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index 0ece712ae7..b744bc6fc7 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -53,8 +53,8 @@ def planning(self, sx, sy, gx, gy): dijkstra path search input: - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gx: goal x position [m] diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py index b72801a6b3..9c13198aff 100644 --- a/PathPlanning/DubinsPath/dubins_path_planning.py +++ b/PathPlanning/DubinsPath/dubins_path_planning.py @@ -9,6 +9,7 @@ import matplotlib.pyplot as plt import numpy as np +from scipy.spatial.transform import Rotation as Rot show_animation = True @@ -136,7 +137,8 @@ def left_right_left(alpha, beta, d): return t, p, q, mode -def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size): +def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, + step_size): dx = end_x dy = end_y D = math.hypot(dx, dy) @@ -146,7 +148,8 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size alpha = mod2pi(- theta) beta = mod2pi(end_yaw - theta) - planners = [left_straight_left, right_straight_right, left_straight_right, right_straight_left, right_left_right, + planners = [left_straight_left, right_straight_right, left_straight_right, + right_straight_left, right_left_right, left_right_left] best_cost = float("inf") @@ -163,13 +166,14 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size best_cost = cost lengths = [bt, bp, bq] - px, py, pyaw, directions = generate_local_course( + x_list, y_list, yaw_list, directions = generate_local_course( sum(lengths), lengths, best_mode, curvature, step_size) - return px, py, pyaw, best_mode, best_cost + return x_list, y_list, yaw_list, best_mode, best_cost -def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions): +def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, + origin_yaw, path_x, path_y, path_yaw, directions): if mode == "S": path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw) path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw) @@ -199,54 +203,49 @@ def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw return path_x, path_y, path_yaw, directions -def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, step_size=0.1): +def dubins_path_planning(s_x, s_y, s_yaw, g_x, g_y, g_yaw, c, step_size=0.1): """ - Dubins path plannner + Dubins path planner input: - sx x position of start point [m] - sy y position of start point [m] - syaw yaw angle of start point [rad] - ex x position of end point [m] - ey y position of end point [m] - eyaw yaw angle of end point [rad] + s_x x position of start point [m] + s_y y position of start point [m] + s_yaw yaw angle of start point [rad] + g_x x position of end point [m] + g_y y position of end point [m] + g_yaw yaw angle of end point [rad] c curvature [1/m] - output: - px - py - pyaw - mode - """ - ex = ex - sx - ey = ey - sy + g_x = g_x - s_x + g_y = g_y - s_y - lex = math.cos(syaw) * ex + math.sin(syaw) * ey - ley = - math.sin(syaw) * ex + math.cos(syaw) * ey - leyaw = eyaw - syaw + l_rot = Rot.from_euler('z', s_yaw).as_matrix()[0:2, 0:2] + le_xy = np.stack([g_x, g_y]).T @ l_rot + le_yaw = g_yaw - s_yaw - lpx, lpy, lpyaw, mode, clen = dubins_path_planning_from_origin( - lex, ley, leyaw, c, step_size) + lp_x, lp_y, lp_yaw, mode, lengths = dubins_path_planning_from_origin( + le_xy[0], le_xy[1], le_yaw, c, step_size) - px = [math.cos(-syaw) * x + math.sin(-syaw) - * y + sx for x, y in zip(lpx, lpy)] - py = [- math.sin(-syaw) * x + math.cos(-syaw) - * y + sy for x, y in zip(lpx, lpy)] - pyaw = [pi_2_pi(iyaw + syaw) for iyaw in lpyaw] + rot = Rot.from_euler('z', -s_yaw).as_matrix()[0:2, 0:2] + converted_xy = np.stack([lp_x, lp_y]).T @ rot + x_list = converted_xy[:, 0] + s_x + y_list = converted_xy[:, 1] + s_y + yaw_list = [pi_2_pi(i_yaw + s_yaw) for i_yaw in lp_yaw] - return px, py, pyaw, mode, clen + return x_list, y_list, yaw_list, mode, lengths -def generate_local_course(total_length, lengths, mode, max_curvature, step_size): +def generate_local_course(total_length, lengths, mode, max_curvature, + step_size): n_point = math.trunc(total_length / step_size) + len(lengths) + 4 path_x = [0.0 for _ in range(n_point)] path_y = [0.0 for _ in range(n_point)] path_yaw = [0.0 for _ in range(n_point)] directions = [0.0 for _ in range(n_point)] - ind = 1 + index = 1 if lengths[0] > 0.0: directions[0] = 1 @@ -262,25 +261,28 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size) d = -step_size # set origin state - origin_x, origin_y, origin_yaw = path_x[ind], path_y[ind], path_yaw[ind] + origin_x, origin_y, origin_yaw = \ + path_x[index], path_y[index], path_yaw[index] - ind -= 1 + index -= 1 if i >= 1 and (lengths[i - 1] * lengths[i]) > 0: pd = - d - ll else: pd = d - ll while abs(pd) <= abs(l): - ind += 1 + index += 1 path_x, path_y, path_yaw, directions = interpolate( - ind, pd, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions) + index, pd, m, max_curvature, origin_x, origin_y, origin_yaw, + path_x, path_y, path_yaw, directions) pd += d ll = l - pd - d # calc remain length - ind += 1 + index += 1 path_x, path_y, path_yaw, directions = interpolate( - ind, l, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions) + index, l, m, max_curvature, origin_x, origin_y, origin_yaw, + path_x, path_y, path_yaw, directions) if len(path_x) <= 1: return [], [], [], [] @@ -295,14 +297,15 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size) return path_x, path_y, path_yaw, directions -def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): # pragma: no cover +def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", + ec="k"): # pragma: no cover """ Plot arrow """ if not isinstance(x, float): - for (ix, iy, iyaw) in zip(x, y, yaw): - plot_arrow(ix, iy, iyaw) + for (i_x, i_y, i_yaw) in zip(x, y, yaw): + plot_arrow(i_x, i_y, i_yaw) else: plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), fc=fc, ec=ec, head_width=width, head_length=width) @@ -322,11 +325,12 @@ def main(): curvature = 1.0 - px, py, pyaw, mode, clen = dubins_path_planning(start_x, start_y, start_yaw, - end_x, end_y, end_yaw, curvature) + path_x, path_y, path_yaw, mode, path_length = dubins_path_planning( + start_x, start_y, start_yaw, + end_x, end_y, end_yaw, curvature) if show_animation: - plt.plot(px, py, label="final course " + "".join(mode)) + plt.plot(path_x, path_y, label="final course " + "".join(mode)) # plotting plot_arrow(start_x, start_y, start_yaw) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index b8395e5ff8..0df40410ab 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -41,11 +41,11 @@ def __init__(self): # robot parameter self.max_speed = 1.0 # [m/s] self.min_speed = -0.5 # [m/s] - self.max_yawrate = 40.0 * math.pi / 180.0 # [rad/s] + self.max_yaw_rate = 40.0 * math.pi / 180.0 # [rad/s] self.max_accel = 0.2 # [m/ss] - self.max_dyawrate = 40.0 * math.pi / 180.0 # [rad/ss] - self.v_reso = 0.01 # [m/s] - self.yawrate_reso = 0.1 * math.pi / 180.0 # [rad/s] + self.max_delta_yaw_rate = 40.0 * math.pi / 180.0 # [rad/ss] + self.v_resolution = 0.01 # [m/s] + self.yaw_rate_resolution = 0.1 * math.pi / 180.0 # [rad/s] self.dt = 0.1 # [s] Time tick for motion prediction self.predict_time = 3.0 # [s] self.to_goal_cost_gain = 0.15 @@ -93,15 +93,15 @@ def calc_dynamic_window(x, config): # Dynamic window from robot specification Vs = [config.min_speed, config.max_speed, - -config.max_yawrate, config.max_yawrate] + -config.max_yaw_rate, config.max_yaw_rate] # Dynamic window from motion model Vd = [x[3] - config.max_accel * config.dt, x[3] + config.max_accel * config.dt, - x[4] - config.max_dyawrate * config.dt, - x[4] + config.max_dyawrate * config.dt] + x[4] - config.max_delta_yaw_rate * config.dt, + x[4] + config.max_delta_yaw_rate * config.dt] - # [vmin, vmax, yaw_rate min, yaw_rate max] + # [v_min, v_max, yaw_rate_min, yaw_rate_max] dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]), max(Vs[2], Vd[2]), min(Vs[3], Vd[3])] @@ -114,14 +114,14 @@ def predict_trajectory(x_init, v, y, config): """ x = np.array(x_init) - traj = np.array(x) + trajectory = np.array(x) time = 0 while time <= config.predict_time: x = motion(x, [v, y], config.dt) - traj = np.vstack((traj, x)) + trajectory = np.vstack((trajectory, x)) time += config.dt - return traj + return trajectory def calc_control_and_trajectory(x, dw, config, goal, ob): @@ -135,8 +135,8 @@ def calc_control_and_trajectory(x, dw, config, goal, ob): best_trajectory = np.array([x]) # evaluate all trajectory with sampled input in dynamic window - for v in np.arange(dw[0], dw[1], config.v_reso): - for y in np.arange(dw[2], dw[3], config.yawrate_reso): + for v in np.arange(dw[0], dw[1], config.v_resolution): + for y in np.arange(dw[2], dw[3], config.yaw_rate_resolution): trajectory = predict_trajectory(x_init, v, y, config) @@ -182,7 +182,7 @@ def calc_obstacle_cost(trajectory, ob, config): np.logical_and(bottom_check, left_check))).any(): return float("Inf") elif config.robot_type == RobotType.circle: - if (r <= config.robot_radius).any(): + if np.array(r <= config.robot_radius).any(): return float("Inf") min_r = np.min(r) @@ -269,8 +269,9 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): if show_animation: plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g") plt.plot(x[0], x[1], "xr") plt.plot(goal[0], goal[1], "xb") @@ -296,4 +297,5 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): if __name__ == '__main__': - main(robot_type=RobotType.circle) + main(robot_type=RobotType.rectangle) + # main(robot_type=RobotType.circle) diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py index c29e37e896..7100b0e604 100644 --- a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py +++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py @@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr): ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ @@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy): Greedy Best-First search input: - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gy: goal y position [m] @@ -132,7 +132,7 @@ def calc_final_path(self, ngoal, closedset): # generate final course rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ self.calc_grid_position(ngoal.y, self.miny)] - n = closedset[ngoal.pind] + n = closedset[ngoal.parent_index] while n is not None: rx.append(self.calc_grid_position(n.x, self.minx)) ry.append(self.calc_grid_position(n.y, self.miny)) @@ -188,15 +188,15 @@ def calc_obstacle_map(self, ox, oy): self.miny = round(min(oy)) self.maxx = round(max(ox)) self.maxy = round(max(oy)) - print("minx:", self.minx) - print("miny:", self.miny) - print("maxx:", self.maxx) - print("maxy:", self.maxy) + print("min_x:", self.minx) + print("min_y:", self.miny) + print("max_x:", self.maxx) + print("max_y:", self.maxy) self.xwidth = round((self.maxx - self.minx) / self.reso) self.ywidth = round((self.maxy - self.miny) / self.reso) - print("xwidth:", self.xwidth) - print("ywidth:", self.ywidth) + print("x_width:", self.xwidth) + print("y_width:", self.ywidth) # obstacle map generation self.obmap = [[False for _ in range(self.ywidth)] diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index 8d9ccd2e2f..09df21c4c8 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -11,6 +11,7 @@ import matplotlib.pyplot as plt import numpy as np +from scipy.spatial.transform import Rotation as Rot sys.path.append(os.path.relpath("../../Mapping/grid_map_lib/")) try: @@ -36,52 +37,55 @@ def __init__(self, self.sweep_direction = sweep_direction self.turing_window = [] self.update_turning_window() - self.xinds_goaly = x_inds_goal_y - self.goaly = goal_y + self.x_indexes_goal_y = x_inds_goal_y + self.goal_y = goal_y - def move_target_grid(self, cxind, cyind, gmap): - nxind = self.moving_direction + cxind - nyind = cyind + def move_target_grid(self, c_x_index, c_y_index, grid_map): + n_x_index = self.moving_direction + c_x_index + n_y_index = c_y_index # found safe grid - if not gmap.check_occupied_from_xy_index(nxind, nyind, - occupied_val=0.5): - return nxind, nyind + if not grid_map.check_occupied_from_xy_index(n_x_index, n_y_index, + occupied_val=0.5): + return n_x_index, n_y_index else: # occupied - ncxind, ncyind = self.find_safe_turning_grid(cxind, cyind, gmap) - if (ncxind is None) and (ncyind is None): + next_c_x_index, next_c_y_index = self.find_safe_turning_grid( + c_x_index, c_y_index, grid_map) + if (next_c_x_index is None) and (next_c_y_index is None): # moving backward - ncxind = -self.moving_direction + cxind - ncyind = cyind - if gmap.check_occupied_from_xy_index(ncxind, ncyind): + next_c_x_index = -self.moving_direction + c_x_index + next_c_y_index = c_y_index + if grid_map.check_occupied_from_xy_index(next_c_x_index, + next_c_y_index): # moved backward, but the grid is occupied by obstacle return None, None else: # keep moving until end - while not gmap.check_occupied_from_xy_index( - ncxind + self.moving_direction, ncyind, - occupied_val=0.5): - ncxind += self.moving_direction + while not grid_map.check_occupied_from_xy_index( + next_c_x_index + self.moving_direction, + next_c_y_index, occupied_val=0.5): + next_c_x_index += self.moving_direction self.swap_moving_direction() - return ncxind, ncyind + return next_c_x_index, next_c_y_index - def find_safe_turning_grid(self, cxind, cyind, gmap): + def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map): for (d_x_ind, d_y_ind) in self.turing_window: - next_x_ind = d_x_ind + cxind - next_y_ind = d_y_ind + cyind + next_x_ind = d_x_ind + c_x_index + next_y_ind = d_y_ind + c_y_index # found safe grid - if not gmap.check_occupied_from_xy_index(next_x_ind, next_y_ind, - occupied_val=0.5): + if not grid_map.check_occupied_from_xy_index(next_x_ind, + next_y_ind, + occupied_val=0.5): return next_x_ind, next_y_ind return None, None def is_search_done(self, grid_map): - for ix in self.xinds_goaly: - if not grid_map.check_occupied_from_xy_index(ix, self.goaly, + for ix in self.x_indexes_goal_y: + if not grid_map.check_occupied_from_xy_index(ix, self.goal_y, occupied_val=0.5): return False @@ -138,64 +142,54 @@ def find_sweep_direction_and_start_position(ox, oy): return vec, sweep_start_pos -def convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi): - tx = [ix - sweep_start_posi[0] for ix in ox] - ty = [iy - sweep_start_posi[1] for iy in oy] - +def convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_position): + tx = [ix - sweep_start_position[0] for ix in ox] + ty = [iy - sweep_start_position[1] for iy in oy] th = math.atan2(sweep_vec[1], sweep_vec[0]) + rot = Rot.from_euler('z', th).as_matrix()[0:2, 0:2] + converted_xy = np.stack([tx, ty]).T @ rot - c = np.cos(-th) - s = np.sin(-th) - - rx = [ix * c - iy * s for (ix, iy) in zip(tx, ty)] - ry = [ix * s + iy * c for (ix, iy) in zip(tx, ty)] - - return rx, ry + return converted_xy[:, 0], converted_xy[:, 1] -def convert_global_coordinate(x, y, sweep_vec, sweep_start_posi): +def convert_global_coordinate(x, y, sweep_vec, sweep_start_position): th = math.atan2(sweep_vec[1], sweep_vec[0]) - c = np.cos(th) - s = np.sin(th) - - tx = [ix * c - iy * s for (ix, iy) in zip(x, y)] - ty = [ix * s + iy * c for (ix, iy) in zip(x, y)] - - rx = [ix + sweep_start_posi[0] for ix in tx] - ry = [iy + sweep_start_posi[1] for iy in ty] - + rot = Rot.from_euler('z', -th).as_matrix()[0:2, 0:2] + converted_xy = np.stack([x, y]).T @ rot + rx = [ix + sweep_start_position[0] for ix in converted_xy[:, 0]] + ry = [iy + sweep_start_position[1] for iy in converted_xy[:, 1]] return rx, ry def search_free_grid_index_at_edge_y(grid_map, from_upper=False): - yind = None - xinds = [] + y_index = None + x_indexes = [] if from_upper: - xrange = range(grid_map.height)[::-1] - yrange = range(grid_map.width)[::-1] + x_range = range(grid_map.height)[::-1] + y_range = range(grid_map.width)[::-1] else: - xrange = range(grid_map.height) - yrange = range(grid_map.width) + x_range = range(grid_map.height) + y_range = range(grid_map.width) - for iy in xrange: - for ix in yrange: + for iy in x_range: + for ix in y_range: if not grid_map.check_occupied_from_xy_index(ix, iy): - yind = iy - xinds.append(ix) - if yind: + y_index = iy + x_indexes.append(ix) + if y_index: break - return xinds, yind + return x_indexes, y_index -def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10): - width = math.ceil((max(ox) - min(ox)) / reso) + offset_grid - height = math.ceil((max(oy) - min(oy)) / reso) + offset_grid - center_x = (np.max(ox)+np.min(ox))/2.0 - center_y = (np.max(oy)+np.min(oy))/2.0 +def setup_grid_map(ox, oy, resolution, sweep_direction, offset_grid=10): + width = math.ceil((max(ox) - min(ox)) / resolution) + offset_grid + height = math.ceil((max(oy) - min(oy)) / resolution) + offset_grid + center_x = (np.max(ox) + np.min(ox)) / 2.0 + center_y = (np.max(oy) + np.min(oy)) / 2.0 - grid_map = GridMap(width, height, reso, center_x, center_y) + grid_map = GridMap(width, height, resolution, center_x, center_y) grid_map.print_grid_map_info() grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False) grid_map.expand_grid() @@ -203,48 +197,51 @@ def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10): x_inds_goal_y = [] goal_y = 0 if sweep_direction == SweepSearcher.SweepDirection.UP: - x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map, - from_upper=True) + x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y( + grid_map, from_upper=True) elif sweep_direction == SweepSearcher.SweepDirection.DOWN: - x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map, - from_upper=False) + x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y( + grid_map, from_upper=False) return grid_map, x_inds_goal_y, goal_y def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False): # search start grid - cxind, cyind = sweep_searcher.search_start_grid(grid_map) - if not grid_map.set_value_from_xy_index(cxind, cyind, 0.5): + c_x_index, c_y_index = sweep_searcher.search_start_grid(grid_map) + if not grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5): print("Cannot find start grid") return [], [] - x, y = grid_map.calc_grid_central_xy_position_from_xy_index(cxind, cyind) + x, y = grid_map.calc_grid_central_xy_position_from_xy_index(c_x_index, + c_y_index) px, py = [x], [y] fig, ax = None, None if grid_search_animation: fig, ax = plt.subplots() # for stopping simulation with the esc key. - fig.canvas.mpl_connect('key_release_event', - lambda event: [ - exit(0) if event.key == 'escape' else None]) + fig.canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) while True: - cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, grid_map) + c_x_index, c_y_index = sweep_searcher.move_target_grid(c_x_index, + c_y_index, + grid_map) if sweep_searcher.is_search_done(grid_map) or ( - cxind is None or cyind is None): + c_x_index is None or c_y_index is None): print("Done") break x, y = grid_map.calc_grid_central_xy_position_from_xy_index( - cxind, cyind) + c_x_index, c_y_index) px.append(x) py.append(y) - grid_map.set_value_from_xy_index(cxind, cyind, 0.5) + grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5) if grid_search_animation: grid_map.plot_grid_map(ax=ax) @@ -255,32 +252,34 @@ def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False): return px, py -def planning(ox, oy, reso, +def planning(ox, oy, resolution, moving_direction=SweepSearcher.MovingDirection.RIGHT, sweeping_direction=SweepSearcher.SweepDirection.UP, ): - sweep_vec, sweep_start_posi = find_sweep_direction_and_start_position( + sweep_vec, sweep_start_position = find_sweep_direction_and_start_position( ox, oy) - rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi) + rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, + sweep_start_position) - gmap, xinds_goaly, goaly = setup_grid_map(rox, roy, reso, - sweeping_direction) + grid_map, x_inds_goal_y, goal_y = setup_grid_map(rox, roy, resolution, + sweeping_direction) sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, - xinds_goaly, goaly) + x_inds_goal_y, goal_y) - px, py = sweep_path_search(sweep_searcher, gmap) + px, py = sweep_path_search(sweep_searcher, grid_map) - rx, ry = convert_global_coordinate(px, py, sweep_vec, sweep_start_posi) + rx, ry = convert_global_coordinate(px, py, sweep_vec, + sweep_start_position) print("Path length:", len(rx)) return rx, ry -def planning_animation(ox, oy, reso): # pragma: no cover - px, py = planning(ox, oy, reso) +def planning_animation(ox, oy, resolution): # pragma: no cover + px, py = planning(ox, oy, resolution) # animation if do_animation: @@ -311,18 +310,18 @@ def main(): # pragma: no cover ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0] oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0] - reso = 5.0 - planning_animation(ox, oy, reso) + resolution = 5.0 + planning_animation(ox, oy, resolution) ox = [0.0, 50.0, 50.0, 0.0, 0.0] oy = [0.0, 0.0, 30.0, 30.0, 0.0] - reso = 1.3 - planning_animation(ox, oy, reso) + resolution = 1.3 + planning_animation(ox, oy, resolution) ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0] oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0] - reso = 5.0 - planning_animation(ox, oy, reso) + resolution = 5.0 + planning_animation(ox, oy, resolution) plt.show() print("done!!") diff --git a/PathPlanning/HybridAStar/a_star_heuristic.py b/PathPlanning/HybridAStar/a_star_heuristic.py index a27293288c..36c3342462 100644 --- a/PathPlanning/HybridAStar/a_star_heuristic.py +++ b/PathPlanning/HybridAStar/a_star_heuristic.py @@ -8,8 +8,9 @@ """ -import math import heapq +import math + import matplotlib.pyplot as plt show_animation = False @@ -17,71 +18,73 @@ class Node: - def __init__(self, x, y, cost, pind): + def __init__(self, x, y, cost, parent_index): self.x = x self.y = y self.cost = cost - self.pind = pind + self.parent_index = parent_index def __str__(self): - return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind) + return str(self.x) + "," + str(self.y) + "," + str( + self.cost) + "," + str(self.parent_index) -def calc_final_path(ngoal, closedset, reso): +def calc_final_path(goal_node, closed_node_set, resolution): # generate final course - rx, ry = [ngoal.x * reso], [ngoal.y * reso] - pind = ngoal.pind - while pind != -1: - n = closedset[pind] - rx.append(n.x * reso) - ry.append(n.y * reso) - pind = n.pind + rx, ry = [goal_node.x * resolution], [goal_node.y * resolution] + parent_index = goal_node.parent_index + while parent_index != -1: + n = closed_node_set[parent_index] + rx.append(n.x * resolution) + ry.append(n.y * resolution) + parent_index = n.parent_index return rx, ry -def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr): +def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr): """ gx: goal x position [m] gx: goal x position [m] ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - reso: grid resolution [m] + resolution: grid resolution [m] rr: robot radius[m] """ - nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1) - ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1) - ox = [iox / reso for iox in ox] - oy = [ioy / reso for ioy in oy] + start_node = Node(round(sx / resolution), round(sy / resolution), 0.0, -1) + goal_node = Node(round(gx / resolution), round(gy / resolution), 0.0, -1) + ox = [iox / resolution for iox in ox] + oy = [ioy / resolution for ioy in oy] - obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr) + obstacle_map, min_x, min_y, max_x, max_y, x_w, y_w = calc_obstacle_map( + ox, oy, resolution, rr) motion = get_motion_model() - openset, closedset = dict(), dict() - openset[calc_index(ngoal, xw, minx, miny)] = ngoal - pq = [] - pq.append((0, calc_index(ngoal, xw, minx, miny))) + open_set, closed_set = dict(), dict() + open_set[calc_index(goal_node, x_w, min_x, min_y)] = goal_node + priority_queue = [(0, calc_index(goal_node, x_w, min_x, min_y))] while 1: - if not pq: + if not priority_queue: break - cost, c_id = heapq.heappop(pq) - if c_id in openset: - current = openset[c_id] - closedset[c_id] = current - openset.pop(c_id) + cost, c_id = heapq.heappop(priority_queue) + if c_id in open_set: + current = open_set[c_id] + closed_set[c_id] = current + open_set.pop(c_id) else: continue # show graph if show_animation: # pragma: no cover - plt.plot(current.x * reso, current.y * reso, "xc") + plt.plot(current.x * resolution, current.y * resolution, "xc") # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) - if len(closedset.keys()) % 10 == 0: + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + if len(closed_set.keys()) % 10 == 0: plt.pause(0.001) # Remove the item from the open set @@ -91,82 +94,82 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr): node = Node(current.x + motion[i][0], current.y + motion[i][1], current.cost + motion[i][2], c_id) - n_id = calc_index(node, xw, minx, miny) + n_id = calc_index(node, x_w, min_x, min_y) - if n_id in closedset: + if n_id in closed_set: continue - if not verify_node(node, obmap, minx, miny, maxx, maxy): + if not verify_node(node, obstacle_map, min_x, min_y, max_x, max_y): continue - if n_id not in openset: - openset[n_id] = node # Discover a new node + if n_id not in open_set: + open_set[n_id] = node # Discover a new node heapq.heappush( - pq, (node.cost, calc_index(node, xw, minx, miny))) + priority_queue, + (node.cost, calc_index(node, x_w, min_x, min_y))) else: - if openset[n_id].cost >= node.cost: + if open_set[n_id].cost >= node.cost: # This path is the best until now. record it! - openset[n_id] = node + open_set[n_id] = node heapq.heappush( - pq, (node.cost, calc_index(node, xw, minx, miny))) + priority_queue, + (node.cost, calc_index(node, x_w, min_x, min_y))) - rx, ry = calc_final_path(closedset[calc_index( - nstart, xw, minx, miny)], closedset, reso) + rx, ry = calc_final_path(closed_set[calc_index( + start_node, x_w, min_x, min_y)], closed_set, resolution) - return rx, ry, closedset + return rx, ry, closed_set def calc_heuristic(n1, n2): w = 1.0 # weight of heuristic - d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2) + d = w * math.sqrt((n1.x - n2.x) ** 2 + (n1.y - n2.y) ** 2) return d -def verify_node(node, obmap, minx, miny, maxx, maxy): - - if node.x < minx: +def verify_node(node, obstacle_map, min_x, min_y, max_x, max_y): + if node.x < min_x: return False - elif node.y < miny: + elif node.y < min_y: return False - elif node.x >= maxx: + elif node.x >= max_x: return False - elif node.y >= maxy: + elif node.y >= max_y: return False - if obmap[node.x][node.y]: + if obstacle_map[node.x][node.y]: return False return True -def calc_obstacle_map(ox, oy, reso, vr): - - minx = round(min(ox)) - miny = round(min(oy)) - maxx = round(max(ox)) - maxy = round(max(oy)) +def calc_obstacle_map(ox, oy, resolution, vr): + min_x = round(min(ox)) + min_y = round(min(oy)) + max_x = round(max(ox)) + max_y = round(max(oy)) - xwidth = round(maxx - minx) - ywidth = round(maxy - miny) + x_width = round(max_x - min_x) + y_width = round(max_y - min_y) # obstacle map generation - obmap = [[False for i in range(ywidth)] for i in range(xwidth)] - for ix in range(xwidth): - x = ix + minx - for iy in range(ywidth): - y = iy + miny + obstacle_map = [[False for _ in range(y_width)] for _ in range(x_width)] + for ix in range(x_width): + x = ix + min_x + for iy in range(y_width): + y = iy + min_y # print(x, y) for iox, ioy in zip(ox, oy): - d = math.sqrt((iox - x)**2 + (ioy - y)**2) - if d <= vr / reso: - obmap[ix][iy] = True + d = math.sqrt((iox - x) ** 2 + (ioy - y) ** 2) + if d <= vr / resolution: + obstacle_map[ix][iy] = True break - return obmap, minx, miny, maxx, maxy, xwidth, ywidth + return obstacle_map, min_x, min_y, max_x, max_y, x_width, y_width -def calc_index(node, xwidth, xmin, ymin): - return (node.y - ymin) * xwidth + (node.x - xmin) +def calc_index(node, x_width, x_min, y_min): + return (node.y - y_min) * x_width + (node.x - x_min) def get_motion_model(): diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py index 13e50b2995..9b956a0877 100644 --- a/PathPlanning/HybridAStar/car.py +++ b/PathPlanning/HybridAStar/car.py @@ -6,9 +6,11 @@ """ +from math import sqrt, cos, sin, tan, pi import matplotlib.pyplot as plt -from math import sqrt, cos, sin, tan, pi +import numpy as np +from scipy.spatial.transform import Rotation as Rot WB = 3. # rear to front wheel W = 2. # width of car @@ -16,25 +18,25 @@ LB = 1.0 # distance from rear to vehicle back end MAX_STEER = 0.6 # [rad] maximum steering angle -WBUBBLE_DIST = (LF - LB) / 2.0 -WBUBBLE_R = sqrt(((LF + LB) / 2.0)**2 + 1) +W_BUBBLE_DIST = (LF - LB) / 2.0 +W_BUBBLE_R = sqrt(((LF + LB) / 2.0) ** 2 + 1) -# vehicle rectangle verticles +# vehicle rectangle vertices VRX = [LF, LF, -LB, -LB, LF] VRY = [W / 2, -W / 2, -W / 2, W / 2, W / 2] -def check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree): - for x, y, yaw in zip(xlist, ylist, yawlist): - cx = x + WBUBBLE_DIST * cos(yaw) - cy = y + WBUBBLE_DIST * sin(yaw) +def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree): + for i_x, i_y, i_yaw in zip(x_list, y_list, yaw_list): + cx = i_x + W_BUBBLE_DIST * cos(i_yaw) + cy = i_y + W_BUBBLE_DIST * sin(i_yaw) - ids = kdtree.search_in_distance([cx, cy], WBUBBLE_R) + ids = kd_tree.search_in_distance([cx, cy], W_BUBBLE_R) if not ids: continue - if not rectangle_check(x, y, yaw, + if not rectangle_check(i_x, i_y, i_yaw, [ox[i] for i in ids], [oy[i] for i in ids]): return False # collision @@ -43,12 +45,12 @@ def check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree): def rectangle_check(x, y, yaw, ox, oy): # transform obstacles to base link frame - c, s = cos(-yaw), sin(-yaw) + rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2] for iox, ioy in zip(ox, oy): tx = iox - x ty = ioy - y - rx = c * tx - s * ty - ry = s * tx + c * ty + converted_xy = np.stack([tx, ty]).T @ rot + rx, ry = converted_xy[0], converted_xy[1] if not (rx > LF or rx < -LB or ry > W / 2.0 or ry < -W / 2.0): return False # no collision @@ -59,24 +61,22 @@ def rectangle_check(x, y, yaw, ox, oy): def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): """Plot arrow.""" if not isinstance(x, float): - for (ix, iy, iyaw) in zip(x, y, yaw): - plot_arrow(ix, iy, iyaw) + for (i_x, i_y, i_yaw) in zip(x, y, yaw): + plot_arrow(i_x, i_y, i_yaw) else: plt.arrow(x, y, length * cos(yaw), length * sin(yaw), fc=fc, ec=ec, head_width=width, head_length=width, alpha=0.4) - # plt.plot(x, y) def plot_car(x, y, yaw): car_color = '-k' c, s = cos(yaw), sin(yaw) - + rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2] car_outline_x, car_outline_y = [], [] for rx, ry in zip(VRX, VRY): - tx = c * rx - s * ry + x - ty = s * rx + c * ry + y - car_outline_x.append(tx) - car_outline_y.append(ty) + converted_xy = np.stack([rx, ry]).T @ rot + car_outline_x.append(converted_xy[0]+x) + car_outline_y.append(converted_xy[1]+y) arrow_x, arrow_y, arrow_yaw = c * 1.5 + x, s * 1.5 + y, yaw plot_arrow(arrow_x, arrow_y, arrow_yaw) @@ -96,8 +96,12 @@ def move(x, y, yaw, distance, steer, L=WB): return x, y, yaw -if __name__ == '__main__': +def main(): x, y, yaw = 0., 0., 1. plt.axis('equal') plot_car(x, y, yaw) - plt.show() \ No newline at end of file + plt.show() + + +if __name__ == '__main__': + main() diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 14e4ac5aec..ff9b3fe43b 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -7,28 +7,27 @@ """ import heapq -import scipy.spatial -import numpy as np import math -import matplotlib.pyplot as plt -import sys import os +import sys + +import matplotlib.pyplot as plt +import numpy as np +import scipy.spatial sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../ReedsSheppPath") try: - from a_star_heuristic import dp_planning # , calc_obstacle_map + from a_star_heuristic import dp_planning import reeds_shepp_path_planning as rs from car import move, check_car_collision, MAX_STEER, WB, plot_car except Exception: raise - XY_GRID_RESOLUTION = 2.0 # [m] YAW_GRID_RESOLUTION = np.deg2rad(15.0) # [rad] -MOTION_RESOLUTION = 0.1 # [m] path interporate resolution +MOTION_RESOLUTION = 0.1 # [m] path interpolate resolution N_STEER = 20 # number of steer command -H_COST = 1.0 VR = 1.0 # robot radius SB_COST = 100.0 # switch back penalty cost @@ -42,29 +41,29 @@ class Node: - def __init__(self, xind, yind, yawind, direction, - xlist, ylist, yawlist, directions, - steer=0.0, pind=None, cost=None): - self.xind = xind - self.yind = yind - self.yawind = yawind + def __init__(self, x_ind, y_ind, yaw_ind, direction, + x_list, y_list, yaw_list, directions, + steer=0.0, parent_index=None, cost=None): + self.x_index = x_ind + self.y_index = y_ind + self.yaw_index = yaw_ind self.direction = direction - self.xlist = xlist - self.ylist = ylist - self.yawlist = yawlist + self.x_list = x_list + self.y_list = y_list + self.yaw_list = yaw_list self.directions = directions self.steer = steer - self.pind = pind + self.parent_index = parent_index self.cost = cost class Path: - def __init__(self, xlist, ylist, yawlist, directionlist, cost): - self.xlist = xlist - self.ylist = ylist - self.yawlist = yawlist - self.directionlist = directionlist + def __init__(self, x_list, y_list, yaw_list, direction_list, cost): + self.x_list = x_list + self.y_list = y_list + self.yaw_list = yaw_list + self.direction_list = direction_list self.cost = cost @@ -88,9 +87,9 @@ def search(self, inp, k=1): dist = [] for i in inp.T: - idist, iindex = self.tree.query(i, k=k) - index.append(iindex) - dist.append(idist) + i_dist, i_index = self.tree.query(i, k=k) + index.append(i_index) + dist.append(i_dist) return index, dist @@ -108,7 +107,7 @@ def search_in_distance(self, inp, r): class Config: - def __init__(self, ox, oy, xyreso, yawreso): + def __init__(self, ox, oy, xy_resolution, yaw_resolution): min_x_m = min(ox) min_y_m = min(oy) max_x_m = max(ox) @@ -119,94 +118,93 @@ def __init__(self, ox, oy, xyreso, yawreso): ox.append(max_x_m) oy.append(max_y_m) - self.minx = round(min_x_m / xyreso) - self.miny = round(min_y_m / xyreso) - self.maxx = round(max_x_m / xyreso) - self.maxy = round(max_y_m / xyreso) + self.min_x = round(min_x_m / xy_resolution) + self.min_y = round(min_y_m / xy_resolution) + self.max_x = round(max_x_m / xy_resolution) + self.max_y = round(max_y_m / xy_resolution) - self.xw = round(self.maxx - self.minx) - self.yw = round(self.maxy - self.miny) + self.x_w = round(self.max_x - self.min_x) + self.y_w = round(self.max_y - self.min_y) - self.minyaw = round(- math.pi / yawreso) - 1 - self.maxyaw = round(math.pi / yawreso) - self.yaww = round(self.maxyaw - self.minyaw) + self.min_yaw = round(- math.pi / yaw_resolution) - 1 + self.max_yaw = round(math.pi / yaw_resolution) + self.yaw_w = round(self.max_yaw - self.min_yaw) def calc_motion_inputs(): - for steer in np.concatenate((np.linspace(-MAX_STEER, MAX_STEER, N_STEER), [0.0])): for d in [1, -1]: yield [steer, d] -def get_neighbors(current, config, ox, oy, kdtree): - +def get_neighbors(current, config, ox, oy, kd_tree): for steer, d in calc_motion_inputs(): - node = calc_next_node(current, steer, d, config, ox, oy, kdtree) + node = calc_next_node(current, steer, d, config, ox, oy, kd_tree) if node and verify_index(node, config): yield node -def calc_next_node(current, steer, direction, config, ox, oy, kdtree): - - x, y, yaw = current.xlist[-1], current.ylist[-1], current.yawlist[-1] +def calc_next_node(current, steer, direction, config, ox, oy, kd_tree): + x, y, yaw = current.x_list[-1], current.y_list[-1], current.yaw_list[-1] arc_l = XY_GRID_RESOLUTION * 1.5 - xlist, ylist, yawlist = [], [], [] + x_list, y_list, yaw_list = [], [], [] for _ in np.arange(0, arc_l, MOTION_RESOLUTION): x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION * direction, steer) - xlist.append(x) - ylist.append(y) - yawlist.append(yaw) + x_list.append(x) + y_list.append(y) + yaw_list.append(yaw) - if not check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree): + if not check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree): return None d = direction == 1 - xind = round(x / XY_GRID_RESOLUTION) - yind = round(y / XY_GRID_RESOLUTION) - yawind = round(yaw / YAW_GRID_RESOLUTION) + x_ind = round(x / XY_GRID_RESOLUTION) + y_ind = round(y / XY_GRID_RESOLUTION) + yaw_ind = round(yaw / YAW_GRID_RESOLUTION) - addedcost = 0.0 + added_cost = 0.0 if d != current.direction: - addedcost += SB_COST + added_cost += SB_COST # steer penalty - addedcost += STEER_COST * abs(steer) + added_cost += STEER_COST * abs(steer) # steer change penalty - addedcost += STEER_CHANGE_COST * abs(current.steer - steer) + added_cost += STEER_CHANGE_COST * abs(current.steer - steer) - cost = current.cost + addedcost + arc_l + cost = current.cost + added_cost + arc_l - node = Node(xind, yind, yawind, d, xlist, - ylist, yawlist, [d], - pind=calc_index(current, config), + node = Node(x_ind, y_ind, yaw_ind, d, x_list, + y_list, yaw_list, [d], + parent_index=calc_index(current, config), cost=cost, steer=steer) return node def is_same_grid(n1, n2): - if n1.xind == n2.xind and n1.yind == n2.yind and n1.yawind == n2.yawind: + if n1.x_index == n2.x_index \ + and n1.y_index == n2.y_index \ + and n1.yaw_index == n2.yaw_index: return True return False -def analytic_expantion(current, goal, c, ox, oy, kdtree): - - sx = current.xlist[-1] - sy = current.ylist[-1] - syaw = current.yawlist[-1] +def analytic_expansion(current, goal, ox, oy, kd_tree): + start_x = current.x_list[-1] + start_y = current.y_list[-1] + start_yaw = current.yaw_list[-1] - gx = goal.xlist[-1] - gy = goal.ylist[-1] - gyaw = goal.yawlist[-1] + goal_x = goal.x_list[-1] + goal_y = goal.y_list[-1] + goal_yaw = goal.yaw_list[-1] max_curvature = math.tan(MAX_STEER) / WB - paths = rs.calc_paths(sx, sy, syaw, gx, gy, gyaw, + paths = rs.calc_paths(start_x, start_y, start_yaw, + goal_x, goal_y, goal_yaw, max_curvature, step_size=MOTION_RESOLUTION) if not paths: @@ -215,7 +213,7 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree): best_path, best = None, None for path in paths: - if check_car_collision(path.x, path.y, path.yaw, ox, oy, kdtree): + if check_car_collision(path.x, path.y, path.yaw, ox, oy, kd_tree): cost = calc_rs_path_cost(path) if not best or best > cost: best = cost @@ -224,99 +222,105 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree): return best_path -def update_node_with_analystic_expantion(current, goal, - c, ox, oy, kdtree): - apath = analytic_expantion(current, goal, c, ox, oy, kdtree) +def update_node_with_analytic_expansion(current, goal, + c, ox, oy, kd_tree): + path = analytic_expansion(current, goal, ox, oy, kd_tree) - if apath: + if path: if show_animation: - plt.plot(apath.x, apath.y) - fx = apath.x[1:] - fy = apath.y[1:] - fyaw = apath.yaw[1:] + plt.plot(path.x, path.y) + f_x = path.x[1:] + f_y = path.y[1:] + f_yaw = path.yaw[1:] - fcost = current.cost + calc_rs_path_cost(apath) - fpind = calc_index(current, c) + f_cost = current.cost + calc_rs_path_cost(path) + f_parent_index = calc_index(current, c) fd = [] - for d in apath.directions[1:]: + for d in path.directions[1:]: fd.append(d >= 0) - fsteer = 0.0 - fpath = Node(current.xind, current.yind, current.yawind, - current.direction, fx, fy, fyaw, fd, - cost=fcost, pind=fpind, steer=fsteer) - return True, fpath + f_steer = 0.0 + f_path = Node(current.x_index, current.y_index, current.yaw_index, + current.direction, f_x, f_y, f_yaw, fd, + cost=f_cost, parent_index=f_parent_index, steer=f_steer) + return True, f_path return False, None -def calc_rs_path_cost(rspath): - +def calc_rs_path_cost(reed_shepp_path): cost = 0.0 - for l in rspath.lengths: - if l >= 0: # forward - cost += l + for length in reed_shepp_path.lengths: + if length >= 0: # forward + cost += length else: # back - cost += abs(l) * BACK_COST + cost += abs(length) * BACK_COST - # swich back penalty - for i in range(len(rspath.lengths) - 1): - if rspath.lengths[i] * rspath.lengths[i + 1] < 0.0: # switch back + # switch back penalty + for i in range(len(reed_shepp_path.lengths) - 1): + # switch back + if reed_shepp_path.lengths[i] * reed_shepp_path.lengths[i + 1] < 0.0: cost += SB_COST - # steer penalyty - for ctype in rspath.ctypes: - if ctype != "S": # curve + # steer penalty + for course_type in reed_shepp_path.ctypes: + if course_type != "S": # curve cost += STEER_COST * abs(MAX_STEER) # ==steer change penalty # calc steer profile - nctypes = len(rspath.ctypes) - ulist = [0.0] * nctypes - for i in range(nctypes): - if rspath.ctypes[i] == "R": - ulist[i] = - MAX_STEER - elif rspath.ctypes[i] == "L": - ulist[i] = MAX_STEER + n_ctypes = len(reed_shepp_path.ctypes) + u_list = [0.0] * n_ctypes + for i in range(n_ctypes): + if reed_shepp_path.ctypes[i] == "R": + u_list[i] = - MAX_STEER + elif reed_shepp_path.ctypes[i] == "L": + u_list[i] = MAX_STEER - for i in range(len(rspath.ctypes) - 1): - cost += STEER_CHANGE_COST * abs(ulist[i + 1] - ulist[i]) + for i in range(len(reed_shepp_path.ctypes) - 1): + cost += STEER_CHANGE_COST * abs(u_list[i + 1] - u_list[i]) return cost -def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso): +def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution): """ - start - goal + start: start node + goal: goal node ox: x position list of Obstacles [m] oy: y position list of Obstacles [m] - xyreso: grid resolution [m] - yawreso: yaw angle resolution [rad] + xy_resolution: grid resolution [m] + yaw_resolution: yaw angle resolution [rad] """ start[2], goal[2] = rs.pi_2_pi(start[2]), rs.pi_2_pi(goal[2]) tox, toy = ox[:], oy[:] - obkdtree = KDTree(np.vstack((tox, toy)).T) + obstacle_kd_tree = KDTree(np.vstack((tox, toy)).T) - config = Config(tox, toy, xyreso, yawreso) + config = Config(tox, toy, xy_resolution, yaw_resolution) - nstart = Node(round(start[0] / xyreso), round(start[1] / xyreso), round(start[2] / yawreso), - True, [start[0]], [start[1]], [start[2]], [True], cost=0) - ngoal = Node(round(goal[0] / xyreso), round(goal[1] / xyreso), round(goal[2] / yawreso), - True, [goal[0]], [goal[1]], [goal[2]], [True]) + start_node = Node(round(start[0] / xy_resolution), + round(start[1] / xy_resolution), + round(start[2] / yaw_resolution), True, + [start[0]], [start[1]], [start[2]], [True], cost=0) + goal_node = Node(round(goal[0] / xy_resolution), + round(goal[1] / xy_resolution), + round(goal[2] / yaw_resolution), True, + [goal[0]], [goal[1]], [goal[2]], [True]) openList, closedList = {}, {} - _, _, h_dp = dp_planning(nstart.xlist[-1], nstart.ylist[-1], - ngoal.xlist[-1], ngoal.ylist[-1], ox, oy, xyreso, VR) + _, _, h_dp = dp_planning(start_node.x_list[-1], start_node.y_list[-1], + goal_node.x_list[-1], goal_node.y_list[-1], + ox, oy, xy_resolution, VR) pq = [] - openList[calc_index(nstart, config)] = nstart - heapq.heappush(pq, (calc_cost(nstart, h_dp, ngoal, config), - calc_index(nstart, config))) + openList[calc_index(start_node, config)] = start_node + heapq.heappush(pq, (calc_cost(start_node, h_dp, config), + calc_index(start_node, config))) + final_path = None while True: if not openList: @@ -331,83 +335,85 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso): continue if show_animation: # pragma: no cover - plt.plot(current.xlist[-1], current.ylist[-1], "xc") + plt.plot(current.x_list[-1], current.y_list[-1], "xc") # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if len(closedList.keys()) % 10 == 0: plt.pause(0.001) - isupdated, fpath = update_node_with_analystic_expantion( - current, ngoal, config, ox, oy, obkdtree) + is_updated, final_path = update_node_with_analytic_expansion( + current, goal_node, config, ox, oy, obstacle_kd_tree) - if isupdated: + if is_updated: print("path found") break - for neighbor in get_neighbors(current, config, ox, oy, obkdtree): + for neighbor in get_neighbors(current, config, ox, oy, + obstacle_kd_tree): neighbor_index = calc_index(neighbor, config) if neighbor_index in closedList: continue if neighbor not in openList \ or openList[neighbor_index].cost > neighbor.cost: heapq.heappush( - pq, (calc_cost(neighbor, h_dp, ngoal, config), + pq, (calc_cost(neighbor, h_dp, config), neighbor_index)) openList[neighbor_index] = neighbor - path = get_final_path(closedList, fpath, nstart, config) + path = get_final_path(closedList, final_path) return path -def calc_cost(n, h_dp, goal, c): - ind = (n.yind - c.miny) * c.xw + (n.xind - c.minx) +def calc_cost(n, h_dp, c): + ind = (n.y_index - c.min_y) * c.x_w + (n.x_index - c.min_x) if ind not in h_dp: return n.cost + 999999999 # collision cost return n.cost + H_COST * h_dp[ind].cost -def get_final_path(closed, ngoal, nstart, config): - rx, ry, ryaw = list(reversed(ngoal.xlist)), list( - reversed(ngoal.ylist)), list(reversed(ngoal.yawlist)) - direction = list(reversed(ngoal.directions)) - nid = ngoal.pind - finalcost = ngoal.cost +def get_final_path(closed, goal_node): + reversed_x, reversed_y, reversed_yaw = \ + list(reversed(goal_node.x_list)), list(reversed(goal_node.y_list)), \ + list(reversed(goal_node.yaw_list)) + direction = list(reversed(goal_node.directions)) + nid = goal_node.parent_index + final_cost = goal_node.cost while nid: n = closed[nid] - rx.extend(list(reversed(n.xlist))) - ry.extend(list(reversed(n.ylist))) - ryaw.extend(list(reversed(n.yawlist))) + reversed_x.extend(list(reversed(n.x_list))) + reversed_y.extend(list(reversed(n.y_list))) + reversed_yaw.extend(list(reversed(n.yaw_list))) direction.extend(list(reversed(n.directions))) - nid = n.pind + nid = n.parent_index - rx = list(reversed(rx)) - ry = list(reversed(ry)) - ryaw = list(reversed(ryaw)) + reversed_x = list(reversed(reversed_x)) + reversed_y = list(reversed(reversed_y)) + reversed_yaw = list(reversed(reversed_yaw)) direction = list(reversed(direction)) # adjust first direction direction[0] = direction[1] - path = Path(rx, ry, ryaw, direction, finalcost) + path = Path(reversed_x, reversed_y, reversed_yaw, direction, final_cost) return path def verify_index(node, c): - xind, yind = node.xind, node.yind - if xind >= c.minx and xind <= c.maxx and yind >= c.miny \ - and yind <= c.maxy: + x_ind, y_ind = node.x_index, node.y_index + if c.min_x <= x_ind <= c.max_x and c.min_y <= y_ind <= c.max_y: return True return False def calc_index(node, c): - ind = (node.yawind - c.minyaw) * c.xw * c.yw + \ - (node.yind - c.miny) * c.xw + (node.xind - c.minx) + ind = (node.yaw_index - c.min_yaw) * c.x_w * c.y_w + \ + (node.y_index - c.min_y) * c.x_w + (node.x_index - c.min_x) if ind <= 0: print("Error(calc_index):", ind) @@ -457,18 +463,18 @@ def main(): path = hybrid_a_star_planning( start, goal, ox, oy, XY_GRID_RESOLUTION, YAW_GRID_RESOLUTION) - x = path.xlist - y = path.ylist - yaw = path.yawlist + x = path.x_list + y = path.y_list + yaw = path.yaw_list if show_animation: - for ix, iy, iyaw in zip(x, y, yaw): + for i_x, i_y, i_yaw in zip(x, y, yaw): plt.cla() plt.plot(ox, oy, ".k") plt.plot(x, y, "-r", label="Hybrid A* path") plt.grid(True) plt.axis("equal") - plot_car(ix, iy, iyaw) + plot_car(i_x, i_y, i_yaw) plt.pause(0.0001) print(__file__ + " done!!") diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 912e56fa4b..baf7dcce91 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -5,7 +5,8 @@ Atsushi Sakai(@Atsushi_twi) Reference: Informed RRT*: Optimal Sampling-based Path planning Focused via -Direct Sampling of an Admissible Ellipsoidal Heuristichttps://arxiv.org/pdf/1404.2334.pdf +Direct Sampling of an Admissible Ellipsoidal Heuristic +https://arxiv.org/pdf/1404.2334.pdf """ @@ -14,6 +15,7 @@ import random import matplotlib.pyplot as plt +from scipy.spatial.transform import Rotation as Rot import numpy as np show_animation = True @@ -38,7 +40,8 @@ def __init__(self, start, goal, def informed_rrt_star_search(self, animation=True): self.node_list = [self.start] - # max length we expect to find in our 'informed' sample space, starts as infinite + # max length we expect to find in our 'informed' sample space, + # starts as infinite cBest = float('inf') solutionSet = set() path = None @@ -51,13 +54,14 @@ def informed_rrt_star_search(self, animation=True): a1 = np.array([[(self.goal.x - self.start.x) / cMin], [(self.goal.y - self.start.y) / cMin], [0]]) - etheta = math.atan2(a1[1], a1[0]) - # first column of idenity matrix transposed + e_theta = math.atan2(a1[1], a1[0]) + # first column of identity matrix transposed id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3) M = a1 @ id1_t U, S, Vh = np.linalg.svd(M, True, True) C = np.dot(np.dot(U, np.diag( - [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), Vh) + [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), + Vh) for i in range(self.max_iter): # Sample space is defined by cBest @@ -66,11 +70,11 @@ def informed_rrt_star_search(self, animation=True): # cBest changes when a new path is found rnd = self.informed_sample(cBest, cMin, xCenter, C) - nind = self.get_nearest_list_index(self.node_list, rnd) - nearestNode = self.node_list[nind] + n_ind = self.get_nearest_list_index(self.node_list, rnd) + nearestNode = self.node_list[n_ind] # steer theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x) - newNode = self.get_new_node(theta, nind, nearestNode) + newNode = self.get_new_node(theta, n_ind, nearestNode) d = self.line_cost(nearestNode, newNode) noCollision = self.check_collision(nearestNode, theta, d) @@ -83,7 +87,8 @@ def informed_rrt_star_search(self, animation=True): self.rewire(newNode, nearInds) if self.is_near_goal(newNode): - if self.check_segment_collision(newNode.x, newNode.y, self.goal.x , self.goal.y): + if self.check_segment_collision(newNode.x, newNode.y, + self.goal.x, self.goal.y): solutionSet.add(newNode) lastIndex = len(self.node_list) - 1 tempPath = self.get_final_course(lastIndex) @@ -94,7 +99,7 @@ def informed_rrt_star_search(self, animation=True): if animation: self.draw_graph(xCenter=xCenter, cBest=cBest, cMin=cMin, - etheta=etheta, rnd=rnd) + e_theta=e_theta, rnd=rnd) return path @@ -117,7 +122,7 @@ def choose_parent(self, newNode, nearInds): minInd = nearInds[dList.index(minCost)] if minCost == float('inf'): - print("mincost is inf") + print("min cost is inf") return newNode newNode.cost = minCost @@ -126,12 +131,12 @@ def choose_parent(self, newNode, nearInds): return newNode def find_near_nodes(self, newNode): - nnode = len(self.node_list) - r = 50.0 * math.sqrt((math.log(nnode) / nnode)) - dlist = [(node.x - newNode.x) ** 2 - + (node.y - newNode.y) ** 2 for node in self.node_list] - nearinds = [dlist.index(i) for i in dlist if i <= r ** 2] - return nearinds + n_node = len(self.node_list) + r = 50.0 * math.sqrt((math.log(n_node) / n_node)) + d_list = [(node.x - newNode.x) ** 2 + (node.y - newNode.y) ** 2 + for node in self.node_list] + near_inds = [d_list.index(i) for i in d_list if i <= r ** 2] + return near_inds def informed_sample(self, cMax, cMin, xCenter, C): if cMax < float('inf'): @@ -192,14 +197,14 @@ def get_nearest_list_index(nodes, rnd): minIndex = dList.index(min(dList)) return minIndex - def get_new_node(self, theta, nind, nearestNode): + def get_new_node(self, theta, n_ind, nearestNode): newNode = copy.deepcopy(nearestNode) newNode.x += self.expand_dis * math.cos(theta) newNode.y += self.expand_dis * math.sin(theta) newNode.cost += self.expand_dis - newNode.parent = nind + newNode.parent = n_ind return newNode def is_near_goal(self, node): @@ -216,28 +221,29 @@ def rewire(self, newNode, nearInds): d = math.sqrt((nearNode.x - newNode.x) ** 2 + (nearNode.y - newNode.y) ** 2) - scost = newNode.cost + d + s_cost = newNode.cost + d - if nearNode.cost > scost: + if nearNode.cost > s_cost: theta = math.atan2(newNode.y - nearNode.y, newNode.x - nearNode.x) if self.check_collision(nearNode, theta, d): nearNode.parent = n_node - 1 - nearNode.cost = scost + nearNode.cost = s_cost @staticmethod def distance_squared_point_to_segment(v, w, p): # Return minimum distance between line segment vw and point p - if (np.array_equal(v, w)): - return (p-v).dot(p-v) # v == w case - l2 = (w-v).dot(w-v) # i.e. |w-v|^2 - avoid a sqrt - # Consider the line extending the segment, parameterized as v + t (w - v). + if np.array_equal(v, w): + return (p - v).dot(p - v) # v == w case + l2 = (w - v).dot(w - v) # i.e. |w-v|^2 - avoid a sqrt + # Consider the line extending the segment, + # parameterized as v + t (w - v). # We find projection of point p onto the line. # It falls where t = [(p-v) . (w-v)] / |w-v|^2 # We clamp t from [0,1] to handle points outside the segment vw. t = max(0, min(1, (p - v).dot(w - v) / l2)) - projection = v + t * (w - v) # Projection falls on the segment - return (p-projection).dot(p-projection) + projection = v + t * (w - v) # Projection falls on the segment + return (p - projection).dot(p - projection) def check_segment_collision(self, x1, y1, x2, y2): for (ox, oy, size) in self.obstacle_list: @@ -245,16 +251,15 @@ def check_segment_collision(self, x1, y1, x2, y2): np.array([x1, y1]), np.array([x2, y2]), np.array([ox, oy])) - if dd <= size**2: + if dd <= size ** 2: return False # collision return True - def check_collision(self, nearNode, theta, d): tmpNode = copy.deepcopy(nearNode) - endx = tmpNode.x + math.cos(theta)*d - endy = tmpNode.y + math.sin(theta)*d - return self.check_segment_collision(tmpNode.x, tmpNode.y, endx, endy) + end_x = tmpNode.x + math.cos(theta) * d + end_y = tmpNode.y + math.sin(theta) * d + return self.check_segment_collision(tmpNode.x, tmpNode.y, end_x, end_y) def get_final_course(self, lastIndex): path = [[self.goal.x, self.goal.y]] @@ -265,15 +270,17 @@ def get_final_course(self, lastIndex): path.append([self.start.x, self.start.y]) return path - def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None): + def draw_graph(self, xCenter=None, cBest=None, cMin=None, e_theta=None, + rnd=None): plt.clf() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd[0], rnd[1], "^k") if cBest != float('inf'): - self.plot_ellipse(xCenter, cBest, cMin, etheta) + self.plot_ellipse(xCenter, cBest, cMin, e_theta) for node in self.node_list: if node.parent is not None: @@ -291,20 +298,18 @@ def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None) plt.pause(0.01) @staticmethod - def plot_ellipse(xCenter, cBest, cMin, etheta): # pragma: no cover + def plot_ellipse(xCenter, cBest, cMin, e_theta): # pragma: no cover a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0 b = cBest / 2.0 - angle = math.pi / 2.0 - etheta + angle = math.pi / 2.0 - e_theta cx = xCenter[0] cy = xCenter[1] - t = np.arange(0, 2 * math.pi + 0.1, 0.1) x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] - R = np.array([[math.cos(angle), math.sin(angle)], - [-math.sin(angle), math.cos(angle)]]) - fx = R @ np.array([x, y]) + rot = Rot.from_euler('z', -angle).as_matrix()[0:2, 0:2] + fx = rot @ np.array([x, y]) px = np.array(fx[0, :] + cx).flatten() py = np.array(fx[1, :] + cy).flatten() plt.plot(cx, cy, "xc") diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 7715174e16..790db074c0 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -1,6 +1,6 @@ """ -Probablistic Road Map (PRM) Planner +Probabilistic Road Map (PRM) Planner author: Atsushi Sakai (@Atsushi_twi) @@ -25,14 +25,15 @@ class Node: Node class for dijkstra search """ - def __init__(self, x, y, cost, pind): + def __init__(self, x, y, cost, parent_index): self.x = x self.y = y self.cost = cost - self.pind = pind + self.parent_index = parent_index def __str__(self): - return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind) + return str(self.x) + "," + str(self.y) + "," +\ + str(self.cost) + "," + str(self.parent_index) class KDTree: @@ -57,9 +58,9 @@ def search(self, inp, k=1): dist = [] for i in inp.T: - idist, iindex = self.tree.query(i, k=k) - index.append(iindex) - dist.append(idist) + i_dist, i_index = self.tree.query(i, k=k) + index.append(i_index) + dist.append(i_dist) return index, dist @@ -75,23 +76,24 @@ def search_in_distance(self, inp, r): return index -def PRM_planning(sx, sy, gx, gy, ox, oy, rr): +def prm_planning(sx, sy, gx, gy, ox, oy, rr): - obkdtree = KDTree(np.vstack((ox, oy)).T) + obstacle_kd_tree = KDTree(np.vstack((ox, oy)).T) - sample_x, sample_y = sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree) + sample_x, sample_y = sample_points(sx, sy, gx, gy, + rr, ox, oy, obstacle_kd_tree) if show_animation: plt.plot(sample_x, sample_y, ".b") - road_map = generate_roadmap(sample_x, sample_y, rr, obkdtree) + road_map = generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree) rx, ry = dijkstra_planning( - sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y) + sx, sy, gx, gy, road_map, sample_x, sample_y) return rx, ry -def is_collision(sx, sy, gx, gy, rr, okdtree): +def is_collision(sx, sy, gx, gy, rr, obstacle_kd_tree): x = sx y = sy dx = gx - sx @@ -103,41 +105,41 @@ def is_collision(sx, sy, gx, gy, rr, okdtree): return True D = rr - nstep = round(d / D) + n_step = round(d / D) - for i in range(nstep): - idxs, dist = okdtree.search(np.array([x, y]).reshape(2, 1)) + for i in range(n_step): + _, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1)) if dist[0] <= rr: return True # collision x += D * math.cos(yaw) y += D * math.sin(yaw) # goal point check - idxs, dist = okdtree.search(np.array([gx, gy]).reshape(2, 1)) + _, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1)) if dist[0] <= rr: return True # collision return False # OK -def generate_roadmap(sample_x, sample_y, rr, obkdtree): +def generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree): """ Road map generation sample_x: [m] x positions of sampled points sample_y: [m] y positions of sampled points rr: Robot Radius[m] - obkdtree: KDTree object of obstacles + obstacle_kd_tree: KDTree object of obstacles """ road_map = [] - nsample = len(sample_x) - skdtree = KDTree(np.vstack((sample_x, sample_y)).T) + n_sample = len(sample_x) + sample_kd_tree = KDTree(np.vstack((sample_x, sample_y)).T) - for (i, ix, iy) in zip(range(nsample), sample_x, sample_y): + for (i, ix, iy) in zip(range(n_sample), sample_x, sample_y): - index, dists = skdtree.search( - np.array([ix, iy]).reshape(2, 1), k=nsample) + index, dists = sample_kd_tree.search( + np.array([ix, iy]).reshape(2, 1), k=n_sample) inds = index[0] edge_id = [] # print(index) @@ -146,7 +148,7 @@ def generate_roadmap(sample_x, sample_y, rr, obkdtree): nx = sample_x[inds[ii]] ny = sample_y[inds[ii]] - if not is_collision(ix, iy, nx, ny, rr, obkdtree): + if not is_collision(ix, iy, nx, ny, rr, obstacle_kd_tree): edge_id.append(inds[ii]) if len(edge_id) >= N_KNN: @@ -159,10 +161,10 @@ def generate_roadmap(sample_x, sample_y, rr, obkdtree): return road_map -def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): +def dijkstra_planning(sx, sy, gx, gy, road_map, sample_x, sample_y): """ - sx: start x position [m] - sy: start y position [m] + s_x: start x position [m] + s_y: start y position [m] gx: goal x position [m] gy: goal y position [m] ox: x position list of Obstacles [m] @@ -175,41 +177,42 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): @return: Two lists of path coordinates ([x1, x2, ...], [y1, y2, ...]), empty list when no path was found """ - nstart = Node(sx, sy, 0.0, -1) - ngoal = Node(gx, gy, 0.0, -1) + start_node = Node(sx, sy, 0.0, -1) + goal_node = Node(gx, gy, 0.0, -1) - openset, closedset = dict(), dict() - openset[len(road_map) - 2] = nstart + open_set, closed_set = dict(), dict() + open_set[len(road_map) - 2] = start_node path_found = True while True: - if not openset: + if not open_set: print("Cannot find path") path_found = False break - c_id = min(openset, key=lambda o: openset[o].cost) - current = openset[c_id] + c_id = min(open_set, key=lambda o: open_set[o].cost) + current = open_set[c_id] # show graph - if show_animation and len(closedset.keys()) % 2 == 0: + if show_animation and len(closed_set.keys()) % 2 == 0: # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(current.x, current.y, "xg") plt.pause(0.001) if c_id == (len(road_map) - 1): print("goal is found!") - ngoal.pind = current.pind - ngoal.cost = current.cost + goal_node.parent_index = current.parent_index + goal_node.cost = current.cost break # Remove the item from the open set - del openset[c_id] + del open_set[c_id] # Add it to the closed set - closedset[c_id] = current + closed_set[c_id] = current # expand search grid based on motion model for i in range(len(road_map[c_id])): @@ -220,27 +223,27 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y): node = Node(sample_x[n_id], sample_y[n_id], current.cost + d, c_id) - if n_id in closedset: + if n_id in closed_set: continue # Otherwise if it is already in the open set - if n_id in openset: - if openset[n_id].cost > node.cost: - openset[n_id].cost = node.cost - openset[n_id].pind = c_id + if n_id in open_set: + if open_set[n_id].cost > node.cost: + open_set[n_id].cost = node.cost + open_set[n_id].parent_index = c_id else: - openset[n_id] = node + open_set[n_id] = node if path_found is False: return [], [] # generate final course - rx, ry = [ngoal.x], [ngoal.y] - pind = ngoal.pind - while pind != -1: - n = closedset[pind] + rx, ry = [goal_node.x], [goal_node.y] + parent_index = goal_node.parent_index + while parent_index != -1: + n = closed_set[parent_index] rx.append(n.x) ry.append(n.y) - pind = n.pind + parent_index = n.parent_index return rx, ry @@ -255,19 +258,19 @@ def plot_road_map(road_map, sample_x, sample_y): # pragma: no cover [sample_y[i], sample_y[ind]], "-k") -def sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree): - maxx = max(ox) - maxy = max(oy) - minx = min(ox) - miny = min(oy) +def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree): + max_x = max(ox) + max_y = max(oy) + min_x = min(ox) + min_y = min(oy) sample_x, sample_y = [], [] while len(sample_x) <= N_SAMPLE: - tx = (random.random() * (maxx - minx)) + minx - ty = (random.random() * (maxy - miny)) + miny + tx = (random.random() * (max_x - min_x)) + min_x + ty = (random.random() * (max_y - min_y)) + min_y - index, dist = obkdtree.search(np.array([tx, ty]).reshape(2, 1)) + index, dist = obstacle_kd_tree.search(np.array([tx, ty]).reshape(2, 1)) if dist[0] >= rr: sample_x.append(tx) @@ -320,12 +323,13 @@ def main(): plt.grid(True) plt.axis("equal") - rx, ry = PRM_planning(sx, sy, gx, gy, ox, oy, robot_size) + rx, ry = prm_planning(sx, sy, gx, gy, ox, oy, robot_size) assert rx, 'Cannot found path' if show_animation: plt.plot(rx, ry, "-r") + plt.pause(0.001) plt.show() diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index 5538731dcd..8ba11a23d2 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -71,9 +71,9 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_ quintic polynomial planner input - sx: start x position [m] - sy: start y position [m] - syaw: start yaw angle [rad] + s_x: start x position [m] + s_y: start y position [m] + s_yaw: start yaw angle [rad] sa: start accel [m/ss] gx: goal x position [m] gy: goal y position [m] diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py index 7bf5425ebf..54d15ea9fb 100644 --- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py +++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py @@ -35,8 +35,8 @@ def search(self, sx, sy, gx, gy, node_x, node_y, edge_ids_list): """ Search shortest path - sx: start x positions [m] - sy: start y positions [m] + s_x: start x positions [m] + s_y: start y positions [m] gx: goal x position [m] gx: goal x position [m] node_x: node x position diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py index 9bb68c2821..9335dbe6a1 100644 --- a/SLAM/GraphBasedSLAM/graph_based_slam.py +++ b/SLAM/GraphBasedSLAM/graph_based_slam.py @@ -6,20 +6,22 @@ Ref -[A Tutorial on Graph-Based SLAM](http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf) +[A Tutorial on Graph-Based SLAM] +(http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf) """ -import numpy as np -import math import copy import itertools -import matplotlib.pyplot as plt +import math +import matplotlib.pyplot as plt +import numpy as np +from scipy.spatial.transform import Rotation as Rot # Simulation parameter -Qsim = np.diag([0.2, np.deg2rad(1.0)])**2 -Rsim = np.diag([0.1, np.deg2rad(10.0)])**2 +Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2 +R_sim = np.diag([0.1, np.deg2rad(10.0)]) ** 2 DT = 2.0 # time tick [s] SIM_TIME = 100.0 # simulation time [s] @@ -33,11 +35,11 @@ MAX_ITR = 20 # Maximum iteration -show_graph_dtime = 20.0 # [s] +show_graph_d_time = 20.0 # [s] show_animation = True -class Edge(): +class Edge: def __init__(self): self.e = np.zeros((3, 1)) @@ -52,26 +54,21 @@ def __init__(self): self.id2 = 0 -def cal_observation_sigma(d): - +def cal_observation_sigma(): sigma = np.zeros((3, 3)) - sigma[0, 0] = C_SIGMA1**2 - sigma[1, 1] = C_SIGMA2**2 - sigma[2, 2] = C_SIGMA3**2 + sigma[0, 0] = C_SIGMA1 ** 2 + sigma[1, 1] = C_SIGMA2 ** 2 + sigma[2, 2] = C_SIGMA3 ** 2 return sigma def calc_rotational_matrix(angle): - - Rt = np.array([[math.cos(angle), -math.sin(angle), 0], - [math.sin(angle), math.cos(angle), 0], - [0, 0, 1.0]]) - return Rt + return Rot.from_euler('z', angle).as_matrix() def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1, - angle1, phi1, d2, angle2, phi2, t1, t2): + angle1, d2, angle2, t1, t2): edge = Edge() tangle1 = pi_2_pi(yaw1 + angle1) @@ -88,8 +85,8 @@ def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1, Rt1 = calc_rotational_matrix(tangle1) Rt2 = calc_rotational_matrix(tangle2) - sig1 = cal_observation_sigma(d1) - sig2 = cal_observation_sigma(d2) + sig1 = cal_observation_sigma() + sig2 = cal_observation_sigma() edge.omega = np.linalg.inv(Rt1 @ sig1 @ Rt1.T + Rt2 @ sig2 @ Rt2.T) @@ -101,34 +98,33 @@ def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1, return edge -def calc_edges(xlist, zlist): - +def calc_edges(x_list, z_list): edges = [] cost = 0.0 - zids = list(itertools.combinations(range(len(zlist)), 2)) + z_ids = list(itertools.combinations(range(len(z_list)), 2)) - for (t1, t2) in zids: - x1, y1, yaw1 = xlist[0, t1], xlist[1, t1], xlist[2, t1] - x2, y2, yaw2 = xlist[0, t2], xlist[1, t2], xlist[2, t2] + for (t1, t2) in z_ids: + x1, y1, yaw1 = x_list[0, t1], x_list[1, t1], x_list[2, t1] + x2, y2, yaw2 = x_list[0, t2], x_list[1, t2], x_list[2, t2] - if zlist[t1] is None or zlist[t2] is None: + if z_list[t1] is None or z_list[t2] is None: continue # No observation - for iz1 in range(len(zlist[t1][:, 0])): - for iz2 in range(len(zlist[t2][:, 0])): - if zlist[t1][iz1, 3] == zlist[t2][iz2, 3]: - d1 = zlist[t1][iz1, 0] - angle1, phi1 = zlist[t1][iz1, 1], zlist[t1][iz1, 2] - d2 = zlist[t2][iz2, 0] - angle2, phi2 = zlist[t2][iz2, 1], zlist[t2][iz2, 2] + for iz1 in range(len(z_list[t1][:, 0])): + for iz2 in range(len(z_list[t2][:, 0])): + if z_list[t1][iz1, 3] == z_list[t2][iz2, 3]: + d1 = z_list[t1][iz1, 0] + angle1, phi1 = z_list[t1][iz1, 1], z_list[t1][iz1, 2] + d2 = z_list[t2][iz2, 0] + angle2, phi2 = z_list[t2][iz2, 1], z_list[t2][iz2, 2] edge = calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1, - angle1, phi1, d2, angle2, phi2, t1, t2) + angle1, d2, angle2, t1, t2) edges.append(edge) - cost += (edge.e.T @ (edge.omega) @ edge.e)[0, 0] + cost += (edge.e.T @ edge.omega @ edge.e)[0, 0] - print("cost:", cost, ",nedge:", len(edges)) + print("cost:", cost, ",n_edge:", len(edges)) return edges @@ -147,7 +143,6 @@ def calc_jacobian(edge): def fill_H_and_b(H, b, edge): - A, B = calc_jacobian(edge) id1 = edge.id1 * STATE_SIZE @@ -167,14 +162,14 @@ def fill_H_and_b(H, b, edge): def graph_based_slam(x_init, hz): print("start graph based slam") - zlist = copy.deepcopy(hz) + z_list = copy.deepcopy(hz) x_opt = copy.deepcopy(x_init) nt = x_opt.shape[1] n = nt * STATE_SIZE for itr in range(MAX_ITR): - edges = calc_edges(x_opt, zlist) + edges = calc_edges(x_opt, z_list) H = np.zeros((n, n)) b = np.zeros((n, 1)) @@ -200,13 +195,12 @@ def graph_based_slam(x_init, hz): def calc_input(): v = 1.0 # [m/s] - yawrate = 0.1 # [rad/s] - u = np.array([[v, yawrate]]).T + yaw_rate = 0.1 # [rad/s] + u = np.array([[v, yaw_rate]]).T return u def observation(xTrue, xd, u, RFID): - xTrue = motion_model(xTrue, u) # add noise to gps x-y @@ -220,16 +214,16 @@ def observation(xTrue, xd, u, RFID): angle = pi_2_pi(math.atan2(dy, dx)) - xTrue[2, 0] phi = pi_2_pi(math.atan2(dy, dx)) if d <= MAX_RANGE: - dn = d + np.random.randn() * Qsim[0, 0] # add noise - angle_noise = np.random.randn() * Qsim[1, 1] + dn = d + np.random.randn() * Q_sim[0, 0] # add noise + angle_noise = np.random.randn() * Q_sim[1, 1] angle += angle_noise phi += angle_noise zi = np.array([dn, angle, phi, i]) z = np.vstack((z, zi)) # add noise to input - ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] - ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] + ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ud = np.array([[ud1, ud2]]).T xd = motion_model(xd, ud) @@ -238,7 +232,6 @@ def observation(xTrue, xd, u, RFID): def motion_model(x, u): - F = np.array([[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]]) @@ -277,7 +270,7 @@ def main(): hxTrue = [] hxDR = [] hz = [] - dtime = 0.0 + d_time = 0.0 init = False while SIM_TIME >= time: @@ -290,22 +283,23 @@ def main(): hxTrue = np.hstack((hxTrue, xTrue)) time += DT - dtime += DT + d_time += DT u = calc_input() xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) hz.append(z) - if dtime >= show_graph_dtime: + if d_time >= show_graph_d_time: x_opt = graph_based_slam(hxDR, hz) - dtime = 0.0 + d_time = 0.0 if show_animation: # pragma: no cover plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") plt.plot(hxTrue[0, :].flatten(), From a36ddb4cfff4f1f7677175c93e8d7935bf5fbbd4 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 8 Jun 2020 17:16:56 +0900 Subject: [PATCH 226/909] use scipy kd-tree directly (#337) --- PathPlanning/HybridAStar/car.py | 4 +- PathPlanning/HybridAStar/hybrid_a_star.py | 42 +---------- .../probabilistic_road_map.py | 71 ++++--------------- PathPlanning/VoronoiRoadMap/kdtree.py | 49 ------------- .../VoronoiRoadMap/voronoi_road_map.py | 29 ++++---- 5 files changed, 31 insertions(+), 164 deletions(-) delete mode 100644 PathPlanning/VoronoiRoadMap/kdtree.py diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py index 9b956a0877..cc7529b9c7 100644 --- a/PathPlanning/HybridAStar/car.py +++ b/PathPlanning/HybridAStar/car.py @@ -31,7 +31,7 @@ def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree): cx = i_x + W_BUBBLE_DIST * cos(i_yaw) cy = i_y + W_BUBBLE_DIST * sin(i_yaw) - ids = kd_tree.search_in_distance([cx, cy], W_BUBBLE_R) + ids = kd_tree.query_ball_point([cx, cy], W_BUBBLE_R) if not ids: continue @@ -71,7 +71,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): def plot_car(x, y, yaw): car_color = '-k' c, s = cos(yaw), sin(yaw) - rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2] + rot = Rot.from_euler('z', -yaw).as_matrix()[0:2, 0:2] car_outline_x, car_outline_y = [], [] for rx, ry in zip(VRX, VRY): converted_xy = np.stack([rx, ry]).T @ rot diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index ff9b3fe43b..45d6e11b18 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -13,7 +13,7 @@ import matplotlib.pyplot as plt import numpy as np -import scipy.spatial +from scipy.spatial import cKDTree sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../ReedsSheppPath") @@ -67,44 +67,6 @@ def __init__(self, x_list, y_list, yaw_list, direction_list, cost): self.cost = cost -class KDTree: - """ - Nearest neighbor search class with KDTree - """ - - def __init__(self, data): - # store kd-tree - self.tree = scipy.spatial.cKDTree(data) - - def search(self, inp, k=1): - """ - Search NN - inp: input data, single frame or multi frame - """ - - if len(inp.shape) >= 2: # multi input - index = [] - dist = [] - - for i in inp.T: - i_dist, i_index = self.tree.query(i, k=k) - index.append(i_index) - dist.append(i_dist) - - return index, dist - - dist, index = self.tree.query(inp, k=k) - return index, dist - - def search_in_distance(self, inp, r): - """ - find points with in a distance r - """ - - index = self.tree.query_ball_point(inp, r) - return index - - class Config: def __init__(self, ox, oy, xy_resolution, yaw_resolution): @@ -297,7 +259,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution): start[2], goal[2] = rs.pi_2_pi(start[2]), rs.pi_2_pi(goal[2]) tox, toy = ox[:], oy[:] - obstacle_kd_tree = KDTree(np.vstack((tox, toy)).T) + obstacle_kd_tree = cKDTree(np.vstack((tox, toy)).T) config = Config(tox, toy, xy_resolution, yaw_resolution) diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 790db074c0..8681420e2d 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -9,8 +9,8 @@ import random import math import numpy as np -import scipy.spatial import matplotlib.pyplot as plt +from scipy.spatial import cKDTree # parameter N_SAMPLE = 500 # number of sample_points @@ -36,49 +36,9 @@ def __str__(self): str(self.cost) + "," + str(self.parent_index) -class KDTree: - """ - Nearest neighbor search class with KDTree - """ - - def __init__(self, data): - # store kd-tree - self.tree = scipy.spatial.cKDTree(data) - - def search(self, inp, k=1): - """ - Search NN - - inp: input data, single frame or multi frame - - """ - - if len(inp.shape) >= 2: # multi input - index = [] - dist = [] - - for i in inp.T: - i_dist, i_index = self.tree.query(i, k=k) - index.append(i_index) - dist.append(i_dist) - - return index, dist - - dist, index = self.tree.query(inp, k=k) - return index, dist - - def search_in_distance(self, inp, r): - """ - find points with in a distance r - """ - - index = self.tree.query_ball_point(inp, r) - return index - - def prm_planning(sx, sy, gx, gy, ox, oy, rr): - obstacle_kd_tree = KDTree(np.vstack((ox, oy)).T) + obstacle_kd_tree = cKDTree(np.vstack((ox, oy)).T) sample_x, sample_y = sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree) @@ -108,15 +68,15 @@ def is_collision(sx, sy, gx, gy, rr, obstacle_kd_tree): n_step = round(d / D) for i in range(n_step): - _, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1)) - if dist[0] <= rr: + dist, _ = obstacle_kd_tree.query([x, y]) + if dist <= rr: return True # collision x += D * math.cos(yaw) y += D * math.sin(yaw) # goal point check - _, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1)) - if dist[0] <= rr: + dist, _ = obstacle_kd_tree.query([gx, gy]) + if dist <= rr: return True # collision return False # OK @@ -134,22 +94,19 @@ def generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree): road_map = [] n_sample = len(sample_x) - sample_kd_tree = KDTree(np.vstack((sample_x, sample_y)).T) + sample_kd_tree = cKDTree(np.vstack((sample_x, sample_y)).T) for (i, ix, iy) in zip(range(n_sample), sample_x, sample_y): - index, dists = sample_kd_tree.search( - np.array([ix, iy]).reshape(2, 1), k=n_sample) - inds = index[0] + dists, indexes = sample_kd_tree.query([ix, iy], k=n_sample) edge_id = [] - # print(index) - for ii in range(1, len(inds)): - nx = sample_x[inds[ii]] - ny = sample_y[inds[ii]] + for ii in range(1, len(indexes)): + nx = sample_x[indexes[ii]] + ny = sample_y[indexes[ii]] if not is_collision(ix, iy, nx, ny, rr, obstacle_kd_tree): - edge_id.append(inds[ii]) + edge_id.append(indexes[ii]) if len(edge_id) >= N_KNN: break @@ -270,9 +227,9 @@ def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree): tx = (random.random() * (max_x - min_x)) + min_x ty = (random.random() * (max_y - min_y)) + min_y - index, dist = obstacle_kd_tree.search(np.array([tx, ty]).reshape(2, 1)) + dist, index = obstacle_kd_tree.query([tx, ty]) - if dist[0] >= rr: + if dist >= rr: sample_x.append(tx) sample_y.append(ty) diff --git a/PathPlanning/VoronoiRoadMap/kdtree.py b/PathPlanning/VoronoiRoadMap/kdtree.py deleted file mode 100644 index d3dc01b1f6..0000000000 --- a/PathPlanning/VoronoiRoadMap/kdtree.py +++ /dev/null @@ -1,49 +0,0 @@ -""" - -Kd tree Search library - -author: Atsushi Sakai (@Atsushi_twi) - -""" - -import scipy.spatial - - -class KDTree: - """ - Nearest neighbor search class with KDTree - """ - - def __init__(self, data): - # store kd-tree - self.tree = scipy.spatial.cKDTree(data) - - def search(self, inp, k=1): - """ - Search NN - - inp: input data, single frame or multi frame - - """ - - if len(inp.shape) >= 2: # multi input - index = [] - dist = [] - - for i in inp.T: - idist, iindex = self.tree.query(i, k=k) - index.append(iindex) - dist.append(idist) - - return index, dist - - dist, index = self.tree.query(inp, k=k) - return index, dist - - def search_in_distance(self, inp, r): - """ - find points with in a distance r - """ - - index = self.tree.query_ball_point(inp, r) - return index diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py index d060c8e46c..d184230fd5 100644 --- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py +++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py @@ -8,10 +8,9 @@ import math import numpy as np -import scipy.spatial import matplotlib.pyplot as plt from dijkstra_search import DijkstraSearch -from kdtree import KDTree +from scipy.spatial import cKDTree, Voronoi show_animation = True @@ -24,7 +23,7 @@ def __init__(self): self.MAX_EDGE_LEN = 30.0 # [m] Maximum edge length def planning(self, sx, sy, gx, gy, ox, oy, robot_radius): - obstacle_tree = KDTree(np.vstack((ox, oy)).T) + obstacle_tree = cKDTree(np.vstack((ox, oy)).T) sample_x, sample_y = self.voronoi_sampling(sx, sy, gx, gy, ox, oy) if show_animation: # pragma: no cover @@ -53,15 +52,15 @@ def is_collision(self, sx, sy, gx, gy, rr, obstacle_kd_tree): n_step = round(d / D) for i in range(n_step): - ids, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1)) - if dist[0] <= rr: + dist, _ = obstacle_kd_tree.query([x, y]) + if dist <= rr: return True # collision x += D * math.cos(yaw) y += D * math.sin(yaw) # goal point check - ids, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1)) - if dist[0] <= rr: + dist, _ = obstacle_kd_tree.query([gx, gy]) + if dist <= rr: return True # collision return False # OK @@ -78,22 +77,20 @@ def generate_road_map_info(self, node_x, node_y, rr, obstacle_tree): road_map = [] n_sample = len(node_x) - node_tree = KDTree(np.vstack((node_x, node_y)).T) + node_tree = cKDTree(np.vstack((node_x, node_y)).T) for (i, ix, iy) in zip(range(n_sample), node_x, node_y): - index, dists = node_tree.search( - np.array([ix, iy]).reshape(2, 1), k=n_sample) + dists, indexes = node_tree.query([ix, iy], k=n_sample) - inds = index[0] edge_id = [] - for ii in range(1, len(inds)): - nx = node_x[inds[ii]] - ny = node_y[inds[ii]] + for ii in range(1, len(indexes)): + nx = node_x[indexes[ii]] + ny = node_y[indexes[ii]] if not self.is_collision(ix, iy, nx, ny, rr, obstacle_tree): - edge_id.append(inds[ii]) + edge_id.append(indexes[ii]) if len(edge_id) >= self.N_KNN: break @@ -119,7 +116,7 @@ def voronoi_sampling(sx, sy, gx, gy, ox, oy): oxy = np.vstack((ox, oy)).T # generate voronoi point - vor = scipy.spatial.Voronoi(oxy) + vor = Voronoi(oxy) sample_x = [ix for [ix, _] in vor.vertices] sample_y = [iy for [_, iy] in vor.vertices] From fa1585d880ea771abd4215077eb699d194486eb4 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 8 Jun 2020 21:43:37 +0900 Subject: [PATCH 227/909] fix scanning error (#339) --- .../random_forward_kinematics.py | 16 +- .../random_inverse_kinematics.py | 12 +- Bipedal/bipedal_planner/bipedal_planner.py | 3 +- .../inverted_pendulum_mpc_control.py | 28 ++- .../lidar_to_grid_map/lidar_to_grid_map.py | 3 +- .../batch_informed_rrtstar.py | 158 ++++++++------ .../bidirectional_a_star.py | 135 ++++++------ .../bidirectional_breadth_first_search.py | 142 ++++++------ .../VoronoiRoadMap/dijkstra_search.py | 1 + PathTracking/cgmres_nmpc/cgmres_nmpc.py | 206 ++++++++++-------- ..._grid_based_sweep_coverage_path_planner.py | 175 ++++++++------- 11 files changed, 487 insertions(+), 392 deletions(-) diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py index b3426e53e8..f9caace300 100644 --- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py @@ -11,10 +11,9 @@ def random_val(min_val, max_val): return min_val + random.random() * (max_val - min_val) -if __name__ == "__main__": +def main(): print("Start solving Forward Kinematics 10 times") - - # init NLinkArm with Denavit-Hartenberg parameters of PR2 + # init NLinkArm with Denavit-Hartenberg parameters of PR2 n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.], [math.pi / 2, math.pi / 2, 0., 0.], [0., -math.pi / 2, 0., .4], @@ -22,10 +21,13 @@ def random_val(min_val, max_val): [0., -math.pi / 2, 0., .321], [0., math.pi / 2, 0., 0.], [0., 0., 0., 0.]]) - # execute FK 10 times - for i in range(10): + for _ in range(10): n_link_arm.set_joint_angles( - [random_val(-1, 1) for j in range(len(n_link_arm.link_list))]) + [random_val(-1, 1) for _ in range(len(n_link_arm.link_list))]) - ee_pose = n_link_arm.forward_kinematics(plot=True) + n_link_arm.forward_kinematics(plot=True) + + +if __name__ == "__main__": + main() diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py index a3838a6a38..91f6f1bba0 100644 --- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py +++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py @@ -11,10 +11,9 @@ def random_val(min_val, max_val): return min_val + random.random() * (max_val - min_val) -if __name__ == "__main__": +def main(): print("Start solving Inverse Kinematics 10 times") - - # init NLinkArm with Denavit-Hartenberg parameters of PR2 + # init NLinkArm with Denavit-Hartenberg parameters of PR2 n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.], [math.pi / 2, math.pi / 2, 0., 0.], [0., -math.pi / 2, 0., .4], @@ -22,12 +21,15 @@ def random_val(min_val, max_val): [0., -math.pi / 2, 0., .321], [0., math.pi / 2, 0., 0.], [0., 0., 0., 0.]]) - # execute IK 10 times - for i in range(10): + for _ in range(10): n_link_arm.inverse_kinematics([random_val(-0.5, 0.5), random_val(-0.5, 0.5), random_val(-0.5, 0.5), random_val(-0.5, 0.5), random_val(-0.5, 0.5), random_val(-0.5, 0.5)], plot=True) + + +if __name__ == "__main__": + main() diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py index 60c03024bf..01df63034f 100644 --- a/Bipedal/bipedal_planner/bipedal_planner.py +++ b/Bipedal/bipedal_planner/bipedal_planner.py @@ -46,9 +46,8 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False): print("No footsteps") return - com_trajectory_for_plot, ax = None, None - # set up plotter + com_trajectory_for_plot, ax = None, None if plot: fig = plt.figure() ax = Axes3D(fig) diff --git a/InvertedPendulumCart/inverted_pendulum_mpc_control.py b/InvertedPendulumCart/inverted_pendulum_mpc_control.py index caf0c23e77..e7b3077740 100644 --- a/InvertedPendulumCart/inverted_pendulum_mpc_control.py +++ b/InvertedPendulumCart/inverted_pendulum_mpc_control.py @@ -3,11 +3,12 @@ author: Atsushi Sakai """ -import matplotlib.pyplot as plt -import numpy as np import math import time + import cvxpy +import matplotlib.pyplot as plt +import numpy as np # Model parameters @@ -39,10 +40,11 @@ def main(): for i in range(50): # calc control input - optimized_x, optimized_delta_x, optimized_theta, optimized_delta_theta, optimized_input = mpc_control(x) + opt_x, opt_delta_x, opt_theta, opt_delta_theta, opt_input = \ + mpc_control(x) # get input - u = optimized_input[0] + u = opt_input[0] # simulate inverted pendulum cart x = simulation(x, u) @@ -86,17 +88,19 @@ def mpc_control(x0): print("calc time:{0} [sec]".format(elapsed_time)) if prob.status == cvxpy.OPTIMAL: - ox = get_nparray_from_matrix(x.value[0, :]) - dx = get_nparray_from_matrix(x.value[1, :]) - theta = get_nparray_from_matrix(x.value[2, :]) - dtheta = get_nparray_from_matrix(x.value[3, :]) + ox = get_numpy_array_from_matrix(x.value[0, :]) + dx = get_numpy_array_from_matrix(x.value[1, :]) + theta = get_numpy_array_from_matrix(x.value[2, :]) + d_theta = get_numpy_array_from_matrix(x.value[3, :]) - ou = get_nparray_from_matrix(u.value[0, :]) + ou = get_numpy_array_from_matrix(u.value[0, :]) + else: + ox, dx, theta, d_theta, ou = None, None, None, None, None - return ox, dx, theta, dtheta, ou + return ox, dx, theta, d_theta, ou -def get_nparray_from_matrix(x): +def get_numpy_array_from_matrix(x): """ get build-in list from matrix """ @@ -133,7 +137,7 @@ def plot_cart(xt, theta): radius = 0.1 cx = np.array([-cart_w / 2.0, cart_w / 2.0, cart_w / - 2.0, -cart_w / 2.0, -cart_w / 2.0]) + 2.0, -cart_w / 2.0, -cart_w / 2.0]) cy = np.array([0.0, 0.0, cart_h, cart_h, 0.0]) cy += radius * 2.0 diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py index 911a0bf0ea..57278d4a47 100644 --- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py +++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py @@ -19,7 +19,8 @@ def file_read(f): """ Reading LIDAR laser beams (angles and corresponding distance data) """ - measures = [line.split(",") for line in open(f)] + with open(f) as data: + measures = [line.split(",") for line in data] angles = [] distances = [] for measure in measures: diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py index 227b3f9d25..b5eabbb39b 100644 --- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py +++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py @@ -27,7 +27,8 @@ class RTree(object): # Class to represent the explicit tree created # while sampling through the state space - def __init__(self, start=None, lowerLimit=None, upperLimit=None, resolution=1.0): + def __init__(self, start=None, lowerLimit=None, upperLimit=None, + resolution=1.0): if upperLimit is None: upperLimit = [10, 10] @@ -96,10 +97,11 @@ def grid_coordinate_to_node_id(self, coord): def real_world_to_node_id(self, real_coord): # first convert the given coordinates to grid space and then # convert the grid space coordinates to a unique node id - return self.grid_coordinate_to_node_id(self.real_coords_to_grid_coord(real_coord)) + return self.grid_coordinate_to_node_id( + self.real_coords_to_grid_coord(real_coord)) def grid_coord_to_real_world_coord(self, coord): - # This function smaps a grid coordinate in discrete space + # This function maps a grid coordinate in discrete space # to a configuration in the full configuration space config = [0] * self.dimension for i in range(0, len(coord)): @@ -126,7 +128,8 @@ def node_id_to_grid_coord(self, node_id): def node_id_to_real_world_coord(self, nid): # This function maps a node in discrete space to a configuration # in the full configuration space - return self.grid_coord_to_real_world_coord(self.node_id_to_grid_coord(nid)) + return self.grid_coord_to_real_world_coord( + self.node_id_to_grid_coord(nid)) class BITStar(object): @@ -137,9 +140,9 @@ def __init__(self, start, goal, self.start = start self.goal = goal - self.minrand = randArea[0] - self.maxrand = randArea[1] - self.maxIter = maxIter + self.min_rand = randArea[0] + self.max_rand = randArea[1] + self.max_iIter = maxIter self.obstacleList = obstacleList self.startId = None self.goalId = None @@ -186,18 +189,19 @@ def setup_planning(self): [(self.start[1] + self.goal[1]) / 2.0], [0]]) a1 = np.array([[(self.goal[0] - self.start[0]) / cMin], [(self.goal[1] - self.start[1]) / cMin], [0]]) - etheta = math.atan2(a1[1], a1[0]) - # first column of idenity matrix transposed + eTheta = math.atan2(a1[1], a1[0]) + # first column of identity matrix transposed id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3) M = np.dot(a1, id1_t) U, S, Vh = np.linalg.svd(M, True, True) C = np.dot(np.dot(U, np.diag( - [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), Vh) + [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), + Vh) self.samples.update(self.informed_sample( 200, cBest, cMin, xCenter, C)) - return etheta, cMin, xCenter, C, cBest + return eTheta, cMin, xCenter, C, cBest def setup_sample(self, iterations, foundGoal, cMin, xCenter, C, cBest): @@ -228,18 +232,19 @@ def setup_sample(self, iterations, foundGoal, cMin, xCenter, C, cBest): def plan(self, animation=True): - etheta, cMin, xCenter, C, cBest = self.setup_planning() + eTheta, cMin, xCenter, C, cBest = self.setup_planning() iterations = 0 foundGoal = False # run until done - while iterations < self.maxIter: + while iterations < self.max_iIter: cBest = self.setup_sample(iterations, foundGoal, cMin, xCenter, C, cBest) # expand the best vertices until an edge is better than the vertex # this is done because the vertex cost represents the lower bound # on the edge cost - while self.best_vertex_queue_value() <= self.best_edge_queue_value(): + while self.best_vertex_queue_value() <= \ + self.best_edge_queue_value(): self.expand_vertex(self.best_in_vertex_queue()) # add the best edge to the tree @@ -247,11 +252,17 @@ def plan(self, animation=True): self.edge_queue.remove(bestEdge) # Check if this can improve the current solution - estimatedCostOfVertex = self.g_scores[bestEdge[0]] + self.compute_distance_cost( - bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(bestEdge[1], self.goalId) - estimatedCostOfEdge = self.compute_distance_cost(self.startId, bestEdge[0]) + self.compute_heuristic_cost( - bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(bestEdge[1], self.goalId) - actualCostOfEdge = self.g_scores[bestEdge[0]] + self.compute_distance_cost(bestEdge[0], bestEdge[1]) + estimatedCostOfVertex = self.g_scores[bestEdge[ + 0]] + self.compute_distance_cost( + bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost( + bestEdge[1], self.goalId) + estimatedCostOfEdge = self.compute_distance_cost( + self.startId, bestEdge[0]) + self.compute_heuristic_cost( + bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost( + bestEdge[1], self.goalId) + actualCostOfEdge = self.g_scores[ + bestEdge[0]] + self.compute_distance_cost( + bestEdge[0], bestEdge[1]) f1 = estimatedCostOfVertex < self.g_scores[self.goalId] f2 = estimatedCostOfEdge < self.g_scores[self.goalId] @@ -277,10 +288,12 @@ def plan(self, animation=True): try: del self.samples[bestEdge[1]] except KeyError: + # invalid sample key pass eid = self.tree.add_vertex(nextCoord) self.vertex_queue.append(eid) - if eid == self.goalId or bestEdge[0] == self.goalId or bestEdge[1] == self.goalId: + if eid == self.goalId or bestEdge[0] == self.goalId or \ + bestEdge[1] == self.goalId: print("Goal found") foundGoal = True @@ -288,14 +301,18 @@ def plan(self, animation=True): g_score = self.compute_distance_cost( bestEdge[0], bestEdge[1]) - self.g_scores[bestEdge[1]] = g_score + self.g_scores[bestEdge[0]] - self.f_scores[bestEdge[1]] = g_score + self.compute_heuristic_cost(bestEdge[1], self.goalId) + self.g_scores[bestEdge[1]] = g_score + self.g_scores[ + bestEdge[0]] + self.f_scores[ + bestEdge[1]] = g_score + self.compute_heuristic_cost( + bestEdge[1], self.goalId) self.update_graph() # visualize new edge if animation: self.draw_graph(xCenter=xCenter, cBest=cBest, - cMin=cMin, etheta=etheta, samples=self.samples.values(), + cMin=cMin, eTheta=eTheta, + samples=self.samples.values(), start=firstCoord, end=secondCoord) self.remove_queue(lastEdge, bestEdge) @@ -330,7 +347,8 @@ def remove_queue(self, lastEdge, bestEdge): for edge in self.edge_queue: if edge[1] == bestEdge[1]: dist_cost = self.compute_distance_cost(edge[1], bestEdge[1]) - if self.g_scores[edge[1]] + dist_cost >= self.g_scores[self.goalId]: + if self.g_scores[edge[1]] + dist_cost >= \ + self.g_scores[self.goalId]: if (lastEdge, bestEdge[1]) in self.edge_queue: self.edge_queue.remove( (lastEdge, bestEdge[1])) @@ -338,8 +356,9 @@ def remove_queue(self, lastEdge, bestEdge): def connect(self, start, end): # A function which attempts to extend from a start coordinates # to goal coordinates - steps = int(self.compute_distance_cost(self.tree.real_world_to_node_id( - start), self.tree.real_world_to_node_id(end)) * 10) + steps = int(self.compute_distance_cost( + self.tree.real_world_to_node_id(start), + self.tree.real_world_to_node_id(end)) * 10) x = np.linspace(start[0], end[0], num=steps) y = np.linspace(start[1], end[1], num=steps) for i in range(len(x)): @@ -409,8 +428,8 @@ def sample_unit_ball(): return np.array([[sample[0]], [sample[1]], [0]]) def sample_free_space(self): - rnd = [random.uniform(self.minrand, self.maxrand), - random.uniform(self.minrand, self.maxrand)] + rnd = [random.uniform(self.min_rand, self.max_rand), + random.uniform(self.min_rand, self.max_rand)] return rnd @@ -418,7 +437,8 @@ def best_vertex_queue_value(self): if len(self.vertex_queue) == 0: return float('inf') values = [self.g_scores[v] - + self.compute_heuristic_cost(v, self.goalId) for v in self.vertex_queue] + + self.compute_heuristic_cost(v, self.goalId) for v in + self.vertex_queue] values.sort() return values[0] @@ -427,21 +447,25 @@ def best_edge_queue_value(self): return float('inf') # return the best value in the queue by score g_tau[v] + c(v,x) + h(x) values = [self.g_scores[e[0]] + self.compute_distance_cost(e[0], e[1]) - + self.compute_heuristic_cost(e[1], self.goalId) for e in self.edge_queue] + + self.compute_heuristic_cost(e[1], self.goalId) for e in + self.edge_queue] values.sort(reverse=True) return values[0] def best_in_vertex_queue(self): # return the best value in the vertex queue - v_plus_vals = [(v, self.g_scores[v] + self.compute_heuristic_cost(v, self.goalId)) - for v in self.vertex_queue] - v_plus_vals = sorted(v_plus_vals, key=lambda x: x[1]) - # print(v_plus_vals) - return v_plus_vals[0][0] + v_plus_values = [(v, self.g_scores[v] + + self.compute_heuristic_cost(v, self.goalId)) + for v in self.vertex_queue] + v_plus_values = sorted(v_plus_values, key=lambda x: x[1]) + # print(v_plus_values) + return v_plus_values[0][0] def best_in_edge_queue(self): - e_and_values = [(e[0], e[1], self.g_scores[e[0]] + self.compute_distance_cost( - e[0], e[1]) + self.compute_heuristic_cost(e[1], self.goalId)) for e in self.edge_queue] + e_and_values = [ + (e[0], e[1], self.g_scores[e[0]] + self.compute_distance_cost( + e[0], e[1]) + self.compute_heuristic_cost(e[1], self.goalId)) + for e in self.edge_queue] e_and_values = sorted(e_and_values, key=lambda x: x[2]) return e_and_values[0][0], e_and_values[0][1] @@ -454,18 +478,19 @@ def expand_vertex(self, vid): # get the nearest value in vertex for every one in samples where difference is # less than the radius - neigbors = [] - for sid, scoord in self.samples.items(): - scoord = np.array(scoord) - if np.linalg.norm(scoord - currCoord, 2) <= self.r and sid != vid: - neigbors.append((sid, scoord)) + neighbors = [] + for sid, s_coord in self.samples.items(): + s_coord = np.array(s_coord) + if np.linalg.norm(s_coord - currCoord, 2) <= self.r and sid != vid: + neighbors.append((sid, s_coord)) # add an edge to the edge queue is the path might improve the solution - for neighbor in neigbors: + for neighbor in neighbors: sid = neighbor[0] h_cost = self.compute_heuristic_cost(sid, self.goalId) estimated_f_score = self.compute_distance_cost( - self.startId, vid) + h_cost + self.compute_distance_cost(vid, sid) + self.startId, vid) + h_cost + self.compute_distance_cost(vid, + sid) if estimated_f_score < self.g_scores[self.goalId]: self.edge_queue.append((vid, sid)) @@ -476,17 +501,21 @@ def add_vertex_to_edge_queue(self, vid, currCoord): if vid not in self.old_vertices: neighbors = [] for v, edges in self.tree.vertices.items(): - if v != vid and (v, vid) not in self.edge_queue and (vid, v) not in self.edge_queue: + if v != vid and (v, vid) not in self.edge_queue and \ + (vid, v) not in self.edge_queue: v_coord = self.tree.node_id_to_real_world_coord(v) if np.linalg.norm(currCoord - v_coord, 2) <= self.r: neighbors.append((vid, v_coord)) for neighbor in neighbors: sid = neighbor[0] - estimated_f_score = self.compute_distance_cost(self.startId, vid) + self.compute_distance_cost( + estimated_f_score = self.compute_distance_cost( + self.startId, vid) + self.compute_distance_cost( vid, sid) + self.compute_heuristic_cost(sid, self.goalId) if estimated_f_score < self.g_scores[self.goalId] and ( - self.g_scores[vid] + self.compute_distance_cost(vid, sid)) < self.g_scores[sid]: + self.g_scores[vid] + + self.compute_distance_cost(vid, sid)) < \ + self.g_scores[sid]: self.edge_queue.append((vid, sid)) def update_graph(self): @@ -511,37 +540,40 @@ def update_graph(self): # find a non visited successor to the current node successors = self.tree.vertices[currId] - for succesor in successors: - if succesor in closedSet: + for successor in successors: + if successor in closedSet: continue else: - # claculate tentative g score + # calculate tentative g score g_score = self.g_scores[currId] + \ - self.compute_distance_cost(currId, succesor) - if succesor not in openSet: + self.compute_distance_cost(currId, successor) + if successor not in openSet: # add the successor to open set - openSet.append(succesor) - elif g_score >= self.g_scores[succesor]: + openSet.append(successor) + elif g_score >= self.g_scores[successor]: continue # update g and f scores - self.g_scores[succesor] = g_score - self.f_scores[succesor] = g_score + self.compute_heuristic_cost(succesor, self.goalId) + self.g_scores[successor] = g_score + self.f_scores[ + successor] = g_score + self.compute_heuristic_cost( + successor, self.goalId) # store the parent and child - self.nodes[succesor] = currId + self.nodes[successor] = currId - def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, + def draw_graph(self, xCenter=None, cBest=None, cMin=None, eTheta=None, samples=None, start=None, end=None): plt.clf() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) for rnd in samples: if rnd is not None: plt.plot(rnd[0], rnd[1], "^k") if cBest != float('inf'): - self.plot_ellipse(xCenter, cBest, cMin, etheta) + self.plot_ellipse(xCenter, cBest, cMin, eTheta) if start is not None and end is not None: plt.plot([start[0], start[1]], [end[0], end[1]], "-g") @@ -556,11 +588,11 @@ def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, plt.pause(0.01) @staticmethod - def plot_ellipse(xCenter, cBest, cMin, etheta): # pragma: no cover + def plot_ellipse(xCenter, cBest, cMin, eTheta): # pragma: no cover a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0 b = cBest / 2.0 - angle = math.pi / 2.0 - etheta + angle = math.pi / 2.0 - eTheta cx = xCenter[0] cy = xCenter[1] diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index cf76ab67cd..ecb875f410 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -17,7 +17,7 @@ class BidirectionalAStarPlanner: - def __init__(self, ox, oy, reso, rr): + def __init__(self, ox, oy, resolution, rr): """ Initialize grid map for a star planning @@ -27,21 +27,24 @@ def __init__(self, ox, oy, reso, rr): rr: robot radius[m] """ - self.reso = reso + self.min_x, self.min_y = None, None + self.max_x, self.max_y = None, None + self.x_width, self.y_width, self.obstacle_map = None, None, None + self.resolution = resolution self.rr = rr self.calc_obstacle_map(ox, oy) self.motion = self.get_motion_model() class Node: - def __init__(self, x, y, cost, pind): + def __init__(self, x, y, cost, parent_index): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.pind = pind + self.parent_index = parent_index def __str__(self): return str(self.x) + "," + str(self.y) + "," + str( - self.cost) + "," + str(self.pind) + self.cost) + "," + str(self.parent_index) def planning(self, sx, sy, gx, gy): """ @@ -58,18 +61,19 @@ def planning(self, sx, sy, gx, gy): ry: y position list of the final path """ - nstart = self.Node(self.calc_xyindex(sx, self.minx), - self.calc_xyindex(sy, self.miny), 0.0, -1) - ngoal = self.Node(self.calc_xyindex(gx, self.minx), - self.calc_xyindex(gy, self.miny), 0.0, -1) + start_node = self.Node(self.calc_xy_index(sx, self.min_x), + self.calc_xy_index(sy, self.min_y), 0.0, -1) + goal_node = self.Node(self.calc_xy_index(gx, self.min_x), + self.calc_xy_index(gy, self.min_y), 0.0, -1) open_set_A, closed_set_A = dict(), dict() open_set_B, closed_set_B = dict(), dict() - open_set_A[self.calc_grid_index(nstart)] = nstart - open_set_B[self.calc_grid_index(ngoal)] = ngoal + open_set_A[self.calc_grid_index(start_node)] = start_node + open_set_B[self.calc_grid_index(goal_node)] = goal_node - current_A = nstart - current_B = ngoal + current_A = start_node + current_B = goal_node + meet_point_A, meet_point_B = None, None while 1: if len(open_set_A) == 0: @@ -94,22 +98,23 @@ def planning(self, sx, sy, gx, gy): # show graph if show_animation: # pragma: no cover - plt.plot(self.calc_grid_position(current_A.x, self.minx), - self.calc_grid_position(current_A.y, self.miny), "xc") - plt.plot(self.calc_grid_position(current_B.x, self.minx), - self.calc_grid_position(current_B.y, self.miny), "xc") + plt.plot(self.calc_grid_position(current_A.x, self.min_x), + self.calc_grid_position(current_A.y, self.min_y), + "xc") + plt.plot(self.calc_grid_position(current_B.x, self.min_x), + self.calc_grid_position(current_B.y, self.min_y), + "xc") # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: - [exit(0) if event.key == 'escape' - else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if len(closed_set_A.keys()) % 10 == 0: plt.pause(0.001) if current_A.x == current_B.x and current_A.y == current_B.y: print("Found goal") - meetpointA = current_A - meetpointB = current_B + meet_point_A = current_A + meet_point_B = current_B break # Remove the item from the open set @@ -158,7 +163,7 @@ def planning(self, sx, sy, gx, gy): open_set_B[n_ids[1]] = c_nodes[1] rx, ry = self.calc_final_bidirectional_path( - meetpointA, meetpointB, closed_set_A, closed_set_B) + meet_point_A, meet_point_B, closed_set_A, closed_set_B) return rx, ry @@ -175,16 +180,16 @@ def calc_final_bidirectional_path(self, n1, n2, setA, setB): return rx, ry - def calc_final_path(self, ngoal, closedset): + def calc_final_path(self, goal_node, closed_set): # generate final course - rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ - self.calc_grid_position(ngoal.y, self.miny)] - pind = ngoal.parent_index - while pind != -1: - n = closedset[pind] - rx.append(self.calc_grid_position(n.x, self.minx)) - ry.append(self.calc_grid_position(n.y, self.miny)) - pind = n.parent_index + rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], \ + [self.calc_grid_position(goal_node.y, self.min_y)] + parent_index = goal_node.parent_index + while parent_index != -1: + n = closed_set[parent_index] + rx.append(self.calc_grid_position(n.x, self.min_x)) + ry.append(self.calc_grid_position(n.y, self.min_y)) + parent_index = n.parent_index return rx, ry @@ -210,69 +215,69 @@ def find_total_cost(self, open_set, lambda_, n1): f_cost = g_cost + h_cost return f_cost - def calc_grid_position(self, index, minp): + def calc_grid_position(self, index, min_position): """ calc grid position :param index: - :param minp: + :param min_position: :return: """ - pos = index * self.reso + minp + pos = index * self.resolution + min_position return pos - def calc_xyindex(self, position, min_pos): - return round((position - min_pos) / self.reso) + def calc_xy_index(self, position, min_pos): + return round((position - min_pos) / self.resolution) def calc_grid_index(self, node): - return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + return (node.y - self.min_y) * self.x_width + (node.x - self.min_x) def verify_node(self, node): - px = self.calc_grid_position(node.x, self.minx) - py = self.calc_grid_position(node.y, self.miny) + px = self.calc_grid_position(node.x, self.min_x) + py = self.calc_grid_position(node.y, self.min_y) - if px < self.minx: + if px < self.min_x: return False - elif py < self.miny: + elif py < self.min_y: return False - elif px >= self.maxx: + elif px >= self.max_x: return False - elif py >= self.maxy: + elif py >= self.max_y: return False # collision check - if self.obmap[node.x][node.y]: + if self.obstacle_map[node.x][node.y]: return False return True def calc_obstacle_map(self, ox, oy): - self.minx = round(min(ox)) - self.miny = round(min(oy)) - self.maxx = round(max(ox)) - self.maxy = round(max(oy)) - print("min_x:", self.minx) - print("min_y:", self.miny) - print("max_x:", self.maxx) - print("max_y:", self.maxy) + self.min_x = round(min(ox)) + self.min_y = round(min(oy)) + self.max_x = round(max(ox)) + self.max_y = round(max(oy)) + print("min_x:", self.min_x) + print("min_y:", self.min_y) + print("max_x:", self.max_x) + print("max_y:", self.max_y) - self.xwidth = round((self.maxx - self.minx) / self.reso) - self.ywidth = round((self.maxy - self.miny) / self.reso) - print("x_width:", self.xwidth) - print("y_width:", self.ywidth) + self.x_width = round((self.max_x - self.min_x) / self.resolution) + self.y_width = round((self.max_y - self.min_y) / self.resolution) + print("x_width:", self.x_width) + print("y_width:", self.y_width) # obstacle map generation - self.obmap = [[False for _ in range(self.ywidth)] - for _ in range(self.xwidth)] - for ix in range(self.xwidth): - x = self.calc_grid_position(ix, self.minx) - for iy in range(self.ywidth): - y = self.calc_grid_position(iy, self.miny) + self.obstacle_map = [[False for _ in range(self.y_width)] + for _ in range(self.x_width)] + for ix in range(self.x_width): + x = self.calc_grid_position(ix, self.min_x) + for iy in range(self.y_width): + y = self.calc_grid_position(iy, self.min_y) for iox, ioy in zip(ox, oy): d = math.hypot(iox - x, ioy - y) if d <= self.rr: - self.obmap[ix][iy] = True + self.obstacle_map[ix][iy] = True break @staticmethod diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py index 328898db85..dd4282573e 100644 --- a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py +++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py @@ -17,7 +17,7 @@ class BidirectionalBreadthFirstSearchPlanner: - def __init__(self, ox, oy, reso, rr): + def __init__(self, ox, oy, resolution, rr): """ Initialize grid map for bfs planning @@ -27,22 +27,25 @@ def __init__(self, ox, oy, reso, rr): rr: robot radius[m] """ - self.reso = reso + self.min_x, self.min_y = None, None + self.max_x, self.max_y = None, None + self.x_width, self.y_width, self.obstacle_map = None, None, None + self.resolution = resolution self.rr = rr self.calc_obstacle_map(ox, oy) self.motion = self.get_motion_model() class Node: - def __init__(self, x, y, cost, pind, parent): + def __init__(self, x, y, cost, parent_index, parent): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.pind = pind + self.parent_index = parent_index self.parent = parent def __str__(self): return str(self.x) + "," + str(self.y) + "," + str( - self.cost) + "," + str(self.pind) + self.cost) + "," + str(self.parent_index) def planning(self, sx, sy, gx, gy): """ @@ -59,15 +62,19 @@ def planning(self, sx, sy, gx, gy): ry: y position list of the final path """ - nstart = self.Node(self.calc_xyindex(sx, self.minx), - self.calc_xyindex(sy, self.miny), 0.0, -1, None) - ngoal = self.Node(self.calc_xyindex(gx, self.minx), - self.calc_xyindex(gy, self.miny), 0.0, -1, None) + start_node = self.Node(self.calc_xy_index(sx, self.min_x), + self.calc_xy_index(sy, self.min_y), 0.0, -1, + None) + goal_node = self.Node(self.calc_xy_index(gx, self.min_x), + self.calc_xy_index(gy, self.min_y), 0.0, -1, + None) open_set_A, closed_set_A = dict(), dict() open_set_B, closed_set_B = dict(), dict() - open_set_B[self.calc_grid_index(ngoal)] = ngoal - open_set_A[self.calc_grid_index(nstart)] = nstart + open_set_B[self.calc_grid_index(goal_node)] = goal_node + open_set_A[self.calc_grid_index(start_node)] = start_node + + meet_point_A, meet_point_B = None, None while 1: if len(open_set_A) == 0: @@ -89,28 +96,29 @@ def planning(self, sx, sy, gx, gy): # show graph if show_animation: # pragma: no cover - plt.plot(self.calc_grid_position(current_A.x, self.minx), - self.calc_grid_position(current_A.y, self.miny), "xc") - plt.plot(self.calc_grid_position(current_B.x, self.minx), - self.calc_grid_position(current_B.y, self.miny), "xc") + plt.plot(self.calc_grid_position(current_A.x, self.min_x), + self.calc_grid_position(current_A.y, self.min_y), + "xc") + plt.plot(self.calc_grid_position(current_B.x, self.min_x), + self.calc_grid_position(current_B.y, self.min_y), + "xc") # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: - [exit(0) if - event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if len(closed_set_A.keys()) % 10 == 0: plt.pause(0.001) if c_id_A in closed_set_B: print("Find goal") - meetpointA = closed_set_A[c_id_A] - meetpointB = closed_set_B[c_id_A] + meet_point_A = closed_set_A[c_id_A] + meet_point_B = closed_set_B[c_id_A] break elif c_id_B in closed_set_A: print("Find goal") - meetpointA = closed_set_A[c_id_B] - meetpointB = closed_set_B[c_id_B] + meet_point_A = closed_set_A[c_id_B] + meet_point_B = closed_set_B[c_id_B] break # expand_grid search grid based on motion model @@ -137,20 +145,18 @@ def planning(self, sx, sy, gx, gy): if not self.verify_node(node_B): breakB = True - if (n_id_A not in closed_set_A) and (n_id_A not in - open_set_A) and (not - breakA): + if (n_id_A not in closed_set_A) and \ + (n_id_A not in open_set_A) and (not breakA): node_A.parent = current_A open_set_A[n_id_A] = node_A - if (n_id_B not in closed_set_B) and (n_id_B not in - open_set_B) and (not - breakB): + if (n_id_B not in closed_set_B) and \ + (n_id_B not in open_set_B) and (not breakB): node_B.parent = current_B open_set_B[n_id_B] = node_B rx, ry = self.calc_final_path_bidir( - meetpointA, meetpointB, closed_set_A, closed_set_B) + meet_point_A, meet_point_B, closed_set_A, closed_set_B) return rx, ry # takes both set and meeting nodes and calculate optimal path @@ -166,81 +172,81 @@ def calc_final_path_bidir(self, n1, n2, setA, setB): return rx, ry - def calc_final_path(self, ngoal, closedset): + def calc_final_path(self, goal_node, closed_set): # generate final course - rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [ - self.calc_grid_position(ngoal.y, self.miny)] - n = closedset[ngoal.parent_index] + rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], [ + self.calc_grid_position(goal_node.y, self.min_y)] + n = closed_set[goal_node.parent_index] while n is not None: - rx.append(self.calc_grid_position(n.x, self.minx)) - ry.append(self.calc_grid_position(n.y, self.miny)) + rx.append(self.calc_grid_position(n.x, self.min_x)) + ry.append(self.calc_grid_position(n.y, self.min_y)) n = n.parent return rx, ry - def calc_grid_position(self, index, minp): + def calc_grid_position(self, index, min_position): """ calc grid position :param index: - :param minp: + :param min_position: :return: """ - pos = index * self.reso + minp + pos = index * self.resolution + min_position return pos - def calc_xyindex(self, position, min_pos): - return round((position - min_pos) / self.reso) + def calc_xy_index(self, position, min_pos): + return round((position - min_pos) / self.resolution) def calc_grid_index(self, node): - return (node.y - self.miny) * self.xwidth + (node.x - self.minx) + return (node.y - self.min_y) * self.x_width + (node.x - self.min_x) def verify_node(self, node): - px = self.calc_grid_position(node.x, self.minx) - py = self.calc_grid_position(node.y, self.miny) + px = self.calc_grid_position(node.x, self.min_x) + py = self.calc_grid_position(node.y, self.min_y) - if px < self.minx: + if px < self.min_x: return False - elif py < self.miny: + elif py < self.min_y: return False - elif px >= self.maxx: + elif px >= self.max_x: return False - elif py >= self.maxy: + elif py >= self.max_y: return False # collision check - if self.obmap[node.x][node.y]: + if self.obstacle_map[node.x][node.y]: return False return True def calc_obstacle_map(self, ox, oy): - self.minx = round(min(ox)) - self.miny = round(min(oy)) - self.maxx = round(max(ox)) - self.maxy = round(max(oy)) - print("min_x:", self.minx) - print("min_y:", self.miny) - print("max_x:", self.maxx) - print("max_y:", self.maxy) + self.min_x = round(min(ox)) + self.min_y = round(min(oy)) + self.max_x = round(max(ox)) + self.max_y = round(max(oy)) + print("min_x:", self.min_x) + print("min_y:", self.min_y) + print("max_x:", self.max_x) + print("max_y:", self.max_y) - self.xwidth = round((self.maxx - self.minx) / self.reso) - self.ywidth = round((self.maxy - self.miny) / self.reso) - print("x_width:", self.xwidth) - print("y_width:", self.ywidth) + self.x_width = round((self.max_x - self.min_x) / self.resolution) + self.y_width = round((self.max_y - self.min_y) / self.resolution) + print("x_width:", self.x_width) + print("y_width:", self.y_width) # obstacle map generation - self.obmap = [[False for _ in range(self.ywidth)] - for _ in range(self.xwidth)] - for ix in range(self.xwidth): - x = self.calc_grid_position(ix, self.minx) - for iy in range(self.ywidth): - y = self.calc_grid_position(iy, self.miny) + self.obstacle_map = [[False for _ in range(self.y_width)] + for _ in range(self.x_width)] + for ix in range(self.x_width): + x = self.calc_grid_position(ix, self.min_x) + for iy in range(self.y_width): + y = self.calc_grid_position(iy, self.min_y) for iox, ioy in zip(ox, oy): d = math.hypot(iox - x, ioy - y) if d <= self.rr: - self.obmap[ix][iy] = True + self.obstacle_map[ix][iy] = True break @staticmethod diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py index 54d15ea9fb..aef07ebbaf 100644 --- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py +++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py @@ -125,6 +125,7 @@ def find_id(self, node_x_list, node_y_list, target_node): if self.is_same_node_with_xy(node_x_list[i], node_y_list[i], target_node): return i + return None @staticmethod def is_same_node_with_xy(node_x, node_y, node_b): diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py index 98a12ab10c..bb5699b888 100644 --- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py +++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py @@ -5,17 +5,18 @@ author Atsushi Sakai (@Atsushi_twi) Ref: - -- Shunichi09/nonlinear_control: Implementing the nonlinear model predictive control, sliding mode control https://github.com/Shunichi09/nonlinear_control +Shunichi09/nonlinear_control: Implementing the nonlinear model predictive +control, sliding mode control https://github.com/Shunichi09/nonlinear_control """ -import numpy as np +from math import cos, sin, radians, atan2 + import matplotlib.pyplot as plt -import math +import numpy as np U_A_MAX = 1.0 -U_OMEGA_MAX = math.radians(45.0) +U_OMEGA_MAX = radians(45.0) PHI_V = 0.01 PHI_OMEGA = 0.01 WB = 0.25 # [m] wheel base @@ -24,19 +25,18 @@ def differential_model(v, yaw, u_1, u_2): - - dx = math.cos(yaw) * v - dy = math.sin(yaw) * v + dx = cos(yaw) * v + dy = sin(yaw) * v dv = u_1 - dyaw = v / WB * math.sin(u_2) # tan is not good for nonlinear optimization + # tangent is not good for nonlinear optimization + d_yaw = v / WB * sin(u_2) - return dx, dy, dyaw, dv + return dx, dy, d_yaw, dv -class TwoWheeledSystem(): +class TwoWheeledSystem: def __init__(self, init_x, init_y, init_yaw, init_v): - self.x = init_x self.y = init_y self.yaw = init_yaw @@ -47,12 +47,11 @@ def __init__(self, init_x, init_y, init_yaw, init_v): self.history_v = [init_v] def update_state(self, u_1, u_2, dt=0.01): - - dx, dy, dyaw, dv = differential_model(self.v, self.yaw, u_1, u_2) + dx, dy, d_yaw, dv = differential_model(self.v, self.yaw, u_1, u_2) self.x += dt * dx self.y += dt * dy - self.yaw += dt * dyaw + self.yaw += dt * d_yaw self.v += dt * dv # save @@ -62,13 +61,15 @@ def update_state(self, u_1, u_2, dt=0.01): self.history_v.append(self.v) -class NMPCSimulatorSystem(): +class NMPCSimulatorSystem: def calc_predict_and_adjoint_state(self, x, y, yaw, v, u_1s, u_2s, N, dt): + # by using state equation x_s, y_s, yaw_s, v_s = self._calc_predict_states( - x, y, yaw, v, u_1s, u_2s, N, dt) # by using state equation + x, y, yaw, v, u_1s, u_2s, N, dt) + # by using adjoint equation lam_1s, lam_2s, lam_3s, lam_4s = self._calc_adjoint_states( - x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt) # by using adjoint equation + x_s, y_s, yaw_s, v_s, u_2s, N, dt) return x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s @@ -88,7 +89,7 @@ def _calc_predict_states(self, x, y, yaw, v, u_1s, u_2s, N, dt): return x_s, y_s, yaw_s, v_s - def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt): + def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_2s, N, dt): lam_1s = [x_s[-1]] lam_2s = [y_s[-1]] lam_3s = [yaw_s[-1]] @@ -97,8 +98,8 @@ def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt): # backward adjoint state calc for i in range(N - 1, 0, -1): temp_lam_1, temp_lam_2, temp_lam_3, temp_lam_4 = self._adjoint_state_with_oylar( - x_s[i], y_s[i], yaw_s[i], v_s[i], lam_1s[0], lam_2s[0], lam_3s[0], lam_4s[0], - u_1s[i], u_2s[i], dt) + yaw_s[i], v_s[i], lam_1s[0], lam_2s[0], lam_3s[0], lam_4s[0], + u_2s[i], dt) lam_1s.insert(0, temp_lam_1) lam_2s.insert(0, temp_lam_2) lam_3s.insert(0, temp_lam_3) @@ -106,7 +107,8 @@ def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt): return lam_1s, lam_2s, lam_3s, lam_4s - def _predict_state_with_oylar(self, x, y, yaw, v, u_1, u_2, dt): + @staticmethod + def _predict_state_with_oylar(x, y, yaw, v, u_1, u_2, dt): dx, dy, dyaw, dv = differential_model( v, yaw, u_1, u_2) @@ -118,21 +120,21 @@ def _predict_state_with_oylar(self, x, y, yaw, v, u_1, u_2, dt): return next_x_1, next_x_2, next_x_3, next_x_4 - def _adjoint_state_with_oylar(self, x, y, yaw, v, lam_1, lam_2, lam_3, lam_4, u_1, u_2, dt): + @staticmethod + def _adjoint_state_with_oylar(yaw, v, lam_1, lam_2, lam_3, lam_4, u_2, dt): # ∂H/∂x pre_lam_1 = lam_1 + dt * 0.0 pre_lam_2 = lam_2 + dt * 0.0 - pre_lam_3 = lam_3 + dt * \ - (- lam_1 * math.sin(yaw) * v + lam_2 * math.cos(yaw) * v) - pre_lam_4 = lam_4 + dt * \ - (lam_1 * math.cos(yaw) + lam_2 * - math.sin(yaw) + lam_3 * math.sin(u_2) / WB) + tmp1 = - lam_1 * sin(yaw) * v + lam_2 * cos(yaw) * v + pre_lam_3 = lam_3 + dt * tmp1 + tmp2 = lam_1 * cos(yaw) + lam_2 * sin(yaw) + lam_3 * sin(u_2) / WB + pre_lam_4 = lam_4 + dt * tmp2 return pre_lam_1, pre_lam_2, pre_lam_3, pre_lam_4 -class NMPCController_with_CGMRES(): +class NMPCControllerCGMRES: """ Attributes ------------ @@ -145,7 +147,7 @@ class NMPCController_with_CGMRES(): alpha : float gain of predict time N : int - predicte step, discritize value + predict step, discrete value threshold : float cgmres's threshold value input_num : int @@ -226,20 +228,22 @@ def calc_input(self, x, y, yaw, v, time): dx_4 = x_4_dot * self.ht x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state( - x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s, self.u_2s, self.N, dt) + x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s, self.u_2s, + self.N, dt) # Fxt:F(U,x+hx˙,t+h) - Fxt = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s, - self.u_1s, self.u_2s, self.dummy_u_1s, self.dummy_u_2s, - self.raw_1s, self.raw_2s, self.N, dt) + Fxt = self._calc_f(v_s, lam_3s, lam_4s, + self.u_1s, self.u_2s, self.dummy_u_1s, + self.dummy_u_2s, + self.raw_1s, self.raw_2s, self.N) # F:F(U,x,t) x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state( x, y, yaw, v, self.u_1s, self.u_2s, self.N, dt) - F = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s, + F = self._calc_f(v_s, lam_3s, lam_4s, self.u_1s, self.u_2s, self.dummy_u_1s, self.dummy_u_2s, - self.raw_1s, self.raw_2s, self.N, dt) + self.raw_1s, self.raw_2s, self.N) right = -self.zeta * F - ((Fxt - F) / self.ht) @@ -251,17 +255,19 @@ def calc_input(self, x, y, yaw, v, time): draw_2 = self.raw_2s * self.ht x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state( - x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1, self.u_2s + du_2, self.N, dt) + x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1, + self.u_2s + du_2, self.N, dt) # Fuxt:F(U+hdU(0),x+hx˙,t+h) - Fuxt = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s, + Fuxt = self._calc_f(v_s, lam_3s, lam_4s, self.u_1s + du_1, self.u_2s + du_2, - self.dummy_u_1s + ddummy_u_1, self.dummy_u_2s + ddummy_u_2, - self.raw_1s + draw_1, self.raw_2s + draw_2, self.N, dt) + self.dummy_u_1s + ddummy_u_1, + self.dummy_u_2s + ddummy_u_2, + self.raw_1s + draw_1, self.raw_2s + draw_2, self.N) left = ((Fuxt - Fxt) / self.ht) - # calculationg cgmres + # calculating cgmres r0 = right - left r0_norm = np.linalg.norm(r0) @@ -276,6 +282,9 @@ def calc_input(self, x, y, yaw, v, time): ys_pre = None + du_1_new, du_2_new, draw_1_new, draw_2_new = None, None, None, None + ddummy_u_1_new, ddummy_u_2_new = None, None + for i in range(self.max_iteration): du_1 = vs[::self.input_num, i] * self.ht du_2 = vs[1::self.input_num, i] * self.ht @@ -285,12 +294,15 @@ def calc_input(self, x, y, yaw, v, time): draw_2 = vs[5::self.input_num, i] * self.ht x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state( - x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1, self.u_2s + du_2, self.N, dt) + x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1, + self.u_2s + du_2, self.N, dt) - Fuxt = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s, + Fuxt = self._calc_f(v_s, lam_3s, lam_4s, self.u_1s + du_1, self.u_2s + du_2, - self.dummy_u_1s + ddummy_u_1, self.dummy_u_2s + ddummy_u_2, - self.raw_1s + draw_1, self.raw_2s + draw_2, self.N, dt) + self.dummy_u_1s + ddummy_u_1, + self.dummy_u_2s + ddummy_u_2, + self.raw_1s + draw_1, self.raw_2s + draw_2, + self.N) Av = ((Fuxt - Fxt) / self.ht) @@ -312,14 +324,17 @@ def calc_input(self, x, y, yaw, v, time): judge_value = r0_norm * e[:i + 1] - np.dot(hs[:i + 1, :i], ys[:i]) - if np.linalg.norm(judge_value) < self.threshold or i == self.max_iteration - 1: - update_value = np.dot(vs[:, :i - 1], ys_pre[:i - 1]).flatten() - du_1_new = du_1 + update_value[::self.input_num] - du_2_new = du_2 + update_value[1::self.input_num] - ddummy_u_1_new = ddummy_u_1 + update_value[2::self.input_num] - ddummy_u_2_new = ddummy_u_2 + update_value[3::self.input_num] - draw_1_new = draw_1 + update_value[4::self.input_num] - draw_2_new = draw_2 + update_value[5::self.input_num] + flag1 = np.linalg.norm(judge_value) < self.threshold + + flag2 = i == self.max_iteration - 1 + if flag1 or flag2: + update_val = np.dot(vs[:, :i - 1], ys_pre[:i - 1]).flatten() + du_1_new = du_1 + update_val[::self.input_num] + du_2_new = du_2 + update_val[1::self.input_num] + ddummy_u_1_new = ddummy_u_1 + update_val[2::self.input_num] + ddummy_u_2_new = ddummy_u_2 + update_val[3::self.input_num] + draw_1_new = draw_1 + update_val[4::self.input_num] + draw_2_new = draw_2 + update_val[5::self.input_num] break ys_pre = ys @@ -335,9 +350,9 @@ def calc_input(self, x, y, yaw, v, time): x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state( x, y, yaw, v, self.u_1s, self.u_2s, self.N, dt) - F = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s, + F = self._calc_f(v_s, lam_3s, lam_4s, self.u_1s, self.u_2s, self.dummy_u_1s, self.dummy_u_2s, - self.raw_1s, self.raw_2s, self.N, dt) + self.raw_1s, self.raw_2s, self.N) print("norm(F) = {0}".format(np.linalg.norm(F))) @@ -352,36 +367,38 @@ def calc_input(self, x, y, yaw, v, time): return self.u_1s, self.u_2s - def _calc_f(self, x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s, - u_1s, u_2s, dummy_u_1s, dummy_u_2s, raw_1s, raw_2s, N, dt): + @staticmethod + def _calc_f(v_s, lam_3s, lam_4s, u_1s, u_2s, dummy_u_1s, dummy_u_2s, + raw_1s, raw_2s, N): F = [] for i in range(N): # ∂H/∂u(xi, ui, λi) F.append(u_1s[i] + lam_4s[i] + 2.0 * raw_1s[i] * u_1s[i]) F.append(u_2s[i] + lam_3s[i] * v_s[i] / - WB * math.cos(u_2s[i])**2 + 2.0 * raw_2s[i] * u_2s[i]) + WB * cos(u_2s[i]) ** 2 + 2.0 * raw_2s[i] * u_2s[i]) F.append(-PHI_V + 2.0 * raw_1s[i] * dummy_u_1s[i]) F.append(-PHI_OMEGA + 2.0 * raw_2s[i] * dummy_u_2s[i]) # C(xi, ui, λi) - F.append(u_1s[i]**2 + dummy_u_1s[i]**2 - U_A_MAX**2) - F.append(u_2s[i]**2 + dummy_u_2s[i]**2 - U_OMEGA_MAX**2) + F.append(u_1s[i] ** 2 + dummy_u_1s[i] ** 2 - U_A_MAX ** 2) + F.append(u_2s[i] ** 2 + dummy_u_2s[i] ** 2 - U_OMEGA_MAX ** 2) return np.array(F) -def plot_figures(plant_system, controller, iteration_num, dt): # pragma: no cover +def plot_figures(plant_system, controller, iteration_num, + dt): # pragma: no cover # figure # time history fig_p = plt.figure() fig_u = plt.figure() fig_f = plt.figure() - # traj + # trajectory fig_t = plt.figure() - fig_traj = fig_t.add_subplot(111) - fig_traj.set_aspect('equal') + fig_trajectory = fig_t.add_subplot(111) + fig_trajectory.set_aspect('equal') x_1_fig = fig_p.add_subplot(411) x_2_fig = fig_p.add_subplot(412) @@ -443,11 +460,11 @@ def plot_figures(plant_system, controller, iteration_num, dt): # pragma: no cov f_fig.set_xlabel("time [s]") f_fig.set_ylabel("optimal error") - fig_traj.plot(plant_system.history_x, - plant_system.history_y, "-r") - fig_traj.set_xlabel("x [m]") - fig_traj.set_ylabel("y [m]") - fig_traj.axis("equal") + fig_trajectory.plot(plant_system.history_x, + plant_system.history_y, "-r") + fig_trajectory.set_xlabel("x [m]") + fig_trajectory.set_ylabel("y [m]") + fig_trajectory.axis("equal") # start state plot_car(plant_system.history_x[0], @@ -462,23 +479,25 @@ def plot_figures(plant_system, controller, iteration_num, dt): # pragma: no cov plt.show() -def plot_car(x, y, yaw, steer=0.0, cabcolor="-r", truckcolor="-k"): # pragma: no cover +def plot_car(x, y, yaw, steer=0.0, truck_color="-k"): # pragma: no cover # Vehicle parameters LENGTH = 0.4 # [m] WIDTH = 0.2 # [m] - BACKTOWHEEL = 0.1 # [m] + BACK_TO_WHEEL = 0.1 # [m] WHEEL_LEN = 0.03 # [m] WHEEL_WIDTH = 0.02 # [m] TREAD = 0.07 # [m] - outline = np.array([[-BACKTOWHEEL, (LENGTH - BACKTOWHEEL), (LENGTH - BACKTOWHEEL), - -BACKTOWHEEL, -BACKTOWHEEL], - [WIDTH / 2, WIDTH / 2, - WIDTH / 2, -WIDTH / 2, WIDTH / 2]]) + outline = np.array( + [[-BACK_TO_WHEEL, (LENGTH - BACK_TO_WHEEL), (LENGTH - BACK_TO_WHEEL), + -BACK_TO_WHEEL, -BACK_TO_WHEEL], + [WIDTH / 2, WIDTH / 2, - WIDTH / 2, -WIDTH / 2, WIDTH / 2]]) - fr_wheel = np.array([[WHEEL_LEN, -WHEEL_LEN, -WHEEL_LEN, WHEEL_LEN, WHEEL_LEN], - [-WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD, WHEEL_WIDTH - - TREAD, WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD]]) + fr_wheel = np.array( + [[WHEEL_LEN, -WHEEL_LEN, -WHEEL_LEN, WHEEL_LEN, WHEEL_LEN], + [-WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD, WHEEL_WIDTH - + TREAD, WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD]]) rr_wheel = np.copy(fr_wheel) @@ -487,10 +506,10 @@ def plot_car(x, y, yaw, steer=0.0, cabcolor="-r", truckcolor="-k"): # pragma: n rl_wheel = np.copy(rr_wheel) rl_wheel[1, :] *= -1 - Rot1 = np.array([[math.cos(yaw), math.sin(yaw)], - [-math.sin(yaw), math.cos(yaw)]]) - Rot2 = np.array([[math.cos(steer), math.sin(steer)], - [-math.sin(steer), math.cos(steer)]]) + Rot1 = np.array([[cos(yaw), sin(yaw)], + [-sin(yaw), cos(yaw)]]) + Rot2 = np.array([[cos(steer), sin(steer)], + [-sin(steer), cos(steer)]]) fr_wheel = (fr_wheel.T.dot(Rot2)).T fl_wheel = (fl_wheel.T.dot(Rot2)).T @@ -516,20 +535,19 @@ def plot_car(x, y, yaw, steer=0.0, cabcolor="-r", truckcolor="-k"): # pragma: n rl_wheel[1, :] += y plt.plot(np.array(outline[0, :]).flatten(), - np.array(outline[1, :]).flatten(), truckcolor) + np.array(outline[1, :]).flatten(), truck_color) plt.plot(np.array(fr_wheel[0, :]).flatten(), - np.array(fr_wheel[1, :]).flatten(), truckcolor) + np.array(fr_wheel[1, :]).flatten(), truck_color) plt.plot(np.array(rr_wheel[0, :]).flatten(), - np.array(rr_wheel[1, :]).flatten(), truckcolor) + np.array(rr_wheel[1, :]).flatten(), truck_color) plt.plot(np.array(fl_wheel[0, :]).flatten(), - np.array(fl_wheel[1, :]).flatten(), truckcolor) + np.array(fl_wheel[1, :]).flatten(), truck_color) plt.plot(np.array(rl_wheel[0, :]).flatten(), - np.array(rl_wheel[1, :]).flatten(), truckcolor) + np.array(rl_wheel[1, :]).flatten(), truck_color) plt.plot(x, y, "*") def animation(plant, controller, dt): - skip = 2 # skip index for animation for t in range(1, len(controller.history_u_1), skip): @@ -543,12 +561,13 @@ def animation(plant, controller, dt): if abs(v) <= 0.01: steer = 0.0 else: - steer = math.atan2(controller.history_u_2[t] * WB / v, 1.0) + steer = atan2(controller.history_u_2[t] * WB / v, 1.0) plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(plant.history_x, plant.history_y, "-r", label="trajectory") plot_car(x, y, yaw, steer=steer) plt.axis("equal") @@ -568,7 +587,7 @@ def main(): init_x = -4.5 init_y = -2.5 - init_yaw = math.radians(45.0) + init_yaw = radians(45.0) init_v = -1.0 # plant @@ -576,14 +595,15 @@ def main(): init_x, init_y, init_yaw, init_v) # controller - controller = NMPCController_with_CGMRES() + controller = NMPCControllerCGMRES() iteration_num = int(iteration_time / dt) for i in range(1, iteration_num): time = float(i) * dt # make input u_1s, u_2s = controller.calc_input( - plant_system.x, plant_system.y, plant_system.yaw, plant_system.v, time) + plant_system.x, plant_system.y, plant_system.yaw, plant_system.v, + time) # update state plant_system.update_state(u_1s[0], u_2s[0]) diff --git a/tests/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py index fa2d0a7a64..12ef98df39 100644 --- a/tests/test_grid_based_sweep_coverage_path_planner.py +++ b/tests/test_grid_based_sweep_coverage_path_planner.py @@ -2,8 +2,10 @@ import sys from unittest import TestCase -sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../PathPlanning/GridBasedSweepCPP") -sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib") +sys.path.append(os.path.dirname( + os.path.abspath(__file__)) + "/../PathPlanning/GridBasedSweepCPP") +sys.path.append( + os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib") try: import grid_based_sweep_coverage_path_planner except ImportError: @@ -14,88 +16,109 @@ class TestPlanning(TestCase): + RIGHT = grid_based_sweep_coverage_path_planner.\ + SweepSearcher.MovingDirection.RIGHT + LEFT = grid_based_sweep_coverage_path_planner. \ + SweepSearcher.MovingDirection.LEFT + UP = grid_based_sweep_coverage_path_planner. \ + SweepSearcher.SweepDirection.UP + DOWN = grid_based_sweep_coverage_path_planner. \ + SweepSearcher.SweepDirection.DOWN + def test_planning1(self): ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0] oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0] - reso = 5.0 - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) + resolution = 5.0 + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.LEFT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.UP, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.UP, + ) + self.assertGreater(len(px), 5) def test_planning2(self): ox = [0.0, 50.0, 50.0, 0.0, 0.0] oy = [0.0, 0.0, 30.0, 30.0, 0.0] - reso = 1.3 - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) + resolution = 1.3 + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.LEFT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.UP, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) def test_planning3(self): ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0] oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0] - reso = 5.1 - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP, - ) - self.assertTrue(len(px) >= 5) - - px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso, - moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT, - sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN, - ) - self.assertTrue(len(px) >= 5) + resolution = 5.1 + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.LEFT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.UP, + ) + self.assertGreater(len(px), 5) + + px, py = grid_based_sweep_coverage_path_planner.planning( + ox, oy, resolution, + moving_direction=self.RIGHT, + sweeping_direction=self.DOWN, + ) + self.assertGreater(len(px), 5) From effddd2f6bdba33f75ca9df3367b4c6fe0fb00df Mon Sep 17 00:00:00 2001 From: adi-vardi-enway <57910756+adi-vardi-enway@users.noreply.github.com> Date: Sat, 4 Jul 2020 11:44:35 +0800 Subject: [PATCH 228/909] Potential field - potential range and ocillations (#345) * Potential cover start and goal as well * Oscillation detection and fix potential look if outside range * Formatting * cleaner and flexible oscillation detection Co-authored-by: Adi Vardi --- .../potential_field_planning.py | 40 +++++++++++++++---- 1 file changed, 33 insertions(+), 7 deletions(-) diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py index 31904fa45d..8f136b5ee3 100644 --- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py +++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py @@ -9,6 +9,7 @@ """ +from collections import deque import numpy as np import matplotlib.pyplot as plt @@ -16,15 +17,17 @@ KP = 5.0 # attractive potential gain ETA = 100.0 # repulsive potential gain AREA_WIDTH = 30.0 # potential area width [m] +# the number of previous positions used to check oscillations +OSCILLATIONS_DETECTION_LENGTH = 3 show_animation = True -def calc_potential_field(gx, gy, ox, oy, reso, rr): - minx = min(ox) - AREA_WIDTH / 2.0 - miny = min(oy) - AREA_WIDTH / 2.0 - maxx = max(ox) + AREA_WIDTH / 2.0 - maxy = max(oy) + AREA_WIDTH / 2.0 +def calc_potential_field(gx, gy, ox, oy, reso, rr, sx, sy): + minx = min(min(ox), sx, gx) - AREA_WIDTH / 2.0 + miny = min(min(oy), sy, gy) - AREA_WIDTH / 2.0 + maxx = max(max(ox), sx, gx) + AREA_WIDTH / 2.0 + maxy = max(max(oy), sy, gy) + AREA_WIDTH / 2.0 xw = int(round((maxx - minx) / reso)) yw = int(round((maxy - miny) / reso)) @@ -84,10 +87,26 @@ def get_motion_model(): return motion +def oscillations_detection(previous_ids, ix, iy): + previous_ids.append((ix, iy)) + + if (len(previous_ids) > OSCILLATIONS_DETECTION_LENGTH): + previous_ids.popleft() + + # check if contains any duplicates by copying into a set + previous_ids_set = set() + for index in previous_ids: + if index in previous_ids_set: + return True + else: + previous_ids_set.add(index) + return False + + def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr): # calc potential field - pmap, minx, miny = calc_potential_field(gx, gy, ox, oy, reso, rr) + pmap, minx, miny = calc_potential_field(gx, gy, ox, oy, reso, rr, sx, sy) # search path d = np.hypot(sx - gx, sy - gy) @@ -106,14 +125,17 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr): rx, ry = [sx], [sy] motion = get_motion_model() + previous_ids = deque() + while d >= reso: minp = float("inf") minix, miniy = -1, -1 for i, _ in enumerate(motion): inx = int(ix + motion[i][0]) iny = int(iy + motion[i][1]) - if inx >= len(pmap) or iny >= len(pmap[0]): + if inx >= len(pmap) or iny >= len(pmap[0]) or inx < 0 or iny < 0: p = float("inf") # outside area + print("outside potential!") else: p = pmap[inx][iny] if minp > p: @@ -128,6 +150,10 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr): rx.append(xp) ry.append(yp) + if (oscillations_detection(previous_ids, ix, iy)): + print("Oscillation detected at ({},{})!".format(ix, iy)) + break + if show_animation: plt.plot(ix, iy, ".r") plt.pause(0.01) From eb42d3937e7de41f8157457d384b52757a30a07a Mon Sep 17 00:00:00 2001 From: "dependabot-preview[bot]" <27856297+dependabot-preview[bot]@users.noreply.github.com> Date: Sun, 5 Jul 2020 20:01:03 +0900 Subject: [PATCH 229/909] Create Dependabot config file (#347) Co-authored-by: dependabot-preview[bot] <27856297+dependabot-preview[bot]@users.noreply.github.com> --- .github/dependabot.yml | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000000..9117f1216c --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,8 @@ +version: 2 +updates: +- package-ecosystem: pip + directory: "/" + schedule: + interval: weekly + time: "20:00" + open-pull-requests-limit: 10 From 5f6e4f855551aac9ece93533ceeacca8e97e1ad1 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 5 Jul 2020 21:15:18 +0900 Subject: [PATCH 230/909] Dependency setting clean up (#348) * requiirements.txt use specific version * use appveyor * fix appveyor * fix appveyor * fix appveyor --- .travis.yml | 35 ----------------------------------- CONTRIBUTING.md | 2 +- appveyor.yml | 1 + environment.yml | 5 ++--- requirements.txt | 10 +++++----- 5 files changed, 9 insertions(+), 44 deletions(-) delete mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 99d239217b..0000000000 --- a/.travis.yml +++ /dev/null @@ -1,35 +0,0 @@ -language: python - -matrix: - include: - - os: linux - -python: - - 3.8 - -before_install: - - sudo apt-get update - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - - chmod +x miniconda.sh - - bash miniconda.sh -b -p $HOME/miniconda - - export PATH="$HOME/miniconda/bin:$PATH" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - # Useful for debugging any issues with conda - - conda info -a - -install: - - conda install numpy - - conda install scipy - - conda install matplotlib - - conda install pandas - - pip install cvxpy - - conda install coveralls - -script: - - python --version - - ./runtests.sh - -after_success: - - coveralls diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index fd41946b65..91f6dfa822 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -18,6 +18,6 @@ Please check this paper to understand the philosophy of this project. ## Check your Python version. -We only accept a PR for Python 3.6.x or higher. +We only accept a PR for Python 3.8.x or higher. We will not accept a PR for Python 2.x. diff --git a/appveyor.yml b/appveyor.yml index f34bcb2b28..b883397d71 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -34,6 +34,7 @@ install: - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH% - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin - conda config --set always_yes yes --set changeps1 no + - conda config --append channels conda-forge - conda update -q conda - conda info -a - conda env create -f C:\\projects\pythonrobotics\environment.yml diff --git a/environment.yml b/environment.yml index da1c3c0a6f..fdfd25ca0a 100644 --- a/environment.yml +++ b/environment.yml @@ -2,10 +2,9 @@ name: python_robotics dependencies: - python - pip -- matplotlib - scipy - numpy - pandas +- cvxpy +- matplotlib - coverage -- pip: - - cvxpy diff --git a/requirements.txt b/requirements.txt index 1398664be8..775486efa3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -numpy -pandas -scipy -matplotlib -cvxpy +numpy == 1.19.0 +scipy == 1.5.1 +pandas == 1.0.5 +matplotlib == 3.2.2 +cvxpy == 1.1.1 From 77f6277e9c86e3b20a74eabfe9be6da3ba339308 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 7 Jul 2020 22:22:53 +0900 Subject: [PATCH 231/909] improve test coverage (#352) * improve test coverage * improve test coverage * add coveralls CI * add coveralls CI * remove cover alls --- Bipedal/bipedal_planner/bipedal_planner.py | 153 +++++++++--------- .../CubicSpline/cubic_spline_planner.py | 2 +- 2 files changed, 80 insertions(+), 75 deletions(-) diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py index 01df63034f..9757fc42d6 100644 --- a/Bipedal/bipedal_planner/bipedal_planner.py +++ b/Bipedal/bipedal_planner/bipedal_planner.py @@ -110,83 +110,88 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False): # plot if plot: - # for plot trajectory, plot in for loop - for c in range(len(self.com_trajectory)): - if c > len(com_trajectory_for_plot): - # set up plotter - plt.cla() - # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect( - 'key_release_event', - lambda event: - [exit(0) if event.key == 'escape' else None]) - ax.set_zlim(0, z_c * 2) - ax.set_xlim(0, 1) - ax.set_ylim(-0.5, 0.5) - - # update com_trajectory_for_plot - com_trajectory_for_plot.append(self.com_trajectory[c]) - - # plot com - ax.plot([p[0] for p in com_trajectory_for_plot], - [p[1] for p in com_trajectory_for_plot], [ - 0 for _ in com_trajectory_for_plot], - color="red") - - # plot inverted pendulum - ax.plot([px_star, com_trajectory_for_plot[-1][0]], - [py_star, com_trajectory_for_plot[-1][1]], - [0, z_c], color="green", linewidth=3) - ax.scatter([com_trajectory_for_plot[-1][0]], - [com_trajectory_for_plot[-1][1]], - [z_c], color="green", s=300) - - # foot rectangle for self.ref_p - foot_width = 0.06 - foot_height = 0.04 - for j in range(len(self.ref_p)): - angle = self.ref_p[j][2] + \ - math.atan2(foot_height, - foot_width) - math.pi - r = math.sqrt( - math.pow(foot_width / 3., 2) + math.pow( - foot_height / 2., 2)) - rec = pat.Rectangle(xy=( - self.ref_p[j][0] + r * math.cos(angle), - self.ref_p[j][1] + r * math.sin(angle)), - width=foot_width, - height=foot_height, - angle=self.ref_p[j][ - 2] * 180 / math.pi, - color="blue", fill=False, - ls=":") - ax.add_patch(rec) - art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z") - - # foot rectangle for self.act_p - for j in range(len(self.act_p)): - angle = self.act_p[j][2] + \ - math.atan2(foot_height, - foot_width) - math.pi - r = math.sqrt( - math.pow(foot_width / 3., 2) + math.pow( - foot_height / 2., 2)) - rec = pat.Rectangle(xy=( - self.act_p[j][0] + r * math.cos(angle), - self.act_p[j][1] + r * math.sin(angle)), - width=foot_width, - height=foot_height, - angle=self.act_p[j][ - 2] * 180 / math.pi, - color="blue", fill=False) - ax.add_patch(rec) - art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z") - - plt.draw() - plt.pause(0.001) + self.plot_animation(ax, com_trajectory_for_plot, px_star, + py_star, z_c) if plot: plt.show() + def plot_animation(self, ax, com_trajectory_for_plot, px_star, py_star, + z_c): # pragma: no cover + # for plot trajectory, plot in for loop + for c in range(len(self.com_trajectory)): + if c > len(com_trajectory_for_plot): + # set up plotter + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: + [exit(0) if event.key == 'escape' else None]) + ax.set_zlim(0, z_c * 2) + ax.set_xlim(0, 1) + ax.set_ylim(-0.5, 0.5) + + # update com_trajectory_for_plot + com_trajectory_for_plot.append(self.com_trajectory[c]) + + # plot com + ax.plot([p[0] for p in com_trajectory_for_plot], + [p[1] for p in com_trajectory_for_plot], [ + 0 for _ in com_trajectory_for_plot], + color="red") + + # plot inverted pendulum + ax.plot([px_star, com_trajectory_for_plot[-1][0]], + [py_star, com_trajectory_for_plot[-1][1]], + [0, z_c], color="green", linewidth=3) + ax.scatter([com_trajectory_for_plot[-1][0]], + [com_trajectory_for_plot[-1][1]], + [z_c], color="green", s=300) + + # foot rectangle for self.ref_p + foot_width = 0.06 + foot_height = 0.04 + for j in range(len(self.ref_p)): + angle = self.ref_p[j][2] + \ + math.atan2(foot_height, + foot_width) - math.pi + r = math.sqrt( + math.pow(foot_width / 3., 2) + math.pow( + foot_height / 2., 2)) + rec = pat.Rectangle(xy=( + self.ref_p[j][0] + r * math.cos(angle), + self.ref_p[j][1] + r * math.sin(angle)), + width=foot_width, + height=foot_height, + angle=self.ref_p[j][ + 2] * 180 / math.pi, + color="blue", fill=False, + ls=":") + ax.add_patch(rec) + art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z") + + # foot rectangle for self.act_p + for j in range(len(self.act_p)): + angle = self.act_p[j][2] + \ + math.atan2(foot_height, + foot_width) - math.pi + r = math.sqrt( + math.pow(foot_width / 3., 2) + math.pow( + foot_height / 2., 2)) + rec = pat.Rectangle(xy=( + self.act_p[j][0] + r * math.cos(angle), + self.act_p[j][1] + r * math.sin(angle)), + width=foot_width, + height=foot_height, + angle=self.act_p[j][ + 2] * 180 / math.pi, + color="blue", fill=False) + ax.add_patch(rec) + art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z") + + plt.draw() + plt.pause(0.001) + if __name__ == "__main__": bipedal_planner = BipedalPlanner() diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py index 239ce16738..7ec01d1f4e 100644 --- a/PathPlanning/CubicSpline/cubic_spline_planner.py +++ b/PathPlanning/CubicSpline/cubic_spline_planner.py @@ -190,7 +190,7 @@ def calc_spline_course(x, y, ds=0.1): return rx, ry, ryaw, rk, s -def main(): +def main(): # pragma: no cover print("Spline 2D test") import matplotlib.pyplot as plt x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0] From b19476f175f7b7730068e5bb7883051e6521ae9d Mon Sep 17 00:00:00 2001 From: Todd Tang Date: Wed, 8 Jul 2020 21:02:27 +0800 Subject: [PATCH 232/909] Wavefront Coverage Path Planning (#351) * First Commit of Wavefront Coverage Planner * Update wavefront_coverage_path_planner.py * fix CI / CodeFactor problem * Fix mypy error * update * Fix PyCodeStyle * Fix Code Scanning Warning and code styling problem * add simple unittest * followed second code review suggestions --- PathPlanning/WavefrontCPP/map/test.png | Bin 0 -> 156 bytes PathPlanning/WavefrontCPP/map/test_2.png | Bin 0 -> 150 bytes PathPlanning/WavefrontCPP/map/test_3.png | Bin 0 -> 132 bytes .../wavefront_coverage_path_planner.py | 219 ++++++++++++++++++ tests/test_wavefront_coverage_path_planner.py | 64 +++++ 5 files changed, 283 insertions(+) create mode 100644 PathPlanning/WavefrontCPP/map/test.png create mode 100644 PathPlanning/WavefrontCPP/map/test_2.png create mode 100644 PathPlanning/WavefrontCPP/map/test_3.png create mode 100644 PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py create mode 100644 tests/test_wavefront_coverage_path_planner.py diff --git a/PathPlanning/WavefrontCPP/map/test.png b/PathPlanning/WavefrontCPP/map/test.png new file mode 100644 index 0000000000000000000000000000000000000000..4abca0bf3090e5b22cb5d6a48ed9c55a4024e787 GIT binary patch literal 156 zcmeAS@N?(olHy`uVBq!ia0vp^Iv~seBp9sUv)%<#>?NMQuIx|P8M&EELc9#81BC)S zT^vI!dXxX0cd$uNa(ufYN#sD2|D=hHjSuaGsttBZ-poD05Ss&JNE+~U7<-f(Z~R#> zVMk@i#}!F2466K(PSYECptdJh!x z@^oA^-pY literal 0 HcmV?d00001 diff --git a/PathPlanning/WavefrontCPP/map/test_3.png b/PathPlanning/WavefrontCPP/map/test_3.png new file mode 100644 index 0000000000000000000000000000000000000000..1a50b87ccff5a966f5e078282cefec2f14ba9112 GIT binary patch literal 132 zcmeAS@N?(olHy`uVBq!ia0vp^20*O90VEg-_b)jBq}WS5eO=j~u(R_RiwlJC@d1S_ zJY5_^EP9jwoNtgfSoCow$BN?&i>2I5d1QGtPRUU#f;|BY-CV2^o55JQI eekEFDFfjD$u~_NcJpT`927{-opUXO@geCxLEGFFm literal 0 HcmV?d00001 diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py new file mode 100644 index 0000000000..20d640f24a --- /dev/null +++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py @@ -0,0 +1,219 @@ +""" +Distance/Path Transform Wavefront Coverage Path Planner + +author: Todd Tang +paper: Planning paths of complete coverage of an unstructured environment + by a mobile robot - Zelinsky et.al. +link: http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf +""" + +import os +import sys + +import matplotlib.pyplot as plt +import numpy as np + +from scipy import ndimage + + +do_animation = True + + +def transform( + gridmap, src, distance_type='chessboard', + transform_type='path', alpha=0.01 +): + """transform + + calculating transform of transform_type from src + in given distance_type + + :param gridmap: 2d binary map + :param src: distance transform source + :param distance_type: type of distance used + :param transform_type: type of transform used + :param alpha: weight of Obstacle Transform usedwhen using path_transform + """ + + nrows, ncols = gridmap.shape + + if nrows == 0 or ncols == 0: + sys.exit('Empty gridmap.') + + inc_order = [[0, 1], [1, 1], [1, 0], [1, -1], + [0, -1], [-1, -1], [-1, 0], [-1, 1]] + if distance_type == 'chessboard': + cost = [1, 1, 1, 1, 1, 1, 1, 1] + elif distance_type == 'eculidean': + cost = [1, np.sqrt(2), 1, np.sqrt(2), 1, np.sqrt(2), 1, np.sqrt(2)] + else: + sys.exit('Unsupported distance type.') + + transform_matrix = float('inf') * np.ones_like(gridmap, dtype=np.float) + transform_matrix[src[0], src[1]] = 0 + if transform_type == 'distance': + eT = np.zeros_like(gridmap) + elif transform_type == 'path': + eT = ndimage.distance_transform_cdt(1-gridmap, distance_type) + else: + sys.exit('Unsupported transform type.') + + # set obstacle transform_matrix value to infinity + for i in range(nrows): + for j in range(ncols): + if gridmap[i][j] == 1.0: + transform_matrix[i][j] = float('inf') + is_visited = np.zeros_like(transform_matrix, dtype=bool) + is_visited[src[0], src[1]] = True + traversal_queue = [src] + calculated = [(src[0]-1)*ncols + src[1]] + + def is_valid_neighbor(i, j): + return ni >= 0 and ni < nrows and nj >= 0 and nj < ncols \ + and not gridmap[ni][nj] + + while traversal_queue != []: + i, j = traversal_queue.pop(0) + for k, inc in enumerate(inc_order): + ni = i + inc[0] + nj = j + inc[1] + if is_valid_neighbor(ni, nj): + is_visited[i][j] = True + + # update transform_matrix + transform_matrix[i][j] = min( + transform_matrix[i][j], + transform_matrix[ni][nj] + cost[k] + alpha*eT[ni][nj]) + + if not is_visited[ni][nj] \ + and ((ni-1)*ncols + nj) not in calculated: + traversal_queue.append((ni, nj)) + calculated.append((ni-1)*ncols + nj) + + return transform_matrix + + +def wavefront(transform_matrix, start, goal): + """wavefront + + performing wavefront coverage path planning + + :param transform_matrix: the transform matrix + :param start: start point of planning + :param goal: goal point of planning + """ + + path = [] + nrows, ncols = transform_matrix.shape + + def get_search_order_increment(start, goal): + if start[0] >= goal[0] and start[1] >= goal[1]: + order = [[1, 0], [0, 1], [-1, 0], [0, -1], + [1, 1], [1, -1], [-1, 1], [-1, -1]] + elif start[0] <= goal[0] and start[1] >= goal[1]: + order = [[-1, 0], [0, 1], [1, 0], [0, -1], + [-1, 1], [-1, -1], [1, 1], [1, -1]] + elif start[0] >= goal[0] and start[1] <= goal[1]: + order = [[1, 0], [0, -1], [-1, 0], [0, 1], + [1, -1], [-1, -1], [1, 1], [-1, 1]] + elif start[0] <= goal[0] and start[1] <= goal[1]: + order = [[-1, 0], [0, -1], [0, 1], [1, 0], + [-1, -1], [-1, 1], [1, -1], [1, 1]] + else: + sys.exit('get_search_order_increment: cannot determine \ + start=>goal increment order') + return order + + def is_valid_neighbor(i, j): + is_i_valid_bounded = i >= 0 and i < nrows + is_j_valid_bounded = j >= 0 and j < ncols + if is_i_valid_bounded and is_j_valid_bounded: + return not is_visited[i][j] and \ + transform_matrix[i][j] != float('inf') + return False + + inc_order = get_search_order_increment(start, goal) + + current_node = start + is_visited = np.zeros_like(transform_matrix, dtype=bool) + + while current_node != goal: + i, j = current_node + path.append((i, j)) + is_visited[i][j] = True + + max_T = float('-inf') + i_max = (-1, -1) + i_last = 0 + for i_last in range(len(path)): + current_node = path[-1 - i_last] # get lastest node in path + for ci, cj in inc_order: + ni, nj = current_node[0] + ci, current_node[1] + cj + if is_valid_neighbor(ni, nj) and \ + transform_matrix[ni][nj] > max_T: + i_max = (ni, nj) + max_T = transform_matrix[ni][nj] + + if i_max != (-1, -1): + break + + if i_max == (-1, -1): + break + else: + current_node = i_max + if i_last != 0: + print('backtracing to', current_node) + path.append(goal) + + return path + + +def visualize_path(grid_map, start, goal, path, resolution): + oy, ox = start + gy, gx = goal + px, py = np.transpose(np.flipud(np.fliplr((path)))) + + if not do_animation: + plt.imshow(grid_map, cmap='Greys') + plt.plot(ox, oy, "-xy") + plt.plot(px, py, "-r") + plt.plot(gx, gy, "-pg") + plt.show() + else: + for ipx, ipy in zip(px, py): + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + plt.imshow(grid_map, cmap='Greys') + plt.plot(ox, oy, "-xb") + plt.plot(px, py, "-r") + plt.plot(gx, gy, "-pg") + plt.plot(ipx, ipy, "or") + plt.axis("equal") + plt.grid(True) + plt.pause(0.1) + + +def main(): + dir_path = os.path.dirname(os.path.realpath(__file__)) + img = plt.imread(os.path.join(dir_path, 'map', 'test.png')) + img = 1 - img # revert pixel values + + start = (43, 0) + goal = (0, 0) + + # distance transform wavefront + DT = transform(img, goal, transform_type='distance') + DT_path = wavefront(DT, start, goal) + visualize_path(img, start, goal, DT_path, 1) + + # path transform wavefront + PT = transform(img, goal, transform_type='path', alpha=0.01) + PT_path = wavefront(PT, start, goal) + visualize_path(img, start, goal, PT_path, 1) + + +if __name__ == "__main__": + main() diff --git a/tests/test_wavefront_coverage_path_planner.py b/tests/test_wavefront_coverage_path_planner.py new file mode 100644 index 0000000000..a72818c259 --- /dev/null +++ b/tests/test_wavefront_coverage_path_planner.py @@ -0,0 +1,64 @@ +import os +import sys +import matplotlib.pyplot as plt +from unittest import TestCase + +sys.path.append(os.path.dirname( + os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP") +try: + import wavefront_coverage_path_planner +except ImportError: + raise + +wavefront_coverage_path_planner.do_animation = False + + +class TestPlanning(TestCase): + def wavefront_cpp(self, img, start, goal): + num_free = 0 + for i in range(img.shape[0]): + for j in range(img.shape[1]): + num_free += 1 - img[i][j] + + DT = wavefront_coverage_path_planner.transform( + img, goal, transform_type='distance') + DT_path = wavefront_coverage_path_planner.wavefront(DT, start, goal) + self.assertEqual(len(DT_path), num_free) # assert complete coverage + + PT = wavefront_coverage_path_planner.transform( + img, goal, transform_type='path', alpha=0.01) + PT_path = wavefront_coverage_path_planner.wavefront(PT, start, goal) + self.assertEqual(len(PT_path), num_free) # assert complete coverage + + def test_wavefront_CPP_1(self): + img_dir = os.path.dirname( + os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP" + img = plt.imread(os.path.join(img_dir, 'map', 'test.png')) + img = 1 - img + + start = (43, 0) + goal = (0, 0) + + self.wavefront_cpp(img, start, goal) + + def test_wavefront_CPP_2(self): + img_dir = os.path.dirname( + os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP" + img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png')) + img = 1 - img + + start = (10, 0) + goal = (10, 40) + + self.wavefront_cpp(img, start, goal) + + def test_wavefront_CPP_3(self): + img_dir = os.path.dirname( + os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP" + img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png')) + img = 1 - img + + start = (0, 0) + goal = (30, 30) + + self.wavefront_cpp(img, start, goal) From 7dd6b6793a5e1e55303103709d151bfa378cba38 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 8 Jul 2020 22:53:20 +0900 Subject: [PATCH 233/909] remove cover alls (#353) --- .travis.yml | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000000..9fcc2dbdc2 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,35 @@ +language: python + +matrix: + include: + - os: linux + +python: + - 3.8 + +before_install: + - sudo apt-get update + - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh + - chmod +x miniconda.sh + - bash miniconda.sh -b -p $HOME/miniconda + - export PATH="$HOME/miniconda/bin:$PATH" + - hash -r + - conda config --set always_yes yes --set changeps1 no + - conda config --append channels conda-forge + - conda update -q conda + # Useful for debugging any issues with conda + - conda info -a + +install: + - conda install numpy + - conda install scipy + - conda install matplotlib + - conda install pandas + - conda install cvxpy + - conda install coveralls + +script: + - python --version + +after_success: + - coveralls \ No newline at end of file From 72c4a891b33d2075df77f97d0bd88f0540fd455b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 10 Jul 2020 21:34:47 +0900 Subject: [PATCH 234/909] remove cover alls (#357) * remove cover alls * add codecov * try coverage * try coverage * try coverage --- .github/workflows/Linux_CI.yml | 2 + .travis.yml | 35 ------ .../wavefront_coverage_path_planner.py | 103 +++++++++--------- README.md | 2 +- 4 files changed, 54 insertions(+), 88 deletions(-) delete mode 100644 .travis.yml diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 293c5621e2..de99bd446f 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -45,6 +45,8 @@ jobs: run: bash rundiffstylecheck.sh - name: do all unit tests run: bash runtests.sh + - name: Codecov + uses: codecov/codecov-action@v1.0.10 diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 9fcc2dbdc2..0000000000 --- a/.travis.yml +++ /dev/null @@ -1,35 +0,0 @@ -language: python - -matrix: - include: - - os: linux - -python: - - 3.8 - -before_install: - - sudo apt-get update - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - - chmod +x miniconda.sh - - bash miniconda.sh -b -p $HOME/miniconda - - export PATH="$HOME/miniconda/bin:$PATH" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda config --append channels conda-forge - - conda update -q conda - # Useful for debugging any issues with conda - - conda info -a - -install: - - conda install numpy - - conda install scipy - - conda install matplotlib - - conda install pandas - - conda install cvxpy - - conda install coveralls - -script: - - python --version - -after_success: - - coveralls \ No newline at end of file diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py index 20d640f24a..aaf77e02fc 100644 --- a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py +++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py @@ -12,33 +12,31 @@ import matplotlib.pyplot as plt import numpy as np - from scipy import ndimage - do_animation = True def transform( - gridmap, src, distance_type='chessboard', - transform_type='path', alpha=0.01 + grid_map, src, distance_type='chessboard', + transform_type='path', alpha=0.01 ): """transform calculating transform of transform_type from src in given distance_type - :param gridmap: 2d binary map + :param grid_map: 2d binary map :param src: distance transform source :param distance_type: type of distance used :param transform_type: type of transform used - :param alpha: weight of Obstacle Transform usedwhen using path_transform + :param alpha: weight of Obstacle Transform used when using path_transform """ - nrows, ncols = gridmap.shape + n_rows, n_cols = grid_map.shape - if nrows == 0 or ncols == 0: - sys.exit('Empty gridmap.') + if n_rows == 0 or n_cols == 0: + sys.exit('Empty grid_map.') inc_order = [[0, 1], [1, 1], [1, 0], [1, -1], [0, -1], [-1, -1], [-1, 0], [-1, 1]] @@ -49,30 +47,30 @@ def transform( else: sys.exit('Unsupported distance type.') - transform_matrix = float('inf') * np.ones_like(gridmap, dtype=np.float) + transform_matrix = float('inf') * np.ones_like(grid_map, dtype=np.float) transform_matrix[src[0], src[1]] = 0 if transform_type == 'distance': - eT = np.zeros_like(gridmap) + eT = np.zeros_like(grid_map) elif transform_type == 'path': - eT = ndimage.distance_transform_cdt(1-gridmap, distance_type) + eT = ndimage.distance_transform_cdt(1 - grid_map, distance_type) else: sys.exit('Unsupported transform type.') # set obstacle transform_matrix value to infinity - for i in range(nrows): - for j in range(ncols): - if gridmap[i][j] == 1.0: + for i in range(n_rows): + for j in range(n_cols): + if grid_map[i][j] == 1.0: transform_matrix[i][j] = float('inf') is_visited = np.zeros_like(transform_matrix, dtype=bool) is_visited[src[0], src[1]] = True traversal_queue = [src] - calculated = [(src[0]-1)*ncols + src[1]] + calculated = [(src[0] - 1) * n_cols + src[1]] - def is_valid_neighbor(i, j): - return ni >= 0 and ni < nrows and nj >= 0 and nj < ncols \ - and not gridmap[ni][nj] + def is_valid_neighbor(g_i, g_j): + return 0 <= g_i < n_rows and 0 <= g_j < n_cols \ + and not grid_map[g_i][g_j] - while traversal_queue != []: + while traversal_queue: i, j = traversal_queue.pop(0) for k, inc in enumerate(inc_order): ni = i + inc[0] @@ -83,16 +81,35 @@ def is_valid_neighbor(i, j): # update transform_matrix transform_matrix[i][j] = min( transform_matrix[i][j], - transform_matrix[ni][nj] + cost[k] + alpha*eT[ni][nj]) + transform_matrix[ni][nj] + cost[k] + alpha * eT[ni][nj]) if not is_visited[ni][nj] \ - and ((ni-1)*ncols + nj) not in calculated: + and ((ni - 1) * n_cols + nj) not in calculated: traversal_queue.append((ni, nj)) - calculated.append((ni-1)*ncols + nj) + calculated.append((ni - 1) * n_cols + nj) return transform_matrix +def get_search_order_increment(start, goal): + if start[0] >= goal[0] and start[1] >= goal[1]: + order = [[1, 0], [0, 1], [-1, 0], [0, -1], + [1, 1], [1, -1], [-1, 1], [-1, -1]] + elif start[0] <= goal[0] and start[1] >= goal[1]: + order = [[-1, 0], [0, 1], [1, 0], [0, -1], + [-1, 1], [-1, -1], [1, 1], [1, -1]] + elif start[0] >= goal[0] and start[1] <= goal[1]: + order = [[1, 0], [0, -1], [-1, 0], [0, 1], + [1, -1], [-1, -1], [1, 1], [-1, 1]] + elif start[0] <= goal[0] and start[1] <= goal[1]: + order = [[-1, 0], [0, -1], [0, 1], [1, 0], + [-1, -1], [-1, 1], [1, -1], [1, 1]] + else: + sys.exit('get_search_order_increment: cannot determine \ + start=>goal increment order') + return order + + def wavefront(transform_matrix, start, goal): """wavefront @@ -104,32 +121,14 @@ def wavefront(transform_matrix, start, goal): """ path = [] - nrows, ncols = transform_matrix.shape - - def get_search_order_increment(start, goal): - if start[0] >= goal[0] and start[1] >= goal[1]: - order = [[1, 0], [0, 1], [-1, 0], [0, -1], - [1, 1], [1, -1], [-1, 1], [-1, -1]] - elif start[0] <= goal[0] and start[1] >= goal[1]: - order = [[-1, 0], [0, 1], [1, 0], [0, -1], - [-1, 1], [-1, -1], [1, 1], [1, -1]] - elif start[0] >= goal[0] and start[1] <= goal[1]: - order = [[1, 0], [0, -1], [-1, 0], [0, 1], - [1, -1], [-1, -1], [1, 1], [-1, 1]] - elif start[0] <= goal[0] and start[1] <= goal[1]: - order = [[-1, 0], [0, -1], [0, 1], [1, 0], - [-1, -1], [-1, 1], [1, -1], [1, 1]] - else: - sys.exit('get_search_order_increment: cannot determine \ - start=>goal increment order') - return order + n_rows, n_cols = transform_matrix.shape - def is_valid_neighbor(i, j): - is_i_valid_bounded = i >= 0 and i < nrows - is_j_valid_bounded = j >= 0 and j < ncols + def is_valid_neighbor(g_i, g_j): + is_i_valid_bounded = 0 <= g_i < n_rows + is_j_valid_bounded = 0 <= g_j < n_cols if is_i_valid_bounded and is_j_valid_bounded: - return not is_visited[i][j] and \ - transform_matrix[i][j] != float('inf') + return not is_visited[g_i][g_j] and \ + transform_matrix[g_i][g_j] != float('inf') return False inc_order = get_search_order_increment(start, goal) @@ -146,7 +145,7 @@ def is_valid_neighbor(i, j): i_max = (-1, -1) i_last = 0 for i_last in range(len(path)): - current_node = path[-1 - i_last] # get lastest node in path + current_node = path[-1 - i_last] # get latest node in path for ci, cj in inc_order: ni, nj = current_node[0] + ci, current_node[1] + cj if is_valid_neighbor(ni, nj) and \ @@ -168,10 +167,10 @@ def is_valid_neighbor(i, j): return path -def visualize_path(grid_map, start, goal, path, resolution): +def visualize_path(grid_map, start, goal, path): # pragma: no cover oy, ox = start gy, gx = goal - px, py = np.transpose(np.flipud(np.fliplr((path)))) + px, py = np.transpose(np.flipud(np.fliplr(path))) if not do_animation: plt.imshow(grid_map, cmap='Greys') @@ -207,12 +206,12 @@ def main(): # distance transform wavefront DT = transform(img, goal, transform_type='distance') DT_path = wavefront(DT, start, goal) - visualize_path(img, start, goal, DT_path, 1) + visualize_path(img, start, goal, DT_path) # path transform wavefront PT = transform(img, goal, transform_type='path', alpha=0.01) PT_path = wavefront(PT, start, goal) - visualize_path(img, start, goal, PT_path, 1) + visualize_path(img, start, goal, PT_path) if __name__ == "__main__": diff --git a/README.md b/README.md index 4a64f53642..49a99959f1 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics) [![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest) [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) -[![Coverage Status](https://coveralls.io/repos/github/AtsushiSakai/PythonRobotics/badge.svg?branch=master)](https://coveralls.io/github/AtsushiSakai/PythonRobotics?branch=master) +[![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python) [![CodeFactor](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/badge/master)](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master) [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics) From 4c5e3ccc9eba866f743feb3ade462fb104f0f4b0 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 10 Jul 2020 23:46:19 +0900 Subject: [PATCH 235/909] fix VisibleDeprecation Warning (#358) * try coverage * add python warning setting * add random seed for test coverage --- SLAM/EKFSLAM/ekf_slam.py | 7 ++++--- runtests.sh | 1 + tests/test_batch_informed_rrt_star.py | 5 ++++- tests/test_closed_loop_rrt_star_car.py | 4 +++- tests/test_dubins_path_planning.py | 1 + tests/test_lqr_rrt_star.py | 5 ++++- tests/test_n_joint_arm_to_point_control.py | 3 +++ tests/test_rrt.py | 3 +++ 8 files changed, 23 insertions(+), 6 deletions(-) diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index 6b349d858b..8fa1a771c2 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -117,7 +117,7 @@ def jacob_motion(x, u): jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])], [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])], - [0.0, 0.0, 0.0]]) + [0.0, 0.0, 0.0]], dtype=np.float64) G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx @@ -236,8 +236,9 @@ def main(): if show_animation: # pragma: no cover plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(RFID[:, 0], RFID[:, 1], "*k") plt.plot(xEst[0], xEst[1], ".r") diff --git a/runtests.sh b/runtests.sh index 52ef50f001..d7df3cff04 100755 --- a/runtests.sh +++ b/runtests.sh @@ -1,5 +1,6 @@ #!/usr/bin/env bash echo "Run test suites! " +export PYTHONWARNINGS=default # show warning #python -m unittest discover tests #python -Wignore -m unittest discover tests #ignore warning coverage run -m unittest discover tests # generate coverage file diff --git a/tests/test_batch_informed_rrt_star.py b/tests/test_batch_informed_rrt_star.py index e83684d92c..bac8941460 100644 --- a/tests/test_batch_informed_rrt_star.py +++ b/tests/test_batch_informed_rrt_star.py @@ -1,14 +1,17 @@ from unittest import TestCase import sys import os +import random sys.path.append(os.path.dirname(__file__) + "/../") try: from PathPlanning.BatchInformedRRTStar import batch_informed_rrtstar as m -except: +except ImportError: raise print(__file__) +random.seed(12345) + class Test(TestCase): diff --git a/tests/test_closed_loop_rrt_star_car.py b/tests/test_closed_loop_rrt_star_car.py index 18513ce665..060389d6a2 100644 --- a/tests/test_closed_loop_rrt_star_car.py +++ b/tests/test_closed_loop_rrt_star_car.py @@ -1,5 +1,6 @@ import os import sys +import random from unittest import TestCase sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../") @@ -7,9 +8,10 @@ "/../PathPlanning/ClosedLoopRRTStar/") try: from PathPlanning.ClosedLoopRRTStar import closed_loop_rrt_star_car as m -except: +except ImportError: raise +random.seed(12345) print(__file__) diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py index f9349217a1..1be70e390e 100644 --- a/tests/test_dubins_path_planning.py +++ b/tests/test_dubins_path_planning.py @@ -1,6 +1,7 @@ from unittest import TestCase import numpy as np +np.random.seed(12345) from PathPlanning.DubinsPath import dubins_path_planning diff --git a/tests/test_lqr_rrt_star.py b/tests/test_lqr_rrt_star.py index a9c2f5eece..0b59475b3d 100644 --- a/tests/test_lqr_rrt_star.py +++ b/tests/test_lqr_rrt_star.py @@ -1,16 +1,19 @@ from unittest import TestCase import sys import os +import random sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../") sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../PathPlanning/LQRRRTStar/") try: from PathPlanning.LQRRRTStar import lqr_rrt_star as m -except: +except ImportError: raise print(__file__) +random.seed(12345) + class Test(TestCase): diff --git a/tests/test_n_joint_arm_to_point_control.py b/tests/test_n_joint_arm_to_point_control.py index a935216475..deb30f83be 100644 --- a/tests/test_n_joint_arm_to_point_control.py +++ b/tests/test_n_joint_arm_to_point_control.py @@ -1,5 +1,6 @@ import os import sys +import random from unittest import TestCase sys.path.append(os.path.dirname(__file__) + "/../ArmNavigation/n_joint_arm_to_point_control/") @@ -8,6 +9,8 @@ print(__file__) +random.seed(12345) + class Test(TestCase): diff --git a/tests/test_rrt.py b/tests/test_rrt.py index 6e8f3f300a..b7c03d25cf 100644 --- a/tests/test_rrt.py +++ b/tests/test_rrt.py @@ -1,5 +1,6 @@ import os import sys +import random from unittest import TestCase sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../") @@ -12,6 +13,8 @@ print(__file__) +random.seed(12345) + class Test(TestCase): From cb796ddeffc83750672846db4353332a1a2243dd Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 11 Jul 2020 14:12:37 +0900 Subject: [PATCH 236/909] Add .circleci/config.yml (#359) * Add .circleci/config.yml * try doc build * try doc build * try doc build * try doc build * try doc build * try doc build * try doc build --- .circleci/artifact_path | 1 + .circleci/config.yml | 25 +++++++++++++++++++++++++ .github/workflows/Linux_CI.yml | 7 +++++++ 3 files changed, 33 insertions(+) create mode 100644 .circleci/artifact_path create mode 100644 .circleci/config.yml diff --git a/.circleci/artifact_path b/.circleci/artifact_path new file mode 100644 index 0000000000..59e6bd06ee --- /dev/null +++ b/.circleci/artifact_path @@ -0,0 +1 @@ +0/html/index.html \ No newline at end of file diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000000..f63f094a3f --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,25 @@ +version: 2.1 + +orbs: + python: circleci/python@0.2.1 + +jobs: + build_doc: + executor: python/default + steps: + - checkout + - python/load-cache + - python/install-deps + - python/save-cache + - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme + - run: + command: cd docs;make html + name: doc_build + - store_artifacts: + path: docs/_build/html/ + destination: html + +workflows: + main: + jobs: + - build_doc diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index de99bd446f..8338a5f4ce 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -43,6 +43,12 @@ jobs: find SLAM -name "*.py" | xargs mypy - name: do diff style check run: bash rundiffstylecheck.sh + - name: run-circleci-artifacts-redirector + uses: larsoner/circleci-artifacts-redirector-action@0.2.0 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + artifact-path: 0/html/index.html + circleci-jobs: build_doc - name: do all unit tests run: bash runtests.sh - name: Codecov @@ -50,3 +56,4 @@ jobs: + From e8a805ef67b8c7ef8f623d1f594c539ffa288d1f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 11 Jul 2020 16:18:13 +0900 Subject: [PATCH 237/909] doc build test (#360) * try doc build * try doc build * try doc build * try doc build * try doc build * try doc build --- .circleci/artifact_path | 2 +- .github/workflows/Linux_CI.yml | 6 ------ .github/workflows/main.yml | 16 ++++++++++++++++ 3 files changed, 17 insertions(+), 7 deletions(-) create mode 100644 .github/workflows/main.yml diff --git a/.circleci/artifact_path b/.circleci/artifact_path index 59e6bd06ee..d2c64880b1 100644 --- a/.circleci/artifact_path +++ b/.circleci/artifact_path @@ -1 +1 @@ -0/html/index.html \ No newline at end of file +0/html/index.html diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 8338a5f4ce..a751c75d4d 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -43,12 +43,6 @@ jobs: find SLAM -name "*.py" | xargs mypy - name: do diff style check run: bash rundiffstylecheck.sh - - name: run-circleci-artifacts-redirector - uses: larsoner/circleci-artifacts-redirector-action@0.2.0 - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - artifact-path: 0/html/index.html - circleci-jobs: build_doc - name: do all unit tests run: bash runtests.sh - name: Codecov diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000000..55d33eb324 --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,16 @@ +name: circleci-artifacts-redirector-action +on: [status] +jobs: + circleci_artifacts_redirector_job: + runs-on: ubuntu-latest + name: Run CircleCI artifacts redirector + steps: + - name: run-circleci-artifacts-redirector + uses: larsoner/circleci-artifacts-redirector-action@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + artifact-path: 0/html/index.html + circleci-jobs: build_doc + - name: Check the URL + run: | + curl --fail ${{ steps.step1.outputs.url }} | grep $GITHUB_SHA From 9fe14e657ba6a6c2aa7ee8bc586312e1c2154b72 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 11 Jul 2020 17:12:47 +0900 Subject: [PATCH 238/909] doc build test again (#361) * try doc build * try doc build --- .../workflows/{main.yml => circleci-artifacts-redirector.yml} | 2 +- docs/index.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) rename .github/workflows/{main.yml => circleci-artifacts-redirector.yml} (87%) diff --git a/.github/workflows/main.yml b/.github/workflows/circleci-artifacts-redirector.yml similarity index 87% rename from .github/workflows/main.yml rename to .github/workflows/circleci-artifacts-redirector.yml index 55d33eb324..849a97f1a7 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/circleci-artifacts-redirector.yml @@ -6,7 +6,7 @@ jobs: name: Run CircleCI artifacts redirector steps: - name: run-circleci-artifacts-redirector - uses: larsoner/circleci-artifacts-redirector-action@master + uses: larsoner/circleci-artifacts-redirector-action@0.2.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/html/index.html diff --git a/docs/index.rst b/docs/index.rst index e8e6052879..3798a1a1a6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -8,7 +8,7 @@ Welcome to PythonRobotics's documentation! Python codes for robotics algorithm. The project is on `GitHub`_. -This is a Python code collection of robotics algorithms, especially for autonomous navigation. +This is a Python code collection of robotics algorithms. Features: From 0c23ebe62be248000151f6fe543bcd551d964177 Mon Sep 17 00:00:00 2001 From: Todd Tang Date: Sun, 12 Jul 2020 12:09:43 +0800 Subject: [PATCH 239/909] Spiral Spanning Tree Coverage Path Planning (#355) * First commit of Spiral Spanning Tree Coverage * Modify followed by first code review * fix pycodestyle error * modifies following 2nd code review --- .../SpiralSpanningTreeCPP/map/test.png | Bin 0 -> 156 bytes .../SpiralSpanningTreeCPP/map/test_2.png | Bin 0 -> 150 bytes .../SpiralSpanningTreeCPP/map/test_3.png | Bin 0 -> 132 bytes ...ral_spanning_tree_coverage_path_planner.py | 313 ++++++++++++++++++ ...ral_spanning_tree_coverage_path_planner.py | 58 ++++ 5 files changed, 371 insertions(+) create mode 100644 PathPlanning/SpiralSpanningTreeCPP/map/test.png create mode 100644 PathPlanning/SpiralSpanningTreeCPP/map/test_2.png create mode 100644 PathPlanning/SpiralSpanningTreeCPP/map/test_3.png create mode 100644 PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py create mode 100644 tests/test_spiral_spanning_tree_coverage_path_planner.py diff --git a/PathPlanning/SpiralSpanningTreeCPP/map/test.png b/PathPlanning/SpiralSpanningTreeCPP/map/test.png new file mode 100644 index 0000000000000000000000000000000000000000..4abca0bf3090e5b22cb5d6a48ed9c55a4024e787 GIT binary patch literal 156 zcmeAS@N?(olHy`uVBq!ia0vp^Iv~seBp9sUv)%<#>?NMQuIx|P8M&EELc9#81BC)S zT^vI!dXxX0cd$uNa(ufYN#sD2|D=hHjSuaGsttBZ-poD05Ss&JNE+~U7<-f(Z~R#> zVMk@i#}!F2466K(PSYECptdJh!x z@^oA^-pY literal 0 HcmV?d00001 diff --git a/PathPlanning/SpiralSpanningTreeCPP/map/test_3.png b/PathPlanning/SpiralSpanningTreeCPP/map/test_3.png new file mode 100644 index 0000000000000000000000000000000000000000..1a50b87ccff5a966f5e078282cefec2f14ba9112 GIT binary patch literal 132 zcmeAS@N?(olHy`uVBq!ia0vp^20*O90VEg-_b)jBq}WS5eO=j~u(R_RiwlJC@d1S_ zJY5_^EP9jwoNtgfSoCow$BN?&i>2I5d1QGtPRUU#f;|BY-CV2^o55JQI eekEFDFfjD$u~_NcJpT`927{-opUXO@geCxLEGFFm literal 0 HcmV?d00001 diff --git a/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py b/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py new file mode 100644 index 0000000000..4a6b42db0b --- /dev/null +++ b/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py @@ -0,0 +1,313 @@ +""" +Spiral Spanning Tree Coverage Path Planner + +author: Todd Tang +paper: Spiral-STC: An On-Line Coverage Algorithm of Grid Environments + by a Mobile Robot - Gabriely et.al. +link: https://ieeexplore.ieee.org/abstract/document/1013479 +""" + +import os +import sys +import math + +import numpy as np +import matplotlib.pyplot as plt + +do_animation = True + + +class SpiralSpanningTreeCoveragePlanner: + def __init__(self, occ_map): + self.origin_map_height = occ_map.shape[0] + self.origin_map_width = occ_map.shape[1] + + # original map resolution must be even + if self.origin_map_height % 2 == 1 or self.origin_map_width % 2 == 1: + sys.exit('original map width/height must be even \ + in grayscale .png format') + + self.occ_map = occ_map + self.merged_map_height = self.origin_map_height // 2 + self.merged_map_width = self.origin_map_width // 2 + + self.edge = [] + + def plan(self, start): + """plan + + performing Spiral Spanning Tree Coverage path planning + + :param start: the start node of Spiral Spanning Tree Coverage + """ + + visit_times = np.zeros( + (self.merged_map_height, self.merged_map_width), dtype=np.int) + visit_times[start[0]][start[1]] = 1 + + # generate route by + # recusively call perform_spanning_tree_coverage() from start node + route = [] + self.perform_spanning_tree_coverage(start, visit_times, route) + + path = [] + # generate path from route + for idx in range(len(route)-1): + dp = abs(route[idx][0] - route[idx+1][0]) + \ + abs(route[idx][1] - route[idx+1][1]) + if dp == 0: + # special handle for round-trip path + path.append(self.get_round_trip_path(route[idx-1], route[idx])) + elif dp == 1: + path.append(self.move(route[idx], route[idx+1])) + elif dp == 2: + # special handle for non-adjacent route nodes + mid_node = self.get_intermediate_node(route[idx], route[idx+1]) + path.append(self.move(route[idx], mid_node)) + path.append(self.move(mid_node, route[idx+1])) + else: + sys.exit('adjacent path node distance larger than 2') + + return self.edge, route, path + + def perform_spanning_tree_coverage(self, current_node, visit_times, route): + """perform_spanning_tree_coverage + + recursive function for function + + :param current_node: current node + """ + + def is_valid_node(i, j): + is_i_valid_bounded = 0 <= i < self.merged_map_height + is_j_valid_bounded = 0 <= j < self.merged_map_width + if is_i_valid_bounded and is_j_valid_bounded: + # free only when the 4 sub-cells are all free + return bool( + self.occ_map[2*i][2*j] + and self.occ_map[2*i+1][2*j] + and self.occ_map[2*i][2*j+1] + and self.occ_map[2*i+1][2*j+1]) + + return False + + # counter-clockwise neighbor finding order + order = [[1, 0], [0, 1], [-1, 0], [0, -1]] + + found = False + route.append(current_node) + for inc in order: + ni, nj = current_node[0] + inc[0], current_node[1] + inc[1] + if is_valid_node(ni, nj) and visit_times[ni][nj] == 0: + neighbor_node = (ni, nj) + self.edge.append((current_node, neighbor_node)) + found = True + visit_times[ni][nj] += 1 + self.perform_spanning_tree_coverage( + neighbor_node, visit_times, route) + + # backtrace route from node with neighbors all visited + # to first node with unvisited neighbor + if not found: + has_node_with_unvisited_ngb = False + for node in reversed(route): + # drop nodes that have been visited twice + if visit_times[node[0]][node[1]] == 2: + continue + + visit_times[node[0]][node[1]] += 1 + route.append(node) + + for inc in order: + ni, nj = node[0] + inc[0], node[1] + inc[1] + if is_valid_node(ni, nj) and visit_times[ni][nj] == 0: + has_node_with_unvisited_ngb = True + break + + if has_node_with_unvisited_ngb: + break + + return route + + def move(self, p, q): + direction = self.get_vector_direction(p, q) + # move east + if direction == 'E': + p = self.get_sub_node(p, 'SE') + q = self.get_sub_node(q, 'SW') + # move west + elif direction == 'W': + p = self.get_sub_node(p, 'NW') + q = self.get_sub_node(q, 'NE') + # move south + elif direction == 'S': + p = self.get_sub_node(p, 'SW') + q = self.get_sub_node(q, 'NW') + # move north + elif direction == 'N': + p = self.get_sub_node(p, 'NE') + q = self.get_sub_node(q, 'SE') + else: + sys.exit('move direction error...') + return [p, q] + + def get_round_trip_path(self, last, pivot): + direction = self.get_vector_direction(last, pivot) + if direction == 'E': + return [self.get_sub_node(pivot, 'SE'), + self.get_sub_node(pivot, 'NE')] + elif direction == 'S': + return [self.get_sub_node(pivot, 'SW'), + self.get_sub_node(pivot, 'SE')] + elif direction == 'W': + return [self.get_sub_node(pivot, 'NW'), + self.get_sub_node(pivot, 'SW')] + elif direction == 'N': + return [self.get_sub_node(pivot, 'NE'), + self.get_sub_node(pivot, 'NW')] + else: + sys.exit('get_round_trip_path: last->pivot direction error.') + + def get_vector_direction(self, p, q): + # east + if p[0] == q[0] and p[1] < q[1]: + return 'E' + # west + elif p[0] == q[0] and p[1] > q[1]: + return 'W' + # south + elif p[0] < q[0] and p[1] == q[1]: + return 'S' + # north + elif p[0] > q[0] and p[1] == q[1]: + return 'N' + else: + sys.exit('get_vector_direction: Only E/W/S/N direction supported.') + + def get_sub_node(self, node, direction): + if direction == 'SE': + return [2*node[0]+1, 2*node[1]+1] + elif direction == 'SW': + return [2*node[0]+1, 2*node[1]] + elif direction == 'NE': + return [2*node[0], 2*node[1]+1] + elif direction == 'NW': + return [2*node[0], 2*node[1]] + else: + sys.exit('get_sub_node: sub-node direction error.') + + def get_interpolated_path(self, p, q): + # direction p->q: southwest / northeast + if (p[0] < q[0]) ^ (p[1] < q[1]): + ipx = [p[0], p[0], q[0]] + ipy = [p[1], q[1], q[1]] + # direction p->q: southeast / northwest + else: + ipx = [p[0], q[0], q[0]] + ipy = [p[1], p[1], q[1]] + return ipx, ipy + + def get_intermediate_node(self, p, q): + p_ngb, q_ngb = set(), set() + + for m, n in self.edge: + if m == p: + p_ngb.add(n) + if n == p: + p_ngb.add(m) + if m == q: + q_ngb.add(n) + if n == q: + q_ngb.add(m) + + itsc = p_ngb.intersection(q_ngb) + if len(itsc) == 0: + sys.exit('get_intermediate_node: \ + no intermediate node between', p, q) + elif len(itsc) == 1: + return list(itsc)[0] + else: + sys.exit('get_intermediate_node: \ + more than 1 intermediate node between', p, q) + + def visualize_path(self, edge, path, start): + def coord_transform(p): + return [2*p[1] + 0.5, 2*p[0] + 0.5] + + if do_animation: + last = path[0][0] + trajectory = [[last[1]], [last[0]]] + for p, q in path: + distance = math.hypot(p[0]-last[0], p[1]-last[1]) + if distance <= 1.0: + trajectory[0].append(p[1]) + trajectory[1].append(p[0]) + else: + ipx, ipy = self.get_interpolated_path(last, p) + trajectory[0].extend(ipy) + trajectory[1].extend(ipx) + + last = q + + trajectory[0].append(last[1]) + trajectory[1].append(last[0]) + + for idx, state in enumerate(np.transpose(trajectory)): + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + + # draw spanning tree + plt.imshow(self.occ_map, 'gray') + for p, q in edge: + p = coord_transform(p) + q = coord_transform(q) + plt.plot([p[0], q[0]], [p[1], q[1]], '-oc') + sx, sy = coord_transform(start) + plt.plot([sx], [sy], 'pr', markersize=10) + + # draw move path + plt.plot(trajectory[0][:idx+1], trajectory[1][:idx+1], '-k') + plt.plot(state[0], state[1], 'or') + plt.axis('equal') + plt.grid(True) + plt.pause(0.01) + + else: + # draw spanning tree + plt.imshow(self.occ_map, 'gray') + for p, q in edge: + p = coord_transform(p) + q = coord_transform(q) + plt.plot([p[0], q[0]], [p[1], q[1]], '-oc') + sx, sy = coord_transform(start) + plt.plot([sx], [sy], 'pr', markersize=10) + + # draw move path + last = path[0][0] + for p, q in path: + distance = math.hypot(p[0]-last[0], p[1]-last[1]) + if distance == 1.0: + plt.plot([last[1], p[1]], [last[0], p[0]], '-k') + else: + ipx, ipy = self.get_interpolated_path(last, p) + plt.plot(ipy, ipx, '-k') + plt.arrow(p[1], p[0], q[1]-p[1], q[0]-p[0], head_width=0.2) + last = q + + plt.show() + + +def main(): + dir_path = os.path.dirname(os.path.realpath(__file__)) + img = plt.imread(os.path.join(dir_path, 'map', 'test_2.png')) + STC_planner = SpiralSpanningTreeCoveragePlanner(img) + start = (10, 0) + edge, route, path = STC_planner.plan(start) + STC_planner.visualize_path(edge, path, start) + + +if __name__ == "__main__": + main() diff --git a/tests/test_spiral_spanning_tree_coverage_path_planner.py b/tests/test_spiral_spanning_tree_coverage_path_planner.py new file mode 100644 index 0000000000..622b6ba18c --- /dev/null +++ b/tests/test_spiral_spanning_tree_coverage_path_planner.py @@ -0,0 +1,58 @@ +import os +import sys +import matplotlib.pyplot as plt +from unittest import TestCase + +sys.path.append(os.path.dirname( + os.path.abspath(__file__)) + "/../PathPlanning/SpiralSpanningTreeCPP") +try: + import spiral_spanning_tree_coverage_path_planner +except ImportError: + raise + +spiral_spanning_tree_coverage_path_planner.do_animation = True + + +class TestPlanning(TestCase): + def spiral_stc_cpp(self, img, start): + num_free = 0 + for i in range(img.shape[0]): + for j in range(img.shape[1]): + num_free += img[i][j] + + STC_planner = spiral_spanning_tree_coverage_path_planner.\ + SpiralSpanningTreeCoveragePlanner(img) + + edge, route, path = STC_planner.plan(start) + + covered_nodes = set() + for p, q in edge: + covered_nodes.add(p) + covered_nodes.add(q) + + # assert complete coverage + self.assertEqual(len(covered_nodes), num_free / 4) + + def test_spiral_stc_cpp_1(self): + img_dir = os.path.dirname( + os.path.abspath(__file__)) + \ + "/../PathPlanning/SpiralSpanningTreeCPP" + img = plt.imread(os.path.join(img_dir, 'map', 'test.png')) + start = (0, 0) + self.spiral_stc_cpp(img, start) + + def test_spiral_stc_cpp_2(self): + img_dir = os.path.dirname( + os.path.abspath(__file__)) + \ + "/../PathPlanning/SpiralSpanningTreeCPP" + img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png')) + start = (10, 0) + self.spiral_stc_cpp(img, start) + + def test_spiral_stc_cpp_3(self): + img_dir = os.path.dirname( + os.path.abspath(__file__)) + \ + "/../PathPlanning/SpiralSpanningTreeCPP" + img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png')) + start = (0, 0) + self.spiral_stc_cpp(img, start) From 808aa0fc4032500982ba2e4111d533ccfc1bc65f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Jul 2020 07:16:33 +0900 Subject: [PATCH 240/909] Bump matplotlib from 3.2.2 to 3.3.0 (#366) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.2.2 to 3.3.0. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.2.2...v3.3.0) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 775486efa3..217a23e50f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.0 scipy == 1.5.1 pandas == 1.0.5 -matplotlib == 3.2.2 +matplotlib == 3.3.0 cvxpy == 1.1.1 From a87c5ab88026b909564b0787f8ef19961ba90d60 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Jul 2020 07:48:21 +0900 Subject: [PATCH 241/909] Bump cvxpy from 1.1.1 to 1.1.3 (#367) Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.1 to 1.1.3. - [Release notes](https://github.com/cvxgrp/cvxpy/releases) - [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md) - [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.1...v1.1.3) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 217a23e50f..2b884c032f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,4 @@ numpy == 1.19.0 scipy == 1.5.1 pandas == 1.0.5 matplotlib == 3.3.0 -cvxpy == 1.1.1 +cvxpy == 1.1.3 From d861cda9d4448f792be655bdfaedc6e8e8a0ed7d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Jul 2020 07:49:42 +0900 Subject: [PATCH 242/909] Bump scipy from 1.5.1 to 1.5.2 (#368) Bumps [scipy](https://github.com/scipy/scipy) from 1.5.1 to 1.5.2. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.5.1...v1.5.2) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2b884c032f..ea6578e457 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.0 -scipy == 1.5.1 +scipy == 1.5.2 pandas == 1.0.5 matplotlib == 3.3.0 cvxpy == 1.1.3 From a3808bca79e22a7fc7a131d6f357fca2f30c6d75 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Jul 2020 08:54:56 +0900 Subject: [PATCH 243/909] Bump numpy from 1.19.0 to 1.19.1 (#369) Bumps [numpy](https://github.com/numpy/numpy) from 1.19.0 to 1.19.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.19.0...v1.19.1) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index ea6578e457..a256740bbd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.19.0 +numpy == 1.19.1 scipy == 1.5.2 pandas == 1.0.5 matplotlib == 3.3.0 From c90ef2ccb3961620581b55fbac4555f1d5aec14b Mon Sep 17 00:00:00 2001 From: "Gaushik M.R" Date: Sun, 2 Aug 2020 16:40:28 +0530 Subject: [PATCH 244/909] Create CODE_OF_CONDUCT.md (#370) --- CODE_OF_CONDUCT.md | 76 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000..e8c4702596 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at asakaig@gmail.com. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq From 88cc03dd217e10f2672bde06b11b5d892c975f15 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 2 Aug 2020 21:50:27 +0900 Subject: [PATCH 245/909] refactor hybrid_a_star codes and clean up heuristic function names (#371) * refactor hybrid_a_star code heuristic function names and codes * removed empty line --- .../circleci-artifacts-redirector.yml | 3 - ...ic.py => dynamic_programming_heuristic.py} | 65 +------------------ PathPlanning/HybridAStar/hybrid_a_star.py | 8 +-- 3 files changed, 6 insertions(+), 70 deletions(-) rename PathPlanning/HybridAStar/{a_star_heuristic.py => dynamic_programming_heuristic.py} (76%) diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml index 849a97f1a7..951e3a5947 100644 --- a/.github/workflows/circleci-artifacts-redirector.yml +++ b/.github/workflows/circleci-artifacts-redirector.yml @@ -11,6 +11,3 @@ jobs: repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/html/index.html circleci-jobs: build_doc - - name: Check the URL - run: | - curl --fail ${{ steps.step1.outputs.url }} | grep $GITHUB_SHA diff --git a/PathPlanning/HybridAStar/a_star_heuristic.py b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py similarity index 76% rename from PathPlanning/HybridAStar/a_star_heuristic.py rename to PathPlanning/HybridAStar/dynamic_programming_heuristic.py index 36c3342462..cc635260d6 100644 --- a/PathPlanning/HybridAStar/a_star_heuristic.py +++ b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py @@ -42,7 +42,7 @@ def calc_final_path(goal_node, closed_node_set, resolution): return rx, ry -def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr): +def calc_distance_heuristic(gx, gy, ox, oy, resolution, rr): """ gx: goal x position [m] gx: goal x position [m] @@ -52,7 +52,6 @@ def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr): rr: robot radius[m] """ - start_node = Node(round(sx / resolution), round(sy / resolution), 0.0, -1) goal_node = Node(round(gx / resolution), round(gy / resolution), 0.0, -1) ox = [iox / resolution for iox in ox] oy = [ioy / resolution for ioy in oy] @@ -115,16 +114,7 @@ def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr): priority_queue, (node.cost, calc_index(node, x_w, min_x, min_y))) - rx, ry = calc_final_path(closed_set[calc_index( - start_node, x_w, min_x, min_y)], closed_set, resolution) - - return rx, ry, closed_set - - -def calc_heuristic(n1, n2): - w = 1.0 # weight of heuristic - d = w * math.sqrt((n1.x - n2.x) ** 2 + (n1.y - n2.y) ** 2) - return d + return closed_set def verify_node(node, obstacle_map, min_x, min_y, max_x, max_y): @@ -184,54 +174,3 @@ def get_motion_model(): [1, 1, math.sqrt(2)]] return motion - - -def main(): - print(__file__ + " start!!") - - # start and goal position - sx = 10.0 # [m] - sy = 10.0 # [m] - gx = 50.0 # [m] - gy = 50.0 # [m] - grid_size = 2.0 # [m] - robot_size = 1.0 # [m] - - ox, oy = [], [] - - for i in range(60): - ox.append(i) - oy.append(0.0) - for i in range(60): - ox.append(60.0) - oy.append(i) - for i in range(61): - ox.append(i) - oy.append(60.0) - for i in range(61): - ox.append(0.0) - oy.append(i) - for i in range(40): - ox.append(20.0) - oy.append(i) - for i in range(40): - ox.append(40.0) - oy.append(60.0 - i) - - if show_animation: # pragma: no cover - plt.plot(ox, oy, ".k") - plt.plot(sx, sy, "xr") - plt.plot(gx, gy, "xb") - plt.grid(True) - plt.axis("equal") - - rx, ry, _ = dp_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size) - - if show_animation: # pragma: no cover - plt.plot(rx, ry, "-r") - plt.show() - - -if __name__ == '__main__': - show_animation = True - main() diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 45d6e11b18..9679e3979b 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -18,7 +18,7 @@ sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../ReedsSheppPath") try: - from a_star_heuristic import dp_planning + from dynamic_programming_heuristic import calc_distance_heuristic import reeds_shepp_path_planning as rs from car import move, check_car_collision, MAX_STEER, WB, plot_car except Exception: @@ -274,9 +274,9 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution): openList, closedList = {}, {} - _, _, h_dp = dp_planning(start_node.x_list[-1], start_node.y_list[-1], - goal_node.x_list[-1], goal_node.y_list[-1], - ox, oy, xy_resolution, VR) + h_dp = calc_distance_heuristic( + goal_node.x_list[-1], goal_node.y_list[-1], + ox, oy, xy_resolution, VR) pq = [] openList[calc_index(start_node, config)] = start_node From 765f752165d157b3342e9175380befc3b92e3d89 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Aug 2020 07:53:43 +0900 Subject: [PATCH 246/909] Bump pandas from 1.0.5 to 1.1.0 (#372) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.0.5 to 1.1.0. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.0.5...v1.1.0) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a256740bbd..1dd8dfe984 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.1 scipy == 1.5.2 -pandas == 1.0.5 +pandas == 1.1.0 matplotlib == 3.3.0 cvxpy == 1.1.3 From 6d29bcd97d531b762b7c7cae39e5676930157d5f Mon Sep 17 00:00:00 2001 From: Mahyar Abdeetedal Date: Tue, 11 Aug 2020 03:31:45 -0400 Subject: [PATCH 247/909] support no obstacle in RRT* (#375) --- PathPlanning/RRTStar/rrt_star.py | 4 ++-- tests/test_rrt_star.py | 11 +++++++++++ 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index 7b46029791..10ad93a6ba 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -81,13 +81,13 @@ def planning(self, animation=True, search_until_max_iter=True): if (not search_until_max_iter) and new_node: # check reaching the goal last_index = self.search_best_goal_node() - if last_index: + if last_index is not None: return self.generate_final_course(last_index) print("reached max iteration") last_index = self.search_best_goal_node() - if last_index: + if last_index is not None: return self.generate_final_course(last_index) return None diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py index 514ed34829..d3661610ff 100644 --- a/tests/test_rrt_star.py +++ b/tests/test_rrt_star.py @@ -20,7 +20,18 @@ def test1(self): m.show_animation = False m.main() + def test_no_obstacle(self): + obstacle_list = [] + + # Set Initial parameters + rrt_star = m.RRTStar(start=[0, 0], + goal=[6, 10], + rand_area=[-2, 15], + obstacle_list=obstacle_list) + path = rrt_star.planning(animation=False) + assert path is not None if __name__ == '__main__': # pragma: no cover test = Test() test.test1() + test.test_no_obstacle() From 750e8a185e20b7f28424f258f503e9ee4870515e Mon Sep 17 00:00:00 2001 From: yashvarshney003 <59112785+yashvarshney003@users.noreply.github.com> Date: Sat, 15 Aug 2020 08:24:36 +0530 Subject: [PATCH 248/909] Update breadth_first_search.py (#374) --- .../breadth_first_search.py | 8 +++--- .../DepthFirstSearch/depth_first_search.py | 8 +++--- PathPlanning/Dijkstra/dijkstra.py | 16 ++++++------ README.md | 2 ++ tests/test_breadth_first_search.py | 26 +++++++++++++++++++ tests/test_depth_first_search.py | 26 +++++++++++++++++++ tests/test_dijkstra.py | 16 +++++++++++- 7 files changed, 85 insertions(+), 17 deletions(-) create mode 100644 tests/test_breadth_first_search.py create mode 100644 tests/test_depth_first_search.py diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index 01f1a31d64..406e5eb51f 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -33,16 +33,16 @@ def __init__(self, ox, oy, reso, rr): self.motion = self.get_motion_model() class Node: - def __init__(self, x, y, cost, pind, parent): + def __init__(self, x, y, cost, parent_index, parent): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.pind = pind + self.parent_index = parent_index self.parent = parent def __str__(self): return str(self.x) + "," + str(self.y) + "," + str( - self.cost) + "," + str(self.pind) + self.cost) + "," + str(self.parent_index) def planning(self, sx, sy, gx, gy): """ @@ -92,7 +92,7 @@ def planning(self, sx, sy, gx, gy): if current.x == ngoal.x and current.y == ngoal.y: print("Find goal") - ngoal.pind = current.pind + ngoal.parent_index = current.parent_index ngoal.cost = current.cost break diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index c8d2d5eaba..523225ee42 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -33,16 +33,16 @@ def __init__(self, ox, oy, reso, rr): self.motion = self.get_motion_model() class Node: - def __init__(self, x, y, cost, pind, parent): + def __init__(self, x, y, cost, parent_index, parent): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.pind = pind + self.parent_index = parent_index self.parent = parent def __str__(self): return str(self.x) + "," + str(self.y) + "," + str( - self.cost) + "," + str(self.pind) + self.cost) + "," + str(self.parent_index) def planning(self, sx, sy, gx, gy): """ @@ -88,7 +88,7 @@ def planning(self, sx, sy, gx, gy): if current.x == ngoal.x and current.y == ngoal.y: print("Find goal") - ngoal.pind = current.pind + ngoal.parent_index = current.parent_index ngoal.cost = current.cost break diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index b744bc6fc7..9956dff326 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -38,15 +38,15 @@ def __init__(self, ox, oy, resolution, robot_radius): self.motion = self.get_motion_model() class Node: - def __init__(self, x, y, cost, parent): + def __init__(self, x, y, cost, parent_index): self.x = x # index of grid self.y = y # index of grid self.cost = cost - self.parent = parent # index of previous Node + self.parent_index = parent_index # index of previous Node def __str__(self): return str(self.x) + "," + str(self.y) + "," + str( - self.cost) + "," + str(self.parent) + self.cost) + "," + str(self.parent_index) def planning(self, sx, sy, gx, gy): """ @@ -88,7 +88,7 @@ def planning(self, sx, sy, gx, gy): if current.x == goal_node.x and current.y == goal_node.y: print("Find goal") - goal_node.parent = current.parent + goal_node.parent_index = current.parent_index goal_node.cost = current.cost break @@ -126,12 +126,12 @@ def calc_final_path(self, goal_node, closed_set): # generate final course rx, ry = [self.calc_position(goal_node.x, self.min_x)], [ self.calc_position(goal_node.y, self.min_y)] - parent = goal_node.parent - while parent != -1: - n = closed_set[parent] + parent_index = goal_node.parent_index + while parent_index != -1: + n = closed_set[parent_index] rx.append(self.calc_position(n.x, self.min_x)) ry.append(self.calc_position(n.y, self.min_y)) - parent = n.parent + parent_index = n.parent_index return rx, ry diff --git a/README.md b/README.md index 49a99959f1..07dbf7ae5a 100644 --- a/README.md +++ b/README.md @@ -124,6 +124,8 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation 2. Install the required libraries. You can use environment.yml with conda command. > conda env create -f environment.yml +> using pip :- + pip install -r requirements.txt 3. Execute python script in each directory. diff --git a/tests/test_breadth_first_search.py b/tests/test_breadth_first_search.py new file mode 100644 index 0000000000..7dc36b2860 --- /dev/null +++ b/tests/test_breadth_first_search.py @@ -0,0 +1,26 @@ +from unittest import TestCase +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../PathPlanning/BreadthFirstSearch/") + + +try: + import breadth_first_search as m +except ImportError: + raise + + +print(__file__) + + +class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main() + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test1() diff --git a/tests/test_depth_first_search.py b/tests/test_depth_first_search.py new file mode 100644 index 0000000000..15aa7a0a53 --- /dev/null +++ b/tests/test_depth_first_search.py @@ -0,0 +1,26 @@ +from unittest import TestCase +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../PathPlanning/DepthFirstSearch/") + + +try: + import depth_first_search as m +except ImportError: + raise + + +print(__file__) + + +class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main() + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test1() diff --git a/tests/test_dijkstra.py b/tests/test_dijkstra.py index b86f2e6ce6..7650eee802 100644 --- a/tests/test_dijkstra.py +++ b/tests/test_dijkstra.py @@ -1,6 +1,15 @@ from unittest import TestCase +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../PathPlanning/Dijkstra/") + + +try: + import dijkstra as m +except ImportError: + raise -from PathPlanning.Dijkstra import dijkstra as m print(__file__) @@ -10,3 +19,8 @@ class Test(TestCase): def test1(self): m.show_animation = False m.main() + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test1() From 007db0c844589a4677eb28a646833c86c549b988 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Aug 2020 07:23:27 +0900 Subject: [PATCH 249/909] Bump cvxpy from 1.1.3 to 1.1.4 (#377) Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.3 to 1.1.4. - [Release notes](https://github.com/cvxgrp/cvxpy/releases) - [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md) - [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.3...v1.1.4) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1dd8dfe984..78ba2686c2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,4 @@ numpy == 1.19.1 scipy == 1.5.2 pandas == 1.1.0 matplotlib == 3.3.0 -cvxpy == 1.1.3 +cvxpy == 1.1.4 From 7442bdceb18b6157b733311ffcc2d544eeab901a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Aug 2020 07:52:23 +0900 Subject: [PATCH 250/909] Bump matplotlib from 3.3.0 to 3.3.1 (#376) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.0 to 3.3.1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.0...v3.3.1) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 78ba2686c2..2a7d5b3a91 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.1 scipy == 1.5.2 pandas == 1.1.0 -matplotlib == 3.3.0 +matplotlib == 3.3.1 cvxpy == 1.1.4 From fa227083372445f0f440b5d5ad878d9201ed7476 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Aug 2020 20:32:47 +0900 Subject: [PATCH 251/909] Bump pandas from 1.1.0 to 1.1.1 (#384) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.0 to 1.1.1. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.0...v1.1.1) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2a7d5b3a91..3bcb2de332 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.1 scipy == 1.5.2 -pandas == 1.1.0 +pandas == 1.1.1 matplotlib == 3.3.1 cvxpy == 1.1.4 From 409be20b472b5ec88077495aeb980b02d8edeb68 Mon Sep 17 00:00:00 2001 From: Sarim Mehdi Date: Sun, 30 Aug 2020 06:04:08 +0200 Subject: [PATCH 252/909] Implemented bug algorithms (#378) * Create bug.py * Update bug.py * Update bug.py * Update bug.py * Update bug.py * Update bug.py * Add files via upload * Update test_bug.py * Update test_bug.py * Update bug.py * Update test_bug.py * Delete bug.py * Create BugPlanning * Delete BugPlanning * Create bug.py * Update bug.py * Update test_bug.py * Update bug.py * Update bug.py --- PathPlanning/BugPlanning/bug.py | 327 ++++++++++++++++++++++++++++++++ tests/test_bug.py | 17 ++ 2 files changed, 344 insertions(+) create mode 100644 PathPlanning/BugPlanning/bug.py create mode 100644 tests/test_bug.py diff --git a/PathPlanning/BugPlanning/bug.py b/PathPlanning/BugPlanning/bug.py new file mode 100644 index 0000000000..21bbbbb1cb --- /dev/null +++ b/PathPlanning/BugPlanning/bug.py @@ -0,0 +1,327 @@ +""" +Bug Planning +author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it) +Source: https://sites.google.com/site/ece452bugalgorithms/ +""" + +import numpy as np +import matplotlib.pyplot as plt + +show_animation = True + + +class BugPlanner: + def __init__(self, start_x, start_y, goal_x, goal_y, obs_x, obs_y): + self.goal_x = goal_x + self.goal_y = goal_y + self.obs_x = obs_x + self.obs_y = obs_y + self.r_x = [start_x] + self.r_y = [start_y] + self.out_x = [] + self.out_y = [] + for o_x, o_y in zip(obs_x, obs_y): + for add_x, add_y in zip([1, 0, -1, -1, -1, 0, 1, 1], + [1, 1, 1, 0, -1, -1, -1, 0]): + cand_x, cand_y = o_x+add_x, o_y+add_y + valid_point = True + for _x, _y in zip(obs_x, obs_y): + if cand_x == _x and cand_y == _y: + valid_point = False + break + if valid_point: + self.out_x.append(cand_x), self.out_y.append(cand_y) + + def mov_normal(self): + return self.r_x[-1] + np.sign(self.goal_x - self.r_x[-1]), \ + self.r_y[-1] + np.sign(self.goal_y - self.r_y[-1]) + + def mov_to_next_obs(self, visited_x, visited_y): + for add_x, add_y in zip([1, 0, -1, 0], [0, 1, 0, -1]): + c_x, c_y = self.r_x[-1] + add_x, self.r_y[-1] + add_y + for _x, _y in zip(self.out_x, self.out_y): + use_pt = True + if c_x == _x and c_y == _y: + for v_x, v_y in zip(visited_x, visited_y): + if c_x == v_x and c_y == v_y: + use_pt = False + break + if use_pt: + return c_x, c_y, False + if not use_pt: + break + return self.r_x[-1], self.r_y[-1], True + + def bug0(self): + '''Greedy algorithm where you move towards goal + until you hit an obstacle. Then you go around it + (pick an arbitrary direction), until it is possible + for you to start moving towards goal in a greedy manner again''' + mov_dir = 'normal' + cand_x, cand_y = -np.inf, -np.inf + if show_animation: + plt.plot(self.obs_x, self.obs_y, ".k") + plt.plot(self.r_x[-1], self.r_y[-1], "og") + plt.plot(self.goal_x, self.goal_y, "xb") + plt.plot(self.out_x, self.out_y, ".") + plt.grid(True) + plt.title('BUG 0') + + for x_ob, y_ob in zip(self.out_x, self.out_y): + if self.r_x[-1] == x_ob and self.r_y[-1] == y_ob: + mov_dir = 'obs' + break + + visited_x, visited_y = [], [] + while True: + if self.r_x[-1] == self.goal_x and \ + self.r_y[-1] == self.goal_y: + break + if mov_dir == 'normal': + cand_x, cand_y = self.mov_normal() + if mov_dir == 'obs': + cand_x, cand_y, _ = self.mov_to_next_obs(visited_x, visited_y) + if mov_dir == 'normal': + found_boundary = False + for x_ob, y_ob in zip(self.out_x, self.out_y): + if cand_x == x_ob and cand_y == y_ob: + self.r_x.append(cand_x), self.r_y.append(cand_y) + visited_x[:], visited_y[:] = [], [] + visited_x.append(cand_x), visited_y.append(cand_y) + mov_dir = 'obs' + found_boundary = True + break + if not found_boundary: + self.r_x.append(cand_x), self.r_y.append(cand_y) + elif mov_dir == 'obs': + can_go_normal = True + for x_ob, y_ob in zip(self.obs_x, self.obs_y): + if self.mov_normal()[0] == x_ob and \ + self.mov_normal()[1] == y_ob: + can_go_normal = False + break + if can_go_normal: + mov_dir = 'normal' + else: + self.r_x.append(cand_x), self.r_y.append(cand_y) + visited_x.append(cand_x), visited_y.append(cand_y) + if show_animation: + plt.plot(self.r_x, self.r_y, "-r") + plt.pause(0.001) + if show_animation: + plt.show() + + def bug1(self): + '''Move towards goal in a greedy manner. + When you hit an obstacle, you go around it and + back to where you hit the obstacle initially. + Then, you go to the point on the obstacle that is + closest to your goal and you start moving towards + goal in a greedy manner from that new point.''' + mov_dir = 'normal' + cand_x, cand_y = -np.inf, -np.inf + exit_x, exit_y = -np.inf, -np.inf + dist = np.inf + back_to_start = False + second_round = False + if show_animation: + plt.plot(self.obs_x, self.obs_y, ".k") + plt.plot(self.r_x[-1], self.r_y[-1], "og") + plt.plot(self.goal_x, self.goal_y, "xb") + plt.plot(self.out_x, self.out_y, ".") + plt.grid(True) + plt.title('BUG 1') + + for xob, yob in zip(self.out_x, self.out_y): + if self.r_x[-1] == xob and self.r_y[-1] == yob: + mov_dir = 'obs' + break + + visited_x, visited_y = [], [] + while True: + if self.r_x[-1] == self.goal_x and \ + self.r_y[-1] == self.goal_y: + break + if mov_dir == 'normal': + cand_x, cand_y = self.mov_normal() + if mov_dir == 'obs': + cand_x, cand_y, back_to_start = \ + self.mov_to_next_obs(visited_x, visited_y) + if mov_dir == 'normal': + found_boundary = False + for x_ob, y_ob in zip(self.out_x, self.out_y): + if cand_x == x_ob and cand_y == y_ob: + self.r_x.append(cand_x), self.r_y.append(cand_y) + visited_x[:], visited_y[:] = [], [] + visited_x.append(cand_x), visited_y.append(cand_y) + mov_dir = 'obs' + dist = np.inf + back_to_start = False + second_round = False + found_boundary = True + break + if not found_boundary: + self.r_x.append(cand_x), self.r_y.append(cand_y) + elif mov_dir == 'obs': + d = np.linalg.norm(np.array([cand_x, cand_y] - + np.array([self.goal_x, + self.goal_y]))) + if d < dist and not second_round: + exit_x, exit_y = cand_x, cand_y + dist = d + if back_to_start and not second_round: + second_round = True + del self.r_x[-len(visited_x):] + del self.r_y[-len(visited_y):] + visited_x[:], visited_y[:] = [], [] + self.r_x.append(cand_x), self.r_y.append(cand_y) + visited_x.append(cand_x), visited_y.append(cand_y) + if cand_x == exit_x and \ + cand_y == exit_y and \ + second_round: + mov_dir = 'normal' + if show_animation: + plt.plot(self.r_x, self.r_y, "-r") + plt.pause(0.001) + if show_animation: + plt.show() + + def bug2(self): + '''Move towards goal in a greedy manner. + When you hit an obstacle, you go around it and + keep track of your distance from the goal. + If the distance from your goal was decreasing before + and now it starts increasing, that means the current + point is probably the closest point to the + goal (this may or may not be true because the algorithm + doesn't explore the entire boundary around the obstacle). + So, you depart from this point and continue towards the + goal in a greedy manner''' + mov_dir = 'normal' + cand_x, cand_y = -np.inf, -np.inf + if show_animation: + plt.plot(self.obs_x, self.obs_y, ".k") + plt.plot(self.r_x[-1], self.r_y[-1], "og") + plt.plot(self.goal_x, self.goal_y, "xb") + plt.plot(self.out_x, self.out_y, ".") + + straight_x, straight_y = [self.r_x[-1]], [self.r_y[-1]] + hit_x, hit_y = [], [] + while True: + if straight_x[-1] == self.goal_x and \ + straight_y[-1] == self.goal_y: + break + c_x = straight_x[-1] + np.sign(self.goal_x - straight_x[-1]) + c_y = straight_y[-1] + np.sign(self.goal_y - straight_y[-1]) + for x_ob, y_ob in zip(self.out_x, self.out_y): + if c_x == x_ob and c_y == y_ob: + hit_x.append(c_x), hit_y.append(c_y) + break + straight_x.append(c_x), straight_y.append(c_y) + if show_animation: + plt.plot(straight_x, straight_y, ",") + plt.plot(hit_x, hit_y, "d") + plt.grid(True) + plt.title('BUG 2') + + for x_ob, y_ob in zip(self.out_x, self.out_y): + if self.r_x[-1] == x_ob and self.r_y[-1] == y_ob: + mov_dir = 'obs' + break + + visited_x, visited_y = [], [] + while True: + if self.r_x[-1] == self.goal_x \ + and self.r_y[-1] == self.goal_y: + break + if mov_dir == 'normal': + cand_x, cand_y = self.mov_normal() + if mov_dir == 'obs': + cand_x, cand_y, _ = self.mov_to_next_obs(visited_x, visited_y) + if mov_dir == 'normal': + found_boundary = False + for x_ob, y_ob in zip(self.out_x, self.out_y): + if cand_x == x_ob and cand_y == y_ob: + self.r_x.append(cand_x), self.r_y.append(cand_y) + visited_x[:], visited_y[:] = [], [] + visited_x.append(cand_x), visited_y.append(cand_y) + del hit_x[0] + del hit_y[0] + mov_dir = 'obs' + found_boundary = True + break + if not found_boundary: + self.r_x.append(cand_x), self.r_y.append(cand_y) + elif mov_dir == 'obs': + self.r_x.append(cand_x), self.r_y.append(cand_y) + visited_x.append(cand_x), visited_y.append(cand_y) + for i_x, i_y in zip(range(len(hit_x)), range(len(hit_y))): + if cand_x == hit_x[i_x] and cand_y == hit_y[i_y]: + del hit_x[i_x] + del hit_y[i_y] + mov_dir = 'normal' + break + if show_animation: + plt.plot(self.r_x, self.r_y, "-r") + plt.pause(0.001) + if show_animation: + plt.show() + + +def main(bug_0, bug_1, bug_2): + # set obstacle positions + o_x, o_y = [], [] + + s_x = 0.0 + s_y = 0.0 + g_x = 167.0 + g_y = 50.0 + + for i in range(20, 40): + for j in range(20, 40): + o_x.append(i) + o_y.append(j) + + for i in range(60, 100): + for j in range(40, 80): + o_x.append(i) + o_y.append(j) + + for i in range(120, 140): + for j in range(80, 100): + o_x.append(i) + o_y.append(j) + + for i in range(80, 140): + for j in range(0, 20): + o_x.append(i) + o_y.append(j) + + for i in range(0, 20): + for j in range(60, 100): + o_x.append(i) + o_y.append(j) + + for i in range(20, 40): + for j in range(80, 100): + o_x.append(i) + o_y.append(j) + + for i in range(120, 160): + for j in range(40, 60): + o_x.append(i) + o_y.append(j) + + if bug_0: + my_Bug = BugPlanner(s_x, s_y, g_x, g_y, o_x, o_y) + my_Bug.bug0() + if bug_1: + my_Bug = BugPlanner(s_x, s_y, g_x, g_y, o_x, o_y) + my_Bug.bug1() + if bug_2: + my_Bug = BugPlanner(s_x, s_y, g_x, g_y, o_x, o_y) + my_Bug.bug2() + + +if __name__ == '__main__': + main(bug_0=True, bug_1=False, bug_2=False) diff --git a/tests/test_bug.py b/tests/test_bug.py new file mode 100644 index 0000000000..31d8b2c297 --- /dev/null +++ b/tests/test_bug.py @@ -0,0 +1,17 @@ +from PathPlanning.BugPlanning import bug as b +from unittest import TestCase +import sys +import os +sys.path.append(os.path.dirname(__file__) + "/../") + + +class Test(TestCase): + + def test(self): + b.show_animation = False + b.main(bug_0=True, bug_1=True, bug_2=True) + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test() From f969069df9a5182dd006b17fdccf52a7c4e64b36 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 30 Aug 2020 13:07:08 +0900 Subject: [PATCH 253/909] fix docstring error --- PathPlanning/BugPlanning/bug.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/PathPlanning/BugPlanning/bug.py b/PathPlanning/BugPlanning/bug.py index 21bbbbb1cb..62fec7c109 100644 --- a/PathPlanning/BugPlanning/bug.py +++ b/PathPlanning/BugPlanning/bug.py @@ -53,10 +53,12 @@ def mov_to_next_obs(self, visited_x, visited_y): return self.r_x[-1], self.r_y[-1], True def bug0(self): - '''Greedy algorithm where you move towards goal + """ + Greedy algorithm where you move towards goal until you hit an obstacle. Then you go around it (pick an arbitrary direction), until it is possible - for you to start moving towards goal in a greedy manner again''' + for you to start moving towards goal in a greedy manner again + """ mov_dir = 'normal' cand_x, cand_y = -np.inf, -np.inf if show_animation: @@ -112,12 +114,14 @@ def bug0(self): plt.show() def bug1(self): - '''Move towards goal in a greedy manner. + """ + Move towards goal in a greedy manner. When you hit an obstacle, you go around it and back to where you hit the obstacle initially. Then, you go to the point on the obstacle that is closest to your goal and you start moving towards - goal in a greedy manner from that new point.''' + goal in a greedy manner from that new point. + """ mov_dir = 'normal' cand_x, cand_y = -np.inf, -np.inf exit_x, exit_y = -np.inf, -np.inf @@ -187,7 +191,8 @@ def bug1(self): plt.show() def bug2(self): - '''Move towards goal in a greedy manner. + """ + Move towards goal in a greedy manner. When you hit an obstacle, you go around it and keep track of your distance from the goal. If the distance from your goal was decreasing before @@ -196,7 +201,8 @@ def bug2(self): goal (this may or may not be true because the algorithm doesn't explore the entire boundary around the obstacle). So, you depart from this point and continue towards the - goal in a greedy manner''' + goal in a greedy manner + """ mov_dir = 'normal' cand_x, cand_y = -np.inf, -np.inf if show_animation: From 46bdd2afd950046d3f57c181efb0b867c63a2536 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 1 Sep 2020 08:26:44 +0900 Subject: [PATCH 254/909] Bump cvxpy from 1.1.4 to 1.1.5 (#389) Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.4 to 1.1.5. - [Release notes](https://github.com/cvxgrp/cvxpy/releases) - [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md) - [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.4...v1.1.5) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 3bcb2de332..bc087081b4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,4 @@ numpy == 1.19.1 scipy == 1.5.2 pandas == 1.1.1 matplotlib == 3.3.1 -cvxpy == 1.1.4 +cvxpy == 1.1.5 From 2a2374ee90c5c101e4f23c7171fc2e7610e32562 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 9 Sep 2020 22:35:56 +0900 Subject: [PATCH 255/909] Create pull_request_template.md (#397) --- .github/pull_request_template.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .github/pull_request_template.md diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000000..9fbb893c1c --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,23 @@ + + +#### Reference issue + + +#### What does this implement/fix? + + +#### Additional information + From 3b92ae8c84f86b02d8c5eab40628319f86c7e40f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 12 Sep 2020 21:36:47 +0900 Subject: [PATCH 256/909] remove code cov check (#400) --- .github/workflows/Linux_CI.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index a751c75d4d..1174438dd5 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -45,8 +45,6 @@ jobs: run: bash rundiffstylecheck.sh - name: do all unit tests run: bash runtests.sh - - name: Codecov - uses: codecov/codecov-action@v1.0.10 From ff3ad5bb9cf1810363191f41c5ed39e5fb923876 Mon Sep 17 00:00:00 2001 From: weicent Date: Sun, 13 Sep 2020 10:17:42 +0800 Subject: [PATCH 257/909] dwa pr (#390) * dwa pr * dwa_pr * dwa_pr * dwa_pr * dwa_pr * make changes rerun CI * rerun CI...again.. * rerun CI..... * rerun CI..... * rerun CI final time! * modified const to class variable * put back missing comment * add test for dwa stuck case * add test dwa stuck case * add test dwa stuck case * add test dwa stuck case * add stuck test in original test file --- .../dynamic_window_approach.py | 54 +++++++++++-------- tests/test_dynamic_window_approach.py | 31 +++++++++++ 2 files changed, 62 insertions(+), 23 deletions(-) diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 0df40410ab..74ff064e86 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -19,7 +19,6 @@ def dwa_control(x, config, goal, ob): """ Dynamic Window Approach control """ - dw = calc_dynamic_window(x, config) u, trajectory = calc_control_and_trajectory(x, dw, config, goal, ob) @@ -51,6 +50,7 @@ def __init__(self): self.to_goal_cost_gain = 0.15 self.speed_cost_gain = 1.0 self.obstacle_cost_gain = 1.0 + self.robot_stuck_flag_cons = 0.001 # constant to prevent robot stucked self.robot_type = RobotType.circle # if robot_type == RobotType.circle @@ -60,6 +60,23 @@ def __init__(self): # if robot_type == RobotType.rectangle self.robot_width = 0.5 # [m] for collision check self.robot_length = 1.2 # [m] for collision check + # obstacles [x(m) y(m), ....] + self.ob = np.array([[-1, -1], + [0, 2], + [4.0, 2.0], + [5.0, 4.0], + [5.0, 5.0], + [5.0, 6.0], + [5.0, 9.0], + [8.0, 9.0], + [7.0, 9.0], + [8.0, 10.0], + [9.0, 11.0], + [12.0, 13.0], + [12.0, 12.0], + [15.0, 15.0], + [13.0, 13.0] + ]) @property def robot_type(self): @@ -72,6 +89,9 @@ def robot_type(self, value): self._robot_type = value +config = Config() + + def motion(x, u, dt): """ motion model @@ -139,7 +159,6 @@ def calc_control_and_trajectory(x, dw, config, goal, ob): for y in np.arange(dw[2], dw[3], config.yaw_rate_resolution): trajectory = predict_trajectory(x_init, v, y, config) - # calc cost to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(trajectory, goal) speed_cost = config.speed_cost_gain * (config.max_speed - trajectory[-1, 3]) @@ -152,13 +171,19 @@ def calc_control_and_trajectory(x, dw, config, goal, ob): min_cost = final_cost best_u = [v, y] best_trajectory = trajectory - + if abs(best_u[0]) < config.robot_stuck_flag_cons \ + and abs(x[3]) < config.robot_stuck_flag_cons: + # to ensure the robot do not get stuck in + # best v=0 m/s (in front of an obstacle) and + # best omega=0 rad/s (heading to the goal with + # angle difference of 0) + best_u[1] = -config.max_delta_yaw_rate return best_u, best_trajectory def calc_obstacle_cost(trajectory, ob, config): """ - calc obstacle cost inf: collision + calc obstacle cost inf: collision """ ox = ob[:, 0] oy = ob[:, 1] @@ -238,29 +263,12 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0]) # goal position [x(m), y(m)] goal = np.array([gx, gy]) - # obstacles [x(m) y(m), ....] - ob = np.array([[-1, -1], - [0, 2], - [4.0, 2.0], - [5.0, 4.0], - [5.0, 5.0], - [5.0, 6.0], - [5.0, 9.0], - [8.0, 9.0], - [7.0, 9.0], - [8.0, 10.0], - [9.0, 11.0], - [12.0, 13.0], - [12.0, 12.0], - [15.0, 15.0], - [13.0, 13.0] - ]) # input [forward speed, yaw_rate] - config = Config() + config.robot_type = robot_type trajectory = np.array(x) - + ob = config.ob while True: u, predicted_trajectory = dwa_control(x, config, goal, ob) x = motion(x, u, config.dt) # simulate robot diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py index 197dda2158..93deb6846d 100644 --- a/tests/test_dynamic_window_approach.py +++ b/tests/test_dynamic_window_approach.py @@ -1,5 +1,6 @@ import os import sys +import numpy as np from unittest import TestCase sys.path.append(os.path.dirname(__file__) + "/../") @@ -20,8 +21,38 @@ def test_main2(self): m.show_animation = False m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle) + def test_stuck_main(self): + m.show_animation = False + # adjust cost + m.config.to_goal_cost_gain = 0.2 + m.config.obstacle_cost_gain = 2.0 + # obstacles and goals for stuck condition + m.config.ob = -1 * np.array([[-1.0, -1.0], + [0.0, 2.0], + [2.0, 6.0], + [2.0, 8.0], + [3.0, 9.27], + [3.79, 9.39], + [7.25, 8.97], + [7.0, 2.0], + [3.0, 4.0], + [6.0, 5.0], + [3.5, 5.8], + [6.0, 9.0], + [8.8, 9.0], + [5.0, 9.0], + [7.5, 3.0], + [9.0, 8.0], + [5.8, 4.4], + [12.0, 12.0], + [3.0, 2.0], + [13.0, 13.0] + ]) + m.main(gx=-5.0, gy=-7.0) + if __name__ == '__main__': # pragma: no cover test = TestDynamicWindowApproach() test.test_main1() test.test_main2() + test.test_stuck_main() From 298f1090749d2789433255c0ea997bc18da53818 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2020 07:52:00 +0900 Subject: [PATCH 258/909] Bump numpy from 1.19.1 to 1.19.2 (#403) Bumps [numpy](https://github.com/numpy/numpy) from 1.19.1 to 1.19.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.19.1...v1.19.2) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index bc087081b4..67ceca7dc1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.19.1 +numpy == 1.19.2 scipy == 1.5.2 pandas == 1.1.1 matplotlib == 3.3.1 From 6f1642076498d9483ab002ff45abe94b854e1459 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Sep 2020 20:12:00 +0900 Subject: [PATCH 259/909] Bump pandas from 1.1.1 to 1.1.2 (#404) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.1 to 1.1.2. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.1...v1.1.2) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 67ceca7dc1..094def17af 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.2 scipy == 1.5.2 -pandas == 1.1.1 +pandas == 1.1.2 matplotlib == 3.3.1 cvxpy == 1.1.5 From 43ab313762d6688b0f27f963bfe2400cfb985137 Mon Sep 17 00:00:00 2001 From: weicent Date: Sun, 20 Sep 2020 11:40:32 +0800 Subject: [PATCH 260/909] New A star algorithm pr (#391) * a star pr * a star pr * a star pr * fix line excede 79 error * fix pycodestyle errors, missing a whitespace * add test file * add test file * rerun CI * rerun CI * rerun CI * rerun CI * rerun CI * modified test file and rerun CI * rerun CI * fix CI error * modified code resubmit pr * fixed some minor error * modified pr as suggested --- .../AStar/A_Star_searching_from_two_side.py | 369 ++++++++++++++++++ tests/test_a_star_searching_two_side.py | 27 ++ 2 files changed, 396 insertions(+) create mode 100644 PathPlanning/AStar/A_Star_searching_from_two_side.py create mode 100644 tests/test_a_star_searching_two_side.py diff --git a/PathPlanning/AStar/A_Star_searching_from_two_side.py b/PathPlanning/AStar/A_Star_searching_from_two_side.py new file mode 100644 index 0000000000..0318970cc8 --- /dev/null +++ b/PathPlanning/AStar/A_Star_searching_from_two_side.py @@ -0,0 +1,369 @@ +""" +A* algorithm +Author: Weicent +randomly generate obstacles, start and goal point +searching path from start and end simultaneously +""" + +import numpy as np +import matplotlib.pyplot as plt +import math + +show_animation = True + + +class Node: + """node with properties of g, h, coordinate and parent node""" + + def __init__(self, G=0, H=0, coordinate=None, parent=None): + self.G = G + self.H = H + self.F = G + H + self.parent = parent + self.coordinate = coordinate + + def reset_f(self): + self.F = self.G + self.H + + +def hcost(node_coordinate, goal): + dx = abs(node_coordinate[0] - goal[0]) + dy = abs(node_coordinate[1] - goal[1]) + hcost = dx + dy + return hcost + + +def gcost(fixed_node, update_node_coordinate): + dx = abs(fixed_node.coordinate[0] - update_node_coordinate[0]) + dy = abs(fixed_node.coordinate[1] - update_node_coordinate[1]) + gc = math.hypot(dx, dy) # gc = move from fixed_node to update_node + gcost = fixed_node.G + gc # gcost = move from start point to update_node + return gcost + + +def boundary_and_obstacles(start, goal, top_vertex, bottom_vertex, obs_number): + """ + :param start: start coordinate + :param goal: goal coordinate + :param top_vertex: top right vertex coordinate of boundary + :param bottom_vertex: bottom left vertex coordinate of boundary + :param obs_number: number of obstacles generated in the map + :return: boundary_obstacle array, obstacle list + """ + # below can be merged into a rectangle boundary + ay = list(range(bottom_vertex[1], top_vertex[1])) + ax = [bottom_vertex[0]] * len(ay) + cy = ay + cx = [top_vertex[0]] * len(cy) + bx = list(range(bottom_vertex[0] + 1, top_vertex[0])) + by = [bottom_vertex[1]] * len(bx) + dx = [bottom_vertex[0]] + bx + [top_vertex[0]] + dy = [top_vertex[1]] * len(dx) + + # generate random obstacles + ob_x = np.random.randint(bottom_vertex[0] + 1, + top_vertex[0], obs_number).tolist() + ob_y = np.random.randint(bottom_vertex[1] + 1, + top_vertex[1], obs_number).tolist() + # x y coordinate in certain order for boundary + x = ax + bx + cx + dx + y = ay + by + cy + dy + obstacle = np.vstack((ob_x, ob_y)).T.tolist() + # remove start and goal coordinate in obstacle list + obstacle = [coor for coor in obstacle if coor != start and coor != goal] + obs_array = np.array(obstacle) + bound = np.vstack((x, y)).T + bound_obs = np.vstack((bound, obs_array)) + return bound_obs, obstacle + + +def find_neighbor(node, ob, closed): + # generate neighbors in certain condition + ob_list = ob.tolist() + neighbor: list = [] + for x in range(node.coordinate[0] - 1, node.coordinate[0] + 2): + for y in range(node.coordinate[1] - 1, node.coordinate[1] + 2): + if [x, y] not in ob_list: + # find all possible neighbor nodes + neighbor.append([x, y]) + # remove node violate the motion rule + # 1. remove node.coordinate itself + neighbor.remove(node.coordinate) + # 2. remove neighbor nodes who cross through two diagonal + # positioned obstacles since there is no enough space for + # robot to go through two diagonal positioned obstacles + + # top bottom left right neighbors of node + top_nei = [node.coordinate[0], node.coordinate[1] + 1] + bottom_nei = [node.coordinate[0], node.coordinate[1] - 1] + left_nei = [node.coordinate[0] - 1, node.coordinate[1]] + right_nei = [node.coordinate[0] + 1, node.coordinate[1]] + # neighbors in four vertex + lt_nei = [node.coordinate[0] - 1, node.coordinate[1] + 1] + rt_nei = [node.coordinate[0] + 1, node.coordinate[1] + 1] + lb_nei = [node.coordinate[0] - 1, node.coordinate[1] - 1] + rb_nei = [node.coordinate[0] + 1, node.coordinate[1] - 1] + + # remove the unnecessary neighbors + if top_nei and left_nei in ob_list and lt_nei in neighbor: + neighbor.remove(lt_nei) + if top_nei and right_nei in ob_list and rt_nei in neighbor: + neighbor.remove(rt_nei) + if bottom_nei and left_nei in ob_list and lb_nei in neighbor: + neighbor.remove(lb_nei) + if bottom_nei and right_nei in ob_list and rb_nei in neighbor: + neighbor.remove(rb_nei) + neighbor = [x for x in neighbor if x not in closed] + return neighbor + + +def find_node_index(coordinate, node_list): + # find node index in the node list via its coordinate + ind = 0 + for node in node_list: + if node.coordinate == coordinate: + target_node = node + ind = node_list.index(target_node) + break + return ind + + +def find_path(open_list, closed_list, goal, obstacle): + # searching for the path, update open and closed list + # obstacle = obstacle and boundary + flag = len(open_list) + for i in range(flag): + node = open_list[0] + open_coordinate_list = [node.coordinate for node in open_list] + closed_coordinate_list = [node.coordinate for node in closed_list] + temp = find_neighbor(node, obstacle, closed_coordinate_list) + for element in temp: + if element in closed_list: + continue + elif element in open_coordinate_list: + # if node in open list, update g value + ind = open_coordinate_list.index(element) + new_g = gcost(node, element) + if new_g <= open_list[ind].G: + open_list[ind].G = new_g + open_list[ind].reset_f() + open_list[ind].parent = node + else: # new coordinate, create corresponding node + ele_node = Node(coordinate=element, parent=node, + G=gcost(node, element), H=hcost(element, goal)) + open_list.append(ele_node) + open_list.remove(node) + closed_list.append(node) + open_list.sort(key=lambda x: x.F) + return open_list, closed_list + + +def node_to_coordinate(node_list): + # convert node list into coordinate list and array + coordinate_list = [node.coordinate for node in node_list] + return coordinate_list + + +def check_node_coincide(close_ls1, closed_ls2): + """ + :param close_ls1: node closed list for searching from start + :param closed_ls2: node closed list for searching from end + :return: intersect node list for above two + """ + # check if node in close_ls1 intersect with node in closed_ls2 + cl1 = node_to_coordinate(close_ls1) + cl2 = node_to_coordinate(closed_ls2) + intersect_ls = [node for node in cl1 if node in cl2] + return intersect_ls + + +def find_surrounding(coordinate, obstacle): + # find obstacles around node, help to draw the borderline + boundary: list = [] + for x in range(coordinate[0] - 1, coordinate[0] + 2): + for y in range(coordinate[1] - 1, coordinate[1] + 2): + if [x, y] in obstacle: + boundary.append([x, y]) + return boundary + + +def get_border_line(node_closed_ls, obstacle): + # if no path, find border line which confine goal or robot + border: list = [] + coordinate_closed_ls = node_to_coordinate(node_closed_ls) + for coordinate in coordinate_closed_ls: + temp = find_surrounding(coordinate, obstacle) + border = border + temp + border_ary = np.array(border) + return border_ary + + +def get_path(org_list, goal_list, coordinate): + # get path from start to end + path_org: list = [] + path_goal: list = [] + ind = find_node_index(coordinate, org_list) + node = org_list[ind] + while node != org_list[0]: + path_org.append(node.coordinate) + node = node.parent + path_org.append(org_list[0].coordinate) + ind = find_node_index(coordinate, goal_list) + node = goal_list[ind] + while node != goal_list[0]: + path_goal.append(node.coordinate) + node = node.parent + path_goal.append(goal_list[0].coordinate) + path_org.reverse() + path = path_org + path_goal + path = np.array(path) + return path + + +def random_coordinate(bottom_vertex, top_vertex): + # generate random coordinates inside maze + coordinate = [np.random.randint(bottom_vertex[0] + 1, top_vertex[0]), + np.random.randint(bottom_vertex[1] + 1, top_vertex[1])] + return coordinate + + +def draw(close_origin, close_goal, start, end, bound): + # plot the map + if not close_goal.tolist(): # ensure the close_goal not empty + # in case of the obstacle number is really large (>4500), the + # origin is very likely blocked at the first search, and then + # the program is over and the searching from goal to origin + # will not start, which remain the closed_list for goal == [] + # in order to plot the map, add the end coordinate to array + close_goal = np.array([end]) + plt.cla() + plt.gcf().set_size_inches(11, 9, forward=True) + plt.axis('equal') + plt.plot(close_origin[:, 0], close_origin[:, 1], 'oy') + plt.plot(close_goal[:, 0], close_goal[:, 1], 'og') + plt.plot(bound[:, 0], bound[:, 1], 'sk') + plt.plot(end[0], end[1], '*b', label='Goal') + plt.plot(start[0], start[1], '^b', label='Origin') + plt.legend() + plt.pause(0.0001) + + +def draw_control(org_closed, goal_closed, flag, start, end, bound, obstacle): + """ + control the plot process, evaluate if the searching finished + flag == 0 : draw the searching process and plot path + flag == 1 or 2 : start or end is blocked, draw the border line + """ + stop_loop = 0 # stop sign for the searching + org_closed_ls = node_to_coordinate(org_closed) + org_array = np.array(org_closed_ls) + goal_closed_ls = node_to_coordinate(goal_closed) + goal_array = np.array(goal_closed_ls) + path = None + if show_animation: # draw the searching process + draw(org_array, goal_array, start, end, bound) + if flag == 0: + node_intersect = check_node_coincide(org_closed, goal_closed) + if node_intersect: # a path is find + path = get_path(org_closed, goal_closed, node_intersect[0]) + stop_loop = 1 + print('Path is find!') + if show_animation: # draw the path + plt.plot(path[:, 0], path[:, 1], '-r') + plt.title('Robot Arrived', size=20, loc='center') + plt.pause(0.01) + plt.show() + elif flag == 1: # start point blocked first + stop_loop = 1 + print('There is no path to the goal! Start point is blocked!') + elif flag == 2: # end point blocked first + stop_loop = 1 + print('There is no path to the goal! End point is blocked!') + if show_animation: # blocked case, draw the border line + info = 'There is no path to the goal!' \ + ' Robot&Goal are split by border' \ + ' shown in red \'x\'!' + if flag == 1: + border = get_border_line(org_closed, obstacle) + plt.plot(border[:, 0], border[:, 1], 'xr') + plt.title(info, size=14, loc='center') + plt.pause(0.01) + plt.show() + elif flag == 2: + border = get_border_line(goal_closed, obstacle) + plt.plot(border[:, 0], border[:, 1], 'xr') + plt.title(info, size=14, loc='center') + plt.pause(0.01) + plt.show() + return stop_loop, path + + +def searching_control(start, end, bound, obstacle): + """manage the searching process, start searching from two side""" + # initial origin node and end node + origin = Node(coordinate=start, H=hcost(start, end)) + goal = Node(coordinate=end, H=hcost(end, start)) + # list for searching from origin to goal + origin_open: list = [origin] + origin_close: list = [] + # list for searching from goal to origin + goal_open = [goal] + goal_close: list = [] + # initial target + target_goal = end + # flag = 0 (not blocked) 1 (start point blocked) 2 (end point blocked) + flag = 0 # init flag + path = None + while True: + # searching from start to end + origin_open, origin_close = \ + find_path(origin_open, origin_close, target_goal, bound) + if not origin_open: # no path condition + flag = 1 # origin node is blocked + draw_control(origin_close, goal_close, flag, start, end, bound, + obstacle) + break + # update target for searching from end to start + target_origin = min(origin_open, key=lambda x: x.F).coordinate + + # searching from end to start + goal_open, goal_close = \ + find_path(goal_open, goal_close, target_origin, bound) + if not goal_open: # no path condition + flag = 2 # goal is blocked + draw_control(origin_close, goal_close, flag, start, end, bound, + obstacle) + break + # update target for searching from start to end + target_goal = min(goal_open, key=lambda x: x.F).coordinate + + # continue searching, draw the process + stop_sign, path = draw_control(origin_close, goal_close, flag, start, + end, bound, obstacle) + if stop_sign: + break + return path + + +def main(obstacle_number=1500): + print(__file__ + ' start!') + + top_vertex = [60, 60] # top right vertex of boundary + bottom_vertex = [0, 0] # bottom left vertex of boundary + + # generate start and goal point randomly + start = random_coordinate(bottom_vertex, top_vertex) + end = random_coordinate(bottom_vertex, top_vertex) + + # generate boundary and obstacles + bound, obstacle = boundary_and_obstacles(start, end, top_vertex, + bottom_vertex, + obstacle_number) + + path = searching_control(start, end, bound, obstacle) + if not show_animation: + print(path) + + +if __name__ == '__main__': + main(obstacle_number=1500) diff --git a/tests/test_a_star_searching_two_side.py b/tests/test_a_star_searching_two_side.py new file mode 100644 index 0000000000..6116c9bbe7 --- /dev/null +++ b/tests/test_a_star_searching_two_side.py @@ -0,0 +1,27 @@ +from unittest import TestCase +import os +import sys + +sys.path.append(os.path.dirname(__file__) + '/../') + +try: + from PathPlanning.AStar import A_Star_searching_from_two_side as m +except ImportError: + raise + + +class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main(800) + + def test2(self): + m.show_animation = False + m.main(5000) # increase obstacle number, block path + + +if __name__ == '__main__': + test = Test() + test.test1() + test.test2() From 5462ed7e6644341446c1efe394b01401f5f4a739 Mon Sep 17 00:00:00 2001 From: Sarim Mehdi Date: Mon, 21 Sep 2020 06:32:38 +0200 Subject: [PATCH 261/909] Added variants of A* (#395) * Add files via upload * Add files via upload * Update test_a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update test_a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update test_a_star_variants.py * Add files via upload * Delete test_a_star_variants.py * Update test_a_star_variants_iterative_deepening.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_dynamic_weighting.py * Update test_a_star_variants_jump_point.py * Update test_a_star_variants_theta_star.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_dynamic_weighting.py * Update test_a_star_variants_iterative_deepening.py * Update test_a_star_variants_jump_point.py * Update test_a_star_variants_theta_star.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_dynamic_weighting.py * Update test_a_star_variants_iterative_deepening.py * Update test_a_star_variants_jump_point.py * Update test_a_star_variants_theta_star.py * Update a_star_variants.py * Add files via upload * Add files via upload * Delete test_a_star_variants_beam_search.py * Delete test_a_star_variants_dynamic_weighting.py * Delete test_a_star_variants_iterative_deepening.py * Delete test_a_star_variants_jump_point.py * Delete test_a_star_variants_theta_star.py * Added requested changes * Added requested changes --- PathPlanning/AStar/a_star_variants.py | 462 ++++++++++++++++++++++++++ tests/test_a_star_variants.py | 47 +++ 2 files changed, 509 insertions(+) create mode 100644 PathPlanning/AStar/a_star_variants.py create mode 100644 tests/test_a_star_variants.py diff --git a/PathPlanning/AStar/a_star_variants.py b/PathPlanning/AStar/a_star_variants.py new file mode 100644 index 0000000000..cbf5395fd2 --- /dev/null +++ b/PathPlanning/AStar/a_star_variants.py @@ -0,0 +1,462 @@ +""" +astar variants +author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it) +Source: http://theory.stanford.edu/~amitp/GameProgramming/Variations.html +""" + +import numpy as np +import matplotlib.pyplot as plt + +show_animation = False +use_beam_search = False +use_iterative_deepening = False +use_dynamic_weighting = False +use_theta_star = False +use_jump_point = False + +beam_capacity = 30 +max_theta = 5 +only_corners = False +max_corner = 5 +w, epsilon, upper_bound_depth = 1, 4, 500 + + +def draw_horizontal_line(start_x, start_y, length, o_x, o_y, o_dict): + for i in range(start_x, start_x + length): + for j in range(start_y, start_y + 2): + o_x.append(i) + o_y.append(j) + o_dict[(i, j)] = True + + +def draw_vertical_line(start_x, start_y, length, o_x, o_y, o_dict): + for i in range(start_x, start_x + 2): + for j in range(start_y, start_y + length): + o_x.append(i) + o_y.append(j) + o_dict[(i, j)] = True + + +def in_line_of_sight(obs_grid, x1, y1, x2, y2): + t = 0 + while t <= 0.5: + xt = (1 - t) * x1 + t * x2 + yt = (1 - t) * y1 + t * y2 + if obs_grid[(int(xt), int(yt))]: + return False, None + xt = (1 - t) * x2 + t * x1 + yt = (1 - t) * y2 + t * y1 + if obs_grid[(int(xt), int(yt))]: + return False, None + t += 0.001 + dist = np.linalg.norm(np.array([x1, y1] - np.array([x2, y2]))) + return True, dist + + +def key_points(o_dict): + offsets1 = [(1, 0), (0, 1), (-1, 0), (1, 0)] + offsets2 = [(1, 1), (-1, 1), (-1, -1), (1, -1)] + offsets3 = [(0, 1), (-1, 0), (0, -1), (0, -1)] + c_list = [] + for grid_point, obs_status in o_dict.items(): + if obs_status: + continue + empty_space = True + x, y = grid_point + for i in [-1, 0, 1]: + for j in [-1, 0, 1]: + if (x + i, y + j) not in o_dict.keys(): + continue + if o_dict[(x + i, y + j)]: + empty_space = False + break + if empty_space: + continue + for offset1, offset2, offset3 in zip(offsets1, offsets2, offsets3): + i1, j1 = offset1 + i2, j2 = offset2 + i3, j3 = offset3 + if ((x + i1, y + j1) not in o_dict.keys()) or \ + ((x + i2, y + j2) not in o_dict.keys()) or \ + ((x + i3, y + j3) not in o_dict.keys()): + continue + obs_count = 0 + if o_dict[(x + i1, y + j1)]: + obs_count += 1 + if o_dict[(x + i2, y + j2)]: + obs_count += 1 + if o_dict[(x + i3, y + j3)]: + obs_count += 1 + if obs_count == 3 or obs_count == 1: + c_list.append((x, y)) + plt.plot(x, y, ".y") + break + if only_corners: + return c_list + + e_list = [] + for corner in c_list: + x1, y1 = corner + for other_corner in c_list: + x2, y2 = other_corner + if x1 == x2 and y1 == y2: + continue + reachable, _ = in_line_of_sight(o_dict, x1, y1, x2, y2) + if not reachable: + continue + x_m, y_m = int((x1 + x2) / 2), int((y1 + y2) / 2) + e_list.append((x_m, y_m)) + if show_animation: + plt.plot(x_m, y_m, ".y") + return c_list + e_list + + +class SearchAlgo: + def __init__(self, obs_grid, goal_x, goal_y, start_x, start_y, + limit_x, limit_y, corner_list=None): + self.start_pt = [start_x, start_y] + self.goal_pt = [goal_x, goal_y] + self.obs_grid = obs_grid + g_cost, h_cost = 0, self.get_hval(start_x, start_y, goal_x, goal_y) + f_cost = g_cost + h_cost + self.all_nodes, self.open_set = {}, [] + + if use_jump_point: + for corner in corner_list: + i, j = corner + h_c = self.get_hval(i, j, goal_x, goal_y) + self.all_nodes[(i, j)] = {'pos': [i, j], 'pred': None, + 'gcost': np.inf, 'hcost': h_c, + 'fcost': np.inf, + 'open': True, 'in_open_list': False} + self.all_nodes[tuple(self.goal_pt)] = \ + {'pos': self.goal_pt, 'pred': None, + 'gcost': np.inf, 'hcost': 0, 'fcost': np.inf, + 'open': True, 'in_open_list': True} + else: + for i in range(limit_x): + for j in range(limit_y): + h_c = self.get_hval(i, j, goal_x, goal_y) + self.all_nodes[(i, j)] = {'pos': [i, j], 'pred': None, + 'gcost': np.inf, 'hcost': h_c, + 'fcost': np.inf, + 'open': True, + 'in_open_list': False} + self.all_nodes[tuple(self.start_pt)] = \ + {'pos': self.start_pt, 'pred': None, + 'gcost': g_cost, 'hcost': h_cost, 'fcost': f_cost, + 'open': True, 'in_open_list': True} + self.open_set.append(self.all_nodes[tuple(self.start_pt)]) + + def get_hval(self, x1, y1, x2, y2): + x, y = x1, y1 + val = 0 + while x != x2 or y != y2: + if x != x2 and y != y2: + val += 14 + else: + val += 10 + x, y = x + np.sign(x2 - x), y + np.sign(y2 - y) + return val + + def get_farthest_point(self, x, y, i, j): + i_temp, j_temp = i, j + counter = 1 + got_goal = False + while not self.obs_grid[(x + i_temp, y + j_temp)] and \ + counter < max_theta: + i_temp += i + j_temp += j + counter += 1 + if [x + i_temp, y + j_temp] == self.goal_pt: + got_goal = True + break + if (x + i_temp, y + j_temp) not in self.obs_grid.keys(): + break + return i_temp - 2*i, j_temp - 2*j, counter, got_goal + + def jump_point(self): + """Jump point: Instead of exploring all empty spaces of the + map, just explore the corners.""" + plt.title('Jump Point') + + goal_found = False + while len(self.open_set) > 0: + self.open_set = sorted(self.open_set, key=lambda x: x['fcost']) + lowest_f = self.open_set[0]['fcost'] + lowest_h = self.open_set[0]['hcost'] + lowest_g = self.open_set[0]['gcost'] + p = 0 + for p_n in self.open_set[1:]: + if p_n['fcost'] == lowest_f and \ + p_n['gcost'] < lowest_g: + lowest_g = p_n['gcost'] + p += 1 + elif p_n['fcost'] == lowest_f and \ + p_n['gcost'] == lowest_g and \ + p_n['hcost'] < lowest_h: + lowest_h = p_n['hcost'] + p += 1 + else: + break + current_node = self.all_nodes[tuple(self.open_set[p]['pos'])] + x1, y1 = current_node['pos'] + + for cand_pt, cand_node in self.all_nodes.items(): + x2, y2 = cand_pt + if x1 == x2 and y1 == y2: + continue + if np.linalg.norm(np.array([x1, y1] - + np.array([x2, y2]))) > max_corner: + continue + reachable, offset = in_line_of_sight(self.obs_grid, x1, + y1, x2, y2) + if not reachable: + continue + + if list(cand_pt) == self.goal_pt: + current_node['open'] = False + self.all_nodes[tuple(cand_pt)]['pred'] = \ + current_node['pos'] + goal_found = True + break + + g_cost = offset + current_node['gcost'] + h_cost = self.all_nodes[cand_pt]['hcost'] + f_cost = g_cost + h_cost + cand_pt = tuple(cand_pt) + if f_cost < self.all_nodes[cand_pt]['fcost']: + self.all_nodes[cand_pt]['pred'] = current_node['pos'] + self.all_nodes[cand_pt]['gcost'] = g_cost + self.all_nodes[cand_pt]['fcost'] = f_cost + if not self.all_nodes[cand_pt]['in_open_list']: + self.open_set.append(self.all_nodes[cand_pt]) + self.all_nodes[cand_pt]['in_open_list'] = True + plt.plot(cand_pt[0], cand_pt[1], "r*") + + if goal_found: + break + plt.pause(0.001) + if goal_found: + current_node = self.all_nodes[tuple(self.goal_pt)] + while goal_found: + if current_node['pred'] is None: + break + x = [current_node['pos'][0], current_node['pred'][0]] + y = [current_node['pos'][1], current_node['pred'][1]] + plt.plot(x, y, "b") + current_node = self.all_nodes[tuple(current_node['pred'])] + plt.pause(0.001) + if goal_found: + break + + current_node['open'] = False + current_node['in_open_list'] = False + plt.plot(current_node['pos'][0], current_node['pos'][1], "g*") + del self.open_set[p] + current_node['fcost'], current_node['hcost'] = np.inf, np.inf + if show_animation: + plt.show() + + def astar(self): + """Beam search: Maintain an open list of just 30 nodes. + If more than 30 nodes, then get rid of ndoes with high + f values. + Iterative deepening: At every iteration, get a cut-off + value for the f cost. This cut-off is minimum of the f + value of all nodes whose f value is higher than the + current cut-off value. Nodes with f value higher than + the current cut off value are not put in the open set. + Dynamic weighting: Multiply heuristic with the following: + (1 + epsilon - (epsilon*d)/N) where d is the current + iteration of loop and N is upper bound on number of + iterations. + Theta star: Same as A star but you don't need to move + one neighbor at a time. In fact, you can look for the + next node as far out as you can as long as there is a + clear line of sight from your current node to that node.""" + if use_beam_search: + plt.title('A* with beam search') + elif use_iterative_deepening: + plt.title('A* with iterative deepening') + elif use_dynamic_weighting: + plt.title('A* with dynamic weighting') + elif use_theta_star: + plt.title('Theta*') + else: + plt.title('A*') + + goal_found = False + curr_f_thresh = np.inf + depth = 0 + no_valid_f = False + while len(self.open_set) > 0: + self.open_set = sorted(self.open_set, key=lambda x: x['fcost']) + lowest_f = self.open_set[0]['fcost'] + lowest_h = self.open_set[0]['hcost'] + lowest_g = self.open_set[0]['gcost'] + p = 0 + for p_n in self.open_set[1:]: + if p_n['fcost'] == lowest_f and \ + p_n['gcost'] < lowest_g: + lowest_g = p_n['gcost'] + p += 1 + elif p_n['fcost'] == lowest_f and \ + p_n['gcost'] == lowest_g and \ + p_n['hcost'] < lowest_h: + lowest_h = p_n['hcost'] + p += 1 + else: + break + current_node = self.all_nodes[tuple(self.open_set[p]['pos'])] + + while len(self.open_set) > beam_capacity and use_beam_search: + del self.open_set[-1] + + f_cost_list = [] + if use_dynamic_weighting: + w = (1 + epsilon - epsilon*depth/upper_bound_depth) + for i in range(-1, 2): + for j in range(-1, 2): + x, y = current_node['pos'] + if (i == 0 and j == 0) or \ + ((x + i, y + j) not in self.obs_grid.keys()): + continue + if (i, j) in [(1, 0), (0, 1), (-1, 0), (0, -1)]: + offset = 10 + else: + offset = 14 + if use_theta_star: + new_i, new_j, counter, goal_found = \ + self.get_farthest_point(x, y, i, j) + offset = offset * counter + cand_pt = [current_node['pos'][0] + new_i, + current_node['pos'][1] + new_j] + else: + cand_pt = [current_node['pos'][0] + i, + current_node['pos'][1] + j] + + if use_theta_star and goal_found: + current_node['open'] = False + cand_pt = self.goal_pt + self.all_nodes[tuple(cand_pt)]['pred'] = \ + current_node['pos'] + break + + if cand_pt == self.goal_pt: + current_node['open'] = False + self.all_nodes[tuple(cand_pt)]['pred'] = \ + current_node['pos'] + goal_found = True + break + + cand_pt = tuple(cand_pt) + if not self.obs_grid[tuple(cand_pt)] and \ + self.all_nodes[cand_pt]['open']: + g_cost = offset + current_node['gcost'] + h_cost = self.all_nodes[cand_pt]['hcost'] + if use_dynamic_weighting: + h_cost = h_cost * w + f_cost = g_cost + h_cost + if f_cost < self.all_nodes[cand_pt]['fcost'] and \ + f_cost <= curr_f_thresh: + f_cost_list.append(f_cost) + self.all_nodes[cand_pt]['pred'] = \ + current_node['pos'] + self.all_nodes[cand_pt]['gcost'] = g_cost + self.all_nodes[cand_pt]['fcost'] = f_cost + if not self.all_nodes[cand_pt]['in_open_list']: + self.open_set.append(self.all_nodes[cand_pt]) + self.all_nodes[cand_pt]['in_open_list'] = True + plt.plot(cand_pt[0], cand_pt[1], "r*") + if curr_f_thresh < f_cost < \ + self.all_nodes[cand_pt]['fcost']: + no_valid_f = True + if goal_found: + break + plt.pause(0.001) + if goal_found: + current_node = self.all_nodes[tuple(self.goal_pt)] + while goal_found: + if current_node['pred'] is None: + break + if use_theta_star or use_jump_point: + x, y = [current_node['pos'][0], current_node['pred'][0]], \ + [current_node['pos'][1], current_node['pred'][1]] + plt.plot(x, y, "b") + else: + plt.plot(current_node['pred'][0], + current_node['pred'][1], "b*") + current_node = self.all_nodes[tuple(current_node['pred'])] + plt.pause(0.001) + if goal_found: + break + + if use_iterative_deepening and f_cost_list: + curr_f_thresh = min(f_cost_list) + if use_iterative_deepening and not f_cost_list: + curr_f_thresh = np.inf + if use_iterative_deepening and not f_cost_list and no_valid_f: + current_node['fcost'], current_node['hcost'] = np.inf, np.inf + continue + + current_node['open'] = False + current_node['in_open_list'] = False + plt.plot(current_node['pos'][0], current_node['pos'][1], "g*") + del self.open_set[p] + current_node['fcost'], current_node['hcost'] = np.inf, np.inf + depth += 1 + if show_animation: + plt.show() + + +def main(): + # set obstacle positions + obs_dict = {} + for i in range(51): + for j in range(51): + obs_dict[(i, j)] = False + o_x, o_y = [], [] + + s_x = 5.0 + s_y = 5.0 + g_x = 35.0 + g_y = 45.0 + + # draw outer border of maze + draw_vertical_line(0, 0, 50, o_x, o_y, obs_dict) + draw_vertical_line(48, 0, 50, o_x, o_y, obs_dict) + draw_horizontal_line(0, 0, 50, o_x, o_y, obs_dict) + draw_horizontal_line(0, 48, 50, o_x, o_y, obs_dict) + + # draw inner walls + all_x = [10, 10, 10, 15, 20, 20, 30, 30, 35, 30, 40, 45] + all_y = [10, 30, 45, 20, 5, 40, 10, 40, 5, 40, 10, 25] + all_len = [10, 10, 5, 10, 10, 5, 20, 10, 25, 10, 35, 15] + for x, y, l in zip(all_x, all_y, all_len): + draw_vertical_line(x, y, l, o_x, o_y, obs_dict) + + all_x[:], all_y[:], all_len[:] = [], [], [] + all_x = [35, 40, 15, 10, 45, 20, 10, 15, 25, 45, 10, 30, 10, 40] + all_y = [5, 10, 15, 20, 20, 25, 30, 35, 35, 35, 40, 40, 45, 45] + all_len = [10, 5, 10, 10, 5, 5, 10, 5, 10, 5, 10, 5, 5, 5] + for x, y, l in zip(all_x, all_y, all_len): + draw_horizontal_line(x, y, l, o_x, o_y, obs_dict) + + plt.plot(o_x, o_y, ".k") + plt.plot(s_x, s_y, "og") + plt.plot(g_x, g_y, "xb") + plt.grid(True) + + if use_jump_point: + keypoint_list = key_points(obs_dict) + search_obj = SearchAlgo(obs_dict, g_x, g_y, s_x, s_y, 101, 101, + keypoint_list) + search_obj.jump_point() + else: + search_obj = SearchAlgo(obs_dict, g_x, g_y, s_x, s_y, 101, 101) + search_obj.astar() + + +if __name__ == '__main__': + main() diff --git a/tests/test_a_star_variants.py b/tests/test_a_star_variants.py new file mode 100644 index 0000000000..53efeddd20 --- /dev/null +++ b/tests/test_a_star_variants.py @@ -0,0 +1,47 @@ +import PathPlanning.AStar.a_star_variants as astar +from unittest import TestCase +import sys +import os +sys.path.append(os.path.dirname(__file__) + "/../") + + +class Test(TestCase): + + def test(self): + # A* with beam search + astar.show_animation = False + astar.use_beam_search = True + astar.main() + self.reset_all() + + # A* with iterative deepening + astar.use_iterative_deepening = True + astar.main() + self.reset_all() + + # A* with dynamic weighting + astar.use_dynamic_weighting = True + astar.main() + self.reset_all() + + # theta* + astar.use_theta_star = True + astar.main() + self.reset_all() + + # A* with jump point + astar.use_jump_point = True + astar.main() + self.reset_all() + + def reset_all(self): + astar.use_beam_search = False + astar.use_iterative_deepening = False + astar.use_dynamic_weighting = False + astar.use_theta_star = False + astar.use_jump_point = False + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test() From d7cc364b2f8d83f614a316cb5916e9f0b17dc10e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Sep 2020 07:13:16 +0900 Subject: [PATCH 262/909] Bump matplotlib from 3.3.1 to 3.3.2 (#406) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.1 to 3.3.2. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.1...v3.3.2) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 094def17af..58511e90f4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.2 scipy == 1.5.2 pandas == 1.1.2 -matplotlib == 3.3.1 +matplotlib == 3.3.2 cvxpy == 1.1.5 From 468be047aa5975265cbdc5b89740bc6b61079a9e Mon Sep 17 00:00:00 2001 From: ajwahab <1449672+ajwahab@users.noreply.github.com> Date: Sun, 27 Sep 2020 00:10:46 -0400 Subject: [PATCH 263/909] robustified 2 joint arm example (#402) two joint arm point control example will no longer fail if target point is out of reach --- .../two_joint_arm_to_point_control.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index 75e7cf301f..85ed612360 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -37,8 +37,11 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0): """ while True: try: - theta2_goal = np.arccos( - (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2)) + if np.sqrt(x**2 + y**2) > (l1 + l2): + theta2_goal = 0 + else: + theta2_goal = np.arccos( + (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2)) theta1_goal = np.math.atan2(y, x) - np.math.atan2(l2 * np.sin(theta2_goal), (l1 + l2 * np.cos(theta2_goal))) From 60e9e8f39b528d664a3d641f916549a2545555d4 Mon Sep 17 00:00:00 2001 From: Sarim Mehdi Date: Tue, 29 Sep 2020 08:32:21 +0200 Subject: [PATCH 264/909] Implemented Flowfield Pathfinding (#408) * Add files via upload * Add files via upload * Update test_a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update test_a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update a_star_variants.py * Update test_a_star_variants.py * Add files via upload * Delete test_a_star_variants.py * Update test_a_star_variants_iterative_deepening.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_dynamic_weighting.py * Update test_a_star_variants_jump_point.py * Update test_a_star_variants_theta_star.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_dynamic_weighting.py * Update test_a_star_variants_iterative_deepening.py * Update test_a_star_variants_jump_point.py * Update test_a_star_variants_theta_star.py * Update test_a_star_variants_beam_search.py * Update test_a_star_variants_dynamic_weighting.py * Update test_a_star_variants_iterative_deepening.py * Update test_a_star_variants_jump_point.py * Update test_a_star_variants_theta_star.py * Update a_star_variants.py * Add files via upload * Add files via upload * Delete test_a_star_variants_beam_search.py * Delete test_a_star_variants_dynamic_weighting.py * Delete test_a_star_variants_iterative_deepening.py * Delete test_a_star_variants_jump_point.py * Delete test_a_star_variants_theta_star.py * Added requested changes * Added requested changes * Added flowfield * Added requested changes * Update flowfield.py --- PathPlanning/FlowField/flowfield.py | 223 ++++++++++++++++++++++++++++ tests/test_flow_field.py | 17 +++ 2 files changed, 240 insertions(+) create mode 100644 PathPlanning/FlowField/flowfield.py create mode 100644 tests/test_flow_field.py diff --git a/PathPlanning/FlowField/flowfield.py b/PathPlanning/FlowField/flowfield.py new file mode 100644 index 0000000000..f222e8ecf2 --- /dev/null +++ b/PathPlanning/FlowField/flowfield.py @@ -0,0 +1,223 @@ +""" +flowfield pathfinding +author: Sarim Mehdi (muhammadsarim.mehdi@studio.unibo.it) +Source: https://leifnode.com/2013/12/flow-field-pathfinding/ +""" + +import numpy as np +import matplotlib.pyplot as plt + +show_animation = True + + +def draw_horizontal_line(start_x, start_y, length, o_x, o_y, o_dict, path): + for i in range(start_x, start_x + length): + for j in range(start_y, start_y + 2): + o_x.append(i) + o_y.append(j) + o_dict[(i, j)] = path + + +def draw_vertical_line(start_x, start_y, length, o_x, o_y, o_dict, path): + for i in range(start_x, start_x + 2): + for j in range(start_y, start_y + length): + o_x.append(i) + o_y.append(j) + o_dict[(i, j)] = path + + +class FlowField: + def __init__(self, obs_grid, goal_x, goal_y, start_x, start_y, + limit_x, limit_y): + self.start_pt = [start_x, start_y] + self.goal_pt = [goal_x, goal_y] + self.obs_grid = obs_grid + self.limit_x, self.limit_y = limit_x, limit_y + self.cost_field = {} + self.integration_field = {} + self.vector_field = {} + + def find_path(self): + self.create_cost_field() + self.create_integration_field() + self.assign_vectors() + self.follow_vectors() + + def create_cost_field(self): + """Assign cost to each grid which defines the energy + it would take to get there.""" + for i in range(self.limit_x): + for j in range(self.limit_y): + if self.obs_grid[(i, j)] == 'free': + self.cost_field[(i, j)] = 1 + elif self.obs_grid[(i, j)] == 'medium': + self.cost_field[(i, j)] = 7 + elif self.obs_grid[(i, j)] == 'hard': + self.cost_field[(i, j)] = 20 + elif self.obs_grid[(i, j)] == 'obs': + continue + + if [i, j] == self.goal_pt: + self.cost_field[(i, j)] = 0 + + def create_integration_field(self): + """Start from the goal node and calculate the value + of the integration field at each node. Start by + assigning a value of infinity to every node except + the goal node which is assigned a value of 0. Put the + goal node in the open list and then get its neighbors + (must not be obstacles). For each neighbor, the new + cost is equal to the cost of the current node in the + integration field (in the beginning, this will simply + be the goal node) + the cost of the neighbor in the + cost field + the extra cost (optional). The new cost + is only assigned if it is less than the previously + assigned cost of the node in the integration field and, + when that happens, the neighbor is put on the open list. + This process continues until the open list is empty.""" + for i in range(self.limit_x): + for j in range(self.limit_y): + if self.obs_grid[(i, j)] == 'obs': + continue + self.integration_field[(i, j)] = np.inf + if [i, j] == self.goal_pt: + self.integration_field[(i, j)] = 0 + + open_list = [(self.goal_pt, 0)] + while open_list: + curr_pos, curr_cost = open_list[0] + curr_x, curr_y = curr_pos + for i in range(-1, 2): + for j in range(-1, 2): + x, y = curr_x + i, curr_y + j + if self.obs_grid[(x, y)] == 'obs': + continue + if (i, j) in [(1, 0), (0, 1), (-1, 0), (0, -1)]: + e_cost = 10 + else: + e_cost = 14 + neighbor_energy = self.cost_field[(x, y)] + neighbor_old_cost = self.integration_field[(x, y)] + neighbor_new_cost = curr_cost + neighbor_energy + e_cost + if neighbor_new_cost < neighbor_old_cost: + self.integration_field[(x, y)] = neighbor_new_cost + open_list.append(([x, y], neighbor_new_cost)) + del open_list[0] + + def assign_vectors(self): + """For each node, assign a vector from itself to the node with + the lowest cost in the integration field. An agent will simply + follow this vector field to the goal""" + for i in range(self.limit_x): + for j in range(self.limit_y): + if self.obs_grid[(i, j)] == 'obs': + continue + if [i, j] == self.goal_pt: + self.vector_field[(i, j)] = (None, None) + continue + offset_list = [(i + a, j + b) + for a in range(-1, 2) + for b in range(-1, 2)] + neighbor_list = [{'loc': pt, + 'cost': self.integration_field[pt]} + for pt in offset_list + if self.obs_grid[pt] != 'obs'] + neighbor_list = sorted(neighbor_list, key=lambda x: x['cost']) + best_neighbor = neighbor_list[0]['loc'] + self.vector_field[(i, j)] = best_neighbor + + def follow_vectors(self): + curr_x, curr_y = self.start_pt + while curr_x is not None and curr_y is not None: + plt.plot(curr_x, curr_y, "b*") + curr_x, curr_y = self.vector_field[(curr_x, curr_y)] + plt.pause(0.001) + if show_animation: + plt.show() + + +def main(): + # set obstacle positions + obs_dict = {} + for i in range(51): + for j in range(51): + obs_dict[(i, j)] = 'free' + o_x, o_y, m_x, m_y, h_x, h_y = [], [], [], [], [], [] + + s_x = 5.0 + s_y = 5.0 + g_x = 35.0 + g_y = 45.0 + + # draw outer border of maze + draw_vertical_line(0, 0, 50, o_x, o_y, obs_dict, 'obs') + draw_vertical_line(48, 0, 50, o_x, o_y, obs_dict, 'obs') + draw_horizontal_line(0, 0, 50, o_x, o_y, obs_dict, 'obs') + draw_horizontal_line(0, 48, 50, o_x, o_y, obs_dict, 'obs') + + # draw inner walls + all_x = [10, 10, 10, 15, 20, 20, 30, 30, 35, 30, 40, 45] + all_y = [10, 30, 45, 20, 5, 40, 10, 40, 5, 40, 10, 25] + all_len = [10, 10, 5, 10, 10, 5, 20, 10, 25, 10, 35, 15] + for x, y, l in zip(all_x, all_y, all_len): + draw_vertical_line(x, y, l, o_x, o_y, obs_dict, 'obs') + + all_x[:], all_y[:], all_len[:] = [], [], [] + all_x = [35, 40, 15, 10, 45, 20, 10, 15, 25, 45, 10, 30, 10, 40] + all_y = [5, 10, 15, 20, 20, 25, 30, 35, 35, 35, 40, 40, 45, 45] + all_len = [10, 5, 10, 10, 5, 5, 10, 5, 10, 5, 10, 5, 5, 5] + for x, y, l in zip(all_x, all_y, all_len): + draw_horizontal_line(x, y, l, o_x, o_y, obs_dict, 'obs') + + # Some points are assigned a slightly higher energy value in the cost + # field. For example, if an agent wishes to go to a point, it might + # encounter different kind of terrain like grass and dirt. Grass is + # assigned medium difficulty of passage (color coded as green on the + # map here). Dirt is assigned hard difficulty of passage (color coded + # as brown here). Hence, this algorithm will take into account how + # difficult it is to go through certain areas of a map when deciding + # the shortest path. + + # draw paths that have medium difficulty (in terms of going through them) + all_x[:], all_y[:], all_len[:] = [], [], [] + all_x = [10, 45] + all_y = [22, 20] + all_len = [8, 5] + for x, y, l in zip(all_x, all_y, all_len): + draw_vertical_line(x, y, l, m_x, m_y, obs_dict, 'medium') + + all_x[:], all_y[:], all_len[:] = [], [], [] + all_x = [20, 30, 42] + [47] * 5 + all_y = [35, 30, 38] + [37 + i for i in range(2)] + all_len = [5, 7, 3] + [1] * 3 + for x, y, l in zip(all_x, all_y, all_len): + draw_horizontal_line(x, y, l, m_x, m_y, obs_dict, 'medium') + + # draw paths that have hard difficulty (in terms of going through them) + all_x[:], all_y[:], all_len[:] = [], [], [] + all_x = [15, 20, 35] + all_y = [45, 20, 35] + all_len = [3, 5, 7] + for x, y, l in zip(all_x, all_y, all_len): + draw_vertical_line(x, y, l, h_x, h_y, obs_dict, 'hard') + + all_x[:], all_y[:], all_len[:] = [], [], [] + all_x = [30] + [47] * 5 + all_y = [10] + [37 + i for i in range(2)] + all_len = [5] + [1] * 3 + for x, y, l in zip(all_x, all_y, all_len): + draw_horizontal_line(x, y, l, h_x, h_y, obs_dict, 'hard') + + plt.plot(o_x, o_y, "sr") + plt.plot(m_x, m_y, "sg") + plt.plot(h_x, h_y, "sy") + plt.plot(s_x, s_y, "og") + plt.plot(g_x, g_y, "o") + plt.grid(True) + + flow_obj = FlowField(obs_dict, g_x, g_y, s_x, s_y, 50, 50) + flow_obj.find_path() + + +if __name__ == '__main__': + main() diff --git a/tests/test_flow_field.py b/tests/test_flow_field.py new file mode 100644 index 0000000000..bf8fe6b802 --- /dev/null +++ b/tests/test_flow_field.py @@ -0,0 +1,17 @@ +import PathPlanning.FlowField.flowfield as flowfield +from unittest import TestCase +import sys +import os +sys.path.append(os.path.dirname(__file__) + "/../") + + +class Test(TestCase): + + def test(self): + flowfield.show_animation = False + flowfield.main() + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test() From 2b316502e336de102888cb62cd0ae39769b191b3 Mon Sep 17 00:00:00 2001 From: Rafael Rojas Date: Thu, 1 Oct 2020 13:15:52 +0200 Subject: [PATCH 265/909] Bug RRT* fix, issues #382 and #383 (#401) --- PathPlanning/RRT/rrt.py | 55 +++++++------ PathPlanning/RRTStar/rrt_star.py | 132 +++++++++++++++++++++++-------- 2 files changed, 131 insertions(+), 56 deletions(-) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 4acea0c10e..834fb24e39 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -32,8 +32,15 @@ def __init__(self, x, y): self.path_y = [] self.parent = None - def __init__(self, start, goal, obstacle_list, rand_area, - expand_dis=3.0, path_resolution=0.5, goal_sample_rate=5, max_iter=500): + def __init__(self, + start, + goal, + obstacle_list, + rand_area, + expand_dis=3.0, + path_resolution=0.5, + goal_sample_rate=5, + max_iter=500): """ Setting Parameter @@ -75,8 +82,10 @@ def planning(self, animation=True): if animation and i % 5 == 0: self.draw_graph(rnd_node) - if self.calc_dist_to_goal(self.node_list[-1].x, self.node_list[-1].y) <= self.expand_dis: - final_node = self.steer(self.node_list[-1], self.end, self.expand_dis) + if self.calc_dist_to_goal(self.node_list[-1].x, + self.node_list[-1].y) <= self.expand_dis: + final_node = self.steer(self.node_list[-1], self.end, + self.expand_dis) if self.check_collision(final_node, self.obstacle_list): return self.generate_final_course(len(self.node_list) - 1) @@ -108,6 +117,8 @@ def steer(self, from_node, to_node, extend_length=float("inf")): if d <= self.path_resolution: new_node.path_x.append(to_node.x) new_node.path_y.append(to_node.y) + new_node.x = to_node.x + new_node.y = to_node.y new_node.parent = from_node @@ -130,8 +141,9 @@ def calc_dist_to_goal(self, x, y): def get_random_node(self): if random.randint(0, 100) > self.goal_sample_rate: - rnd = self.Node(random.uniform(self.min_rand, self.max_rand), - random.uniform(self.min_rand, self.max_rand)) + rnd = self.Node( + random.uniform(self.min_rand, self.max_rand), + random.uniform(self.min_rand, self.max_rand)) else: # goal point sampling rnd = self.Node(self.end.x, self.end.y) return rnd @@ -139,8 +151,9 @@ def get_random_node(self): def draw_graph(self, rnd=None): plt.clf() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") for node in self.node_list: @@ -167,8 +180,8 @@ def plot_circle(x, y, size, color="-b"): # pragma: no cover @staticmethod def get_nearest_node_index(node_list, rnd_node): - dlist = [(node.x - rnd_node.x) ** 2 + (node.y - rnd_node.y) - ** 2 for node in node_list] + dlist = [(node.x - rnd_node.x)**2 + (node.y - rnd_node.y)**2 + for node in node_list] minind = dlist.index(min(dlist)) return minind @@ -184,7 +197,7 @@ def check_collision(node, obstacleList): dy_list = [oy - y for y in node.path_y] d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)] - if min(d_list) <= size ** 2: + if min(d_list) <= size**2: return False # collision return True # safe @@ -202,20 +215,14 @@ def main(gx=6.0, gy=10.0): print("start " + __file__) # ====Search Path with RRT==== - obstacleList = [ - (5, 5, 1), - (3, 6, 2), - (3, 8, 2), - (3, 10, 2), - (7, 5, 2), - (9, 5, 2), - (8, 10, 1) - ] # [x, y, radius] + obstacleList = [(5, 5, 1), (3, 6, 2), (3, 8, 2), (3, 10, 2), (7, 5, 2), + (9, 5, 2), (8, 10, 1)] # [x, y, radius] # Set Initial parameters - rrt = RRT(start=[0, 0], - goal=[gx, gy], - rand_area=[-2, 15], - obstacle_list=obstacleList) + rrt = RRT( + start=[0, 0], + goal=[gx, gy], + rand_area=[-2, 15], + obstacle_list=obstacleList) path = rrt.planning(animation=show_animation) if path is None: diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index 10ad93a6ba..bef0086095 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -12,8 +12,7 @@ import matplotlib.pyplot as plt -sys.path.append(os.path.dirname(os.path.abspath(__file__)) + - "/../RRT/") +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRT/") try: from rrt import RRT @@ -33,15 +32,17 @@ def __init__(self, x, y): super().__init__(x, y) self.cost = 0.0 - def __init__(self, start, goal, obstacle_list, rand_area, + def __init__(self, + start, + goal, + obstacle_list, + rand_area, expand_dis=30.0, path_resolution=1.0, goal_sample_rate=20, max_iter=300, - connect_circle_dist=50.0 - ): - super().__init__(start, goal, obstacle_list, - rand_area, expand_dis, path_resolution, goal_sample_rate, max_iter) + connect_circle_dist=50.0, + search_until_max_iter=False): """ Setting Parameter @@ -51,15 +52,17 @@ def __init__(self, start, goal, obstacle_list, rand_area, randArea:Random Sampling Area [min,max] """ + super().__init__(start, goal, obstacle_list, rand_area, expand_dis, + path_resolution, goal_sample_rate, max_iter) self.connect_circle_dist = connect_circle_dist self.goal_node = self.Node(goal[0], goal[1]) + self.search_until_max_iter = search_until_max_iter - def planning(self, animation=True, search_until_max_iter=True): + def planning(self, animation=True): """ rrt star path planning - animation: flag for animation on or off - search_until_max_iter: search until max iteration for path improving or not + animation: flag for animation on or off . """ self.node_list = [self.start] @@ -67,19 +70,28 @@ def planning(self, animation=True, search_until_max_iter=True): print("Iter:", i, ", number of nodes:", len(self.node_list)) rnd = self.get_random_node() nearest_ind = self.get_nearest_node_index(self.node_list, rnd) - new_node = self.steer(self.node_list[nearest_ind], rnd, self.expand_dis) + new_node = self.steer(self.node_list[nearest_ind], rnd, + self.expand_dis) + near_node = self.node_list[nearest_ind] + new_node.cost = near_node.cost + \ + math.hypot(new_node.x-near_node.x, + new_node.y-near_node.y) if self.check_collision(new_node, self.obstacle_list): near_inds = self.find_near_nodes(new_node) - new_node = self.choose_parent(new_node, near_inds) - if new_node: + node_with_updated_parent = self.choose_parent( + new_node, near_inds) + if node_with_updated_parent: + self.rewire(node_with_updated_parent, near_inds) + self.node_list.append(node_with_updated_parent) + else: self.node_list.append(new_node) - self.rewire(new_node, near_inds) - if animation and i % 5 == 0: + if animation: self.draw_graph(rnd) - if (not search_until_max_iter) and new_node: # check reaching the goal + if ((not self.search_until_max_iter) + and new_node): # if reaches goal last_index = self.search_best_goal_node() if last_index is not None: return self.generate_final_course(last_index) @@ -93,6 +105,21 @@ def planning(self, animation=True, search_until_max_iter=True): return None def choose_parent(self, new_node, near_inds): + """ + Computes the cheapest point to new_node contained in the list + near_inds and set such a node as the parent of new_node. + Arguments: + -------- + new_node, Node + randomly generated node with a path from its neared point + There are not coalitions between this node and th tree. + near_inds: list + Indices of indices of the nodes what are near to new_node + + Returns. + ------ + Node, a copy of new_node + """ if not near_inds: return None @@ -113,14 +140,18 @@ def choose_parent(self, new_node, near_inds): min_ind = near_inds[costs.index(min_cost)] new_node = self.steer(self.node_list[min_ind], new_node) - new_node.parent = self.node_list[min_ind] new_node.cost = min_cost return new_node def search_best_goal_node(self): - dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list] - goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expand_dis] + dist_to_goal_list = [ + self.calc_dist_to_goal(n.x, n.y) for n in self.node_list + ] + goal_inds = [ + dist_to_goal_list.index(i) for i in dist_to_goal_list + if i <= self.expand_dis + ] safe_goal_inds = [] for goal_ind in goal_inds: @@ -139,17 +170,48 @@ def search_best_goal_node(self): return None def find_near_nodes(self, new_node): + """ + 1) defines a ball centered on new_node + 2) Returns all nodes of the three that are inside this ball + Arguments: + --------- + new_node: Node + new randomly generated node, without collisions between + its nearest node + Returns: + ------- + list + List with the indices of the nodes inside the ball of + radius r + """ nnode = len(self.node_list) + 1 r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode)) - # if expand_dist exists, search vertices in a range no more than expand_dist - if hasattr(self, 'expand_dis'): + # if expand_dist exists, search vertices in a range no more than + # expand_dist + if hasattr(self, 'expand_dis'): r = min(r, self.expand_dis) - dist_list = [(node.x - new_node.x) ** 2 + - (node.y - new_node.y) ** 2 for node in self.node_list] - near_inds = [dist_list.index(i) for i in dist_list if i <= r ** 2] + dist_list = [(node.x - new_node.x)**2 + (node.y - new_node.y)**2 + for node in self.node_list] + near_inds = [dist_list.index(i) for i in dist_list if i <= r**2] return near_inds def rewire(self, new_node, near_inds): + """ + For each node in near_inds, this will check if it is cheaper to + arrive to them from new_node. + In such a case, this will re-assign the parent of the nodes in + near_inds to new_node. + Parameters: + ---------- + new_node, Node + Node randomly added which can be joined to the tree + + near_inds, list of uints + A list of indices of the self.new_node which contains + nodes within a circle of a given radius. + Remark: parent is designated in choose_parent. + + """ for i in near_inds: near_node = self.node_list[i] edge_node = self.steer(new_node, near_node) @@ -161,7 +223,12 @@ def rewire(self, new_node, near_inds): improved_cost = near_node.cost > edge_node.cost if no_collision and improved_cost: - self.node_list[i] = edge_node + near_node.x = edge_node.x + near_node.y = edge_node.y + near_node.cost = edge_node.cost + near_node.path_x = edge_node.path_x + near_node.path_y = edge_node.path_y + near_node.parent = edge_node.parent self.propagate_cost_to_leaves(new_node) def calc_new_cost(self, from_node, to_node): @@ -192,10 +259,12 @@ def main(): ] # [x,y,size(radius)] # Set Initial parameters - rrt_star = RRTStar(start=[0, 0], - goal=[6, 10], - rand_area=[-2, 15], - obstacle_list=obstacle_list) + rrt_star = RRTStar( + start=[0, 0], + goal=[6, 10], + rand_area=[-2, 15], + obstacle_list=obstacle_list, + expand_dis=1) path = rrt_star.planning(animation=show_animation) if path is None: @@ -206,10 +275,9 @@ def main(): # Draw final path if show_animation: rrt_star.draw_graph() - plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r') + plt.plot([x for (x, y) in path], [y for (x, y) in path], 'r--') plt.grid(True) - plt.pause(0.01) # Need for Mac - plt.show() + plt.show() if __name__ == '__main__': From 2a5bbdc0be30a9adfa1b074727548f5d0160a873 Mon Sep 17 00:00:00 2001 From: hotsuyuki Date: Thu, 1 Oct 2020 20:21:51 +0900 Subject: [PATCH 266/909] More intuitive way of plot_covariance_ellipse() (#407) * More intuitive way of plot_covariance_ellipse() in EKF and UKF * Add the same changes in UKF as well * Modified rotation matrix to scipy.spatial.transform.Rotation * Modified angle of covariance matrix * Fixed typos --- .../ensemble_kalman_filter/ensemble_kalman_filter.py | 2 +- .../extended_kalman_filter/extended_kalman_filter.py | 6 +++--- Localization/particle_filter/particle_filter.py | 2 +- .../unscented_kalman_filter/unscented_kalman_filter.py | 6 +++--- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index 1307b1ebd1..8ef2089b01 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -167,7 +167,7 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] - angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0]) + angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind]) rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2] fx = np.stack([x, y]).T @ rot diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py index 2d0d86bf77..51e8f22f4d 100644 --- a/Localization/extended_kalman_filter/extended_kalman_filter.py +++ b/Localization/extended_kalman_filter/extended_kalman_filter.py @@ -10,6 +10,7 @@ import matplotlib.pyplot as plt import numpy as np +from scipy.spatial.transform import Rotation as Rot # Covariance for EKF simulation Q = np.diag([ @@ -147,9 +148,8 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover b = math.sqrt(eigval[smallind]) x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] - angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) - rot = np.array([[math.cos(angle), math.sin(angle)], - [-math.sin(angle), math.cos(angle)]]) + angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind]) + rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2] fx = rot @ (np.array([x, y])) px = np.array(fx[0, :] + xEst[0, 0]).flatten() py = np.array(fx[1, :] + xEst[1, 0]).flatten() diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 89a43ea600..002718f028 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -188,7 +188,7 @@ def plot_covariance_ellipse(x_est, p_est): # pragma: no cover x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] - angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0]) + angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind]) rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2] fx = rot.dot(np.array([[x, y]])) px = np.array(fx[0, :] + x_est[0, 0]).flatten() diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py index 7bf279ced0..7f80bccf51 100644 --- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py +++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py @@ -10,6 +10,7 @@ import matplotlib.pyplot as plt import numpy as np +from scipy.spatial.transform import Rotation as Rot import scipy.linalg # Covariance for UKF simulation @@ -180,9 +181,8 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover b = math.sqrt(eigval[smallind]) x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] - angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) - rot = np.array([[math.cos(angle), math.sin(angle)], - [-math.sin(angle), math.cos(angle)]]) + angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind]) + rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2] fx = rot @ np.array([x, y]) px = np.array(fx[0, :] + xEst[0, 0]).flatten() py = np.array(fx[1, :] + xEst[1, 0]).flatten() From b10bfbbc40988c8d475427d88188b7d261cf9342 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 7 Oct 2020 19:57:58 +0900 Subject: [PATCH 267/909] Bump pandas from 1.1.2 to 1.1.3 (#416) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.2 to 1.1.3. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.2...v1.1.3) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 58511e90f4..da40bbae8f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.2 scipy == 1.5.2 -pandas == 1.1.2 +pandas == 1.1.3 matplotlib == 3.3.2 cvxpy == 1.1.5 From b3ab809890d0d27c6242266ddc05e4fcae7226e8 Mon Sep 17 00:00:00 2001 From: Raghuram Shankar Date: Fri, 9 Oct 2020 16:43:21 +0200 Subject: [PATCH 268/909] Add Cubature Kalman Filter (#410) * cubature kalman filter * Revert "cubature kalman filter" This reverts commit 172772897c3f8407390fb9d57476c15b4f21fa75. * add ckf test * update flags for CI * update flags for CI * update flags for CI * remove comments * remove comments * change postpross * changes requested * remove comments * Changes to comments, remove linear_update * changes to comments * removed comments * change comments * update comments * update comments * update comments * update comments * fix comment --- .../cubature_kalman_filter.py | 246 ++++++++++++++++++ tests/test_cubature_kalman_filter.py | 14 + 2 files changed, 260 insertions(+) create mode 100644 Localization/cubature_kalman_filter/cubature_kalman_filter.py create mode 100644 tests/test_cubature_kalman_filter.py diff --git a/Localization/cubature_kalman_filter/cubature_kalman_filter.py b/Localization/cubature_kalman_filter/cubature_kalman_filter.py new file mode 100644 index 0000000000..0fc4c93760 --- /dev/null +++ b/Localization/cubature_kalman_filter/cubature_kalman_filter.py @@ -0,0 +1,246 @@ +""" +Cubature Kalman filter using Constant Turn Rate and Velocity (CTRV) model +Fuse sensor data from IMU and GPS to obtain accurate position + +https://ieeexplore.ieee.org/document/4982682 + +Author: Raghuram Shankar + +state matrix: 2D x-y position, yaw, velocity and yaw rate +measurement matrix: 2D x-y position, velocity and yaw rate + +dt: Duration of time step +N: Number of time steps +show_final: Flag for showing final result +show_animation: Flag for showing each animation frame +show_ellipse: Flag for showing covariance ellipse +z_noise: Measurement noise +x_0: Prior state estimate matrix +P_0: Prior state estimate covariance matrix +q: Process noise covariance +hx: Measurement model matrix +r: Sensor noise covariance +SP: Sigma Points +W: Weights + +x_est: State estimate +P_est: State estimate covariance +x_true: Ground truth value of state +x_true_cat: Concatenate all ground truth states +x_est_cat: Concatenate all state estimates +z_cat: Concatenate all measurements + +""" + +import math +import matplotlib.pyplot as plt +import numpy as np +from scipy.linalg import sqrtm + + +dt = 0.1 +N = 100 + +show_final = 1 +show_animation = 0 +show_ellipse = 0 + + +z_noise = np.array([[0.1, 0.0, 0.0, 0.0], # x position [m] + [0.0, 0.1, 0.0, 0.0], # y position [m] + [0.0, 0.0, 0.1, 0.0], # velocity [m/s] + [0.0, 0.0, 0.0, 0.1]]) # yaw rate [rad/s] + + +x_0 = np.array([[0.0], # x position [m] + [0.0], # y position [m] + [0.0], # yaw [rad] + [1.0], # velocity [m/s] + [0.1]]) # yaw rate [rad/s] + + +p_0 = np.array([[1e-3, 0.0, 0.0, 0.0, 0.0], + [0.0, 1e-3, 0.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 1.0]]) + + +q = np.array([[1e-11, 0.0, 0.0, 0.0, 0.0], + [0.0, 1e-11, 0.0, 0.0, 0.0], + [0.0, 0.0, np.deg2rad(1e-4), 0.0, 0.0], + [0.0, 0.0, 0.0, 1e-4, 0.0], + [0.0, 0.0, 0.0, 0.0, np.deg2rad(1e-4)]]) + + +hx = np.array([[1.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 1.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 1.0]]) + + +r = np.array([[0.015, 0.0, 0.0, 0.0], + [0.0, 0.010, 0.0, 0.0], + [0.0, 0.0, 0.1, 0.0], + [0.0, 0.0, 0.0, 0.01]])**2 + + +def cubature_kalman_filter(x_est, p_est, z): + x_pred, p_pred = cubature_prediction(x_est, p_est) + x_upd, p_upd = cubature_update(x_pred, p_pred, z) + return x_upd, p_upd + + +def f(x): + """ + Motion Model + References: + http://fusion.isif.org/proceedings/fusion08CD/papers/1569107835.pdf + https://github.com/balzer82/Kalman + """ + x[0] = x[0] + (x[3]/x[4]) * (np.sin(x[4] * dt + x[2]) - np.sin(x[2])) + x[1] = x[1] + (x[3]/x[4]) * (- np.cos(x[4] * dt + x[2]) + np.cos(x[2])) + x[2] = x[2] + x[4] * dt + x[3] = x[3] + x[4] = x[4] + return x + + +def h(x): + """Measurement Model""" + x = hx @ x + return x + + +def sigma(x, p): + """ + Unscented Transform with Cubature Rule + Generate 2n Sigma Points to represent the nonlinear motion + Assign Weights to each Sigma Point, Wi = 1/2n + Cubature Rule - Special Case of Unscented Transform + W0 = 0, no extra tuning parameters, no negative weights + """ + n = np.shape(x)[0] + SP = np.zeros((n, 2*n)) + W = np.zeros((1, 2*n)) + for i in range(n): + SD = sqrtm(p) + SP[:, i] = (x + (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten() + SP[:, i+n] = (x - (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten() + W[:, i] = 1/(2*n) + W[:, i+n] = W[:, i] + return SP, W + + +def cubature_prediction(x_pred, p_pred): + n = np.shape(x_pred)[0] + [SP, W] = sigma(x_pred, p_pred) + x_pred = np.zeros((n, 1)) + p_pred = q + for i in range(2*n): + x_pred = x_pred + (f(SP[:, i]).reshape((n, 1)) * W[0, i]) + for i in range(2*n): + p_step = (f(SP[:, i]).reshape((n, 1)) - x_pred) + p_pred = p_pred + (p_step @ p_step.T * W[0, i]) + return x_pred, p_pred + + +def cubature_update(x_pred, p_pred, z): + n = np.shape(x_pred)[0] + m = np.shape(z)[0] + [SP, W] = sigma(x_pred, p_pred) + y_k = np.zeros((m, 1)) + P_xy = np.zeros((n, m)) + s = r + for i in range(2*n): + y_k = y_k + (h(SP[:, i]).reshape((m, 1)) * W[0, i]) + for i in range(2*n): + p_step = (h(SP[:, i]).reshape((m, 1)) - y_k) + P_xy = P_xy + ((SP[:, i]).reshape((n, 1)) - + x_pred) @ p_step.T * W[0, i] + s = s + p_step @ p_step.T * W[0, i] + x_pred = x_pred + P_xy @ np.linalg.pinv(s) @ (z - y_k) + p_pred = p_pred - P_xy @ np.linalg.pinv(s) @ P_xy.T + return x_pred, p_pred + + +def generate_measurement(x_true): + gz = hx @ x_true + z = gz + z_noise @ np.random.randn(4, 1) + return z + + +def plot_animation(i, x_true_cat, x_est_cat, z): + if i == 0: + plt.plot(x_true_cat[0], x_true_cat[1], '.r') + plt.plot(x_est_cat[0], x_est_cat[1], '.b') + else: + plt.plot(x_true_cat[0:, 0], x_true_cat[0:, 1], 'r') + plt.plot(x_est_cat[0:, 0], x_est_cat[0:, 1], 'b') + plt.plot(z[0], z[1], '+g') + plt.grid(True) + plt.pause(0.001) + + +def plot_ellipse(x_est, p_est): + phi = np.linspace(0, 2 * math.pi, 100) + p_ellipse = np.array( + [[p_est[0, 0], p_est[0, 1]], [p_est[1, 0], p_est[1, 1]]]) + x0 = 3 * sqrtm(p_ellipse) + xy_1 = np.array([]) + xy_2 = np.array([]) + for i in range(100): + arr = np.array([[math.sin(phi[i])], [math.cos(phi[i])]]) + arr = x0 @ arr + xy_1 = np.hstack([xy_1, arr[0]]) + xy_2 = np.hstack([xy_2, arr[1]]) + plt.plot(xy_1 + x_est[0], xy_2 + x_est[1], 'r') + plt.pause(0.00001) + + +def plot_final(x_true_cat, x_est_cat, z_cat): + fig = plt.figure() + subplot = fig.add_subplot(111) + subplot.plot(x_true_cat[0:, 0], x_true_cat[0:, 1], + 'r', label='True Position') + subplot.plot(x_est_cat[0:, 0], x_est_cat[0:, 1], + 'b', label='Estimated Position') + subplot.plot(z_cat[0:, 0], z_cat[0:, 1], '+g', label='Noisy Measurements') + subplot.set_xlabel('x [m]') + subplot.set_ylabel('y [m]') + subplot.set_title('Cubature Kalman Filter - CTRV Model') + subplot.legend(loc='upper left', shadow=True, fontsize='large') + plt.grid(True) + plt.show() + + +def main(): + print(__file__ + " start!!") + x_est = x_0 + p_est = p_0 + x_true = x_0 + x_true_cat = np.array([x_0[0, 0], x_0[1, 0]]) + x_est_cat = np.array([x_0[0, 0], x_0[1, 0]]) + z_cat = np.array([x_0[0, 0], x_0[1, 0]]) + for i in range(N): + x_true = f(x_true) + z = generate_measurement(x_true) + if i == (N - 1) and show_final == 1: + show_final_flag = 1 + else: + show_final_flag = 0 + if show_animation == 1: + plot_animation(i, x_true_cat, x_est_cat, z) + if show_ellipse == 1: + plot_ellipse(x_est[0:2], p_est) + if show_final_flag == 1: + plot_final(x_true_cat, x_est_cat, z_cat) + x_est, p_est = cubature_kalman_filter(x_est, p_est, z) + x_true_cat = np.vstack((x_true_cat, x_true[0:2].T)) + x_est_cat = np.vstack((x_est_cat, x_est[0:2].T)) + z_cat = np.vstack((z_cat, z[0:2].T)) + print('CKF Over') + + +if __name__ == '__main__': + main() diff --git a/tests/test_cubature_kalman_filter.py b/tests/test_cubature_kalman_filter.py new file mode 100644 index 0000000000..e6ac71a8ee --- /dev/null +++ b/tests/test_cubature_kalman_filter.py @@ -0,0 +1,14 @@ +from unittest import TestCase + +from Localization.cubature_kalman_filter import cubature_kalman_filter as m + +print(__file__) + + +class Test(TestCase): + + def test1(self): + m.show_final = False + m.show_animation = False + m.show_ellipse = False + m.main() From 1b1e649b7d74dc3eda20ac3d63d43f60a3681dcd Mon Sep 17 00:00:00 2001 From: Raghuram Shankar Date: Sat, 10 Oct 2020 03:51:16 +0200 Subject: [PATCH 269/909] fixed TypeError (#414) * fixed TypeError * updated global variables * updated global variables * fix comment * Change try-except to if --- .../two_joint_arm_to_point_control.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index 85ed612360..90fcea1df9 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -34,9 +34,14 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0): """ Computes the inverse kinematics for a planar 2DOF arm + When out of bounds, rewrite x and y with last correct values """ + global x, y while True: try: + if x is not None and y is not None: + x_prev = x + y_prev = y if np.sqrt(x**2 + y**2) > (l1 + l2): theta2_goal = 0 else: @@ -54,11 +59,15 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0): theta2 = theta2 + Kp * ang_diff(theta2_goal, theta2) * dt except ValueError as e: print("Unreachable goal") + except TypeError: + x = x_prev + y = y_prev wrist = plot_arm(theta1, theta2, x, y) # check goal - d2goal = np.hypot(wrist[0] - x, wrist[1] - y) + if x is not None and y is not None: + d2goal = np.hypot(wrist[0] - x, wrist[1] - y) if abs(d2goal) < GOAL_TH and x is not None: return theta1, theta2 @@ -118,8 +127,8 @@ def main(): # pragma: no cover fig = plt.figure() fig.canvas.mpl_connect("button_press_event", click) # for stopping simulation with the esc key. - fig.canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + fig.canvas.mpl_connect('key_release_event', lambda event: [ + exit(0) if event.key == 'escape' else None]) two_joint_arm() From d90fba3717c556229a2cac8778324b915438b65e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 10 Oct 2020 16:29:57 +0900 Subject: [PATCH 270/909] fix circle ci (#419) * fix circle ci * fix circle ci * fix circle ci --- .circleci/config.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index f63f094a3f..d65f0fdf3c 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -5,7 +5,8 @@ orbs: jobs: build_doc: - executor: python/default + docker: + - image: circleci/python:3.8 steps: - checkout - python/load-cache From fa636b26406baad31e8c2840657ff0dcbb6fc4b2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Oct 2020 06:46:11 +0900 Subject: [PATCH 271/909] Bump cvxpy from 1.1.5 to 1.1.6 (#421) Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.5 to 1.1.6. - [Release notes](https://github.com/cvxgrp/cvxpy/releases) - [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md) - [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.5...v1.1.6) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index da40bbae8f..932089a068 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,4 @@ numpy == 1.19.2 scipy == 1.5.2 pandas == 1.1.3 matplotlib == 3.3.2 -cvxpy == 1.1.5 +cvxpy == 1.1.6 From 840d6afb6d8bb4835fdb1682a535cea034ea4ca9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 20 Oct 2020 07:22:35 +0900 Subject: [PATCH 272/909] Bump scipy from 1.5.2 to 1.5.3 (#424) Bumps [scipy](https://github.com/scipy/scipy) from 1.5.2 to 1.5.3. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.5.2...v1.5.3) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 932089a068..e175ed2d00 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.2 -scipy == 1.5.2 +scipy == 1.5.3 pandas == 1.1.3 matplotlib == 3.3.2 cvxpy == 1.1.6 From 48b6ee3cf9ee65fec86124948c277f43b8cf06ff Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Oct 2020 07:23:42 +0900 Subject: [PATCH 273/909] Bump cvxpy from 1.1.6 to 1.1.7 (#426) Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.6 to 1.1.7. - [Release notes](https://github.com/cvxgrp/cvxpy/releases) - [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md) - [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.6...v1.1.7) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index e175ed2d00..92f1b04fb6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,4 @@ numpy == 1.19.2 scipy == 1.5.3 pandas == 1.1.3 matplotlib == 3.3.2 -cvxpy == 1.1.6 +cvxpy == 1.1.7 From 2bf0dcedc906332e1b4d1b5b4a307fb32de4afc5 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 1 Nov 2020 14:05:46 +0900 Subject: [PATCH 274/909] fix unittest animation bugs (#429) * fix unittest animation bugs * exstract a function --- ...e.py => a_star_searching_from_two_side.py} | 0 PathPlanning/AStar/a_star_variants.py | 129 ++++++++++-------- PathPlanning/FlowField/flowfield.py | 20 +-- tests/test_a_star_searching_two_side.py | 2 +- tests/test_a_star_variants.py | 5 +- 5 files changed, 92 insertions(+), 64 deletions(-) rename PathPlanning/AStar/{A_Star_searching_from_two_side.py => a_star_searching_from_two_side.py} (100%) diff --git a/PathPlanning/AStar/A_Star_searching_from_two_side.py b/PathPlanning/AStar/a_star_searching_from_two_side.py similarity index 100% rename from PathPlanning/AStar/A_Star_searching_from_two_side.py rename to PathPlanning/AStar/a_star_searching_from_two_side.py diff --git a/PathPlanning/AStar/a_star_variants.py b/PathPlanning/AStar/a_star_variants.py index cbf5395fd2..e0053ee224 100644 --- a/PathPlanning/AStar/a_star_variants.py +++ b/PathPlanning/AStar/a_star_variants.py @@ -1,5 +1,5 @@ """ -astar variants +a star variants author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it) Source: http://theory.stanford.edu/~amitp/GameProgramming/Variations.html """ @@ -7,7 +7,7 @@ import numpy as np import matplotlib.pyplot as plt -show_animation = False +show_animation = True use_beam_search = False use_iterative_deepening = False use_dynamic_weighting = False @@ -89,7 +89,8 @@ def key_points(o_dict): obs_count += 1 if obs_count == 3 or obs_count == 1: c_list.append((x, y)) - plt.plot(x, y, ".y") + if show_animation: + plt.plot(x, y, ".y") break if only_corners: return c_list @@ -148,7 +149,8 @@ def __init__(self, obs_grid, goal_x, goal_y, start_x, start_y, 'open': True, 'in_open_list': True} self.open_set.append(self.all_nodes[tuple(self.start_pt)]) - def get_hval(self, x1, y1, x2, y2): + @staticmethod + def get_hval(x1, y1, x2, y2): x, y = x1, y1 val = 0 while x != x2 or y != y2: @@ -178,7 +180,6 @@ def get_farthest_point(self, x, y, i, j): def jump_point(self): """Jump point: Instead of exploring all empty spaces of the map, just explore the corners.""" - plt.title('Jump Point') goal_found = False while len(self.open_set) > 0: @@ -232,11 +233,13 @@ def jump_point(self): if not self.all_nodes[cand_pt]['in_open_list']: self.open_set.append(self.all_nodes[cand_pt]) self.all_nodes[cand_pt]['in_open_list'] = True - plt.plot(cand_pt[0], cand_pt[1], "r*") + if show_animation: + plt.plot(cand_pt[0], cand_pt[1], "r*") if goal_found: break - plt.pause(0.001) + if show_animation: + plt.pause(0.001) if goal_found: current_node = self.all_nodes[tuple(self.goal_pt)] while goal_found: @@ -244,23 +247,26 @@ def jump_point(self): break x = [current_node['pos'][0], current_node['pred'][0]] y = [current_node['pos'][1], current_node['pred'][1]] - plt.plot(x, y, "b") current_node = self.all_nodes[tuple(current_node['pred'])] - plt.pause(0.001) + if show_animation: + plt.plot(x, y, "b") + plt.pause(0.001) if goal_found: break current_node['open'] = False current_node['in_open_list'] = False - plt.plot(current_node['pos'][0], current_node['pos'][1], "g*") + if show_animation: + plt.plot(current_node['pos'][0], current_node['pos'][1], "g*") del self.open_set[p] current_node['fcost'], current_node['hcost'] = np.inf, np.inf if show_animation: + plt.title('Jump Point') plt.show() - def astar(self): + def a_star(self): """Beam search: Maintain an open list of just 30 nodes. - If more than 30 nodes, then get rid of ndoes with high + If more than 30 nodes, then get rid of nodes with high f values. Iterative deepening: At every iteration, get a cut-off value for the f cost. This cut-off is minimum of the f @@ -275,21 +281,23 @@ def astar(self): one neighbor at a time. In fact, you can look for the next node as far out as you can as long as there is a clear line of sight from your current node to that node.""" - if use_beam_search: - plt.title('A* with beam search') - elif use_iterative_deepening: - plt.title('A* with iterative deepening') - elif use_dynamic_weighting: - plt.title('A* with dynamic weighting') - elif use_theta_star: - plt.title('Theta*') - else: - plt.title('A*') + if show_animation: + if use_beam_search: + plt.title('A* with beam search') + elif use_iterative_deepening: + plt.title('A* with iterative deepening') + elif use_dynamic_weighting: + plt.title('A* with dynamic weighting') + elif use_theta_star: + plt.title('Theta*') + else: + plt.title('A*') goal_found = False curr_f_thresh = np.inf depth = 0 no_valid_f = False + w = None while len(self.open_set) > 0: self.open_set = sorted(self.open_set, key=lambda x: x['fcost']) lowest_f = self.open_set[0]['fcost'] @@ -351,30 +359,14 @@ def astar(self): break cand_pt = tuple(cand_pt) - if not self.obs_grid[tuple(cand_pt)] and \ - self.all_nodes[cand_pt]['open']: - g_cost = offset + current_node['gcost'] - h_cost = self.all_nodes[cand_pt]['hcost'] - if use_dynamic_weighting: - h_cost = h_cost * w - f_cost = g_cost + h_cost - if f_cost < self.all_nodes[cand_pt]['fcost'] and \ - f_cost <= curr_f_thresh: - f_cost_list.append(f_cost) - self.all_nodes[cand_pt]['pred'] = \ - current_node['pos'] - self.all_nodes[cand_pt]['gcost'] = g_cost - self.all_nodes[cand_pt]['fcost'] = f_cost - if not self.all_nodes[cand_pt]['in_open_list']: - self.open_set.append(self.all_nodes[cand_pt]) - self.all_nodes[cand_pt]['in_open_list'] = True - plt.plot(cand_pt[0], cand_pt[1], "r*") - if curr_f_thresh < f_cost < \ - self.all_nodes[cand_pt]['fcost']: - no_valid_f = True + no_valid_f = self.update_node_cost(cand_pt, curr_f_thresh, + current_node, + f_cost_list, no_valid_f, + offset, w) if goal_found: break - plt.pause(0.001) + if show_animation: + plt.pause(0.001) if goal_found: current_node = self.all_nodes[tuple(self.goal_pt)] while goal_found: @@ -383,12 +375,13 @@ def astar(self): if use_theta_star or use_jump_point: x, y = [current_node['pos'][0], current_node['pred'][0]], \ [current_node['pos'][1], current_node['pred'][1]] - plt.plot(x, y, "b") + if show_animation: + plt.plot(x, y, "b") else: - plt.plot(current_node['pred'][0], - current_node['pred'][1], "b*") + if show_animation: + plt.plot(current_node['pred'][0], + current_node['pred'][1], "b*") current_node = self.all_nodes[tuple(current_node['pred'])] - plt.pause(0.001) if goal_found: break @@ -402,13 +395,40 @@ def astar(self): current_node['open'] = False current_node['in_open_list'] = False - plt.plot(current_node['pos'][0], current_node['pos'][1], "g*") + if show_animation: + plt.plot(current_node['pos'][0], current_node['pos'][1], "g*") del self.open_set[p] current_node['fcost'], current_node['hcost'] = np.inf, np.inf depth += 1 if show_animation: plt.show() + def update_node_cost(self, cand_pt, curr_f_thresh, current_node, + f_cost_list, no_valid_f, offset, w): + if not self.obs_grid[tuple(cand_pt)] and \ + self.all_nodes[cand_pt]['open']: + g_cost = offset + current_node['gcost'] + h_cost = self.all_nodes[cand_pt]['hcost'] + if use_dynamic_weighting: + h_cost = h_cost * w + f_cost = g_cost + h_cost + if f_cost < self.all_nodes[cand_pt]['fcost'] and \ + f_cost <= curr_f_thresh: + f_cost_list.append(f_cost) + self.all_nodes[cand_pt]['pred'] = \ + current_node['pos'] + self.all_nodes[cand_pt]['gcost'] = g_cost + self.all_nodes[cand_pt]['fcost'] = f_cost + if not self.all_nodes[cand_pt]['in_open_list']: + self.open_set.append(self.all_nodes[cand_pt]) + self.all_nodes[cand_pt]['in_open_list'] = True + if show_animation: + plt.plot(cand_pt[0], cand_pt[1], "r*") + if curr_f_thresh < f_cost < \ + self.all_nodes[cand_pt]['fcost']: + no_valid_f = True + return no_valid_f + def main(): # set obstacle positions @@ -443,10 +463,11 @@ def main(): for x, y, l in zip(all_x, all_y, all_len): draw_horizontal_line(x, y, l, o_x, o_y, obs_dict) - plt.plot(o_x, o_y, ".k") - plt.plot(s_x, s_y, "og") - plt.plot(g_x, g_y, "xb") - plt.grid(True) + if show_animation: + plt.plot(o_x, o_y, ".k") + plt.plot(s_x, s_y, "og") + plt.plot(g_x, g_y, "xb") + plt.grid(True) if use_jump_point: keypoint_list = key_points(obs_dict) @@ -455,7 +476,7 @@ def main(): search_obj.jump_point() else: search_obj = SearchAlgo(obs_dict, g_x, g_y, s_x, s_y, 101, 101) - search_obj.astar() + search_obj.a_star() if __name__ == '__main__': diff --git a/PathPlanning/FlowField/flowfield.py b/PathPlanning/FlowField/flowfield.py index f222e8ecf2..e50430de3c 100644 --- a/PathPlanning/FlowField/flowfield.py +++ b/PathPlanning/FlowField/flowfield.py @@ -129,9 +129,12 @@ def assign_vectors(self): def follow_vectors(self): curr_x, curr_y = self.start_pt while curr_x is not None and curr_y is not None: - plt.plot(curr_x, curr_y, "b*") curr_x, curr_y = self.vector_field[(curr_x, curr_y)] - plt.pause(0.001) + + if show_animation: + plt.plot(curr_x, curr_y, "b*") + plt.pause(0.001) + if show_animation: plt.show() @@ -208,12 +211,13 @@ def main(): for x, y, l in zip(all_x, all_y, all_len): draw_horizontal_line(x, y, l, h_x, h_y, obs_dict, 'hard') - plt.plot(o_x, o_y, "sr") - plt.plot(m_x, m_y, "sg") - plt.plot(h_x, h_y, "sy") - plt.plot(s_x, s_y, "og") - plt.plot(g_x, g_y, "o") - plt.grid(True) + if show_animation: + plt.plot(o_x, o_y, "sr") + plt.plot(m_x, m_y, "sg") + plt.plot(h_x, h_y, "sy") + plt.plot(s_x, s_y, "og") + plt.plot(g_x, g_y, "o") + plt.grid(True) flow_obj = FlowField(obs_dict, g_x, g_y, s_x, s_y, 50, 50) flow_obj.find_path() diff --git a/tests/test_a_star_searching_two_side.py b/tests/test_a_star_searching_two_side.py index 6116c9bbe7..013e9253b8 100644 --- a/tests/test_a_star_searching_two_side.py +++ b/tests/test_a_star_searching_two_side.py @@ -5,7 +5,7 @@ sys.path.append(os.path.dirname(__file__) + '/../') try: - from PathPlanning.AStar import A_Star_searching_from_two_side as m + from PathPlanning.AStar import a_star_searching_from_two_side as m except ImportError: raise diff --git a/tests/test_a_star_variants.py b/tests/test_a_star_variants.py index 53efeddd20..13009c1365 100644 --- a/tests/test_a_star_variants.py +++ b/tests/test_a_star_variants.py @@ -10,6 +10,7 @@ class Test(TestCase): def test(self): # A* with beam search astar.show_animation = False + astar.use_beam_search = True astar.main() self.reset_all() @@ -34,7 +35,9 @@ def test(self): astar.main() self.reset_all() - def reset_all(self): + @staticmethod + def reset_all(): + astar.show_animation = False astar.use_beam_search = False astar.use_iterative_deepening = False astar.use_dynamic_weighting = False From 0c76141b8057630865880fd57368be3312f58c6f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 3 Nov 2020 07:30:43 +0900 Subject: [PATCH 275/909] Bump numpy from 1.19.2 to 1.19.4 (#430) Bumps [numpy](https://github.com/numpy/numpy) from 1.19.2 to 1.19.4. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.19.2...v1.19.4) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 92f1b04fb6..526d314410 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.19.2 +numpy == 1.19.4 scipy == 1.5.3 pandas == 1.1.3 matplotlib == 3.3.2 From 35e23c89cdd789e09a4849eb87b345790ac2efcc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 3 Nov 2020 07:30:59 +0900 Subject: [PATCH 276/909] Bump pandas from 1.1.3 to 1.1.4 (#431) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.3 to 1.1.4. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.3...v1.1.4) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 526d314410..600dbc480e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.4 scipy == 1.5.3 -pandas == 1.1.3 +pandas == 1.1.4 matplotlib == 3.3.2 cvxpy == 1.1.7 From 31b75f135865bcd9e92376088b1064b84d155068 Mon Sep 17 00:00:00 2001 From: Nguyen Mau Dung Date: Sun, 8 Nov 2020 14:09:59 +0900 Subject: [PATCH 277/909] Using vectorization to speed up computation (#427) --- .../rectangle_fitting/rectangle_fitting.py | 41 ++++++------------- 1 file changed, 12 insertions(+), 29 deletions(-) diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index ae41b3c53e..a60a6fbfd1 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -69,15 +69,11 @@ def _calc_closeness_criterion(self, c1, c2): c1_min = min(c1) c2_min = min(c2) - D1 = [min([np.linalg.norm(c1_max - ic1), - np.linalg.norm(ic1 - c1_min)]) for ic1 in c1] - D2 = [min([np.linalg.norm(c2_max - ic2), - np.linalg.norm(ic2 - c2_min)]) for ic2 in c2] - - beta = 0 - for i, _ in enumerate(D1): - d = max(min([D1[i], D2[i]]), self.min_dist_of_closeness_criteria) - beta += (1.0 / d) + # Vectorization + D1 = np.minimum(c1_max - c1, c1 - c1_min) + D2 = np.minimum(c2_max - c2, c2 - c2_min) + d = np.maximum(np.minimum(D1, D2), self.min_dist_of_closeness_criteria) + beta = (1.0 / d).sum() return beta @@ -88,26 +84,13 @@ def _calc_variance_criterion(c1, c2): c1_min = min(c1) c2_min = min(c2) - D1 = [min([np.linalg.norm(c1_max - ic1), - np.linalg.norm(ic1 - c1_min)]) for ic1 in c1] - D2 = [min([np.linalg.norm(c2_max - ic2), - np.linalg.norm(ic2 - c2_min)]) for ic2 in c2] - - E1, E2 = [], [] - for (d1, d2) in zip(D1, D2): - if d1 < d2: - E1.append(d1) - else: - E2.append(d2) - - V1 = 0.0 - if E1: - V1 = - np.var(E1) - - V2 = 0.0 - if E2: - V2 = - np.var(E2) - + # Vectorization + D1 = np.minimum(c1_max - c1, c1 - c1_min) + D2 = np.minimum(c2_max - c2, c2 - c2_min) + E1 = D1[D1 < D2] + E2 = D2[D1 >= D2] + V1 = - np.var(E1) if len(E1) > 0 else 0. + V2 = - np.var(E2) if len(E2) > 0 else 0. gamma = V1 + V2 return gamma From 7e312daa705e4b9939719fca3719e5956614116f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Nov 2020 06:55:51 +0900 Subject: [PATCH 278/909] Bump scipy from 1.5.3 to 1.5.4 (#438) Bumps [scipy](https://github.com/scipy/scipy) from 1.5.3 to 1.5.4. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.5.3...v1.5.4) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 600dbc480e..5035bbbc69 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.4 -scipy == 1.5.3 +scipy == 1.5.4 pandas == 1.1.4 matplotlib == 3.3.2 cvxpy == 1.1.7 From 40df009180e7541e6bc85e7f455d7d973a064aed Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 17 Nov 2020 20:18:04 +0900 Subject: [PATCH 279/909] Bump matplotlib from 3.3.2 to 3.3.3 (#441) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.2 to 3.3.3. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.2...v3.3.3) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5035bbbc69..5cc619a841 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.4 scipy == 1.5.4 pandas == 1.1.4 -matplotlib == 3.3.2 +matplotlib == 3.3.3 cvxpy == 1.1.7 From f9e2293a8d0478c51752809efb878c9f3cea4a4d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Dec 2020 07:49:13 +0900 Subject: [PATCH 280/909] Bump pandas from 1.1.4 to 1.1.5 (#443) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.4 to 1.1.5. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.4...v1.1.5) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5cc619a841..b66011e4a4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.4 scipy == 1.5.4 -pandas == 1.1.4 +pandas == 1.1.5 matplotlib == 3.3.3 cvxpy == 1.1.7 From 2d5024dc386b1d84ade7cdbdda7d5aaab58cf57b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 20 Dec 2020 16:16:35 +0900 Subject: [PATCH 281/909] add test_rectangle_fitting.py (#444) * add test_rectangle_fitting.py * add test_rectangle_fitting.py --- tests/test_rectangle_fitting.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 tests/test_rectangle_fitting.py diff --git a/tests/test_rectangle_fitting.py b/tests/test_rectangle_fitting.py new file mode 100644 index 0000000000..2a28320626 --- /dev/null +++ b/tests/test_rectangle_fitting.py @@ -0,0 +1,22 @@ +from unittest import TestCase + +import sys +import os + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../Mapping/rectangle_fitting/") + +try: + from Mapping.rectangle_fitting import rectangle_fitting as m +except ImportError: + raise + + +print(__file__) + + +class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main() From 4264cb8b1010e5eaa448b3ef6d0a1c096bba65e8 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 20 Dec 2020 20:29:36 +0900 Subject: [PATCH 282/909] fix pure_pursuit sample (#445) --- .../ClosedLoopRRTStar/pure_pursuit.py | 41 +------------------ 1 file changed, 2 insertions(+), 39 deletions(-) diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py index f3984f87b0..ca763f0d4f 100644 --- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py +++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py @@ -250,7 +250,7 @@ def main(): # pragma: no cover yaw = [state.yaw] v = [state.v] t = [0.0] - target_ind = calc_target_index(state, cx, cy) + target_ind, dis = calc_target_index(state, cx, cy) while T >= time and lastIndex > target_ind: ai = PIDControl(target_speed, state.v) @@ -291,43 +291,6 @@ def main(): # pragma: no cover plt.show() -def main2(): # pragma: no cover - import pandas as pd - data = pd.read_csv("rrt_course.csv") - cx = np.array(data["x"]) - cy = np.array(data["y"]) - cyaw = np.array(data["yaw"]) - - target_speed = 10.0 / 3.6 - - goal = [cx[-1], cy[-1]] - - cx, cy, cyaw = extend_path(cx, cy, cyaw) - - speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed) - - t, x, y, yaw, v, a, d, flag = closed_loop_prediction( - cx, cy, cyaw, speed_profile, goal) - - plt.subplots(1) - plt.plot(cx, cy, ".r", label="course") - plt.plot(x, y, "-b", label="trajectory") - plt.plot(goal[0], goal[1], "xg", label="goal") - plt.legend() - plt.xlabel("x[m]") - plt.ylabel("y[m]") - plt.axis("equal") - plt.grid(True) - - plt.subplots(1) - plt.plot(t, [iv * 3.6 for iv in v], "-r") - plt.xlabel("Time[s]") - plt.ylabel("Speed[km/h]") - plt.grid(True) - plt.show() - - if __name__ == '__main__': # pragma: no cover print("Pure pursuit path tracking simulation start") - # main() - main2() + main() From e0a8ab2368d83797d2246af1f9b4fa80788d74dd Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 29 Dec 2020 07:28:19 +0900 Subject: [PATCH 283/909] Bump pandas from 1.1.5 to 1.2.0 (#449) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.5 to 1.2.0. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.5...v1.2.0) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b66011e4a4..c8aa78aeb7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.19.4 scipy == 1.5.4 -pandas == 1.1.5 +pandas == 1.2.0 matplotlib == 3.3.3 cvxpy == 1.1.7 From 65debdc332b23083a434c24fa5b73e917ee71aa2 Mon Sep 17 00:00:00 2001 From: Mahyar Abdeetedal Date: Tue, 29 Dec 2020 06:32:13 -0500 Subject: [PATCH 284/909] RRT* for seven joint arm control (#439) --- .../rrt_star_seven_joint_arm.py | 403 ++++++++++++++++++ tests/test_rrt_star_seven_joint_arm.py | 26 ++ 2 files changed, 429 insertions(+) create mode 100755 ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py create mode 100644 tests/test_rrt_star_seven_joint_arm.py diff --git a/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py b/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py new file mode 100755 index 0000000000..d10733b41e --- /dev/null +++ b/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py @@ -0,0 +1,403 @@ +""" +RRT* path planner for a seven joint arm +Author: Mahyar Abdeetedal (mahyaret) +""" +import math +import os +import sys +import random +import numpy as np +from mpl_toolkits import mplot3d +import matplotlib.pyplot as plt +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../n_joint_arm_3d/") +try: + from NLinkArm3d import NLinkArm +except ImportError: + raise + +show_animation = True +verbose = False + + +class RobotArm(NLinkArm): + def get_points(self, joint_angle_list): + self.set_joint_angles(joint_angle_list) + + x_list = [] + y_list = [] + z_list = [] + + trans = np.identity(4) + + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + for i in range(len(self.link_list)): + trans = np.dot(trans, self.link_list[i].transformation_matrix()) + x_list.append(trans[0, 3]) + y_list.append(trans[1, 3]) + z_list.append(trans[2, 3]) + + return x_list, y_list, z_list + + +class RRTStar: + """ + Class for RRT Star planning + """ + + class Node: + def __init__(self, x): + self.x = x + self.parent = None + self.cost = 0.0 + + def __init__(self, start, goal, robot, obstacle_list, rand_area, + expand_dis=.30, + path_resolution=.1, + goal_sample_rate=20, + max_iter=300, + connect_circle_dist=50.0 + ): + """ + Setting Parameter + + start:Start Position [q1,...,qn] + goal:Goal Position [q1,...,qn] + obstacleList:obstacle Positions [[x,y,z,size],...] + randArea:Random Sampling Area [min,max] + + """ + self.start = self.Node(start) + self.end = self.Node(goal) + self.dimension = len(start) + self.min_rand = rand_area[0] + self.max_rand = rand_area[1] + self.expand_dis = expand_dis + self.path_resolution = path_resolution + self.goal_sample_rate = goal_sample_rate + self.max_iter = max_iter + self.robot = robot + self.obstacle_list = obstacle_list + self.connect_circle_dist = connect_circle_dist + self.goal_node = self.Node(goal) + self.ax = plt.axes(projection='3d') + self.node_list = [] + + def planning(self, animation=False, search_until_max_iter=False): + """ + rrt star path planning + + animation: flag for animation on or off + search_until_max_iter: search until max iteration for path + improving or not + """ + + self.node_list = [self.start] + for i in range(self.max_iter): + if verbose: + print("Iter:", i, ", number of nodes:", len(self.node_list)) + rnd = self.get_random_node() + nearest_ind = self.get_nearest_node_index(self.node_list, rnd) + new_node = self.steer(self.node_list[nearest_ind], + rnd, + self.expand_dis) + + if self.check_collision(new_node, self.robot, self.obstacle_list): + near_inds = self.find_near_nodes(new_node) + new_node = self.choose_parent(new_node, near_inds) + if new_node: + self.node_list.append(new_node) + self.rewire(new_node, near_inds) + + if animation and i % 5 == 0 and self.dimension <= 3: + self.draw_graph(rnd) + + if (not search_until_max_iter) and new_node: + last_index = self.search_best_goal_node() + if last_index is not None: + return self.generate_final_course(last_index) + if verbose: + print("reached max iteration") + + last_index = self.search_best_goal_node() + if last_index is not None: + return self.generate_final_course(last_index) + + return None + + def choose_parent(self, new_node, near_inds): + if not near_inds: + return None + + # search nearest cost in near_inds + costs = [] + for i in near_inds: + near_node = self.node_list[i] + t_node = self.steer(near_node, new_node) + if t_node and self.check_collision(t_node, + self.robot, + self.obstacle_list): + costs.append(self.calc_new_cost(near_node, new_node)) + else: + costs.append(float("inf")) # the cost of collision node + min_cost = min(costs) + + if min_cost == float("inf"): + print("There is no good path.(min_cost is inf)") + return None + + min_ind = near_inds[costs.index(min_cost)] + new_node = self.steer(self.node_list[min_ind], new_node) + new_node.parent = self.node_list[min_ind] + new_node.cost = min_cost + + return new_node + + def search_best_goal_node(self): + dist_to_goal_list = [self.calc_dist_to_goal(n.x) + for n in self.node_list] + goal_inds = [dist_to_goal_list.index(i) + for i in dist_to_goal_list if i <= self.expand_dis] + + safe_goal_inds = [] + for goal_ind in goal_inds: + t_node = self.steer(self.node_list[goal_ind], self.goal_node) + if self.check_collision(t_node, self.robot, self.obstacle_list): + safe_goal_inds.append(goal_ind) + + if not safe_goal_inds: + return None + + min_cost = min([self.node_list[i].cost for i in safe_goal_inds]) + for i in safe_goal_inds: + if self.node_list[i].cost == min_cost: + return i + + return None + + def find_near_nodes(self, new_node): + nnode = len(self.node_list) + 1 + r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode)) + # if expand_dist exists, search vertices in + # a range no more than expand_dist + if hasattr(self, 'expand_dis'): + r = min(r, self.expand_dis) + dist_list = [np.sum((np.array(node.x) - np.array(new_node.x)) ** 2) + for node in self.node_list] + near_inds = [dist_list.index(i) for i in dist_list if i <= r ** 2] + return near_inds + + def rewire(self, new_node, near_inds): + for i in near_inds: + near_node = self.node_list[i] + edge_node = self.steer(new_node, near_node) + if not edge_node: + continue + edge_node.cost = self.calc_new_cost(new_node, near_node) + + no_collision = self.check_collision(edge_node, + self.robot, + self.obstacle_list) + improved_cost = near_node.cost > edge_node.cost + + if no_collision and improved_cost: + self.node_list[i] = edge_node + self.propagate_cost_to_leaves(new_node) + + def calc_new_cost(self, from_node, to_node): + d, _, _ = self.calc_distance_and_angle(from_node, to_node) + return from_node.cost + d + + def propagate_cost_to_leaves(self, parent_node): + + for node in self.node_list: + if node.parent == parent_node: + node.cost = self.calc_new_cost(parent_node, node) + self.propagate_cost_to_leaves(node) + + def generate_final_course(self, goal_ind): + path = [self.end.x] + node = self.node_list[goal_ind] + while node.parent is not None: + path.append(node.x) + node = node.parent + path.append(node.x) + + return path + + def calc_dist_to_goal(self, x): + distance = np.linalg.norm(np.array(x) - np.array(self.end.x)) + return distance + + def get_random_node(self): + if random.randint(0, 100) > self.goal_sample_rate: + rnd = self.Node(np.random.uniform(self.min_rand, + self.max_rand, + self.dimension)) + else: # goal point sampling + rnd = self.Node(self.end.x) + return rnd + + def steer(self, from_node, to_node, extend_length=float("inf")): + new_node = self.Node(list(from_node.x)) + d, phi, theta = self.calc_distance_and_angle(new_node, to_node) + + new_node.path_x = [list(new_node.x)] + + if extend_length > d: + extend_length = d + + n_expand = math.floor(extend_length / self.path_resolution) + + start, end = np.array(from_node.x), np.array(to_node.x) + v = end - start + u = v / (np.sqrt(np.sum(v ** 2))) + for _ in range(n_expand): + new_node.x += u * self.path_resolution + new_node.path_x.append(list(new_node.x)) + + d, _, _ = self.calc_distance_and_angle(new_node, to_node) + if d <= self.path_resolution: + new_node.path_x.append(list(to_node.x)) + + new_node.parent = from_node + + return new_node + + def draw_graph(self, rnd=None): + plt.cla() + self.ax.axis([-1, 1, -1, 1]) + self.ax.set_zlim(0, 1) + self.ax.grid(True) + for (ox, oy, oz, size) in self.obstacle_list: + self.plot_sphere(self.ax, ox, oy, oz, size=size) + if self.dimension > 3: + return self.ax + if rnd is not None: + self.ax.plot([rnd.x[0]], [rnd.x[1]], [rnd.x[2]], "^k") + for node in self.node_list: + if node.parent: + path = np.array(node.path_x) + plt.plot(path[:, 0], path[:, 1], path[:, 2], "-g") + self.ax.plot([self.start.x[0]], [self.start.x[1]], + [self.start.x[2]], "xr") + self.ax.plot([self.end.x[0]], [self.end.x[1]], [self.end.x[2]], "xr") + plt.pause(0.01) + return self.ax + + @staticmethod + def get_nearest_node_index(node_list, rnd_node): + dlist = [np.sum((np.array(node.x) - np.array(rnd_node.x))**2) + for node in node_list] + minind = dlist.index(min(dlist)) + + return minind + + @staticmethod + def plot_sphere(ax, x, y, z, size=1, color="k"): + u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j] + xl = x+size*np.cos(u)*np.sin(v) + yl = y+size*np.sin(u)*np.sin(v) + zl = z+size*np.cos(v) + ax.plot_wireframe(xl, yl, zl, color=color) + + @staticmethod + def calc_distance_and_angle(from_node, to_node): + dx = to_node.x[0] - from_node.x[0] + dy = to_node.x[1] - from_node.x[1] + dz = to_node.x[2] - from_node.x[2] + d = np.sqrt(np.sum((np.array(to_node.x) - np.array(from_node.x))**2)) + phi = math.atan2(dy, dx) + theta = math.atan2(math.hypot(dx, dy), dz) + return d, phi, theta + + @staticmethod + def calc_distance_and_angle2(from_node, to_node): + dx = to_node.x[0] - from_node.x[0] + dy = to_node.x[1] - from_node.x[1] + dz = to_node.x[2] - from_node.x[2] + d = math.sqrt(dx**2 + dy**2 + dz**2) + phi = math.atan2(dy, dx) + theta = math.atan2(math.hypot(dx, dy), dz) + return d, phi, theta + + @staticmethod + def check_collision(node, robot, obstacleList): + + if node is None: + return False + + for (ox, oy, oz, size) in obstacleList: + for x in node.path_x: + x_list, y_list, z_list = robot.get_points(x) + dx_list = [ox - x_point for x_point in x_list] + dy_list = [oy - y_point for y_point in y_list] + dz_list = [oz - z_point for z_point in z_list] + d_list = [dx * dx + dy * dy + dz * dz + for (dx, dy, dz) in zip(dx_list, + dy_list, + dz_list)] + + if min(d_list) <= size ** 2: + return False # collision + + return True # safe + + +def main(): + print("Start " + __file__) + + # init NLinkArm with Denavit-Hartenberg parameters of panda + # https://frankaemika.github.io/docs/control_parameters.html + # [theta, alpha, a, d] + seven_joint_arm = RobotArm([[0., math.pi/2., 0., .333], + [0., -math.pi/2., 0., 0.], + [0., math.pi/2., 0.0825, 0.3160], + [0., -math.pi/2., -0.0825, 0.], + [0., math.pi/2., 0., 0.3840], + [0., math.pi/2., 0.088, 0.], + [0., 0., 0., 0.107]]) + # ====Search Path with RRT==== + obstacle_list = [ + (-.3, -.3, .7, .1), + (.0, -.3, .7, .1), + (.2, -.1, .3, .15), + ] # [x,y,size(radius)] + start = [0 for _ in range(len(seven_joint_arm.link_list))] + end = [1.5 for _ in range(len(seven_joint_arm.link_list))] + # Set Initial parameters + rrt_star = RRTStar(start=start, + goal=end, + rand_area=[0, 2], + max_iter=200, + robot=seven_joint_arm, + obstacle_list=obstacle_list) + path = rrt_star.planning(animation=show_animation, + search_until_max_iter=False) + + if path is None: + print("Cannot find path") + else: + print("found path!!") + + # Draw final path + if show_animation: + ax = rrt_star.draw_graph() + for i, q in enumerate(reversed(path)): + x_points, y_points, z_points = seven_joint_arm.get_points(q) + if i == 0 or i == len(path) - 1: + color = None + else: + color = "grey" + ax.plot([x for x in x_points], + [y for y in y_points], + [z for z in z_points], + "o-", color=color, ms=4, mew=0.5) + plt.pause(0.01) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/tests/test_rrt_star_seven_joint_arm.py b/tests/test_rrt_star_seven_joint_arm.py new file mode 100644 index 0000000000..fe90801b3b --- /dev/null +++ b/tests/test_rrt_star_seven_joint_arm.py @@ -0,0 +1,26 @@ +import os +import sys +from unittest import TestCase + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../ArmNavigation/seven_joint_arm_to_point_control/") + +try: + import rrt_star_seven_joint_arm as m +except ImportError: + raise + + +print(__file__) + + +class Test(TestCase): + + def test1(self): + m.show_animation = False + m.main() + + +if __name__ == '__main__': # pragma: no cover + test = Test() + test.test1() From d29dacbf9e89db2e06be3fcb799a5cf78f22e4d9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 29 Dec 2020 21:41:14 +0900 Subject: [PATCH 285/909] clean up seven joint arm script --- .../rrt_star_seven_joint_arm_control.py} | 19 ++++++++++++------- tests/test_rrt_star_seven_joint_arm.py | 4 ++-- 2 files changed, 14 insertions(+), 9 deletions(-) rename ArmNavigation/{seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py => rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py} (96%) diff --git a/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py similarity index 96% rename from ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py rename to ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py index d10733b41e..0de4c7edb8 100755 --- a/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py +++ b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py @@ -224,7 +224,7 @@ def generate_final_course(self, goal_ind): path.append(node.x) node = node.parent path.append(node.x) - + reversed(path) return path def calc_dist_to_goal(self, x): @@ -385,17 +385,22 @@ def main(): # Draw final path if show_animation: ax = rrt_star.draw_graph() - for i, q in enumerate(reversed(path)): + + # Plot final configuration + x_points, y_points, z_points = seven_joint_arm.get_points(path[-1]) + ax.plot([x for x in x_points], + [y for y in y_points], + [z for z in z_points], + "o-", color="red", ms=5, mew=0.5) + + for i, q in enumerate(path): x_points, y_points, z_points = seven_joint_arm.get_points(q) - if i == 0 or i == len(path) - 1: - color = None - else: - color = "grey" ax.plot([x for x in x_points], [y for y in y_points], [z for z in z_points], - "o-", color=color, ms=4, mew=0.5) + "o-", color="grey", ms=4, mew=0.5) plt.pause(0.01) + plt.show() diff --git a/tests/test_rrt_star_seven_joint_arm.py b/tests/test_rrt_star_seven_joint_arm.py index fe90801b3b..0788b3675a 100644 --- a/tests/test_rrt_star_seven_joint_arm.py +++ b/tests/test_rrt_star_seven_joint_arm.py @@ -3,10 +3,10 @@ from unittest import TestCase sys.path.append(os.path.dirname(os.path.abspath(__file__)) + - "/../ArmNavigation/seven_joint_arm_to_point_control/") + "/../ArmNavigation/rrt_star_seven_joint_arm_control/") try: - import rrt_star_seven_joint_arm as m + import rrt_star_seven_joint_arm_control as m except ImportError: raise From 581b3cf27ab188a319162dc13dc926503b52cda9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 30 Dec 2020 13:52:00 +0900 Subject: [PATCH 286/909] bump python version to 3.9. (#433) --- .circleci/config.yml | 4 +++- .github/workflows/Linux_CI.yml | 6 ++++-- .github/workflows/MacOS_CI.yml | 4 +++- README.md | 2 +- appveyor.yml | 16 ++++++---------- docs/getting_started.rst | 2 +- 6 files changed, 18 insertions(+), 16 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index d65f0fdf3c..0ec05a3a20 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,10 +6,12 @@ orbs: jobs: build_doc: docker: - - image: circleci/python:3.8 + - image: circleci/python:3.9 steps: + - run: sudo apt-get update && sudo apt-get install -y cmake # TODO: remove after osqp wheels released - checkout - python/load-cache + - run: pip install numpy # TODO: remove after osqp wheels released - python/install-deps - python/save-cache - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 1174438dd5..61be099152 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -8,7 +8,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.8'] + python-version: ['3.9'] name: Python ${{ matrix.python-version }} CI @@ -21,9 +21,11 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Install dependencies + # TODO: remove numpy installation after fix cvxpy install issue run: | python -m pip install --upgrade pip - pip install -r requirements.txt + pip install numpy + python -m pip install -r requirements.txt - name: install coverage run: pip install coverage - name: install mypy diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 3a28347b18..84ef75157f 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -11,7 +11,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [ '3.8' ] + python-version: [ '3.9' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v2 @@ -23,8 +23,10 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies + # TODO: remove numpy installation after fix cvxpy install issue run: | python -m pip install --upgrade pip + pip install numpy pip install -r requirements.txt - name: install coverage run: pip install coverage diff --git a/README.md b/README.md index 07dbf7ae5a..2dcb4bab7e 100644 --- a/README.md +++ b/README.md @@ -92,7 +92,7 @@ See this paper for more details: # Requirements -- Python 3.8.x +- Python 3.9.x - numpy diff --git a/appveyor.yml b/appveyor.yml index b883397d71..9fd7430223 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -8,11 +8,10 @@ environment: CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd" matrix: - - MINICONDA: C:\Miniconda38-x64 - PYTHON_VERSION: "3.8" + - PYTHON_DIR: C:\Python39-x64 init: - - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%" + - "ECHO %PYTHON_DIR%" install: # If there is a newer build queued for the same PR, cancel this one. @@ -30,15 +29,12 @@ install: # Prepend newly installed Python to the PATH of this build (this cannot be # done from inside the powershell script as it would require to restart # the parent CMD process). - - SET PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH% + - SET PATH=%PYTHON_DIR%;%PYTHON_DIR%\\Scripts;%PATH% - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH% - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin - - conda config --set always_yes yes --set changeps1 no - - conda config --append channels conda-forge - - conda update -q conda - - conda info -a - - conda env create -f C:\\projects\pythonrobotics\environment.yml - - activate python_robotics + - "python -m pip install --upgrade pip" + - "pip install numpy" # TODO: remove this line after osqp wheel released + - "python -m pip install -r requirements.txt" # Check that we have the expected version and architecture for Python - "python --version" diff --git a/docs/getting_started.rst b/docs/getting_started.rst index 93ab70503a..83565eb872 100644 --- a/docs/getting_started.rst +++ b/docs/getting_started.rst @@ -25,7 +25,7 @@ See this paper for more details: Requirements ------------- -- Python 3.6.x +- Python 3.9.x - numpy - scipy - matplotlib From c2debe3b05b095046f63f72bb9b9c3b0c52c2635 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 31 Dec 2020 22:54:40 +0900 Subject: [PATCH 287/909] use pytest for test runner (#452) --- .github/pull_request_template.md | 2 +- .../two_joint_arm_to_point_control.py | 26 ++- .../Eta3SplinePath/eta3_spline_path.py | 178 ++++++++++-------- .../eta3_spline_trajectory.py | 48 ++--- README.md | 2 + requirements.txt | 1 + runtests.sh | 8 +- tests/__init__.py | 0 8 files changed, 152 insertions(+), 113 deletions(-) create mode 100644 tests/__init__.py diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 9fbb893c1c..fb412d571c 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1,7 @@ 2\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"hoge.png\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m 1149\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munconfined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munconfined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1150\u001b[0m super(Image, self).__init__(data=data, url=url, filename=filename, \n\u001b[0;32m-> 1151\u001b[0;31m metadata=metadata)\n\u001b[0m\u001b[1;32m 1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1153\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'width'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, metadata)\u001b[0m\n\u001b[1;32m 607\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 609\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 610\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1180\u001b[0m \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1181\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1182\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1183\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1184\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retina_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 633\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 635\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'hoge.png'" - ] - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename=\"hoge.png\",width=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![gif](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/extended_kalman_filter/animation.gif)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Section1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Section2\n", - "\n", - "$\\begin{equation*}\n", - "F=\n", - "\\begin{bmatrix}\n", - "1 & 0 & 0 & 0\\\\\n", - "0 & 1 & 0 & 0\\\\\n", - "0 & 0 & 1 & 0 \\\\\n", - "0 & 0 & 0 & 0 \\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ref" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 2fef7bc476929a62f304b8aeaebb2452659fb5c0 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 16 Nov 2021 23:06:25 +0900 Subject: [PATCH 364/909] clean up doc dir (#565) * clean up doc dir * clean up doc dir * clean up doc dir --- .circleci/config.yml | 2 +- docs/conf.py | 3 ++- docs/modules/path_planning/path_planning.rst | 2 +- .../cgmres_nmpc_files/cgmres_nmpc_1_0.png | Bin .../cgmres_nmpc_files/cgmres_nmpc_2_0.png | Bin .../cgmres_nmpc_files/cgmres_nmpc_3_0.png | Bin .../cgmres_nmpc_files/cgmres_nmpc_4_0.png | Bin 7 files changed, 4 insertions(+), 3 deletions(-) rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_1_0.png (100%) rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_2_0.png (100%) rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_3_0.png (100%) rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_4_0.png (100%) diff --git a/.circleci/config.yml b/.circleci/config.yml index 4cb628ecd8..4ced9fb90b 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -12,7 +12,7 @@ jobs: - python/load-cache - python/install-deps - python/save-cache - - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme pytest + - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme pytest IPython - run: command: cd docs;make html name: doc_build diff --git a/docs/conf.py b/docs/conf.py index 7422e2b106..64b32d5047 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -20,7 +20,7 @@ # -- Project information ----------------------------------------------------- project = 'PythonRobotics' -copyright = '2018, Atsushi Sakai' +copyright = '2018-2021, Atsushi Sakai' author = 'Atsushi Sakai' # The short X.Y version @@ -43,6 +43,7 @@ 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.viewcode', + 'IPython.sphinxext.ipython_console_highlighting', ] # Add any paths that contain templates here, relative to this directory. diff --git a/docs/modules/path_planning/path_planning.rst b/docs/modules/path_planning/path_planning.rst index cfe7b6ad40..60651dd5ff 100644 --- a/docs/modules/path_planning/path_planning.rst +++ b/docs/modules/path_planning/path_planning.rst @@ -39,7 +39,7 @@ In the animation, cyan points are searched nodes. Its heuristic is 2D Euclid distance. -.. _a*-algorithm: +.. _D*-algorithm: D\* algorithm ~~~~~~~~~~~~~ diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_1_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png similarity index 100% rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_1_0.png rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_2_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png similarity index 100% rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_2_0.png rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_3_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png similarity index 100% rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_3_0.png rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_4_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png similarity index 100% rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_4_0.png rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png From 7b0522b588301109cb0936e3412c36220ce70aff Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 16 Nov 2021 23:14:26 +0900 Subject: [PATCH 365/909] clean up doc system (#566) * clean up doc dir * clean up doc dir --- README.md | 1 - requirements.txt | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c9e45d9363..74c19d7a5e 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,6 @@ [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) [![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python) -[![CodeFactor](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/badge/master)](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master) [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics) Python codes for robotics algorithm. diff --git a/requirements.txt b/requirements.txt index 77a0bea427..7c60c4a24c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,3 +3,4 @@ scipy == 1.7.2 pandas == 1.3.4 matplotlib == 3.4.3 cvxpy == 1.1.17 +IPython == 7.29.0 # For sphinx documentation From b812cb1fc9baf2197e7ff8d834d1187d012c7021 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 17 Nov 2021 19:42:14 +0900 Subject: [PATCH 366/909] clean up doc dir (#567) --- README.md | 2 +- requirements.txt | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 74c19d7a5e..5a06f09ca8 100644 --- a/README.md +++ b/README.md @@ -117,7 +117,7 @@ For development: - mypy (for type check) -- Sphinx (for document generation) +- sphinx (for document generation) - pycodestyle (for code style check) diff --git a/requirements.txt b/requirements.txt index 7c60c4a24c..dbe243878d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,7 @@ scipy == 1.7.2 pandas == 1.3.4 matplotlib == 3.4.3 cvxpy == 1.1.17 +pytest == 6.2.5 # For unit test +pytest-xdist == 2.4.0 # For unit test +sphinx == 4.3.0 # For sphinx documentation IPython == 7.29.0 # For sphinx documentation From 6f309828ba0e204833574b9f72f1c6ee6e74369f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 17 Nov 2021 20:04:09 +0900 Subject: [PATCH 367/909] clean up doc dir (#568) * clean up doc dir * clean up doc dir * clean up doc dir * clean up doc dir --- .circleci/config.yml | 2 +- requirements.txt | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 4ced9fb90b..539c0eeac0 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -12,7 +12,7 @@ jobs: - python/load-cache - python/install-deps - python/save-cache - - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme pytest IPython + - run: pip install -r requirements.txt - run: command: cd docs;make html name: doc_build diff --git a/requirements.txt b/requirements.txt index dbe243878d..e11ba13b8d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,5 +5,6 @@ matplotlib == 3.4.3 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test pytest-xdist == 2.4.0 # For unit test -sphinx == 4.3.0 # For sphinx documentation +sphinx < 4.0 # For sphinx documentation +sphinx_rtd_theme == 1.0.0 IPython == 7.29.0 # For sphinx documentation From 137e372db1fb6455cfe6fdce263d63e7dd8e259c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 17 Nov 2021 22:58:23 +0900 Subject: [PATCH 368/909] fix CI (#569) * fix CI * fix CI * fix CI * fix CI * fix CI * fix CI * fix CI * fix CI --- .circleci/config.yml | 12 ++++++------ .github/workflows/Linux_CI.yml | 6 +++++- .github/workflows/MacOS_CI.yml | 7 ++++++- .github/workflows/codeql.yml | 4 ++-- appveyor.yml | 4 ++++ 5 files changed, 23 insertions(+), 10 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 539c0eeac0..4866240bd4 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -1,7 +1,7 @@ version: 2.1 orbs: - python: circleci/python@0.2.1 + python: circleci/python@1.4.0 jobs: build_doc: @@ -9,13 +9,13 @@ jobs: - image: circleci/python:3.9 steps: - checkout - - python/load-cache - - python/install-deps - - python/save-cache - - run: pip install -r requirements.txt - run: - command: cd docs;make html name: doc_build + command: | + python -m venv venv + . venv/bin/activate + pip install -r requirements.txt + cd docs;make html - store_artifacts: path: docs/_build/html/ destination: html diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 2962028d6f..4e2e57f8e4 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -1,6 +1,10 @@ name: Linux_CI -on: [push, pull_request] +on: + push: + branches: + - master + pull_request: jobs: build: diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 6b3ffa2588..9bfb4b6fd3 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -4,7 +4,12 @@ name: MacOS_CI # Controls when the action will run. Triggers the workflow on push or pull request # events but only for the master branch -on: [push, pull_request] +on: + push: + branches: + - master + pull_request: + jobs: build: diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 5e6c03aa16..148260a835 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -2,8 +2,8 @@ name: "Code scanning - action" on: push: - branches-ignore: - - 'dependabot/**' + branches: + - master pull_request: schedule: - cron: '0 19 * * 0' diff --git a/appveyor.yml b/appveyor.yml index efbb96131c..16a5d7751d 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -10,6 +10,10 @@ environment: matrix: - PYTHON_DIR: C:\Python39-x64 +branches: + only: + - master + init: - "ECHO %PYTHON_DIR%" From 35984e8978cff3f71bcdff26aea235d3ea433ef0 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 18 Nov 2021 22:35:10 +0900 Subject: [PATCH 369/909] clean up localization docs (#570) * clean up localization docs * clean up localization docs * clean up localization docs --- Localization/Kalmanfilter_basics.ipynb | 769 ------------------ Localization/Kalmanfilter_basics_2.ipynb | 381 --------- Localization/bayes_filter.png | Bin 1088102 -> 0 bytes Localization/extended_kalman_filter/ekf.png | Bin 113010 -> 0 bytes .../extended_kalman_filter_localization.ipynb | 264 ------ .../particle_filter/particle_filter.ipynb | 72 -- docs/modules/appendix/Kalmanfilter_basics.rst | 8 +- .../extended_kalman_filter_localization.rst | 12 +- docs/modules/localization/localization.rst | 26 +- 9 files changed, 28 insertions(+), 1504 deletions(-) delete mode 100644 Localization/Kalmanfilter_basics.ipynb delete mode 100644 Localization/Kalmanfilter_basics_2.ipynb delete mode 100644 Localization/bayes_filter.png delete mode 100644 Localization/extended_kalman_filter/ekf.png delete mode 100644 Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb delete mode 100644 Localization/particle_filter/particle_filter.ipynb diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb deleted file mode 100644 index c501b117de..0000000000 --- a/Localization/Kalmanfilter_basics.ipynb +++ /dev/null @@ -1,769 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## KF Basics - Part I\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction\n", - "#### What is the need to describe belief in terms of PDF's?\n", - "This is because robot environments are stochastic. A robot environment may have cows with Tesla by side. That is a robot and it's environment cannot be deterministically modelled(e.g as a function of something like time t). In the real world sensors are also error prone, and hence there'll be a set of values with a mean and variance that it can take. Hence, we always have to model around some mean and variances associated." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### What is Expectation of a Random Variables?\n", - " Expectation is nothing but an average of the probabilites\n", - " \n", - "$$\\mathbb E[X] = \\sum_{i=1}^n p_ix_i$$\n", - "\n", - "In the continous form,\n", - "\n", - "$$\\mathbb E[X] = \\int_{-\\infty}^\\infty x\\, f(x) \\,dx$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.4000000000000001\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import random\n", - "x=[3,1,2]\n", - "p=[0.1,0.3,0.4]\n", - "E_x=np.sum(np.multiply(x,p))\n", - "print(E_x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### What is the advantage of representing the belief as a unimodal as opposed to multimodal?\n", - "Obviously, it makes sense because we can't multiple probabilities to a car moving for two locations. This would be too confusing and the information will not be useful." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Variance, Covariance and Correlation\n", - "\n", - "#### Variance\n", - "Variance is the spread of the data. The mean does'nt tell much **about** the data. Therefore the variance tells us about the **story** about the data meaning the spread of the data.\n", - "\n", - "$$\\mathit{VAR}(X) = \\frac{1}{n}\\sum_{i=1}^n (x_i - \\mu)^2$$" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0224618077401504" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x=np.random.randn(10)\n", - "np.var(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Covariance\n", - "\n", - "This is for a multivariate distribution. For example, a robot in 2-D space can take values in both x and y. To describe them, a normal distribution with mean in both x and y is needed.\n", - "\n", - "For a multivariate distribution, mean $\\mu$ can be represented as a matrix, \n", - "\n", - "$$\n", - "\\mu = \\begin{bmatrix}\\mu_1\\\\\\mu_2\\\\ \\vdots \\\\\\mu_n\\end{bmatrix}\n", - "$$\n", - "\n", - "\n", - "Similarly, variance can also be represented.\n", - "\n", - "But an important concept is that in the same way as every variable or dimension has a variation in its values, it is also possible that there will be values on how they **together vary**. This is also a measure of how two datasets are related to each other or **correlation**.\n", - "\n", - "For example, as height increases weight also generally increases. These variables are correlated. They are positively correlated because as one variable gets larger so does the other.\n", - "\n", - "We use a **covariance matrix** to denote covariances of a multivariate normal distribution:\n", - "$$\n", - "\\Sigma = \\begin{bmatrix}\n", - " \\sigma_1^2 & \\sigma_{12} & \\cdots & \\sigma_{1n} \\\\\n", - " \\sigma_{21} &\\sigma_2^2 & \\cdots & \\sigma_{2n} \\\\\n", - " \\vdots & \\vdots & \\ddots & \\vdots \\\\\n", - " \\sigma_{n1} & \\sigma_{n2} & \\cdots & \\sigma_n^2\n", - " \\end{bmatrix}\n", - "$$\n", - "\n", - "**Diagonal** - Variance of each variable associated. \n", - "\n", - "**Off-Diagonal** - covariance between ith and jth variables.\n", - "\n", - "$$\\begin{aligned}VAR(X) = \\sigma_x^2 &= \\frac{1}{n}\\sum_{i=1}^n(X - \\mu)^2\\\\\n", - "COV(X, Y) = \\sigma_{xy} &= \\frac{1}{n}\\sum_{i=1}^n[(X-\\mu_x)(Y-\\mu_y)\\big]\\end{aligned}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.08868895, 0.05064471, 0.08855629],\n", - " [0.05064471, 0.06219243, 0.11555291],\n", - " [0.08855629, 0.11555291, 0.21534324]])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x=np.random.random((3,3))\n", - "np.cov(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Covariance taking the data as **sample** with $\\frac{1}{N-1}$" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.1571437 , -0.00766623],\n", - " [-0.00766623, 0.13957621]])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x_cor=np.random.rand(1,10)\n", - "y_cor=np.random.rand(1,10)\n", - "np.cov(x_cor,y_cor)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Covariance taking the data as **population** with $\\frac{1}{N}$" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.14142933, -0.0068996 ],\n", - " [-0.0068996 , 0.12561859]])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.cov(x_cor,y_cor,bias=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gaussians \n", - "\n", - "#### Central Limit Theorem\n", - "\n", - "According to this theorem, the average of n samples of random and independent variables tends to follow a normal distribution as we increase the sample size.(Generally, for n>=30)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 1., 4., 9., 12., 12., 20., 16., 16., 4., 6.]),\n", - " array([5.30943011, 5.34638597, 5.38334183, 5.42029769, 5.45725355,\n", - " 5.49420941, 5.53116527, 5.56812114, 5.605077 , 5.64203286,\n", - " 5.67898872]),\n", - " )" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import random\n", - "a=np.zeros((100,))\n", - "for i in range(100):\n", - " x=[random.uniform(1,10) for _ in range(1000)]\n", - " a[i]=np.sum(x,axis=0)/1000\n", - "plt.hist(a)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Gaussian Distribution\n", - "A Gaussian is a *continuous probability distribution* that is completely described with two parameters, the mean ($\\mu$) and the variance ($\\sigma^2$). It is defined as:\n", - "\n", - "$$ \n", - "f(x, \\mu, \\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}} \\exp\\big [{-\\frac{(x-\\mu)^2}{2\\sigma^2} }\\big ]\n", - "$$\n", - "Range is $$[-\\inf,\\inf] $$\n", - "\n", - "\n", - "This is just a function of mean($\\mu$) and standard deviation ($\\sigma$) and what gives the normal distribution the charecteristic **bell curve**. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.mlab as mlab\n", - "import math\n", - "import scipy.stats\n", - "\n", - "mu = 0\n", - "variance = 5\n", - "sigma = math.sqrt(variance)\n", - "x = np.linspace(mu - 5*sigma, mu + 5*sigma, 100)\n", - "plt.plot(x,scipy.stats.norm.pdf(x, mu, sigma))\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Why do we need Gaussian distributions?\n", - "Since it becomes really difficult in the real world to deal with multimodal distribution as we cannot put the belief in two seperate location of the robots. This becomes really confusing and in practice impossible to comprehend. \n", - "Gaussian probability distribution allows us to drive the robots using only one mode with peak at the mean with some variance." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gaussian Properties\n", - "\n", - "**Multiplication**\n", - "\n", - "\n", - "For the measurement update in a Bayes Filter, the algorithm tells us to multiply the Prior P(X_t) and measurement P(Z_t|X_t) to calculate the posterior:\n", - "\n", - "$$P(X \\mid Z) = \\frac{P(Z \\mid X)P(X)}{P(Z)}$$\n", - "\n", - "Here for the numerator, $P(Z \\mid X),P(X)$ both are gaussian.\n", - "\n", - "$N(\\mu_1, \\sigma_1^2)$ and $N(\\mu_2, \\sigma_2^2)$ are their mean and variances.\n", - "\n", - "New mean is \n", - "\n", - "$$\\mu_\\mathtt{new} = \\frac{\\mu_1 \\sigma_2^2 + \\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}$$\n", - "New variance is\n", - "$$\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New mean is at: 5.0\n", - "New variance is: 1.0\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.mlab as mlab\n", - "import math\n", - "mu1 = 0\n", - "variance1 = 2\n", - "sigma = math.sqrt(variance1)\n", - "x1 = np.linspace(mu1 - 3*sigma, mu1 + 3*sigma, 100)\n", - "plt.plot(x1,scipy.stats.norm.pdf(x1, mu1, sigma),label='prior')\n", - "\n", - "mu2 = 10\n", - "variance2 = 2\n", - "sigma = math.sqrt(variance2)\n", - "x2 = np.linspace(mu2 - 3*sigma, mu2 + 3*sigma, 100)\n", - "plt.plot(x2,scipy.stats.norm.pdf(x2, mu2, sigma),\"g-\",label='measurement')\n", - "\n", - "\n", - "mu_new=(mu1*variance2+mu2*variance1)/(variance1+variance2)\n", - "print(\"New mean is at: \",mu_new)\n", - "var_new=(variance1*variance2)/(variance1+variance2)\n", - "print(\"New variance is: \",var_new)\n", - "sigma = math.sqrt(var_new)\n", - "x3 = np.linspace(mu_new - 3*sigma, mu_new + 3*sigma, 100)\n", - "plt.plot(x3,scipy.stats.norm.pdf(x3, mu_new, var_new),label=\"posterior\")\n", - "plt.legend(loc='upper left')\n", - "plt.xlim(-10,20)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Addition**\n", - "\n", - "The motion step involves a case of adding up probability (Since it has to abide the Law of Total Probability). This means their beliefs are to be added and hence two gaussians. They are simply arithmetic additions of the two.\n", - "\n", - "$$\\begin{gathered}\\mu_x = \\mu_p + \\mu_z \\\\\n", - "\\sigma_x^2 = \\sigma_z^2+\\sigma_p^2\\, \\square\\end{gathered}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New mean is at: 15\n", - "New variance is: 2\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.mlab as mlab\n", - "import math\n", - "mu1 = 5\n", - "variance1 = 1\n", - "sigma = math.sqrt(variance1)\n", - "x1 = np.linspace(mu1 - 3*sigma, mu1 + 3*sigma, 100)\n", - "plt.plot(x1,scipy.stats.norm.pdf(x1, mu1, sigma),label='prior')\n", - "\n", - "mu2 = 10\n", - "variance2 = 1\n", - "sigma = math.sqrt(variance2)\n", - "x2 = np.linspace(mu2 - 3*sigma, mu2 + 3*sigma, 100)\n", - "plt.plot(x2,scipy.stats.norm.pdf(x2, mu2, sigma),\"g-\",label='measurement')\n", - "\n", - "\n", - "mu_new=mu1+mu2\n", - "print(\"New mean is at: \",mu_new)\n", - "var_new=(variance1+variance2)\n", - "print(\"New variance is: \",var_new)\n", - "sigma = math.sqrt(var_new)\n", - "x3 = np.linspace(mu_new - 3*sigma, mu_new + 3*sigma, 100)\n", - "plt.plot(x3,scipy.stats.norm.pdf(x3, mu_new, var_new),label=\"posterior\")\n", - "plt.legend(loc='upper left')\n", - "plt.xlim(-10,20)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 188, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Example from:\n", - "#https://scipython.com/blog/visualizing-the-bivariate-gaussian-distribution/\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib import cm\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "# Our 2-dimensional distribution will be over variables X and Y\n", - "N = 60\n", - "X = np.linspace(-3, 3, N)\n", - "Y = np.linspace(-3, 4, N)\n", - "X, Y = np.meshgrid(X, Y)\n", - "\n", - "# Mean vector and covariance matrix\n", - "mu = np.array([0., 1.])\n", - "Sigma = np.array([[ 1. , -0.5], [-0.5, 1.5]])\n", - "\n", - "# Pack X and Y into a single 3-dimensional array\n", - "pos = np.empty(X.shape + (2,))\n", - "pos[:, :, 0] = X\n", - "pos[:, :, 1] = Y\n", - "\n", - "def multivariate_gaussian(pos, mu, Sigma):\n", - " \"\"\"Return the multivariate Gaussian distribution on array pos.\n", - "\n", - " pos is an array constructed by packing the meshed arrays of variables\n", - " x_1, x_2, x_3, ..., x_k into its _last_ dimension.\n", - "\n", - " \"\"\"\n", - "\n", - " n = mu.shape[0]\n", - " Sigma_det = np.linalg.det(Sigma)\n", - " Sigma_inv = np.linalg.inv(Sigma)\n", - " N = np.sqrt((2*np.pi)**n * Sigma_det)\n", - " # This einsum call calculates (x-mu)T.Sigma-1.(x-mu) in a vectorized\n", - " # way across all the input variables.\n", - " fac = np.einsum('...k,kl,...l->...', pos-mu, Sigma_inv, pos-mu)\n", - "\n", - " return np.exp(-fac / 2) / N\n", - "\n", - "# The distribution on the variables X, Y packed into pos.\n", - "Z = multivariate_gaussian(pos, mu, Sigma)\n", - "\n", - "# Create a surface plot and projected filled contour plot under it.\n", - "fig = plt.figure()\n", - "ax = fig.gca(projection='3d')\n", - "ax.plot_surface(X, Y, Z, rstride=3, cstride=3, linewidth=1, antialiased=True,\n", - " cmap=cm.viridis)\n", - "\n", - "cset = ax.contourf(X, Y, Z, zdir='z', offset=-0.15, cmap=cm.viridis)\n", - "\n", - "# Adjust the limits, ticks and view angle\n", - "ax.set_zlim(-0.15,0.2)\n", - "ax.set_zticks(np.linspace(0,0.2,5))\n", - "ax.view_init(27, -21)\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a 3D projection of the gaussians involved with the lower surface showing the 2D projection of the 3D projection above. The innermost ellipse represents the highest peak, that is the maximum probability for a given (X,Y) value.\n", - "\n", - "\n", - "\n", - "\n", - "** numpy einsum examples **" - ] - }, - { - "cell_type": "code", - "execution_count": 175, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 1 2 3 4]\n", - " [ 5 6 7 8 9]\n", - " [10 11 12 13 14]\n", - " [15 16 17 18 19]\n", - " [20 21 22 23 24]]\n", - "[0 1 2 3 4]\n", - "[[0 1 2]\n", - " [3 4 5]]\n" - ] - } - ], - "source": [ - "a = np.arange(25).reshape(5,5)\n", - "b = np.arange(5)\n", - "c = np.arange(6).reshape(2,3)\n", - "print(a)\n", - "print(b)\n", - "print(c)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 30, 80, 130, 180, 230])" - ] - }, - "execution_count": 204, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#this is the diagonal sum, i repeated means the diagonal\n", - "np.einsum('ij', a)\n", - "#this takes the output ii which is the diagonal and outputs to a\n", - "np.einsum('ii->i',a)\n", - "#this takes in the array A represented by their axes 'ij' and B by its only axes'j' \n", - "#and multiples them element wise\n", - "np.einsum('ij,j',a, b)" - ] - }, - { - "cell_type": "code", - "execution_count": 199, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 22, 76])" - ] - }, - "execution_count": 199, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "A = np.arange(3).reshape(3,1)\n", - "B = np.array([[ 0, 1, 2, 3],\n", - " [ 4, 5, 6, 7],\n", - " [ 8, 9, 10, 11]])\n", - "C=np.multiply(A,B)\n", - "np.sum(C,axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 22, 76])" - ] - }, - "execution_count": 197, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "D = np.array([0,1,2])\n", - "E = np.array([[ 0, 1, 2, 3],\n", - " [ 4, 5, 6, 7],\n", - " [ 8, 9, 10, 11]])\n", - "\n", - "np.einsum('i,ij->i',D,E)" - ] - }, - { - "cell_type": "code", - "execution_count": 238, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 238, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from scipy.stats import multivariate_normal\n", - "x, y = np.mgrid[-5:5:.1, -5:5:.1]\n", - "pos = np.empty(x.shape + (2,))\n", - "pos[:, :, 0] = x; pos[:, :, 1] = y\n", - "rv = multivariate_normal([0.5, -0.2], [[2.0, 0.9], [0.9, 0.5]])\n", - "plt.contourf(x, y, rv.pdf(pos))\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### References:\n", - "\n", - "1. Roger Labbe's [repo](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) on Kalman Filters. (Majority of the examples in the notes are from this)\n", - "\n", - "\n", - "\n", - "2. Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press.\n", - "\n", - "\n", - "\n", - "3. Scipy [Documentation](https://scipython.com/blog/visualizing-the-bivariate-gaussian-distribution/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Localization/Kalmanfilter_basics_2.ipynb b/Localization/Kalmanfilter_basics_2.ipynb deleted file mode 100644 index 43f34ec6f2..0000000000 --- a/Localization/Kalmanfilter_basics_2.ipynb +++ /dev/null @@ -1,381 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## KF Basics - Part 2\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Probabilistic Generative Laws\n", - " \n", - "#### 1st Law:\n", - "The belief representing the state $x_{t}$, is conditioned on all past states, measurements and controls. This can be shown mathematically by the conditional probability shown below:\n", - "\n", - "$$p(x_{t} | x_{0:t-1},z_{1:t-1},u_{1:t})$$\n", - "\n", - "1) $z_{t}$ represents the **measurement**\n", - "\n", - "2) $u_{t}$ the **motion command**\n", - "\n", - "3) $x_{t}$ the **state** (can be the position, velocity, etc) of the robot or its environment at time t.\n", - "\n", - "\n", - "'If we know the state $x_{t-1}$ and $u_{t}$, then knowing the states $x_{0:t-2}$, $z_{1:t-1}$ becomes immaterial through the property of **conditional independence**'. The state $x_{t-1}$ introduces a conditional independence between $x_{t}$ and $z_{1:t-1}$, $u_{1:t-1}$\n", - "\n", - "Therefore the law now holds as:\n", - "\n", - "$$p(x_{t} | x_{0:t-1},z_{1:t-1},u_{1:t})=p(x_{t} | x_{t-1},u_{t})$$\n", - "\n", - "#### 2nd Law:\n", - "\n", - "If $x_{t}$ is complete, then:\n", - "\n", - "$$p(z_{t} | x_{0:t},z_{1:t-1},u_{1:t})=p(z_{t} | x_{t})$$\n", - "\n", - "$x_{t}$ is **complete** means that the past states, controls or measurements carry no additional information to predict future.\n", - "\n", - "$x_{0:t-1}$, $z_{1:t-1}$ and $u_{1:t}$ are **conditionally independent** of $z_{t}$ given $x_{t}$ of complete.\n", - "\n", - "The filter works in two parts:\n", - "\n", - "$p(x_{t} | x_{t-1},u_{t})$ -> **State Transition Probability**\n", - "\n", - "$p(z_{t} | x_{t})$ -> **Measurement Probability**\n", - "\n", - "\n", - "### Conditional dependence and independence example:\n", - "\n", - "\n", - "$\\bullet$**Independent but conditionally dependent**\n", - "\n", - "Let's say you flip two fair coins\n", - "\n", - "A - Your first coin flip is heads\n", - "\n", - "B - Your second coin flip is heads\n", - "\n", - "C - Your first two flips were the same\n", - "\n", - "\n", - "A and B here are independent. However, A and B are conditionally dependent given C, since if you know C then your first coin flip will inform the other one.\n", - "\n", - "$\\bullet$**Dependent but conditionally independent**\n", - "\n", - "A box contains two coins: a regular coin and one fake two-headed coin ((P(H)=1). I choose a coin at random and toss it twice. Define the following events.\n", - "\n", - "A= First coin toss results in an H.\n", - "\n", - "B= Second coin toss results in an H.\n", - "\n", - "C= Coin 1 (regular) has been selected. \n", - "\n", - "If we know A has occurred (i.e., the first coin toss has resulted in heads), we would guess that it is more likely that we have chosen Coin 2 than Coin 1. This in turn increases the conditional probability that B occurs. This suggests that A and B are not independent. On the other hand, given C (Coin 1 is selected), A and B are independent.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bayes Rule:\n", - "\n", - "\n", - "Posterior = $$\\frac{Likelihood*Prior}{Marginal} $$\n", - "\n", - "Here,\n", - "\n", - "**Posterior** = Probability of an event occurring based on certain evidence.\n", - "\n", - "**Likelihood** = How probable is the evidence given the event.\n", - "\n", - "**Prior** = Probability of the just the event occurring without having any evidence.\n", - "\n", - "**Marginal** = Probability of the evidence given all the instances of events possible.\n", - "\n", - "\n", - "\n", - "Example:\n", - "\n", - "1% of women have breast cancer (and therefore 99% do not).\n", - "80% of mammograms detect breast cancer when it is there (and therefore 20% miss it).\n", - "9.6% of mammograms detect breast cancer when its not there (and therefore 90.4% correctly return a negative result).\n", - "\n", - "We can turn the process above into an equation, which is Bayes Theorem. Here is the equation:\n", - "\n", - "$\\displaystyle{\\Pr(\\mathrm{A}|\\mathrm{X}) = \\frac{\\Pr(\\mathrm{X}|\\mathrm{A})\\Pr(\\mathrm{A})}{\\Pr(\\mathrm{X|A})\\Pr(\\mathrm{A})+ \\Pr(\\mathrm{X | not \\ A})\\Pr(\\mathrm{not \\ A})}}$\n", - "\n", - "\n", - "$\\bullet$Pr(A|X) = Chance of having cancer (A) given a positive test (X). This is what we want to know: How likely is it to have cancer with a positive result? In our case it was 7.8%.\n", - "\n", - "$\\bullet$Pr(X|A) = Chance of a positive test (X) given that you had cancer (A). This is the chance of a true positive, 80% in our case.\n", - "\n", - "$\\bullet$Pr(A) = Chance of having cancer (1%).\n", - "\n", - "$\\bullet$Pr(not A) = Chance of not having cancer (99%).\n", - "\n", - "$\\bullet$Pr(X|not A) = Chance of a positive test (X) given that you didn't have cancer (~A). This is a false positive, 9.6% in our case.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bayes Filter Algorithm\n", - "\n", - "The basic filter algorithm is:\n", - "\n", - "for all $x_{t}$:\n", - "\n", - "1. $\\overline{bel}(x_t) = \\int p(x_t | u_t, x_{t-1}) bel(x_{t-1})dx$\n", - "\n", - "2. $bel(x_t) = \\eta p(z_t | x_t) \\overline{bel}(x_t)$\n", - "\n", - "end.\n", - "\n", - "\n", - "$\\rightarrow$The first step in filter is to calculate the prior for the next step that uses the bayes theorem. This is the **Prediction** step. The belief, $\\overline{bel}(x_t)$, is **before** incorporating measurement($z_{t}$) at time t=t. This is the step where the motion occurs and information is lost because the means and covariances of the gaussians are added. The RHS of the equation incorporates the law of total probability for prior calculation.\n", - "\n", - "$\\rightarrow$ This is the **Correction** or update step that calculates the belief of the robot **after** taking into account the measurement($z_{t}$) at time t=t. This is where we incorporate the sensor information for the whereabouts of the robot. We gain information here as the gaussians get multiplied here. (Multiplication of gaussian values allows the resultant to lie in between these numbers and the resultant covariance is smaller.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bayes filter localization example:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": { - "image/png": { - "width": 400 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename=\"bayes_filter.png\",width=400)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "Given - A robot with a sensor to detect doorways along a hallway. Also, the robot knows how the hallway looks like but doesn't know where it is in the map. \n", - "\n", - "\n", - "1. Initially(first scenario), it doesn't know where it is with respect to the map and hence the belief assigns equal probability to each location in the map.\n", - "\n", - "\n", - "2. The first sensor reading is incorporated and it shows the presence of a door. Now the robot knows how the map looks like but cannot localize yet as map has 3 doors present. Therefore it assigns equal probability to each door present. \n", - "\n", - "\n", - "3. The robot now moves forward. This is the prediction step and the motion causes the robot to lose some of the information and hence the variance of the gaussians increase (diagram 4.). The final belief is **convolution** of posterior from previous step and the current state after motion. Also, the means shift on the right due to the motion.\n", - "\n", - "\n", - "4. Again, incorporating the measurement, the sensor senses a door and this time too the possibility of door is equal for the three door. This is where the filter's magic kicks in. For the final belief (diagram 5.), the posterior calculated after sensing is mixed or **convolution** of previous posterior and measurement. It improves the robot's belief at location near to the second door. The variance **decreases** and **peaks**.\n", - "\n", - "\n", - "5. Finally after series of iterations of motion and correction, the robot is able to localize itself with respect to the environment.(diagram 6.)\n", - "\n", - "Do note that the robot knows the map but doesn't know where exactly it is on the map." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bayes and Kalman filter structure\n", - "\n", - "The basic structure and the concept remains the same as bayes filter for Kalman. The only key difference is the mathematical representation of Kalman filter. The Kalman filter is nothing but a bayesian filter that uses Gaussians.\n", - "\n", - "For a bayes filter to be a Kalman filter, **each term of belief is now a gaussian**, unlike histograms. The basic formulation for the **bayes filter** algorithm is:\n", - "\n", - "$$\\begin{aligned} \n", - "\\bar {\\mathbf x} &= \\mathbf x \\ast f_{\\mathbf x}(\\bullet)\\, \\, &\\text{Prediction} \\\\\n", - "\\mathbf x &= \\mathcal L \\cdot \\bar{\\mathbf x}\\, \\, &\\text{Correction}\n", - "\\end{aligned}$$\n", - "\n", - "\n", - "$\\bar{\\mathbf x}$ is the *prior* \n", - "\n", - "$\\mathcal L$ is the *likelihood* of a measurement given the prior $\\bar{\\mathbf x}$\n", - "\n", - "$f_{\\mathbf x}(\\bullet)$ is the *process model* or the gaussian term that helps predict the next state like velocity\n", - "to track position or acceleration.\n", - "\n", - "$\\ast$ denotes *convolution*.\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Kalman Gain\n", - "\n", - "\n", - "$$ x = (\\mathcal L \\bar x)$$\n", - "\n", - "Where x is posterior and $\\mathcal L$ and $\\bar x$ are gaussians.\n", - "\n", - "Therefore the mean of the posterior is given by:\n", - "\n", - "$$\n", - "\\mu=\\frac{\\bar\\sigma^2\\, \\mu_z + \\sigma_z^2 \\, \\bar\\mu} {\\bar\\sigma^2 + \\sigma_z^2}\n", - "$$\n", - "\n", - "\n", - "$$\\mu = \\left( \\frac{\\bar\\sigma^2}{\\bar\\sigma^2 + \\sigma_z^2}\\right) \\mu_z + \\left(\\frac{\\sigma_z^2}{\\bar\\sigma^2 + \\sigma_z^2}\\right)\\bar\\mu$$\n", - "\n", - "In this form it is easy to see that we are scaling the measurement and the prior by weights: \n", - "\n", - "$$\\mu = W_1 \\mu_z + W_2 \\bar\\mu$$\n", - "\n", - "\n", - "The weights sum to one because the denominator is a normalization term. We introduce a new term, $K=W_1$, giving us:\n", - "\n", - "$$\\begin{aligned}\n", - "\\mu &= K \\mu_z + (1-K) \\bar\\mu\\\\\n", - "&= \\bar\\mu + K(\\mu_z - \\bar\\mu)\n", - "\\end{aligned}$$\n", - "\n", - "where\n", - "\n", - "$$K = \\frac {\\bar\\sigma^2}{\\bar\\sigma^2 + \\sigma_z^2}$$\n", - "\n", - "The variance in terms of the Kalman gain:\n", - "\n", - "$$\\begin{aligned}\n", - "\\sigma^2 &= \\frac{\\bar\\sigma^2 \\sigma_z^2 } {\\bar\\sigma^2 + \\sigma_z^2} \\\\\n", - "&= K\\sigma_z^2 \\\\\n", - "&= (1-K)\\bar\\sigma^2 \n", - "\\end{aligned}$$\n", - "\n", - "\n", - "$K$ is the *Kalman gain*. It's the crux of the Kalman filter. It is a scaling term that chooses a value partway between $\\mu_z$ and $\\bar\\mu$." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Kalman Filter - Univariate and Multivariate\n", - "\n", - "\n", - "**Prediction**\n", - "\n", - "$\\begin{array}{|l|l|l|}\n", - "\\hline\n", - "\\text{Univariate} & \\text{Univariate} & \\text{Multivariate}\\\\\n", - "& \\text{(Kalman form)} & \\\\\n", - "\\hline\n", - "\\bar \\mu = \\mu + \\mu_{f_x} & \\bar x = x + dx & \\bar{\\mathbf x} = \\mathbf{Fx} + \\mathbf{Bu}\\\\\n", - "\\bar\\sigma^2 = \\sigma_x^2 + \\sigma_{f_x}^2 & \\bar P = P + Q & \\bar{\\mathbf P} = \\mathbf{FPF}^\\mathsf T + \\mathbf Q \\\\\n", - "\\hline\n", - "\\end{array}$\n", - "\n", - "$\\mathbf x,\\, \\mathbf P$ are the state mean and covariance. They correspond to $x$ and $\\sigma^2$.\n", - "\n", - "$\\mathbf F$ is the *state transition function*. When multiplied by $\\bf x$ it computes the prior. \n", - "\n", - "$\\mathbf Q$ is the process covariance. It corresponds to $\\sigma^2_{f_x}$.\n", - "\n", - "$\\mathbf B$ and $\\mathbf u$ are model control inputs to the system.\n", - "\n", - "**Correction**\n", - "\n", - "$\\begin{array}{|l|l|l|}\n", - "\\hline\n", - "\\text{Univariate} & \\text{Univariate} & \\text{Multivariate}\\\\\n", - "& \\text{(Kalman form)} & \\\\\n", - "\\hline\n", - "& y = z - \\bar x & \\mathbf y = \\mathbf z - \\mathbf{H\\bar x} \\\\\n", - "& K = \\frac{\\bar P}{\\bar P+R}&\n", - "\\mathbf K = \\mathbf{\\bar{P}H}^\\mathsf T (\\mathbf{H\\bar{P}H}^\\mathsf T + \\mathbf R)^{-1} \\\\\n", - "\\mu=\\frac{\\bar\\sigma^2\\, \\mu_z + \\sigma_z^2 \\, \\bar\\mu} {\\bar\\sigma^2 + \\sigma_z^2} & x = \\bar x + Ky & \\mathbf x = \\bar{\\mathbf x} + \\mathbf{Ky} \\\\\n", - "\\sigma^2 = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2} & P = (1-K)\\bar P &\n", - "\\mathbf P = (\\mathbf I - \\mathbf{KH})\\mathbf{\\bar{P}} \\\\\n", - "\\hline\n", - "\\end{array}$\n", - "\n", - "$\\mathbf H$ is the measurement function.\n", - "\n", - "$\\mathbf z,\\, \\mathbf R$ are the measurement mean and noise covariance. They correspond to $z$ and $\\sigma_z^2$ in the univariate filter. \n", - "$\\mathbf y$ and $\\mathbf K$ are the residual and Kalman gain. \n", - "\n", - "The details will be different than the univariate filter because these are vectors and matrices, but the concepts are exactly the same: \n", - "\n", - "- Use a Gaussian to represent our estimate of the state and error\n", - "- Use a Gaussian to represent the measurement and its error\n", - "- Use a Gaussian to represent the process model\n", - "- Use the process model to predict the next state (the prior)\n", - "- Form an estimate part way between the measurement and the prior" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### References:\n", - "\n", - "1. Roger Labbe's [repo](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) on Kalman Filters. (Majority of text in the notes are from this)\n", - "\n", - "\n", - "\n", - "2. Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/Localization/bayes_filter.png b/Localization/bayes_filter.png deleted file mode 100644 index 50e509bdac47d79a9db7429fe4d770dc80c53378..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1088102 zcmZU(1yCH(vp2ffq6;A;xCIF=!QI^hAwY0h+;?&J1SiM_hXBFd9YSz|yW8Tfi@*Hu 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zz1blgYIyCDYUVRioQ(XrB=?S3^;9oT^}3zhf!Q2x63>IJu(S{%xekHND}rWif@h`D zH5}f<%_R5Lmk%oJXXLWDmj()93En6B_U*g_cT3o=DG-amN$UT&gw8^20Zj2F)n) zqoT?Vx~~3-x&D0JnHCn9<3am?fqVOYNOmLrVgLj0l79OK&64QC+5=0!Evjk6yGP(T zUDt{DTj+okTNSDrJO+E" - ] - }, - "execution_count": 2, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename=\"ekf.png\",width=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![EKF](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif)\n", - "\n", - "This is a sensor fusion localization with Extended Kalman Filter(EKF).\n", - "\n", - "The blue line is true trajectory, the black line is dead reckoning\n", - "trajectory,\n", - "\n", - "the green point is positioning observation (ex. GPS), and the red line\n", - "is estimated trajectory with EKF.\n", - "\n", - "The red ellipse is estimated covariance ellipse with EKF.\n", - "\n", - "Code: [PythonRobotics/extended\\_kalman\\_filter\\.py at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter.py)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Filter design\n", - "\n", - "In this simulation, the robot has a state vector includes 4 states at time $t$.\n", - "\n", - "$$\\textbf{x}_t=[x_t, y_t, \\phi_t, v_t]$$\n", - "\n", - "x, y are a 2D x-y position, $\\phi$ is orientation, and v is velocity.\n", - "\n", - "In the code, \"xEst\" means the state vector. [code](https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L168)\n", - "\n", - "And, $P_t$ is covariace matrix of the state,\n", - "\n", - "$Q$ is covariance matrix of process noise, \n", - "\n", - "$R$ is covariance matrix of observation noise at time $t$ \n", - "\n", - "　\n", - "\n", - "The robot has a speed sensor and a gyro sensor.\n", - "\n", - "So, the input vecor can be used as each time step\n", - "\n", - "$$\\textbf{u}_t=[v_t, \\omega_t]$$\n", - "\n", - "Also, the robot has a GNSS sensor, it means that the robot can observe x-y position at each time.\n", - "\n", - "$$\\textbf{z}_t=[x_t,y_t]$$\n", - "\n", - "The input and observation vector includes sensor noise.\n", - "\n", - "In the code, \"observation\" function generates the input and observation vector with noise [code](https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L34-L50)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Motion Model\n", - "\n", - "The robot model is \n", - "\n", - "$$ \\dot{x} = vcos(\\phi)$$\n", - "\n", - "$$ \\dot{y} = vsin((\\phi)$$\n", - "\n", - "$$ \\dot{\\phi} = \\omega$$\n", - "\n", - "\n", - "So, the motion model is\n", - "\n", - "$$\\textbf{x}_{t+1} = F\\textbf{x}_t+B\\textbf{u}_t$$\n", - "\n", - "where\n", - "\n", - "$\\begin{equation*}\n", - "F=\n", - "\\begin{bmatrix}\n", - "1 & 0 & 0 & 0\\\\\n", - "0 & 1 & 0 & 0\\\\\n", - "0 & 0 & 1 & 0 \\\\\n", - "0 & 0 & 0 & 0 \\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "B=\n", - "\\begin{bmatrix}\n", - "cos(\\phi)dt & 0\\\\\n", - "sin(\\phi)dt & 0\\\\\n", - "0 & dt\\\\\n", - "1 & 0\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$dt$ is a time interval.\n", - "\n", - "This is implemented at [code](https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L53-L67)\n", - "\n", - "Its Jacobian matrix is\n", - "\n", - "$\\begin{equation*}\n", - "J_F=\n", - "\\begin{bmatrix}\n", - "\\frac{dx}{dx}& \\frac{dx}{dy} & \\frac{dx}{d\\phi} & \\frac{dx}{dv}\\\\\n", - "\\frac{dy}{dx}& \\frac{dy}{dy} & \\frac{dy}{d\\phi} & \\frac{dy}{dv}\\\\\n", - "\\frac{d\\phi}{dx}& \\frac{d\\phi}{dy} & \\frac{d\\phi}{d\\phi} & \\frac{d\\phi}{dv}\\\\\n", - "\\frac{dv}{dx}& \\frac{dv}{dy} & \\frac{dv}{d\\phi} & \\frac{dv}{dv}\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "　=\n", - "\\begin{bmatrix}\n", - "1& 0 & -v sin(\\phi)dt & cos(\\phi)dt\\\\\n", - "0 & 1 & v cos(\\phi)dt & sin(\\phi) dt\\\\\n", - "0 & 0 & 1 & 0\\\\\n", - "0 & 0 & 0 & 1\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Observation Model\n", - "\n", - "The robot can get x-y position infomation from GPS.\n", - "\n", - "So GPS Observation model is\n", - "\n", - "$$\\textbf{z}_{t} = H\\textbf{x}_t$$\n", - "\n", - "where\n", - "\n", - "$\\begin{equation*}\n", - "B=\n", - "\\begin{bmatrix}\n", - "1 & 0 & 0& 0\\\\\n", - "0 & 1 & 0& 0\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "Its Jacobian matrix is\n", - "\n", - "$\\begin{equation*}\n", - "J_H=\n", - "\\begin{bmatrix}\n", - "\\frac{dx}{dx}& \\frac{dx}{dy} & \\frac{dx}{d\\phi} & \\frac{dx}{dv}\\\\\n", - "\\frac{dy}{dx}& \\frac{dy}{dy} & \\frac{dy}{d\\phi} & \\frac{dy}{dv}\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "　=\n", - "\\begin{bmatrix}\n", - "1& 0 & 0 & 0\\\\\n", - "0 & 1 & 0 & 0\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extented Kalman Filter\n", - "\n", - "Localization process using Extendted Kalman Filter:EKF is\n", - "\n", - "=== Predict ===\n", - "\n", - "$x_{Pred} = Fx_t+Bu_t$\n", - "\n", - "$P_{Pred} = J_FP_t J_F^T + Q$\n", - "\n", - "=== Update ===\n", - "\n", - "$z_{Pred} = Hx_{Pred}$ \n", - "\n", - "$y = z - z_{Pred}$\n", - "\n", - "$S = J_H P_{Pred}.J_H^T + R$\n", - "\n", - "$K = P_{Pred}.J_H^T S^{-1}$\n", - "\n", - "$x_{t+1} = x_{Pred} + Ky$\n", - "\n", - "$P_{t+1} = ( I - K J_H) P_{Pred}$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ref:\n", - "\n", - "- [PROBABILISTIC\\-ROBOTICS\\.ORG](http://www.probabilistic-robotics.org/)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Localization/particle_filter/particle_filter.ipynb b/Localization/particle_filter/particle_filter.ipynb deleted file mode 100644 index 65f1e026df..0000000000 --- a/Localization/particle_filter/particle_filter.ipynb +++ /dev/null @@ -1,72 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Particle Filter Localization\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## How to calculate covariance matrix from particles\n", - "\n", - "The covariance matrix $\\Xi$ from particle information is calculated by the following equation: \n", - "\n", - "$\\Xi_{j,k}=\\frac{1}{1-\\sum^N_{i=1}(w^i)^2}\\sum^N_{i=1}w^i(x^i_j-\\mu_j)(x^i_k-\\mu_k)$\n", - "\n", - "- $\\Xi_{j,k}$ is covariance matrix element at row $i$ and column $k$.\n", - "\n", - "- $w^i$ is the weight of the $i$ th particle. \n", - "\n", - "- $x^i_j$ is the $j$ th state of the $i$ th particle. \n", - "\n", - "- $\\mu_j$ is the $j$ th mean state of particles.\n", - "\n", - "Ref:\n", - "\n", - "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://arxiv.org/pdf/1801.07000.pdf)\n" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/modules/appendix/Kalmanfilter_basics.rst b/docs/modules/appendix/Kalmanfilter_basics.rst index 7325dafdea..6548377e07 100644 --- a/docs/modules/appendix/Kalmanfilter_basics.rst +++ b/docs/modules/appendix/Kalmanfilter_basics.rst @@ -222,9 +222,8 @@ described with two parameters, the mean (:math:`\mu`) and the variance f(x, \mu, \sigma) = \frac{1}{\sigma\sqrt{2\pi}} \exp\big [{-\frac{(x-\mu)^2}{2\sigma^2} }\big ] - Range is -.. math:: [-\inf,\inf] +Range is :math:`[-\inf,\inf]` This is just a function of mean(\ :math:`\mu`) and standard deviation (:math:`\sigma`) and what gives the normal distribution the @@ -279,7 +278,8 @@ New mean is .. math:: \mu_\mathtt{new} = \frac{\sigma_z^2\bar\mu + \bar\sigma^2z}{\bar\sigma^2+\sigma_z^2} - New variance is + +New variance is .. math:: @@ -336,7 +336,7 @@ of the two. .. math:: \begin{gathered}\mu_x = \mu_p + \mu_z \\ - \sigma_x^2 = \sigma_z^2+\sigma_p^2\, \square\end{gathered} + \sigma_x^2 = \sigma_z^2+\sigma_p^2\, \end{gathered} .. code-block:: ipython3 diff --git a/docs/modules/localization/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization.rst index 5021563b5b..507ea972a4 100644 --- a/docs/modules/localization/extended_kalman_filter_localization.rst +++ b/docs/modules/localization/extended_kalman_filter_localization.rst @@ -2,14 +2,6 @@ Extended Kalman Filter Localization ----------------------------------- -.. code-block:: ipython3 - - from IPython.display import Image - Image(filename="ekf.png",width=600) - - - - .. image:: extended_kalman_filter_localization_files/extended_kalman_filter_localization_1_0.png :width: 600px @@ -18,8 +10,6 @@ Extended Kalman Filter Localization .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif :alt: EKF - EKF - This is a sensor fusion localization with Extended Kalman Filter(EKF). The blue line is true trajectory, the black line is dead reckoning @@ -127,7 +117,7 @@ The observation function states that Its Jacobian matrix is -:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partialv}\\ \end{bmatrix} \end{equation*}` +:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}` :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}` diff --git a/docs/modules/localization/localization.rst b/docs/modules/localization/localization.rst index ddbf056c82..c665eba694 100644 --- a/docs/modules/localization/localization.rst +++ b/docs/modules/localization/localization.rst @@ -15,7 +15,8 @@ This is a sensor fusion localization with Unscented Kalman Filter(UKF). The lines and points are same meaning of the EKF simulation. -Ref: +References: +~~~~~~~~~~~ - `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization`_ @@ -37,9 +38,26 @@ It is assumed that the robot can measure a distance from landmarks This measurements are used for PF localization. -Ref: +How to calculate covariance matrix from particles +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The covariance matrix :math:`\Xi` from particle information is calculated by the following equation: + +.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k) + +- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`. + +- :math:`w^i` is the weight of the :math:`i` th particle. + +- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle. + +- :math:`\mu_j` is the :math:`j` th mean state of particles. + +References: +~~~~~~~~~~~ - `PROBABILISTIC ROBOTICS`_ +- `Improving the particle filter in high dimensions using conjugate artificial process noise`_ Histogram filter localization ----------------------------- @@ -59,12 +77,14 @@ localization. Initial position is not needed. -Ref: +References: +~~~~~~~~~~~ - `PROBABILISTIC ROBOTICS`_ .. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ .. _Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization: https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization +.. _Improving the particle filter in high dimensions using conjugate artificial process noise: https://arxiv.org/pdf/1801.07000.pdf .. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif .. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif From 1f4e5ed8c32249e07ba3493a5b195da33e5de77b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 19 Nov 2021 15:54:52 +0900 Subject: [PATCH 370/909] clean up mapping docs (#571) * clean up mapping docs * clean up mapping docs * clean up mapping docs --- Mapping/lidar_to_grid_map/animation.gif | Bin 285406 -> 0 bytes README.md | 3 +- .../mapping/lidar_to_grid_map_tutorial.rst | 195 ++++++++++++++++++ .../grid_map_example.png | Bin .../lidar_to_grid_map_tutorial_12_0.png | Bin 0 -> 5053 bytes .../lidar_to_grid_map_tutorial_14_1.png | Bin 0 -> 14082 bytes .../lidar_to_grid_map_tutorial_5_0.png | Bin 0 -> 121806 bytes .../lidar_to_grid_map_tutorial_7_0.png | Bin 0 -> 5025 bytes .../lidar_to_grid_map_tutorial_8_0.png | Bin 0 -> 5035 bytes docs/modules/mapping/mapping.rst | 8 + 10 files changed, 204 insertions(+), 2 deletions(-) delete mode 100644 Mapping/lidar_to_grid_map/animation.gif create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial.rst rename {Mapping/lidar_to_grid_map => docs/modules/mapping/lidar_to_grid_map_tutorial_files}/grid_map_example.png (100%) create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png create mode 100644 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with zeros: + +.. code:: ipython3 + + flood_fill((10, 20), map1) + map_float = np.array(map1)/10.0 + plt.imshow(map1) + plt.colorbar() + plt.show() + + + +.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png + + +Let’s use this flood fill on real data: + +.. code:: ipython3 + + xyreso = 0.02 # x-y grid resolution + yawreso = math.radians(3.1) # yaw angle resolution [rad] + ang, dist = file_read("lidar01.csv") + ox = np.sin(ang) * dist + oy = np.cos(ang) * dist + pmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, False) + xyres = np.array(pmap).shape + plt.figure(figsize=(20,8)) + plt.subplot(122) + plt.imshow(pmap, cmap = "PiYG_r") + plt.clim(-0.4, 1.4) + plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True) + plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True) + plt.grid(True, which="minor", color="w", linewidth = .6, alpha = 0.5) + plt.colorbar() + plt.show() + + +.. parsed-literal:: + + The grid map is 150 x 100 . + + + +.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png + diff --git a/Mapping/lidar_to_grid_map/grid_map_example.png b/docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png similarity index 100% rename from Mapping/lidar_to_grid_map/grid_map_example.png rename to docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png new file mode 100644 index 0000000000000000000000000000000000000000..0a03f61c8653f52a6d95aa43c33b5f55e2c2e974 GIT binary patch literal 5053 zcmZ{o2{@E%|Hp@6WQjA$lC_437K4&4OUp!qXv$J!%@UItON=nZs8go0oQ7zzgsCBG zOhXvvh-qB{Vg*u@3Ju&U^mv|NURr-*w^f%>CT={dhj#@9+D2?w)nD-7cjf z1p%P6uO+}?8g;LCWluT-M$S! z$-40A>?TXHc6;XKk2?l@#^@i!A9Cdg=sypM*2#;i*#54$%Y^#EZ(BG5jFC7rcxQ3Y 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Date: Sun, 21 Nov 2021 22:39:18 +0900 Subject: [PATCH 371/909] clean up SLAM docs (#572) * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs --- .../lidar_to_grid_map_tutorial.ipynb | 318 --- SLAM/EKFSLAM/ekf_slam.ipynb | 1784 ----------------- SLAM/FastSLAM1/FastSLAM1.ipynb | 676 ------- SLAM/FastSLAM1/animation.png | Bin 44443 -> 0 bytes SLAM/GraphBasedSLAM/LaTeX/.gitignore | 3 - SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib | 30 - .../LaTeX/graphSLAM_formulation.tex | 175 -- .../graphSLAM_SE2_example.ipynb | 332 --- SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf | Bin 276368 -> 0 bytes docs/_static/custom.css | 23 + docs/conf.py | 5 +- ...g_started.rst => getting_started_main.rst} | 0 docs/{index.rst => index_main.rst} | 0 ...igation.rst => aerial_navigation_main.rst} | 0 .../{appendix.rst => appendix_main.rst} | 0 ...navigation.rst => arm_navigation_main.rst} | 0 ...introduction.rst => introduction_main.rst} | 0 ...localization.rst => localization_main.rst} | 0 .../mapping/{mapping.rst => mapping_main.rst} | 0 ...th_planning.rst => path_planning_main.rst} | 0 ...th_tracking.rst => path_tracking_main.rst} | 0 docs/modules/slam/FastSLAM1.rst | 17 +- docs/modules/slam/ekf_slam.rst | 593 ++++++ .../slam/ekf_slam_files/ekf_slam_1_0.png | Bin docs/modules/slam/graphSLAM_SE2_example.rst | 209 ++ .../Graph_SLAM_optimization.gif | Bin .../graphSLAM_SE2_example_13_0.png | Bin 0 -> 32748 bytes .../graphSLAM_SE2_example_15_0.png | Bin 0 -> 30123 bytes .../graphSLAM_SE2_example_16_0.png | Bin 0 -> 29350 bytes .../graphSLAM_SE2_example_4_0.png | Bin 0 -> 52368 bytes .../graphSLAM_SE2_example_8_0.png | Bin 0 -> 31076 bytes .../graphSLAM_SE2_example_9_0.png | Bin 0 -> 49650 bytes docs/modules/slam/graphSLAM_doc.rst | 557 +++++ docs/modules/slam/graphSLAM_formulation.rst | 216 ++ docs/modules/slam/{slam.rst => slam_main.rst} | 34 +- 35 files changed, 1617 insertions(+), 3355 deletions(-) delete mode 100644 Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb delete mode 100644 SLAM/EKFSLAM/ekf_slam.ipynb delete mode 100644 SLAM/FastSLAM1/FastSLAM1.ipynb delete mode 100644 SLAM/FastSLAM1/animation.png delete mode 100644 SLAM/GraphBasedSLAM/LaTeX/.gitignore delete mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib delete mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex delete mode 100644 SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb delete mode 100644 SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf create mode 100644 docs/_static/custom.css rename docs/{getting_started.rst => getting_started_main.rst} (100%) rename docs/{index.rst => index_main.rst} (100%) rename docs/modules/aerial_navigation/{aerial_navigation.rst => aerial_navigation_main.rst} (100%) rename docs/modules/appendix/{appendix.rst => appendix_main.rst} (100%) rename docs/modules/arm_navigation/{arm_navigation.rst => arm_navigation_main.rst} (100%) rename docs/modules/{introduction.rst => introduction_main.rst} (100%) rename docs/modules/localization/{localization.rst => localization_main.rst} (100%) rename docs/modules/mapping/{mapping.rst => mapping_main.rst} (100%) rename docs/modules/path_planning/{path_planning.rst => path_planning_main.rst} (100%) rename docs/modules/path_tracking/{path_tracking.rst => path_tracking_main.rst} (100%) create mode 100644 docs/modules/slam/ekf_slam.rst rename SLAM/EKFSLAM/animation.png => docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png (100%) create mode 100644 docs/modules/slam/graphSLAM_SE2_example.rst rename {SLAM/GraphBasedSLAM/images => docs/modules/slam/graphSLAM_SE2_example_files}/Graph_SLAM_optimization.gif (100%) create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png create mode 100644 docs/modules/slam/graphSLAM_doc.rst create mode 100644 docs/modules/slam/graphSLAM_formulation.rst rename docs/modules/slam/{slam.rst => slam_main.rst} (75%) diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb deleted file mode 100644 index 9e8a00f009..0000000000 --- a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb +++ /dev/null @@ -1,318 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## LIDAR to 2D grid map example\n", - "\n", - "This simple tutorial shows how to read LIDAR (range) measurements from a file and convert it to occupancy grid.\n", - "\n", - "Occupancy grid maps (_Hans Moravec, A.E. Elfes: High resolution maps from wide angle sonar, Proc. IEEE Int. Conf. Robotics Autom. (1985)_) are a popular, probabilistic approach to represent the environment. The grid is basically discrete representation of the environment, which shows if a grid cell is occupied or not. Here the map is represented as a `numpy array`, and numbers close to 1 means the cell is occupied (_marked with red on the next image_), numbers close to 0 means they are free (_marked with green_). The grid has the ability to represent unknown (unobserved) areas, which are close to 0.5.\n", - "\n", - "![Example](grid_map_example.png)\n", - "\n", - "\n", - "In order to construct the grid map from the measurement we need to discretise the values. But, first let's need to `import` some necessary packages." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from math import cos, sin, radians, pi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The measurement file contains the distances and the corresponding angles in a `csv` (comma separated values) format. Let's write the `file_read` method:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def file_read(f):\n", - " \"\"\"\n", - " Reading LIDAR laser beams (angles and corresponding distance data)\n", - " \"\"\"\n", - " measures = [line.split(\",\") for line in open(f)]\n", - " angles = []\n", - " distances = []\n", - " for measure in measures:\n", - " angles.append(float(measure[0]))\n", - " distances.append(float(measure[1]))\n", - " angles = np.array(angles)\n", - " distances = np.array(distances)\n", - " return angles, distances" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the distances and the angles it is easy to determine the `x` and `y` coordinates with `sin` and `cos`. \n", - "In order to display it `matplotlib.pyplot` (`plt`) is used." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "ang, dist = file_read(\"lidar01.csv\")\n", - "ox = np.sin(ang) * dist\n", - "oy = np.cos(ang) * dist\n", - "plt.figure(figsize=(6,10))\n", - "plt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], \"ro-\") # lines from 0,0 to the \n", - "plt.axis(\"equal\")\n", - "bottom, top = plt.ylim() # return the current ylim\n", - "plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `lidar_to_grid_map.py` contains handy functions which can used to convert a 2D range measurement to a grid map. For example the `bresenham` gives the a straight line between two points in a grid map. Let's see how this works." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import lidar_to_grid_map as lg\n", - "map1 = np.ones((50, 50)) * 0.5\n", - "line = lg.bresenham((2, 2), (40, 30))\n", - "for l in line:\n", - " map1[l[0]][l[1]] = 1\n", - "plt.imshow(map1)\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "line = lg.bresenham((2, 30), (40, 30))\n", - "for l in line:\n", - " map1[l[0]][l[1]] = 1\n", - "line = lg.bresenham((2, 30), (2, 2))\n", - "for l in line:\n", - " map1[l[0]][l[1]] = 1\n", - "plt.imshow(map1)\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To fill empty areas, a queue-based algorithm can be used that can be used on an initialized occupancy map. The center point is given: the algorithm checks for neighbour elements in each iteration, and stops expansion on obstacles and free boundaries." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import deque\n", - "def flood_fill(cpoint, pmap):\n", - " \"\"\"\n", - " cpoint: starting point (x,y) of fill\n", - " pmap: occupancy map generated from Bresenham ray-tracing\n", - " \"\"\"\n", - " # Fill empty areas with queue method\n", - " sx, sy = pmap.shape\n", - " fringe = deque()\n", - " fringe.appendleft(cpoint)\n", - " while fringe:\n", - " n = fringe.pop()\n", - " nx, ny = n\n", - " # West\n", - " if nx > 0:\n", - " if pmap[nx - 1, ny] == 0.5:\n", - " pmap[nx - 1, ny] = 0.0\n", - " fringe.appendleft((nx - 1, ny))\n", - " # East\n", - " if nx < sx - 1:\n", - " if pmap[nx + 1, ny] == 0.5:\n", - " pmap[nx + 1, ny] = 0.0\n", - " fringe.appendleft((nx + 1, ny))\n", - " # North\n", - " if ny > 0:\n", - " if pmap[nx, ny - 1] == 0.5:\n", - " pmap[nx, ny - 1] = 0.0\n", - " fringe.appendleft((nx, ny - 1))\n", - " # South\n", - " if ny < sy - 1:\n", - " if pmap[nx, ny + 1] == 0.5:\n", - " pmap[nx, ny + 1] = 0.0\n", - " fringe.appendleft((nx, ny + 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This algotihm will fill the area bounded by the yellow lines starting from a center point (e.g. (10, 20)) with zeros:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "flood_fill((10, 20), map1)\n", - "map_float = np.array(map1)/10.0\n", - "plt.imshow(map1)\n", - "plt.colorbar()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's use this flood fill on real data:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The grid map is 150 x 100 .\n" - ] - }, - { - "data": { - "image/png": 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35uFVRNZj3SkWW3XC6VFEKnI2y6mIrLyIzKycWya7+4WzvO7Au/P2KiLrsZ7ce/CIiGz3ssNjjaZGKnI2y6mITBFZKOI5UiGEEEFRRNZjrYhMzqY4FZGVOIpMqymHQxFZPSIVOZvlVERWbkQWExpFJoQQwajsTP5CUETWY62ITM6mOBWRlRuRlbTYZSkoIuuxVkQmZ1OcisgUkYVCEZkQQgTEhnSRPwiKyAar5ZRzLrUishIjsshQRNZjrYhMzqY4FZGVGJGZQUTXYOI5UiGEEEFRRNZjveiC01g4NcVXd3+CJccOs3/NOKtQRCZn/ZyKyMqLyCx1xsLADYyZzQPGgMfc/TwzWwlsBU4A7gXe4e6TnX62ThFZq54pymhqpCJns5yKyMqMyOIappzHkb4XeCDz+OPAp9x9GHgauCQHhxBCiJoxUA/GzJYDvwh8DHifmRlwFvD2dJMbgY8A13b5+dpEZK36vaffjA0N4VNTHLzlPub7Qs7cu3H6O8D4Kbumj7EJkYqczXIqIitzFJlpmHIf/DHw28Bx6eOXA8+4+6H08QSwrNMPmtkWYAvAC5OTteyCt0cMre/714w3KlKRs1lORWSaaBmKOTfeZnYecMDd78k+3WFT7/Tz7j7q7iPuPrL4xBPn+jaEEKI+GDA0lP9XRRmkB/M64I1mdi7wUuB4kh7NIjM7Ku3FLAf2dttBHSOybN0tamhapCJns5yKyMqdaBnTRf45NzDu/kHggwBmth74gLv/kpl9EXgzyUiyi4CvzLCP2o0iy9aKyOSso1MRmSKyUBQxD+ZyYKuZ/T7wbeCzBTiEEKJ+mJbr7xt3vwO4I60fAtb18nN1j8iWXHYWNjTEwqmp6e8A5w+djaf183fvmR5dlvdIsybHOHIqImtqRBYTWousYGenNcwgvxitqTGOnIrImhiRGWC6ZbIQQojcaY0iiwStRVaws9My/wAbntw8cFzW5BhHTkVkisjqjyKygp3dIrJsPUhc1tQYR05FZE2MyMA0TDkUMfdg8pg30+T/ZcupHox6MPVHPZiCnQvWreDoqWS1nGe37VQPRs7SnerBlNiDMYhpLTI13kIIIQpBEVlAZ+umZa3nW/WGu4fnNFemyTGOnIrImhqR6RpMIGKIyHqpB5kr09QYR05FZM2MyAwUkQkhhBCDoYisAs5e5sq0orM719waRYwjpyKyJkZkBiT3ZYwDRWQVcPYyV6ZV7152eJTS1BhHTkVkjYzIIkNLxQghREgiugajiKwCzm6jy56/e8/076oVZVz82JXTcVkeKzL3Wzc5OorFqYisxFFkkc2DUURWYeds0VnIG5tlaznr7VREpogsFIrIhBAiJLrIHwZFZDPXrejs4C33dRxp1mmUGeRzM7NudZOjo1ic3SKy5+/ew+Teg0By7tXpb6U2EVlkKCKrgbM9ysjWRd7MrFvd1OgoFme3iCxbFxmXVfXvMwwW1TUYNd5CCBEKI/lXN++vXtRm55jZg2a2y8yu6PD6KWb2dTP7tpn9q5mdO8CRAorIauFcctlZh40ua9WdRplBPjcz61Y3OTqKxTl2wT0sOXaY/WvG2fDk5unXQ40oq+rfZ5Mxs3nANcAbgAlgh5ltc/edmc1+F7jZ3a81s1cC24FTB/EqIquxs+ibmXWrmxodxeg8c+9GRWSBR5GVNJN/HbDL3R9K38NWYBOQbWAcOD6tFwJ7B5VqFJkQQjSfZcCezOMJ4Iy2bT4C/I2ZvQd4GXD2oFJFZDV2ZidohorLmhwdxeic753XwWva30qlIrJiLvIvNrOxzONRdx/NPO4k9bbHFwI3uPsnzey1wJ+b2Wp3n3MXTxFZQ5wh47IYoqNYnIrIwkdkBfGEu4/M8PoEsCLzeDlHRmCXAOcAuPs3zeylwGLgAHMkeOMthBDRYiQ9mLy/ZmcHMGxmK81sPrAZ2Na2zaPABgAz+2ngpcB3BzlcRWQNcYaKy2KJjmJxKiILH5GVcZHf3Q+Z2aXAbcA84Hp3v9/MPgqMufs24P3An5rZb5HEZxe7e3uM1heKyBroLDouiyE6isWpiKwxEdmsuPt2kqHH2eeuzNQ7gdfl6dQoMiGECElEFyYUkTXQWWRcFkt0FItTEVkJo8giQhFZw51FxGUxREexOBWRhY3IzOJai0wRmRBChETL9YdBEVnxzrzjsliio7z21+mWCneuuTUX5/nDl+PpZ2tDQ3Oqn/fO50TZ560ismagiCwiZ15xWQzRUV776BRB7V7Wff/97NtziLG6nROKyIpDEVkg1IMJ6+x007L2usm9iTKcnS6irzrh9FyceZxD3c6DKp236sHUF/VgInKqB1ONHsy5b3w/Q0NDTJ2YfkZp3em5meo8ejAL1q3g6Kll0/tu1Xnsu1td1b+VILRm8keCLvILIURASlquvxQUkUXkVERWjYhsaGiotudQk50ifxSRReRURFaNiOy4Lp9dHc6hJjuDEVGLFtGhCiGECIkisoicvcyJaXJcVZWIbPvDn2TJscPsf258evv9z41z/vDllT+HmuwMg+kaTCgUkZXn7BaXKSIrbx5MkSO3utV1O2+LdAYhslFkisiEEEIUgiKyiJzP372Hyb0HAY0iC+UcP2UX+9dkorA146yi80TLscdvOSI2AwqNzupw3jYrIgPN5A+EIjKNIuu1jtkZ46THxkZkkaGJlkIIERJd5A+DIrKwzm6x2PhrD49xWjQtrqqrs8jorA7nbdMisphQRBaRc5CRY9m6KtFR7M48orM6nLdNisjMdA1GCCFEUUTUZVJEFpFzriPHsnWVoqPYnXlEZ3U4bxWR1ZeBGhgzWwRcB6wGHPgV4EHgJuBU4BHgre7+dKefV0SmiEzO/J39RGd1OG+bFJER2Uz+QRvvq4GvuftPAa8CHgCuAG5392Hg9vSxEEKIyJhzD8bMjgd+HrgYwN0ngUkz2wSsTze7EbgDuLzLPhSRBXQqIovD2Sk6O2/VZbU9bxsVkUW2VMwgEdmPA98F/szMXgXcA7wXONnd9wG4+z4zO6nTD5vZFmALwAuTk1F0wct2KiKL1zm1UrcIqEZERtLIRMIgjfdRwM8C17r7q4Hv0Ucc5u6j7j7i7iOLTzxxgLchhBCiigzSg5kAJtz9W+njL5E0MI+b2dK097IUONBtB4rIwjoVkcXr1C0CKhKRAZrJ3wPuvt/M9pjZT7r7g8AGYGf6dRFwVfr9KzPsQ6PIAjoVkcnZXusWASVEZBEx6DyY9wCfN7P5wEPAO0k+s5vN7BLgUeAtAzqEEKIxeDwdmMEaGHf/Z2Ckw0sbevl5RWRhnYrI8nVueHIzZ+7dCCR3rpytHj9lV+WOs/0WDq160QWnVea8bVREZkR1kV9rkUXkVESWr7PT3Spnqqt4K4Rux7CwwOisqn8rIn+0FpkQQoREF/nDoIiseGced7HM1lWNq4p0do3Cuvw+63S30Pne+b2Wfd42NiKLDEVkDXfmdRfLbF3FuKpIZ79RWLdaEVm1/1ZE/qgH03BnHv+zztZl9ybKcHb7X756MM36WxH5ox5Mw53dejB3rrmV3Zb8b3bVc6fXpjdRhnOQHszCN63m6KllAOx/+NbKHad6MGF7MI6GKQshhCgE00X+UCgiK96ZRyyWrcuOq6oUkS264DQWpv/7taGhjnX7ysZVO05FZIrIikQRWcOdec19ydaKyHqPkfY/V+0BFIrISrjIX1IHxszOIbmH1zzgOne/qsM2bwU+QpLm/Yu7v30QpyIyIYRoOGY2D7gGeAPJQsU7zGybu+/MbDMMfBB4nbs/3e1WK/2giKzhTkVkxUVkvfz+zx++HM9EZz41xVd3f6KjM7vt2OO3BDlORWSBI7LylopZB+xy94cAzGwrsIlkceIWvwpc07rFvbt3XQm/VxSRNdypiKzciKxT3e2Ysisb9xutzbVWRBY+IitpFNkyYE/m8QRwRts2/xHAzP6RJEb7iLt/bRCpIjIhhKg/i81sLPN41N1HM487NWve9vgoYBhYDywH7jSz1e7+zFzflCKyhjsVkQ3uHD9lF/vXZG7UldbnD509p8/tvaff3DEuy444CxWXKSIrYRRZMcOUn3D3Tivbt5gAVmQeLwf2dtjmLnd/AXjYzB4kaXB2MEcUkTXcqYisOGceN+tqd3Xad5FxmSKyEkaRlcMOYNjMVgKPAZuB9hFitwAXAjeY2WKSyOyhQaSKyIQQIiQlXINx90NmdilwG8n1levd/X4z+ygw5u7b0td+wcx2Ai8Cl7n7k4N4FZE13KmIrDhnHp9hNi7LRmHd4rLWtq1tBj1ORWRlRGShhQnuvh3Y3vbclZnagfelX7mgiKzhTkVk1Y7IsnW3KKybJ4/oTBFZNBFZKSgiE0KIUBi41iILgyKyYpx532QsW1c1rqprRJatu40ca1/PLM+RZorISojIIkIRWQOdRdxkLFtXMa4qw5l3RNZv/JXHSDNFZIrIikSNtxBCiEJQRNZAZ7dYbPy1h08YbNGEuKoMZ/tIrzw/z15GjuUx0kwRWTyjyMpAEVkDnUWMHMvWVYyrynbmEZf1sm3eI80UkZUQkUV0kV8RmRBCiEJQRNZAZ94jx7J1VeOqsp2hzqFeRppl47rW9t1uEaCITBFZkSgia6BTEVlzI7K5xmXdjkkRmUaRFYkmWgohRCistPvBlIIisgY6FZE1NyKba1yWXfOstR+fmuJ5/9E9qBSRBSKii/yKyBroVEQWR0Q217gsW3c7VxSRiTxQRCaEECGJpwOjiKyJzvYJlYNOrszWdYirynDmMelykHNitrjsvFWXdfzZbnFqLH8rpURkEaGIrIHOIu+AWMT+muaca1yW1/nR6fOfWtl5/4rISojI1IMJg3owxTir9D/7GJ1ln0Od3tfQ0JB6MLNsGwbTcv2hUA+mGOfwN3+C5ePHA7Bg+KTpeuyCe6LrTZThLLsHM3Lym5g6Mam3P/xJdj+1g6ufeiurTjid3U/t4D1rt05vv2DdCo6eWpbsY2hous57pegijrO2PZiI0EV+IYQIhaGILBSKyIpxah5M3BFZt4v/NjR02LIx+58bz2UV6CocZ30isrhQRNZAp+bBxB2RzVZnj6/IKKzs4+zXGQZPv+JAjbcQQohCUETWEOfzd+9hcu9BIP+bjGXrusVVZTjnOicm1HmTXTam/bxp1YsuOK2xfytlRmRJ/yWeHowisoY4i47FsnWd4qqynf1EUGWcQ5oHE34UmRPPqDVFZEIIIQpBEVlDnEWOHMvWdYurynZW/RzSRMsyRpEpIguCIjJFZE13KiKrxnFWKSKLCU20FEKIYHhU12AUkTXEWeQKytm6znGVIrIj60UXnMbC9H/wNjR0WN3Uv5WyIzKNIguEIrL8nEWvoJyt6xpXleGsekQmpyKyIlFEJoQQoXAHj6dBU0RWY2d2Ql/doqNYnFU/h+QsYxRZPAzUwJjZbwHvIhl39x3gncBSYCtwAnAv8A53n+z084rIBqtDxmLZWk5FZE10hiKmazBzbrzNbBnwG8CIu68G5gGbgY8Dn3L3YeBp4JI83qgQQtSdZKmYqdy/qsqgEdlRwAIzewE4BtgHnAW8PX39RuAjwLWdflgR2WD1hic3c+bejQDM94XT9fgpu2hR1egoFmc/65LFct5W1SnyZ84NjLs/ZmZ/CDwKPA/8DXAP8Iy7H0o3mwCWdfp5M9sCbAF4YXIyii543nXIyZXZuq5xVdnO2eKyWM7bqjrD4FDhHkfeDBKR/RiwCVgJvAJ4GbCxw6YdA0d3H3X3EXcfWXziiXN9G0IIISrKIBHZ2cDD7v5dADP7MvBzwCIzOyrtxSwH9nbbgSKywepu60hlo7O847I6x1VlO6t4DskZPiKL6SL/IA3Mo8BrzOwYkohsAzAGfB14M8lIsouAr3TbgUaRFRORZWutRVYdpyKyajtF/gxyDeZbZvYlkqHIh4BvA6PAXwNbzez30+c+m8cbFUKIJlDlUV95M9AoMnf/MPDhtqcfAtb18vOKyAars+tIPX/3HloUGZc1Ja5SRCZnex2OciIyMzsHuJpkSsl17n5Vl+3eDHwRON3dxwZxai2yhjhDxmVNiKvKcCoiq7azyZjZPOAa4A0ko3t3mNk2d9/Ztt1xJPON9dMyAAAWU0lEQVQbv5WHN3zjLYQQ0eJlTbRcB+xy94fSlVW2kowCbuf3gD8AfpDH0WotsoY4e4nLqhodxeJsTbo8b9VllTyHYnc2nGXAnszjCeCM7AZm9mpghbt/1cw+kIdUEVkDnUVPwGxCXFWmc2pl58+uSudQjM4QJEvFFHINZrGZZa+XjLr7aOaxdXk7yYtmQ8CngIvzfFPqwTTQ2W1+TFX/Zx+bc2hoqPLnUIzOcBTSoD3h7iMzvD4BrMg8bp+jeBywGrjDzACWANvM7I2DXOhXD6aBzgXrVnD0VLJCz7Pbdk73YIb5CZaPHw/A2AX3VO5/9rE41YOpprPh7ACGzWwl8BjJwsStNSNx94PA4tZjM7sD+ECtR5EJIURceCkz+d39kJldCtxGMkz5ene/38w+Coy5+7YivIrIGu7MezmZpsVVZTgVkVXT2XTcfTuwve25K7tsuz4PpyKyhjuLmB/TpLiqDKcismo6Q6GZ/EIIIQpCi10GQRFZ8c6858c0La4qw7n94U+y5NjhI+bEVPUcisUp8kcRWUTOvObHNCmuKtuZjcvqcA412RkGxz2eiEyNtxBCiEJQRBaRs1tc1s+IsqbFVWU7syPK6nAONdkZCt1wLBCKyMpzDhKXNTWuKsOpiKw6TpE/GkUmhBBBiadBU0QWqXOu65U1Oa4qw6mIrDrOEBS42GUlUUQWqVMRWTWcisiq4xT5o4hMCCGC4ZrJHwpFZOU5ZxtRdueaW6OLq8pwZiddnj98ea3OoaY5Rf4oIpOzY1y2e1n3mKipcVXZTp+aqu051ARnOHQNRgghRAEoIguEIrJqODuNKFt1wunRxVVlO+t8DjXBKfJHEZmcisgq4lREFkNE5kz5iwF95aLGWwghRCEoIpOz44iyix+7kvm+8IgRZbHEVYrI4nOGwCPrwSgik/Owupe4LIa4qgynIrIYIjJw4mlgQjbeQgghIkIRmZyH1bONKIslrirDOfb4Lex/bhwg2KTLppy3dYnIYrvIr4hMzsNqRWTVcIaKy5py3ubhFPmjiZZCCBEId9SDCYUisuo5WyPKvrr7E8kaWWvGWYUishB19s6iz/seJvceBJLPpE7nUF2dIn8UkcnZsZ4pJootrgpVn7l3Y8dbKCwsMC5r2nk7iDMMzpTHE8mpByNnx1pLxYR3zvfON4Gr6zlUN2cIHJiKaJiyejBydqxHvryWBcMnsXz8eIDpeuyCe6LoTZThVA8mhh5MXOgivxBCBMNxXeQPgyKy6joXDJ/UcU5MLHFVkfs+8zsbp5fhAabr7JI9NjR0WF3Hc6huTpE/isjk7Fi34pn2yGb/mvEo4qoi9z0yvhY48ndbZBTWrW7aeTuIMwyaaCmEEKIANA8mIIrIqutcctlZ0zHNwVvum47IsnM1xk/ZRYu6xlVlOLvFj007h+rmFPmjiEzOWev2uKz1PWRcpois3udQHZxhiCsiU+MthBCiEBSRyTlrnY10stFOE+KqkM7peHH4R7/HhW9aPR1FNvkcqoMzFJpoGQhFZPVwKiLLp+40kfK4zOfY5HOoDs4QeGTzYEI33kIIISJBEZmcs9bZEWVjj9/C/jXJTbHqHFeV4ey01tjQ0FAU51AdnGGI6yK/IjI5+6r3P6eJlorImukU+RO28RZCiMiZ8hdz/+oFMzvHzB40s11mdkWH199nZjvN7F/N7HYz+w+DHqsiMjn7qrMTLTutp9Ve5zEZs0oRWb/Hf1jdYeQYxHcOVdXZZMxsHnAN8AZgAthhZtvcfWdms28DI+7+fTP778AfAG8bxDtrA2Nm1wPnAQfcfXX63AnATcCpwCPAW939aTMz4GrgXOD7wMXufm+3fSsiq59zcu/BjkvKd6vzGmlWlYis25L6/dTHdfnsYjmHquoMgXtpNxxbB+xy94cAzGwrsAmYbmDc/euZ7e8CfnlQaS+N9w3AOW3PXQHc7u7DwO3pY4CNwHD6tQW4dtA3KIQQTWKKF3P/6oFlwJ7M44n0uW5cAtw6wGECPfRg3P0bZnZq29ObgPVpfSNwB3B5+vzn3N2Bu8xskZktdfd9nfatiKx+zk5raM1U57GMfd9RVA71bDHXIHV25BjEdw5V1VlzFpvZWObxqLuPZh5bh5/xTjsys18GRoDXD/qm5noN5uRWo+Hu+8zspPT5bq3kEQ2MmW0h6eXwwuRkFF3wJjiLjMi6rdHVi6eIuqh9KyKrpjMMhU20fMLdR2Z4fQJYkXm8HNjbvpGZnQ18CHi9u/9w0DeVd+Pdcyvp7qPuPuLuI4tPPDHntyGEECLDDmDYzFaa2XxgM7Atu4GZvRr4n8Ab3f1Ah330zVx7MI+3oi8zWwq03kxPrWQLRWT1c85018VO9Ya7h3uPqIY7R0p5xFL91t1e7/f4Z6qr8HnKGTYi85ImWrr7ITO7FLgNmAdc7+73m9lHgTF33wZ8AjgW+GIyXotH3f2Ng3jn2sBsAy4Crkq/fyXz/KXpCIUzgIPdrr+ARpHF4Ow3UutWVyUiK3JJ/Tp8nk12hqKsmfzuvh3Y3vbclZn67LydvQxT/gKwnuQi0gTwYZKG5WYzuwR4FHhLuvl2kiHKu0iGKb8z7zcshBCiHvQyiuzCLi9t6LCtA+/uVa6IrPnOfmKpThMQW8vY5xVL9Vr3E22V9buVs6YRmZbrD4MisuY7+4nINLpKzhgispgotYERQojY0GrKgVBE1nznXEZd1fE45ay/Mwge1w3HFJHJKaeccopCUA9GTjnllDMQZc2DKQv1YOSUU045RSHoIr8QQgREPZhAKCKTU045q+IMQ2n3gykFRWRyyimnnKIQFJEJIUQgHEVkwVBEJqecclbFKfJHEZmccsopZyjceXEqnh6MGm8hhBCFoIhMTjnllDMQyTWYeAYVKCKTU0455QyGM6WITAghhBgMRWRyyimnnIFwR8OUQ6GITE455ayKU+SPJloKIUQwnBfVgwmDIjI55ZSzKs4QOHH1mBSRySmnnHKKQlBEJoQQwdANx4KhiExOOeWsilPkjyIyOeWUU85QeFwTLRWRCSFEILRUTEAUkckpp5xVcYr8UUQmp5xyyhkIR8v1CyGEEAOjiExOOeWUMxRaiywcisjklFPOqjhF/mgUmRBCBEPDlIOhiExOOeWsijMEGqYcEEVkcsopZ1WcIn/Ug5FTTjnlDIaW6w+GejByyilnVZwif3SRXwghAuEOusgfCEVkcsopZ1WcYXBd5A+FIjI55ZSzKs6mY2bnAFcD84Dr3P2qttePBj4HrAWeBN7m7o8M4lREJoQQgUhumRw+IjOzecA1wBuACWCHmW1z952ZzS4Bnnb3nzCzzcDHgbcN4lVEJqeccsrZfNYBu9z9IQAz2wpsArINzCbgI2n9JeDTZmbu7nOVKiKTU0455QyFlzZMeRmwJ/N4Ajij2zbufsjMDgIvB56Yq1QRmRBCBMLxohq0xWY2lnk86u6jmcfW8e0cTi/b9IUiMjnllFPO+vOEu4/M8PoEsCLzeDmwt8s2E2Z2FLAQeGqQN6WITE455ZQzICUt178DGDazlcBjwGbg7W3bbAMuAr4JvBn4+0Guv4AiMiGEaDzpNZVLgdtIhilf7+73m9lHgTF33wZ8FvhzM9tF0nPZPKhXEZmccsopZyi8vOX63X07sL3tuSsz9Q+At+TpVEQmp5xyyikKQRGZEEIEwkGrKYdCEZmccspZFWcYChumXElmbWDM7HrgPOCAu69On/sEcD4wCewG3unuz6SvfZBkyYEXgd9w99u67VsRmZxyylkVp8ifXhrvG4Bz2p77W2C1u58G/BvwQQAzeyXJyIP/lP7Mn6Rr4AghRPQkt0x+MfevqjJrD8bdv2Fmp7Y99zeZh3eRjJmGZC2bre7+Q+DhdLjbOpJx1UegiExOOeWsilPkTx7XYH4FuCmtl5E0OC0m0ueOwMy2AFsAXpicjKILLqecclbXGQR3pqYGmrtYKwZqYMzsQ8Ah4POtpzps1vG3ma6TMwqwdu3aeH7jQohoSSKyeK75zLmBMbOLSC7+b8gsJ9DLejfZfSgik1NOOSvhFPkzpwYmvTPa5cDr3f37mZe2Af/LzP4IeAUwDNzdbT8aRSannHJWxRkKRWQZzOwLwHqS5aAngA+TjBo7GvhbMwO4y91/LV3b5maSm9gcAt7tXuEhDkIIIQqjl1FkF3Z4+rMzbP8x4GO9yBWRySmnnFVxBsHVgwmGIjI55ZSzKs4QOM7UYCvg14qgjbcQQoh40Fpkcsopp5wBCT2ooEwUkckpp5xyikJQD0ZOOeWUMxCui/zhUA9GTjnlrIpT5I9uOCaEEAGJaRSZIjI55ZRTzmBosctgKCKTU045q+IU+aOITAghAuGOVlMOhSIyOeWUsypOkT+KyOSUU045A6JrMEIIIXLHUQMTDEVkcsopZ1WcIn8Ukckpp5xyhsIdj2gejBpvIYQQhaCITE455ZQzIKEHFZSJIjI55ZRTzkDEdpE/dOMthBAiEhSRySmnnHKGwtFil6FQRCannHJWxSnyRxMthRAiGFpNORiKyOSUU86qOEX+KCKTU0455QxEcsvkeCI5RWRCCBEQXeQPhCIyOeWUsypOkT+KyOSUU045A6GJlkIIIUQOKCKTU0455QyFV2+YspmdANwEnAo8ArzV3Z9u2+ZngGuB44EXgY+5+02z7VsRmZxyyilnQKY8rK8HrgBud/erzOyK9PHlbdt8H/hv7j5uZq8A7jGz29z9mZl2HLTxFkIIUTk2ATem9Y3Am9o3cPd/c/fxtN4LHABOnG3HisjklFNOOQNR4EX+xWY2lnk86u6jPf7sye6+D8Dd95nZSTNtbGbrgPnA7tl2rIhMTjnllLP+POHuI91eNLO/A5Z0eOlD/UjMbCnw58BF7rNnferByCmnnHKGwgvrwcysdT+722tm9riZLU17L0tJ4q9O2x0P/DXwu+5+Vy9e9WDklFNOOQPheBVn8m8DLgKuSr9/pX0DM5sP/G/gc+7+xV53HLTxFkIIUTmuAt5gZuPAG9LHmNmImV2XbvNW4OeBi83sn9Ovn5ltx4rI5JRTTjkDUrV5MO7+JLChw/NjwLvS+i+Av+h334rI5JRTTjlFIWg1ZSGECIVD9eZZFociMjnllFNOUQiKyOSUU045AxHbasqKyIQQIiDVG6VcHIrI5JRTTjlFISgik1NOOeUMhUNIXdmo8RZCCFEIisjklFNOOQPhgOsifxgUkckpp5xVcYZCEZkQQggxIIrI5JRTTjlDoZn8h2Nm1wPnAQfcfXXbax8APgGc6O5PmJkBVwPnktzD+WJ3v7fbvhWRySmnnFVxivzppfG+ATin/UkzW0GytPOjmac3AsPp1xbg2sHfohBCNAMHptxz/6oqs/Zg3P0bZnZqh5c+Bfw2h9+cZhPJDWkcuMvMFrXulNZp34rI5JRTzqo4Q6GIbBbM7I3AY+7+L0kqNs0yYE/m8UT63BENjJltIenl8MLkZBRdcDnllLO6TpE/fTcwZnYM8CHgFzq93OG5jv03dx8FRgHWrl1b3T6eEELkRWQz+efSg1kFrARavZflwL1mto6kx7Iis+1yYG+3HSkik1NOOaviFPnTdwPj7t8BTmo9NrNHgJF0FNk24FIz2wqcARzsdv0l3ZdGkckpp5yVcIbBo5rJP2vjbWZfAL4J/KSZTZjZJTNsvh14CNgF/Cnw67m8SyGEELWjl1FkF87y+qmZ2oF39ypXRCannHJWxRkC1zWYcCgik1NOOaviDEWFp63kTsjGWwghRERoLTI55ZRTzoBMRXSRXxGZnHLKKacoBPVg5JRTTjkD4VpNORzqwcgpp5xVcYYipg5TyMZbCCFERCgik1NOOeUMiCKyQCgik1NOOaviFPlTagMjhBAx4U6lbxCWN4rI5JRTTjkDoogsEIrI5JRTzqo4Rf4oIhNCiIDE1J4pIpNTTjnlFIWgiExOOeWUMxSObjgmhBBCDIoiMjnllFPOQDi6BhMMRWRyyilnVZyhiGgaTNDGWwghRMUwsxPM7G/NbDz9/mMzbHu8mT1mZp/uZd+KyOSUU045Q+FQwRuOXQHc7u5XmdkV6ePLu2z7e8D/6XXHisjklFNOOeNmE7A+rW8E7qBDA2Nma4GTga8BI73sWBMthRAiEE4ll4o52d33Abj7PjM7qX0DMxsCPgm8A9jQ644rEZEB319wzDEPAIuBF4CD6SYL51jP9edCOXWcOk4dZ/WOc5iC+eETL9z24HX7Fhew65ea2Vjm8ai7j7YemNnfAUs6/NyHetz/rwPb3X2PmfX+rty99K/0lwEw1qqzz/db57GPIp06Th2njrO6xxnbF/AgsDStlwIPdtjm88CjwCPAE8C/A1fNtu+qRGR/lXOd9/7klFPOuJwxsQ24CLgq/f6V9g3c/ZdatZldDIy4+xWz7djS1qkSmNmYu/d08ajO6DibhY5T1BkzezlwM3AKSS/lLe7+lJmNAL/m7u9q2/5ikgbm0tn2XZUeTIvR2TdpBDrOZqHjFLXF3Z+kw4V7dx8D3tXh+RuAG3rZd6V6MEIIIZpD0DlGQggh4qESDYyZnWNmD5rZrnQmaSMwsxVm9nUze8DM7jez96bP97w0Q50ws3lm9m0z+2r6eKWZfSs9zpvMbH7Z73FQzGyRmX3JzP5v+rm+tomfp5n9VnrO3mdmXzCzlzbx8xTFUnoDY2bzgGuAjcArgQvN7JXlvqvcOAS8391/GngN8O702FpLMwwDt6ePm8B7gQcyjz8OfCo9zqeBS0p5V/lyNfA1d/8p4FUkx9uoz9PMlgG/QXIhdzUwD9hMMz9PUSClNzDAOmCXuz/k7pPAVpKlC2qPu+9z93vT+lmSf4yWkRzfjelmNwJvKucd5oeZLQd+EbgufWzAWcCX0k1qf5xmdjzw88BnAdx90t2foYGfJ8kAoAVmdhRwDLCPhn2eoniq0MAsA/ZkHk+kzzUKMzsVeDXwLdqWZgCOWJqhhvwx8NtAayGMlwPPuPuh9HETPtcfB74L/FkaBV5nZi+jYZ+nuz8G/CHJkNV9JDPd76F5n6comCo0MJ3WHWjU0DYzOxb4S+A33f3fy34/eWNm5wEH3P2e7NMdNq3753oU8LPAte7+auB71DwO60R6DWkTsBJ4BfAykgi7nbp/nqJgqtDATAArMo+XA3tLei+5Y2YvIWlcPu/uX06fftzMlqavLwUOlPX+cuJ1wBvN7BGSiPMskh7NojRigWZ8rhPAhLt/K338JZIGp2mf59nAw+7+XXd/Afgy8HM07/MUBVOFBmYHMJyOUJlPcjFxW8nvKRfS6xCfBR5w9z/KvNRamgG6LM1QJ9z9g+6+3N1PJfn8/j5dWuLrwJvTzZpwnPuBPWb2k+lTG4CdNOzzJInGXmNmx6TncOs4G/V5iuKpxERLMzuX5H+884Dr3f1jJb+lXDCz/wzcCXyHH12b+B2S6zBHLM1QypvMGTNbD3zA3c8zsx8n6dGcAHwb+GV3/2GZ729QzOxnSAYyzAceAt5J8h+1Rn2eZvY/gLeRjIT8NsmM7mU07PMUxVKJBkYIIUTzqEJEJoQQooGogRFCCFEIamCEEEIUghoYIYQQhaAGRgghRCGogRFCCFEIamCEEEIUghoYIYQQhfD/AdzbWJCWx3FQAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "xyreso = 0.02 # x-y grid resolution\n", - "yawreso = math.radians(3.1) # yaw angle resolution [rad]\n", - "ang, dist = file_read(\"lidar01.csv\")\n", - "ox = np.sin(ang) * dist\n", - "oy = np.cos(ang) * dist\n", - "pmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, False)\n", - "xyres = np.array(pmap).shape\n", - "plt.figure(figsize=(20,8))\n", - "plt.subplot(122)\n", - "plt.imshow(pmap, cmap = \"PiYG_r\") \n", - "plt.clim(-0.4, 1.4)\n", - "plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True)\n", - "plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True)\n", - "plt.grid(True, which=\"minor\", color=\"w\", linewidth = .6, alpha = 0.5)\n", - "plt.colorbar()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb deleted file mode 100644 index 7634bfde9e..0000000000 --- a/SLAM/EKFSLAM/ekf_slam.ipynb +++ /dev/null @@ -1,1784 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# EKF SLAM" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename=\"animation.png\",width=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulation\n", - "\n", - "This is a simulation of EKF SLAM. \n", - "\n", - "- Black stars: landmarks\n", - "- Green crosses: estimates of landmark positions\n", - "- Black line: dead reckoning \n", - "- Blue line: ground truth\n", - "- Red line: EKF SLAM position estimation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "EKF SLAM models the SLAM problem in a single EKF where the modeled state is both the pose $(x, y, \\theta)$ and \n", - "an array of landmarks $[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]$ for $n$ landmarks. The covariance between each of the positions and landmarks are also tracked. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$\\begin{equation}\n", - "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n \\end{bmatrix}\n", - "\\end{equation}$\n", - "\n", - "$\\begin{equation}\n", - "P = \\begin{bmatrix} \n", - "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n", - "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n", - " & & & & \\vdots & & & & & \\\\\n", - "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n", - "\\end{bmatrix}\n", - "\\end{equation}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A single estimate of the pose is tracked over time, while the confidence in the pose is tracked by the \n", - "covariance matrix $P$. $P$ is a symmetric square matrix whith each element in the matrix corresponding to the \n", - "covariance between two parts of the system. For example, $\\sigma_{xy}$ represents the covariance between the \n", - "belief of $x$ and $y$ and is equal to $\\sigma_{yx}$. \n", - "\n", - "The state can be represented more concisely as follows. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "$\\begin{equation}\n", - "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n", - "\\end{equation}$\n", - "$\\begin{equation}\n", - "P = \\begin{bmatrix} \n", - "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n", - "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the state simplifies to a combination of pose ($x$) and map ($m$). The covariance matrix becomes easier to \n", - "understand and simply reads as the uncertainty of the robots pose ($\\Sigma_{xx}$), the uncertainty of the \n", - "map ($\\Sigma_{mm}$), and the uncertainty of the robots pose with respect to the map and vice versa \n", - "($\\Sigma_{xm}$, $\\Sigma_{mx}$).\n", - "\n", - "Take care to note the difference between $X$ (state) and $x$ (pose). " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "\"\"\"\n", - "Extended Kalman Filter SLAM example\n", - "original author: Atsushi Sakai (@Atsushi_twi)\n", - "notebook author: Andrew Tu (drewtu2)\n", - "\"\"\"\n", - "\n", - "import math\n", - "import numpy as np\n", - "%matplotlib notebook\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "# EKF state covariance\n", - "Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2 # Change in covariance\n", - "\n", - "# Simulation parameter\n", - "Qsim = np.diag([0.2, np.deg2rad(1.0)])**2 # Sensor Noise\n", - "Rsim = np.diag([1.0, np.deg2rad(10.0)])**2 # Process Noise\n", - "\n", - "DT = 0.1 # time tick [s]\n", - "SIM_TIME = 50.0 # simulation time [s]\n", - "MAX_RANGE = 20.0 # maximum observation range\n", - "M_DIST_TH = 2.0 # Threshold of Mahalanobis distance for data association.\n", - "STATE_SIZE = 3 # State size [x,y,yaw]\n", - "LM_SIZE = 2 # LM state size [x,y]\n", - "\n", - "show_animation = True" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Algorithm Walkthrough\n", - "\n", - "At each time step, the following is done. \n", - "- predict the new state using the control functions\n", - "- update the belief in landmark positions based on the estimated state and measurements" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def ekf_slam(xEst, PEst, u, z):\n", - " \"\"\"\n", - " Performs an iteration of EKF SLAM from the available information. \n", - " \n", - " :param xEst: the belief in last position\n", - " :param PEst: the uncertainty in last position\n", - " :param u: the control function applied to the last position \n", - " :param z: measurements at this step\n", - " :returns: the next estimated position and associated covariance\n", - " \"\"\"\n", - " S = STATE_SIZE\n", - "\n", - " # Predict\n", - " xEst, PEst, G, Fx = predict(xEst, PEst, u)\n", - " initP = np.eye(2)\n", - "\n", - " # Update\n", - " xEst, PEst = update(xEst, PEst, u, z, initP)\n", - "\n", - " return xEst, PEst\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1- Predict\n", - "**Predict State update:** The following equations describe the predicted motion model of the robot in case we provide only the control $(v,w)$, which are the linear and angular velocity repsectively. \n", - "\n", - "$\\begin{equation*}\n", - "F=\n", - "\\begin{bmatrix}\n", - "1 & 0 & 0 \\\\\n", - "0 & 1 & 0 \\\\\n", - "0 & 0 & 1 \n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "B=\n", - "\\begin{bmatrix}\n", - "\\Delta t cos(\\theta) & 0\\\\\n", - "\\Delta t sin(\\theta) & 0\\\\\n", - "0 & \\Delta t\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "U=\n", - "\\begin{bmatrix}\n", - "v_t\\\\\n", - "w_t\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "X = FX + BU \n", - "\\end{equation*}$\n", - "\n", - "\n", - "$\\begin{equation*}\n", - "\\begin{bmatrix}\n", - "x_{t+1} \\\\\n", - "y_{t+1} \\\\\n", - "\\theta_{t+1}\n", - "\\end{bmatrix}=\n", - "\\begin{bmatrix}\n", - "1 & 0 & 0 \\\\\n", - "0 & 1 & 0 \\\\\n", - "0 & 0 & 1 \n", - "\\end{bmatrix}\\begin{bmatrix}\n", - "x_{t} \\\\\n", - "y_{t} \\\\\n", - "\\theta_{t}\n", - "\\end{bmatrix}+\n", - "\\begin{bmatrix}\n", - "\\Delta t cos(\\theta) & 0\\\\\n", - "\\Delta t sin(\\theta) & 0\\\\\n", - "0 & \\Delta t\n", - "\\end{bmatrix}\n", - "\\begin{bmatrix}\n", - "v_{t} + \\sigma_v\\\\\n", - "w_{t} + \\sigma_w\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "Notice that while $U$ is only defined by $v_t$ and $w_t$, in the actual calcuations, a $+\\sigma_v$ and \n", - "$+\\sigma_w$ appear. These values represent the error bewteen the given control inputs and the actual control \n", - "inputs. \n", - "\n", - "As a result, the simulation is set up as the following. $R$ represents the process noise which is added to the \n", - "control inputs to simulate noise experienced in the real world. A set of truth values are computed from the raw \n", - "control values while the values dead reckoning values incorporate the error into the estimation. \n", - "\n", - "$\\begin{equation*}\n", - "R=\n", - "\\begin{bmatrix}\n", - "\\sigma_v\\\\\n", - "\\sigma_w\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "X_{true} = FX + B(U)\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "X_{DR} = FX + B(U + R)\n", - "\\end{equation*}$\n", - "\n", - "The implementation of the motion model prediciton code is shown in `motion_model`. The `observation` function \n", - "shows how the simulation uses (or doesn't use) the process noise `Rsim` to the find the ground truth and dead reckoning estimtates of the pose.\n", - "\n", - "**Predict covariance:** Add the state covariance to the the current uncertainty of the EKF. At each time step, the uncertainty in the system grows by the covariance of the pose, $Cx$. \n", - "\n", - "$\n", - "P = G^TPG + Cx\n", - "$\n", - "\n", - "Notice this uncertainty is only growing with respect to the pose, not the landmarks. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def predict(xEst, PEst, u):\n", - " \"\"\"\n", - " Performs the prediction step of EKF SLAM\n", - " \n", - " :param xEst: nx1 state vector\n", - " :param PEst: nxn covariacne matrix\n", - " :param u: 2x1 control vector\n", - " :returns: predicted state vector, predicted covariance, jacobian of control vector, transition fx\n", - " \"\"\"\n", - " S = STATE_SIZE\n", - " G, Fx = jacob_motion(xEst[0:S], u)\n", - " xEst[0:S] = motion_model(xEst[0:S], u)\n", - " # Fx is an an identity matrix of size (STATE_SIZE)\n", - " # sigma = G*sigma*G.T + Noise\n", - " PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n", - " return xEst, PEst, G, Fx" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def motion_model(x, u):\n", - " \"\"\"\n", - " Computes the motion model based on current state and input function. \n", - " \n", - " :param x: 3x1 pose estimation\n", - " :param u: 2x1 control input [v; w]\n", - " :returns: the resutling state after the control function is applied\n", - " \"\"\"\n", - " F = np.array([[1.0, 0, 0],\n", - " [0, 1.0, 0],\n", - " [0, 0, 1.0]])\n", - "\n", - " B = np.array([[DT * math.cos(x[2, 0]), 0],\n", - " [DT * math.sin(x[2, 0]), 0],\n", - " [0.0, DT]])\n", - "\n", - " x = (F @ x) + (B @ u)\n", - " return x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2 - Update\n", - "In the update phase, the observations of nearby landmarks are used to correct the location estimate. \n", - "\n", - "For every landmark observed, it is associated to a particular landmark in the known map. If no landmark exists \n", - "in the position surrounding the landmark, it is taken as a NEW landmark. The distance threshold for how far a \n", - "landmark must be from the next known landmark before its considered to be a new landmark is set by `M_DIST_TH`.\n", - "\n", - "With an observation associated to the appropriate landmark, the **innovation** can be calculated. Innovation \n", - "($y$) is the difference between the observation and the observation that *should* have been made if the \n", - "observation were made from the pose predicted in the predict stage.\n", - "\n", - "$\n", - "y = z_t - h(X)\n", - "$\n", - "\n", - "With the innovation calculated, the question becomes which to trust more - the observations or the predictions? \n", - "To determine this, we calculate the Kalman Gain - a percent of how much of the innovation to add to the \n", - "prediction based on the uncertainty in the predict step and the update step. \n", - "\n", - "$\n", - "K = \\bar{P_t}H_t^T(H_t\\bar{P_t}H_t^T + Q_t)^{-1}\n", - "$\n", - "In these equations, $H$ is the jacobian of the measurement function. The multiplications by $H^T$ and $H$ \n", - "represent the application of the delta to the measurement covariance. \n", - "Intuitively, this equation is applying the following from the single variate Kalman equation but in the \n", - "multivariate form, i.e. finding the ratio of the uncertianty of the process compared the measurment. \n", - "\n", - "$\n", - "K = \\frac{\\bar{P_t}}{\\bar{P_t} + Q_t}\n", - "$\n", - "\n", - "If $Q_t << \\bar{P_t}$, (i.e. the measurement covariance is low relative to the current estimate), then the \n", - "Kalman gain will be $~1$. This results in adding all of the innovation to the estimate -- and therefore \n", - "completely believing the measurement. \n", - "\n", - "However, if $Q_t >> \\bar{P_t}$ then the Kalman gain will go to 0, signaling a high trust in the process \n", - "and little trust in the measurement.\n", - "\n", - "The update is captured in the following. \n", - "\n", - "$\n", - "xUpdate = xEst + (K * y)\n", - "$\n", - "\n", - "Of course, the covariance must also be updated as well to account for the changing uncertainty. \n", - "\n", - "$\n", - "P_{t} = (I-K_tH_t)\\bar{P_t}\n", - "$" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def update(xEst, PEst, u, z, initP):\n", - " \"\"\"\n", - " Performs the update step of EKF SLAM\n", - " \n", - " :param xEst: nx1 the predicted pose of the system and the pose of the landmarks\n", - " :param PEst: nxn the predicted covariance\n", - " :param u: 2x1 the control function \n", - " :param z: the measurements read at new position\n", - " :param initP: 2x2 an identity matrix acting as the initial covariance\n", - " :returns: the updated state and covariance for the system\n", - " \"\"\"\n", - " for iz in range(len(z[:, 0])): # for each observation\n", - " minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark\n", - "\n", - " nLM = calc_n_LM(xEst) # number of landmarks we currently know about\n", - " \n", - " if minid == nLM: # Landmark is a NEW landmark\n", - " print(\"New LM\")\n", - " # Extend state and covariance matrix\n", - " xAug = np.vstack((xEst, calc_LM_Pos(xEst, z[iz, :])))\n", - " PAug = np.vstack((np.hstack((PEst, np.zeros((len(xEst), LM_SIZE)))),\n", - " np.hstack((np.zeros((LM_SIZE, len(xEst))), initP))))\n", - " xEst = xAug\n", - " PEst = PAug\n", - " \n", - " lm = get_LM_Pos_from_state(xEst, minid)\n", - " y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)\n", - "\n", - " K = (PEst @ H.T) @ np.linalg.inv(S) # Calculate Kalman Gain\n", - " xEst = xEst + (K @ y)\n", - " PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst\n", - " \n", - " xEst[2] = pi_2_pi(xEst[2])\n", - " return xEst, PEst\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def calc_innovation(lm, xEst, PEst, z, LMid):\n", - " \"\"\"\n", - " Calculates the innovation based on expected position and landmark position\n", - " \n", - " :param lm: landmark position\n", - " :param xEst: estimated position/state\n", - " :param PEst: estimated covariance\n", - " :param z: read measurements\n", - " :param LMid: landmark id\n", - " :returns: returns the innovation y, and the jacobian H, and S, used to calculate the Kalman Gain\n", - " \"\"\"\n", - " delta = lm - xEst[0:2]\n", - " q = (delta.T @ delta)[0, 0]\n", - " zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]\n", - " zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])\n", - " # zp is the expected measurement based on xEst and the expected landmark position\n", - " \n", - " y = (z - zp).T # y = innovation\n", - " y[1] = pi_2_pi(y[1])\n", - " \n", - " H = jacobH(q, delta, xEst, LMid + 1)\n", - " S = H @ PEst @ H.T + Cx[0:2, 0:2]\n", - "\n", - " return y, S, H\n", - "\n", - "def jacobH(q, delta, x, i):\n", - " \"\"\"\n", - " Calculates the jacobian of the measurement function\n", - " \n", - " :param q: the range from the system pose to the landmark\n", - " :param delta: the difference between a landmark position and the estimated system position\n", - " :param x: the state, including the estimated system position\n", - " :param i: landmark id + 1\n", - " :returns: the jacobian H\n", - " \"\"\"\n", - " sq = math.sqrt(q)\n", - " G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],\n", - " [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]])\n", - "\n", - " G = G / q\n", - " nLM = calc_n_LM(x)\n", - " F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM))))\n", - " F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))),\n", - " np.eye(2), np.zeros((2, 2 * nLM - 2 * i))))\n", - "\n", - " F = np.vstack((F1, F2))\n", - "\n", - " H = G @ F\n", - "\n", - " return H" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Observation Step\n", - "The observation step described here is outside the main EKF SLAM process and is primarily used as a method of\n", - "driving the simulation. The observations funciton is in charge of calcualting how the poses of the robots change \n", - "and accumulate error over time, and the theoretical measuremnts that are expected as a result of each \n", - "measurement. \n", - "\n", - "Observations are based on the TRUE position of the robot. Error in dead reckoning and control functions are \n", - "passed along here as well. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def observation(xTrue, xd, u, RFID):\n", - " \"\"\"\n", - " :param xTrue: the true pose of the system\n", - " :param xd: the current noisy estimate of the system\n", - " :param u: the current control input\n", - " :param RFID: the true position of the landmarks\n", - " \n", - " :returns: Computes the true position, observations, dead reckoning (noisy) position, \n", - " and noisy control function\n", - " \"\"\"\n", - " xTrue = motion_model(xTrue, u)\n", - "\n", - " # add noise to gps x-y\n", - " z = np.zeros((0, 3))\n", - "\n", - " for i in range(len(RFID[:, 0])): # Test all beacons, only add the ones we can see (within MAX_RANGE)\n", - "\n", - " dx = RFID[i, 0] - xTrue[0, 0]\n", - " dy = RFID[i, 1] - xTrue[1, 0]\n", - " d = math.sqrt(dx**2 + dy**2)\n", - " angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])\n", - " if d <= MAX_RANGE:\n", - " dn = d + np.random.randn() * Qsim[0, 0] # add noise\n", - " anglen = angle + np.random.randn() * Qsim[1, 1] # add noise\n", - " zi = np.array([dn, anglen, i])\n", - " z = np.vstack((z, zi))\n", - "\n", - " # add noise to input\n", - " ud = np.array([[\n", - " u[0, 0] + np.random.randn() * Rsim[0, 0],\n", - " u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T\n", - "\n", - " xd = motion_model(xd, ud)\n", - " return xTrue, z, xd, ud" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def calc_n_LM(x):\n", - " \"\"\"\n", - " Calculates the number of landmarks currently tracked in the state\n", - " :param x: the state\n", - " :returns: the number of landmarks n\n", - " \"\"\"\n", - " n = int((len(x) - STATE_SIZE) / LM_SIZE)\n", - " return n\n", - "\n", - "\n", - "def jacob_motion(x, u):\n", - " \"\"\"\n", - " Calculates the jacobian of motion model. \n", - " \n", - " :param x: The state, including the estimated position of the system\n", - " :param u: The control function\n", - " :returns: G: Jacobian\n", - " Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix\n", - " \"\"\"\n", - " \n", - " # [eye(3) [0 x y; 0 x y; 0 x y]]\n", - " Fx = np.hstack((np.eye(STATE_SIZE), np.zeros(\n", - " (STATE_SIZE, LM_SIZE * calc_n_LM(x)))))\n", - "\n", - " jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],\n", - " [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],\n", - " [0.0, 0.0, 0.0]],dtype=object)\n", - "\n", - " G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx\n", - " if calc_n_LM(x) > 0:\n", - " print(Fx.shape)\n", - " return G, Fx,\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def calc_LM_Pos(x, z):\n", - " \"\"\"\n", - " Calcualtes the pose in the world coordinate frame of a landmark at the given measurement. \n", - "\n", - " :param x: [x; y; theta]\n", - " :param z: [range; bearing]\n", - " :returns: [x; y] for given measurement\n", - " \"\"\"\n", - " zp = np.zeros((2, 1))\n", - "\n", - " zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1])\n", - " zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1])\n", - " #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])\n", - " #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])\n", - "\n", - " return zp\n", - "\n", - "\n", - "def get_LM_Pos_from_state(x, ind):\n", - " \"\"\"\n", - " Returns the position of a given landmark\n", - " \n", - " :param x: The state containing all landmark positions\n", - " :param ind: landmark id\n", - " :returns: The position of the landmark\n", - " \"\"\"\n", - " lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :]\n", - "\n", - " return lm\n", - "\n", - "\n", - "def search_correspond_LM_ID(xAug, PAug, zi):\n", - " \"\"\"\n", - " Landmark association with Mahalanobis distance.\n", - " \n", - " If this landmark is at least M_DIST_TH units away from all known landmarks, \n", - " it is a NEW landmark.\n", - " \n", - " :param xAug: The estimated state\n", - " :param PAug: The estimated covariance\n", - " :param zi: the read measurements of specific landmark\n", - " :returns: landmark id\n", - " \"\"\"\n", - "\n", - " nLM = calc_n_LM(xAug)\n", - "\n", - " mdist = []\n", - "\n", - " for i in range(nLM):\n", - " lm = get_LM_Pos_from_state(xAug, i)\n", - " y, S, H = calc_innovation(lm, xAug, PAug, zi, i)\n", - " mdist.append(y.T @ np.linalg.inv(S) @ y)\n", - "\n", - " mdist.append(M_DIST_TH) # new landmark\n", - "\n", - " minid = mdist.index(min(mdist))\n", - "\n", - " return minid\n", - "\n", - "def calc_input():\n", - " v = 1.0 # [m/s]\n", - " yawrate = 0.1 # [rad/s]\n", - " u = np.array([[v, yawrate]]).T\n", - " return u\n", - "\n", - "def pi_2_pi(angle):\n", - " return (angle + math.pi) % (2 * math.pi) - math.pi" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def main():\n", - " print(\" start!!\")\n", - "\n", - " time = 0.0\n", - "\n", - " # RFID positions [x, y]\n", - " RFID = np.array([[10.0, -2.0],\n", - " [15.0, 10.0],\n", - " [3.0, 15.0],\n", - " [-5.0, 20.0]])\n", - "\n", - " # State Vector [x y yaw v]'\n", - " xEst = np.zeros((STATE_SIZE, 1))\n", - " xTrue = np.zeros((STATE_SIZE, 1))\n", - " PEst = np.eye(STATE_SIZE)\n", - "\n", - " xDR = np.zeros((STATE_SIZE, 1)) # Dead reckoning\n", - "\n", - " # history\n", - " hxEst = xEst\n", - " hxTrue = xTrue\n", - " hxDR = xTrue\n", - "\n", - " while SIM_TIME >= time:\n", - " time += DT\n", - " u = calc_input()\n", - "\n", - " xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)\n", - "\n", - " xEst, PEst = ekf_slam(xEst, PEst, ud, z)\n", - "\n", - " x_state = xEst[0:STATE_SIZE]\n", - "\n", - " # store data history\n", - " hxEst = np.hstack((hxEst, x_state))\n", - " hxDR = np.hstack((hxDR, xDR))\n", - " hxTrue = np.hstack((hxTrue, xTrue))\n", - "\n", - " if show_animation: # pragma: no cover\n", - " plt.cla()\n", - "\n", - " plt.plot(RFID[:, 0], RFID[:, 1], \"*k\")\n", - " plt.plot(xEst[0], xEst[1], \".r\")\n", - "\n", - " # plot landmark\n", - " for i in range(calc_n_LM(xEst)):\n", - " plt.plot(xEst[STATE_SIZE + i * 2],\n", - " xEst[STATE_SIZE + i * 2 + 1], \"xg\")\n", - "\n", - " plt.plot(hxTrue[0, :],\n", - " hxTrue[1, :], \"-b\")\n", - " plt.plot(hxDR[0, :],\n", - " hxDR[1, :], \"-k\")\n", - " plt.plot(hxEst[0, :],\n", - " hxEst[1, :], \"-r\")\n", - " plt.axis(\"equal\")\n", - " plt.grid(True)\n", - " plt.pause(0.001)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " start!!\n", - "New LM\n", - "New LM\n", - "New LM\n" - ] - }, - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", - " if (this.ResizeObserver === undefined) {\n", - " if (window.ResizeObserver !== undefined) {\n", - " this.ResizeObserver = window.ResizeObserver;\n", - " } else {\n", - " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", - " this.ResizeObserver = obs.ResizeObserver;\n", - " }\n", - " }\n", - "\n", - " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " this.resizeObserverInstance.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'dblclick',\n", - " on_mouse_event_closure('dblclick')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " var img = evt.data;\n", - " if (img.type !== 'image/png') {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " img.type = 'image/png';\n", - " }\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " img\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.key === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.key;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.key !== 'Control') {\n", - " value += 'ctrl+';\n", - " }\n", - " else if (event.altKey && event.key !== 'Alt') {\n", - " value += 'alt+';\n", - " }\n", - " else if (event.shiftKey && event.key !== 'Shift') {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k' + event.key;\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "\n", - "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", - "// prettier-ignore\n", - "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.binaryType = comm.kernel.ws.binaryType;\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " function updateReadyState(_event) {\n", - " if (comm.kernel.ws) {\n", - " ws.readyState = comm.kernel.ws.readyState;\n", - " } else {\n", - " ws.readyState = 3; // Closed state.\n", - " }\n", - " }\n", - " comm.kernel.ws.addEventListener('open', updateReadyState);\n", - " comm.kernel.ws.addEventListener('close', updateReadyState);\n", - " comm.kernel.ws.addEventListener('error', updateReadyState);\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " var data = msg['content']['data'];\n", - " if (data['blob'] !== undefined) {\n", - " data = {\n", - " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", - " };\n", - " }\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(data);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.on(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " if (event.target !== this) {\n", - " // Ignore bubbled events from children.\n", - " return;\n", - " }\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New LM\n" - ] - } - ], - "source": [ - "%matplotlib notebook\n", - "%matplotlib notebook\n", - "main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/SLAM/FastSLAM1/FastSLAM1.ipynb b/SLAM/FastSLAM1/FastSLAM1.ipynb deleted file mode 100644 index 60faed6e88..0000000000 --- a/SLAM/FastSLAM1/FastSLAM1.ipynb +++ /dev/null @@ -1,676 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## FastSLAM1.0" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename=\"animation.png\",width=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Simulation\n", - "\n", - "This is a feature based SLAM example using FastSLAM 1.0.\n", - "\n", - "The blue line is ground truth, the black line is dead reckoning, the red\n", - "line is the estimated trajectory with FastSLAM.\n", - "\n", - "The red points are particles of FastSLAM.\n", - "\n", - "Black points are landmarks, blue crosses are estimated landmark\n", - "positions by FastSLAM.\n", - "\n", - "\n", - "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction\n", - "\n", - "FastSLAM algorithm implementation is based on particle filters and belongs to the family of probabilistic SLAM approaches. It is used with feature-based maps (see gif above) or with occupancy grid maps.\n", - "\n", - "As it is shown, the particle filter differs from EKF by representing the robot's estimation through a set of particles. Each single particle has an independent belief, as it holds the pose $(x, y, \\theta)$ and an array of landmark locations $[(x_1, y_1), (x_2, y_2), ... (x_n, y_n)]$ for n landmarks.\n", - "\n", - "* The blue line is the true trajectory\n", - "* The red line is the estimated trajectory\n", - "* The red dots represent the distribution of particles\n", - "* The black line represent dead reckoning tracjectory\n", - "* The blue x is the observed and estimated landmarks\n", - "* The black x is the true landmark\n", - "\n", - "I.e. Each particle maintains a deterministic pose and n-EKFs for each landmark and update it with each measurement." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Algorithm walkthrough\n", - "\n", - "The particles are initially drawn from a uniform distribution the represent the initial uncertainty.\n", - "At each time step we do:\n", - "\n", - "* Predict the pose for each particle by using $u$ and the motion model (the landmarks are not updated). \n", - "* Update the particles with observations $z$, where the weights are adjusted based on how likely the particle to have the correct pose given the sensor measurement\n", - "* Resampling such that the particles with the largest weights survive and the unlikely ones with the lowest weights die out.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1- Predict\n", - "The following equations and code snippets we can see how the particles distribution evolves in case we provide only the control $(v,w)$, which are the linear and angular velocity respectively. \n", - "\n", - "$\\begin{equation*}\n", - "F=\n", - "\\begin{bmatrix}\n", - "1 & 0 & 0 \\\\\n", - "0 & 1 & 0 \\\\\n", - "0 & 0 & 1 \n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "B=\n", - "\\begin{bmatrix}\n", - "\\Delta t cos(\\theta) & 0\\\\\n", - "\\Delta t sin(\\theta) & 0\\\\\n", - "0 & \\Delta t\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n", - "$\\begin{equation*}\n", - "X = FX + BU \n", - "\\end{equation*}$\n", - "\n", - "\n", - "$\\begin{equation*}\n", - "\\begin{bmatrix}\n", - "x_{t+1} \\\\\n", - "y_{t+1} \\\\\n", - "\\theta_{t+1}\n", - "\\end{bmatrix}=\n", - "\\begin{bmatrix}\n", - "1 & 0 & 0 \\\\\n", - "0 & 1 & 0 \\\\\n", - "0 & 0 & 1 \n", - "\\end{bmatrix}\\begin{bmatrix}\n", - "x_{t} \\\\\n", - "y_{t} \\\\\n", - "\\theta_{t}\n", - "\\end{bmatrix}+\n", - "\\begin{bmatrix}\n", - "\\Delta t cos(\\theta) & 0\\\\\n", - "\\Delta t sin(\\theta) & 0\\\\\n", - "0 & \\Delta t\n", - "\\end{bmatrix}\n", - "\\begin{bmatrix}\n", - "v_{t} + \\sigma_v\\\\\n", - "w_{t} + \\sigma_w\\\\\n", - "\\end{bmatrix}\n", - "\\end{equation*}$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following snippets playsback the recorded trajectory of each particle.\n", - "\n", - "To get the insight of the motion model change the value of $R$ and re-run the cells again. As R is the parameters that indicates how much we trust that the robot executed the motion commands.\n", - "\n", - "It is interesting to notice also that only motion will increase the uncertainty in the system as the particles start to spread out more. If observations are included the uncertainty will decrease and particles will converge to the correct estimate." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# CODE SNIPPET #\n", - "import numpy as np\n", - "import math\n", - "from copy import deepcopy\n", - "# Fast SLAM covariance\n", - "Q = np.diag([3.0, np.deg2rad(10.0)])**2\n", - "R = np.diag([1.0, np.deg2rad(20.0)])**2\n", - "\n", - "# Simulation parameter\n", - "Qsim = np.diag([0.3, np.deg2rad(2.0)])**2\n", - "Rsim = np.diag([0.5, np.deg2rad(10.0)])**2\n", - "OFFSET_YAWRATE_NOISE = 0.01\n", - "\n", - "DT = 0.1 # time tick [s]\n", - "SIM_TIME = 50.0 # simulation time [s]\n", - "MAX_RANGE = 20.0 # maximum observation range\n", - "M_DIST_TH = 2.0 # Threshold of Mahalanobis distance for data association.\n", - "STATE_SIZE = 3 # State size [x,y,yaw]\n", - "LM_SIZE = 2 # LM srate size [x,y]\n", - "N_PARTICLE = 100 # number of particle\n", - "NTH = N_PARTICLE / 1.5 # Number of particle for re-sampling\n", - "\n", - "class Particle:\n", - "\n", - " def __init__(self, N_LM):\n", - " self.w = 1.0 / N_PARTICLE\n", - " self.x = 0.0\n", - " self.y = 0.0\n", - " self.yaw = 0.0\n", - " # landmark x-y positions\n", - " self.lm = np.zeros((N_LM, LM_SIZE))\n", - " # landmark position covariance\n", - " self.lmP = np.zeros((N_LM * LM_SIZE, LM_SIZE))\n", - "\n", - "def motion_model(x, u):\n", - " F = np.array([[1.0, 0, 0],\n", - " [0, 1.0, 0],\n", - " [0, 0, 1.0]])\n", - "\n", - " B = np.array([[DT * math.cos(x[2, 0]), 0],\n", - " [DT * math.sin(x[2, 0]), 0],\n", - " [0.0, DT]])\n", - " x = F @ x + B @ u\n", - " \n", - " x[2, 0] = pi_2_pi(x[2, 0])\n", - " return x\n", - " \n", - "def predict_particles(particles, u):\n", - " for i in range(N_PARTICLE):\n", - " px = np.zeros((STATE_SIZE, 1))\n", - " px[0, 0] = particles[i].x\n", - " px[1, 0] = particles[i].y\n", - " px[2, 0] = particles[i].yaw\n", - " ud = u + (np.random.randn(1, 2) @ R).T # add noise\n", - " px = motion_model(px, ud)\n", - " particles[i].x = px[0, 0]\n", - " particles[i].y = px[1, 0]\n", - " particles[i].yaw = px[2, 0]\n", - "\n", - " return particles\n", - "\n", - "def pi_2_pi(angle):\n", - " return (angle + math.pi) % (2 * math.pi) - math.pi\n", - "\n", - "# END OF SNIPPET\n", - "\n", - "N_LM = 0 \n", - "particles = [Particle(N_LM) for i in range(N_PARTICLE)]\n", - "time= 0.0\n", - "v = 1.0 # [m/s]\n", - "yawrate = 0.1 # [rad/s]\n", - "u = np.array([v, yawrate]).reshape(2, 1)\n", - "history = []\n", - "while SIM_TIME >= time:\n", - " time += DT\n", - " particles = predict_particles(particles, u)\n", - " history.append(deepcopy(particles))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3f279cf1dcaf4ec886c01942c36e601d", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=0, description='t', max=499), Output()), _dom_classes=('widget-interact'…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# from IPython.html.widgets import *\n", - "from ipywidgets import *\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "\n", - "# playback the recorded motion of the particles\n", - "def plot_particles(t=0):\n", - " x = []\n", - " y = []\n", - " for i in range(len(history[t])):\n", - " x.append(history[t][i].x)\n", - " y.append(history[t][i].y)\n", - " plt.figtext(0.15,0.82,'t = ' + str(t))\n", - " plt.plot(x, y, '.r')\n", - " plt.axis([-20,20, -5,25])\n", - "\n", - "interact(plot_particles, t=(0,len(history)-1,1));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2- Update\n", - "\n", - "For the update step it is useful to observe a single particle and the effect of getting a new measurements on the weight of the particle.\n", - "\n", - "As mentioned earlier, each particle maintains $N$ $2x2$ EKFs to estimate the landmarks, which includes the EKF process described in the EKF notebook. The difference is the change in the weight of the particle according to how likely the measurement is.\n", - "\n", - "The weight is updated according to the following equation: \n", - "\n", - "$\\begin{equation*}\n", - "w_i = |2\\pi Q|^{\\frac{-1}{2}} exp\\{\\frac{-1}{2}(z_t - \\hat z_i)^T Q^{-1}(z_t-\\hat z_i)\\}\n", - "\\end{equation*}$\n", - "\n", - "Where, $w_i$ is the computed weight, $Q$ is the measurement covariance, $z_t$ is the actual measurment and $\\hat z_i$ is the predicted measurement of particle $i$.\n", - "\n", - "To experiment this, a single particle is initialized then passed an initial measurement, which results in a relatively average weight. However, setting the particle coordinate to a wrong value to simulate wrong estimation will result in a very low weight. The lower the weight the less likely that this particle will be drawn during resampling and probably will die out." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "initial weight 1.0\n", - "weight after landmark intialization 1.0\n", - "weight after update 0.023098460073039763\n", - "weight after wrong prediction 7.951154575772496e-07\n" - ] - } - ], - "source": [ - "# CODE SNIPPET #\n", - "def observation(xTrue, xd, u, RFID):\n", - "\n", - " # calc true state\n", - " xTrue = motion_model(xTrue, u)\n", - "\n", - " # add noise to range observation\n", - " z = np.zeros((3, 0))\n", - " for i in range(len(RFID[:, 0])):\n", - "\n", - " dx = RFID[i, 0] - xTrue[0, 0]\n", - " dy = RFID[i, 1] - xTrue[1, 0]\n", - " d = math.sqrt(dx**2 + dy**2)\n", - " angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])\n", - " if d <= MAX_RANGE:\n", - " dn = d + np.random.randn() * Qsim[0, 0] # add noise\n", - " anglen = angle + np.random.randn() * Qsim[1, 1] # add noise\n", - " zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)\n", - " z = np.hstack((z, zi))\n", - "\n", - " # add noise to input\n", - " ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]\n", - " ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + OFFSET_YAWRATE_NOISE\n", - " ud = np.array([ud1, ud2]).reshape(2, 1)\n", - "\n", - " xd = motion_model(xd, ud)\n", - "\n", - " return xTrue, z, xd, ud\n", - "\n", - "def update_with_observation(particles, z):\n", - " for iz in range(len(z[0, :])):\n", - "\n", - " lmid = int(z[2, iz])\n", - "\n", - " for ip in range(N_PARTICLE):\n", - " # new landmark\n", - " if abs(particles[ip].lm[lmid, 0]) <= 0.01:\n", - " particles[ip] = add_new_lm(particles[ip], z[:, iz], Q)\n", - " # known landmark\n", - " else:\n", - " w = compute_weight(particles[ip], z[:, iz], Q)\n", - " particles[ip].w *= w\n", - " particles[ip] = update_landmark(particles[ip], z[:, iz], Q)\n", - "\n", - " return particles\n", - "\n", - "def compute_weight(particle, z, Q):\n", - " lm_id = int(z[2])\n", - " xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)\n", - " Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2])\n", - " zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)\n", - " dx = z[0:2].reshape(2, 1) - zp\n", - " dx[1, 0] = pi_2_pi(dx[1, 0])\n", - "\n", - " try:\n", - " invS = np.linalg.inv(Sf)\n", - " except np.linalg.linalg.LinAlgError:\n", - " print(\"singuler\")\n", - " return 1.0\n", - "\n", - " num = math.exp(-0.5 * dx.T @ invS @ dx)\n", - " den = 2.0 * math.pi * math.sqrt(np.linalg.det(Sf))\n", - " w = num / den\n", - "\n", - " return w\n", - "\n", - "def compute_jacobians(particle, xf, Pf, Q):\n", - " dx = xf[0, 0] - particle.x\n", - " dy = xf[1, 0] - particle.y\n", - " d2 = dx**2 + dy**2\n", - " d = math.sqrt(d2)\n", - "\n", - " zp = np.array(\n", - " [d, pi_2_pi(math.atan2(dy, dx) - particle.yaw)]).reshape(2, 1)\n", - "\n", - " Hv = np.array([[-dx / d, -dy / d, 0.0],\n", - " [dy / d2, -dx / d2, -1.0]])\n", - "\n", - " Hf = np.array([[dx / d, dy / d],\n", - " [-dy / d2, dx / d2]])\n", - "\n", - " Sf = Hf @ Pf @ Hf.T + Q\n", - "\n", - " return zp, Hv, Hf, Sf\n", - "\n", - "def add_new_lm(particle, z, Q):\n", - "\n", - " r = z[0]\n", - " b = z[1]\n", - " lm_id = int(z[2])\n", - "\n", - " s = math.sin(pi_2_pi(particle.yaw + b))\n", - " c = math.cos(pi_2_pi(particle.yaw + b))\n", - "\n", - " particle.lm[lm_id, 0] = particle.x + r * c\n", - " particle.lm[lm_id, 1] = particle.y + r * s\n", - "\n", - " # covariance\n", - " Gz = np.array([[c, -r * s],\n", - " [s, r * c]])\n", - "\n", - " particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q @ Gz.T\n", - "\n", - " return particle\n", - "\n", - "def update_KF_with_cholesky(xf, Pf, v, Q, Hf):\n", - " PHt = Pf @ Hf.T\n", - " S = Hf @ PHt + Q\n", - "\n", - " S = (S + S.T) * 0.5\n", - " SChol = np.linalg.cholesky(S).T\n", - " SCholInv = np.linalg.inv(SChol)\n", - " W1 = PHt @ SCholInv\n", - " W = W1 @ SCholInv.T\n", - "\n", - " x = xf + W @ v\n", - " P = Pf - W1 @ W1.T\n", - "\n", - " return x, P\n", - "\n", - "def update_landmark(particle, z, Q):\n", - "\n", - " lm_id = int(z[2])\n", - " xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)\n", - " Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2, :])\n", - "\n", - " zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)\n", - "\n", - " dz = z[0:2].reshape(2, 1) - zp\n", - " dz[1, 0] = pi_2_pi(dz[1, 0])\n", - "\n", - " xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf)\n", - "\n", - " particle.lm[lm_id, :] = xf.T\n", - " particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf\n", - "\n", - " return particle\n", - "\n", - "# END OF CODE SNIPPET #\n", - "\n", - "\n", - "\n", - "# Setting up the landmarks\n", - "RFID = np.array([[10.0, -2.0],\n", - " [15.0, 10.0]])\n", - "N_LM = RFID.shape[0]\n", - "\n", - "# Initialize 1 particle\n", - "N_PARTICLE = 1\n", - "particles = [Particle(N_LM) for i in range(N_PARTICLE)]\n", - "\n", - "xTrue = np.zeros((STATE_SIZE, 1))\n", - "xDR = np.zeros((STATE_SIZE, 1))\n", - "\n", - "print(\"initial weight\", particles[0].w)\n", - "\n", - "xTrue, z, _, ud = observation(xTrue, xDR, u, RFID)\n", - "# Initialize landmarks\n", - "particles = update_with_observation(particles, z)\n", - "print(\"weight after landmark intialization\", particles[0].w)\n", - "particles = update_with_observation(particles, z)\n", - "print(\"weight after update \", particles[0].w)\n", - "\n", - "particles[0].x = -10\n", - "particles = update_with_observation(particles, z)\n", - "print(\"weight after wrong prediction\", particles[0].w)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3- Resampling\n", - "\n", - "In the reseampling steps a new set of particles are chosen from the old set. This is done according to the weight of each particle. \n", - "\n", - "The figure shows 100 particles distributed uniformly between [-0.5, 0.5] with the weights of each particle distributed according to a Gaussian funciton. \n", - "\n", - "The resampling picks \n", - "\n", - "$i \\in 1,...,N$ particles with probability to pick particle with index $i ∝ \\omega_i$, where $\\omega_i$ is the weight of that particle\n", - "\n", - "\n", - "To get the intuition of the resampling step we will look at a set of particles which are initialized with a given x location and weight. After the resampling the particles are more concetrated in the location where they had the highest weights. This is also indicated by the indices \n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# CODE SNIPPET #\n", - "def normalize_weight(particles):\n", - "\n", - " sumw = sum([p.w for p in particles])\n", - "\n", - " try:\n", - " for i in range(N_PARTICLE):\n", - " particles[i].w /= sumw\n", - " except ZeroDivisionError:\n", - " for i in range(N_PARTICLE):\n", - " particles[i].w = 1.0 / N_PARTICLE\n", - "\n", - " return particles\n", - "\n", - " return particles\n", - "\n", - "\n", - "def resampling(particles):\n", - " \"\"\"\n", - " low variance re-sampling\n", - " \"\"\"\n", - "\n", - " particles = normalize_weight(particles)\n", - "\n", - " pw = []\n", - " for i in range(N_PARTICLE):\n", - " pw.append(particles[i].w)\n", - "\n", - " pw = np.array(pw)\n", - "\n", - " Neff = 1.0 / (pw @ pw.T) # Effective particle number\n", - " # print(Neff)\n", - "\n", - " if Neff < NTH: # resampling\n", - " wcum = np.cumsum(pw)\n", - " base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE\n", - " resampleid = base + np.random.rand(base.shape[0]) / N_PARTICLE\n", - "\n", - " inds = []\n", - " ind = 0\n", - " for ip in range(N_PARTICLE):\n", - " while ((ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind])):\n", - " ind += 1\n", - " inds.append(ind)\n", - "\n", - " tparticles = particles[:]\n", - " for i in range(len(inds)):\n", - " particles[i].x = tparticles[inds[i]].x\n", - " particles[i].y = tparticles[inds[i]].y\n", - " particles[i].yaw = tparticles[inds[i]].yaw\n", - " particles[i].w = 1.0 / N_PARTICLE\n", - "\n", - " return particles, inds\n", - "# END OF SNIPPET #\n", - "\n", - "\n", - "\n", - "def gaussian(x, mu, sig):\n", - " return np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))\n", - "N_PARTICLE = 100\n", - "particles = [Particle(N_LM) for i in range(N_PARTICLE)]\n", - "x_pos = []\n", - "w = []\n", - "for i in range(N_PARTICLE):\n", - " particles[i].x = np.linspace(-0.5,0.5,N_PARTICLE)[i]\n", - " x_pos.append(particles[i].x)\n", - " particles[i].w = gaussian(i, N_PARTICLE/2, N_PARTICLE/20)\n", - " w.append(particles[i].w)\n", - " \n", - "\n", - "# Normalize weights\n", - "sw = sum(w)\n", - "for i in range(N_PARTICLE):\n", - " w[i] /= sw\n", - "\n", - "particles, new_indices = resampling(particles)\n", - "x_pos2 = []\n", - "for i in range(N_PARTICLE):\n", - " x_pos2.append(particles[i].x)\n", - " \n", - "# Plot results\n", - "fig, ((ax1,ax2,ax3)) = plt.subplots(nrows=3, ncols=1)\n", - "fig.tight_layout()\n", - "ax1.plot(x_pos,np.ones((N_PARTICLE,1)), '.r', markersize=2)\n", - "ax1.set_title(\"Particles before resampling\")\n", - "ax1.axis((-1, 1, 0, 2))\n", - "ax2.plot(w)\n", - "ax2.set_title(\"Weights distribution\")\n", - "ax3.plot(x_pos2,np.ones((N_PARTICLE,1)), '.r')\n", - "ax3.set_title(\"Particles after resampling\")\n", - "ax3.axis((-1, 1, 0, 2))\n", - "fig.subplots_adjust(hspace=0.8)\n", - "plt.show()\n", - "\n", - "plt.figure()\n", - "plt.hist(new_indices)\n", - "plt.xlabel(\"Particles indices to be resampled\")\n", - "plt.ylabel(\"# of time index is used\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### References\n", - "\n", - "http://www.probabilistic-robotics.org//n", - "\n", - "http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/SLAM/FastSLAM1/animation.png b/SLAM/FastSLAM1/animation.png deleted file mode 100644 index 86c2950f709e3a0b97fa7b5509ae9951b7f8e883..0000000000000000000000000000000000000000 GIT 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-graphSLAM_formulation.* -!graphSLAM_formulation.tex diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib deleted file mode 100644 index 4d3b71ae50..0000000000 --- a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib +++ /dev/null @@ -1,30 +0,0 @@ -@article{blanco2010tutorial, - title={A tutorial on ${SE}(3)$ transformation parameterizations and on-manifold optimization}, - author={Blanco, Jose-Luis}, - journal={University of Malaga, Tech. Rep}, - volume={3}, - year={2010}, - publisher={Citeseer} -} - -@article{grisetti2010tutorial, - title={A tutorial on graph-based {SLAM}}, - author={Grisetti, Giorgio and Kummerle, Rainer and Stachniss, Cyrill and Burgard, Wolfram}, - journal={IEEE Intelligent Transportation Systems Magazine}, - volume={2}, - number={4}, - pages={31--43}, - year={2010}, - publisher={IEEE} -} - -@article{thrun2006graph, - title={The graph {SLAM} algorithm with applications to large-scale mapping of urban structures}, - author={Thrun, Sebastian and Montemerlo, Michael}, - journal={The International Journal of Robotics Research}, - volume={25}, - number={5-6}, - pages={403--429}, - year={2006}, - publisher={SAGE Publications} -} diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex deleted file mode 100644 index 25f02cd3bd..0000000000 --- a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex +++ /dev/null @@ -1,175 +0,0 @@ -\documentclass{article} - -\usepackage{amsfonts} -\usepackage{amsmath,amssymb,amsfonts} -\usepackage{textcomp} -\usepackage{fullpage} -\usepackage{setspace} -\usepackage{float} -\usepackage{cite} -\usepackage{graphicx} -\usepackage{caption} -\usepackage{subcaption} -\usepackage[pdfborder={0 0 0}, pdfpagemode=UseNone, pdfstartview=FitH]{hyperref} - -\DeclareMathOperator*{\argmax}{arg\,max} -\DeclareMathOperator*{\argmin}{arg\,min} - -\def\keyterm{\textit} - -\newcommand{\transp}{{\scriptstyle{\mathsf{T}}}} - - - -\begin{document} - -\title{Graph SLAM Formulation} -\author{Jeff Irion} -\date{} - -\maketitle -\vspace{3em} - - -\section{Problem Formulation} - -Let a robot's trajectory through its environment be represented by a sequence of $N$ poses: $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$. Each pose lies on a manifold: $\mathbf{p}_i \in \mathcal{M}$. Simple examples of manifolds used in Graph SLAM include 1-D, 2-D, and 3-D space, i.e., $\mathbb{R}$, $\mathbb{R}^2$, and $\mathbb{R}^3$. These environments are \keyterm{rectilinear}, meaning that there is no concept of orientation. By contrast, in $SE(2)$ problem settings a robot's pose consists of its location in $\mathbb{R}^2$ and its orientation $\theta$. Similarly, in $SE(3)$ a robot's pose consists of its location in $\mathbb{R}^3$ and its orientation, which can be represented via Euler angles, quaternions, or $SO(3)$ rotation matrices. - -As the robot explores its environment, it collects a set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_j\}$. Examples of such measurements include odometry, GPS, and IMU data. Given a set of poses $\mathbf{p}_1, \ldots, \mathbf{p}_N$, we can compute the estimated measurement $\hat{\mathbf{z}}_j(\mathbf{p}_1, \ldots, \mathbf{p}_N)$. We can then compute the \keyterm{residual} $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j)$ for measurement $j$. The formula for the residual depends on the type of measurement. As an example, let $\mathbf{z}_1$ be an odometry measurement that was collected when the robot traveled from $\mathbf{p}_1$ to $\mathbf{p}_2$. The expected measurement and the residual are computed as -% -\begin{align*} - \hat{\mathbf{z}}_1(\mathbf{p}_1, \mathbf{p}_2) &= \mathbf{p}_2 \ominus \mathbf{p}_1 \\ - \mathbf{e}_1(\mathbf{z}_1, \hat{\mathbf{z}}_1) &= \mathbf{z}_1 \ominus \hat{\mathbf{z}}_1 = \mathbf{z}_1 \ominus (\mathbf{p}_2 \ominus \mathbf{p}_1), -\end{align*} -% -where the $\ominus$ operator indicates inverse pose composition. We model measurement $\mathbf{z}_j$ as having independent Gaussian noise with zero mean and covariance matrix $\Omega_j^{-1}$; we refer to $\Omega_j$ as the \keyterm{information matrix} for measurement $j$. That is, -\begin{equation} - p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) = \eta_j \exp \left( (-\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right), \label{eq:observation_probability} -\end{equation} -where $\eta_j$ is the normalization constant. - -The objective of Graph SLAM is to find the maximum likelihood set of poses given the measurements $\mathcal{Z} = \{\mathbf{z}_j\}$; in other words, we want to find -% -\begin{equation*} - \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) -\end{equation*} -% -Using Bayes' rule, we can write this probability as -% -\begin{align} - p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \frac{p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) p(\mathbf{p}_1, \ldots, \mathbf{p}_N) }{ p(\mathcal{Z}) } \notag \\ - &\propto p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N), \label{eq:bayes} -\end{align} -% -since $p(\mathcal{Z})$ is a constant (albeit, an unknown constant) and we assume that $p(\mathbf{p}_1, \ldots, \mathbf{p}_N)$ is uniformly distributed \cite{thrun2006graph}. Therefore, we can use \eqref{eq:observation_probability} and \eqref{eq:bayes} to simplify the Graph SLAM optimization as follows: -% -\begin{align*} - \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\ - &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\ - &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M \exp \left( -(\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right) \\ - &= \argmin_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j). -\end{align*} -% -We define -% -\begin{equation*} - \chi^2 := \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j), -\end{equation*} -% -and this is what we seek to minimize. - - -\section{Dimensionality and Pose Representation} - -Before proceeding further, it is helpful to discuss the dimensionality of the problem. We have: -\begin{itemize} - \item A set of $N$ poses $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$, where each pose lies on the manifold $\mathcal{M}$ - \begin{itemize} - \item Each pose $\mathbf{p}_i$ is represented as a vector in (a subset of) $\mathbb{R}^d$. For example: - \begin{itemize} - \item[$\circ$] An $SE(2)$ pose is typically represented as $(x, y, \theta)$, and thus $d = 3$. - \item[$\circ$] An $SE(3)$ pose is typically represented as $(x, y, z, q_x, q_y, q_z, q_w)$, where $(x, y, z)$ is a point in $\mathbb{R}^3$ and $(q_x, q_y, q_z, q_w)$ is a \keyterm{quaternion}, and so $d = 7$. For more information about $SE(3)$ parameterizations and pose transformations, see \cite{blanco2010tutorial}. - \end{itemize} - \item We also need to be able to represent each pose compactly as a vector in (a subset of) $\mathbb{R}^c$. - \begin{itemize} - \item[$\circ$] Since an $SE(2)$ pose has three degrees of freedom, the $(x, y, \theta)$ representation is again sufficient and $c=3$. - \item[$\circ$] An $SE(3)$ pose only has six degrees of freedom, and we can represent it compactly as $(x, y, z, q_x, q_y, q_z)$, and thus $c=6$. - \end{itemize} - \item We use the $\boxplus$ operator to indicate pose composition when one or both of the poses are represented compactly. The output can be a pose in $\mathcal{M}$ or a vector in $\mathbb{R}^c$, as required by context. - \end{itemize} - \item A set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_1, \mathbf{z}_2, \ldots, \mathbf{z}_M\}$ - \begin{itemize} - \item Each measurement's dimensionality can be unique, and we will use $\bullet$ to denote a ``wildcard'' variable. - \item Measurement $\mathbf{z}_j \in \mathbb{R}^\bullet$ has an associated information matrix $\Omega_j \in \mathbb{R}^{\bullet \times \bullet}$ and residual function $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) = \mathbf{e}_j(\mathbf{z}_j, \mathbf{p}_1, \ldots, \mathbf{p}_N) \in \mathbb{R}^\bullet$. - \item A measurement could, in theory, constrain anywhere from 1 pose to all $N$ poses. In practice, each measurement usually constrains only 1 or 2 poses. - \end{itemize} -\end{itemize} - - -\section{Graph SLAM Algorithm} - -The ``Graph'' in Graph SLAM refers to the fact that we view the problem as a graph. The graph has a set $\mathcal{V}$ of $N$ vertices, where each vertex $v_i$ has an associated pose $\mathbf{p}_i$. Similarly, the graph has a set $\mathcal{E}$ of $M$ edges, where each edge $e_j$ has an associated measurement $\mathbf{z}_j$. In practice, the edges in this graph are either unary (i.e., a loop) or binary. (Note: $e_j$ refers to the edge in the graph associated with measurement $\mathbf{z}_j$, whereas $\mathbf{e}_j$ refers to the residual function associated with $\mathbf{z}_j$.) For more information about the Graph SLAM algorithm, see \cite{grisetti2010tutorial}. - -We want to optimize -% -\begin{equation*} - \chi^2 = \sum_{e_j \in \mathcal{E}} \mathbf{e}_j^\transp \Omega_j \mathbf{e}_j. -\end{equation*} -% -Let $\mathbf{x}_i \in \mathbb{R}^c$ be the compact representation of pose $\mathbf{p}_i \in \mathcal{M}$, and let -% -\begin{equation*} - \mathbf{x} := \begin{bmatrix} \mathbf{x}_1 \\ \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \end{bmatrix} \in \mathbb{R}^{cN} -\end{equation*} -% -We will solve this optimization problem iteratively. Let -% -\begin{equation} - \mathbf{x}^{k+1} := \mathbf{x}^k \boxplus \Delta \mathbf{x}^k = \begin{bmatrix} \mathbf{x}_1 \boxplus \Delta \mathbf{x}_1 \\ \mathbf{x}_2 \boxplus \Delta \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \boxplus \Delta \mathbf{x}_2 \end{bmatrix} \label{eq:update} -\end{equation} -% -The $\chi^2$ error at iteration $k+1$ is -\begin{equation} - \chi_{k+1}^2 = \sum_{e_j \in \mathcal{E}} \underbrace{\left[ \mathbf{e}_j(\mathbf{x}^{k+1}) \right]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^{k+1})}_{\bullet \times 1}. \label{eq:chisq_at_kplusone} -\end{equation} -% -We will linearize the residuals as: -% -\begin{align} - \mathbf{e}_j(\mathbf{x}^{k+1}) &= \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \notag \\ - &\approx \mathbf{e}_j(\mathbf{x}^{k}) + \frac{\partial}{\partial \Delta \mathbf{x}^k} \left[ \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \right] \Delta \mathbf{x}^k \notag \\ - &= \mathbf{e}_j(\mathbf{x}^{k}) + \left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right) \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \Delta \mathbf{x}^k. \label{eq:linearization} -\end{align} -% -Plugging \eqref{eq:linearization} into \eqref{eq:chisq_at_kplusone}, we get: -% -\small -\begin{align} - \chi_{k+1}^2 &\approx \ \ \ \ \ \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x}^k)]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^k)}_{\bullet \times 1} \notag \\ - &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\ - &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{(\Delta \mathbf{x}^k)^\transp}_{1 \times cN} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\ - &= \chi_k^2 + 2 \mathbf{b}^\transp \Delta \mathbf{x}^k + (\Delta \mathbf{x}^k)^\transp H \Delta \mathbf{x}^k, \notag -\end{align} -\normalsize -% -where -% -\begin{align*} - \mathbf{b}^\transp &= \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \\ - H &= \sum_{e_j \in \mathcal{E}} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN}. -\end{align*} -% -Using this notation, we obtain the optimal update as -% -\begin{equation} - \Delta \mathbf{x}^k = -H^{-1} \mathbf{b}. \label{eq:deltax} -\end{equation} -% -We apply this update to the poses via \eqref{eq:update} and repeat until convergence. - - - -\bibliographystyle{acm} -\bibliography{graphSLAM}{} - -\end{document} diff --git a/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb deleted file mode 100644 index cd3d9fd5db..0000000000 --- a/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb +++ /dev/null @@ -1,332 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from graphslam.graph import Graph\n", - "from graphslam.load import load_g2o_se2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction\n", - "\n", - "For a complete derivation of the Graph SLAM algorithm, please see [graphSLAM_formulation.pdf](./graphSLAM_formulation.pdf). \n", - "\n", - "This notebook illustrates the iterative optimization of a real-world $SE(2)$ dataset. The code can be found in the `graphslam` folder. For simplicity, numerical differentiation is used in lieu of analytic Jacobians. This code originated from the [python-graphslam](https://github.com/JeffLIrion/python-graphslam) repo, which is a full-featured Graph SLAM solver. The dataset in this example is used with permission from Luca Carlone and was downloaded from his [website](https://lucacarlone.mit.edu/datasets/). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# The Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of edges: 1483\n", - "Number of vertices: 1228\n" - ] - } - ], - "source": [ - "g = load_g2o_se2(\"data/input_INTEL.g2o\")\n", - "\n", - "print(\"Number of edges: {}\".format(len(g._edges)))\n", - "print(\"Number of vertices: {}\".format(len(g._vertices)))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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UhqCA2z4y3LCB6O23Wbbq358ng1lHUvIaH2zCk+qcNM3/TjiEX4ux9HIl5xTD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RpstvfdV/V9PP00ricqbH1fJyPiOuzxcKkiC4aLDrx8uh4uDpJJeniy8JHG8+35PCRVD2fMJiaa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\nnQRuCt4U4/PCC8pjvv/+9I97+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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "g.plot(title=r\"Original ($\\chi^2 = {:.0f}$)\".format(g.calc_chi2()))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each edge in this dataset is a constraint that compares the measured $SE(2)$ transformation between two poses to the expected $SE(2)$ transformation between them, as computed using the current pose estimates. These edges can be classified into two categories:\n", - "\n", - "1. Odometry edges constrain two consecutive vertices, and the measurement for the $SE(2)$ transformation comes directly from odometry data.\n", - "2. Scan-matching edges constrain two non-consecutive vertices. These scan matches can be computed using, for example, 2-D LiDAR data or landmarks; the details of how these contstraints are determined is beyond the scope of this example. This is often referred to as *loop closure* in the Graph SLAM literature.\n", - "\n", - "We can easily parse out the two different types of edges present in this dataset and plot them." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def parse_edges(g):\n", - " \"\"\"Split the graph `g` into two graphs, one with only odometry edges and the other with only scan-matching edges.\n", - "\n", - " Parameters\n", - " ----------\n", - " g : graphslam.graph.Graph\n", - " The input graph\n", - "\n", - " Returns\n", - " -------\n", - " g_odom : graphslam.graph.Graph\n", - " A graph consisting of the vertices and odometry edges from `g`\n", - " g_scan : graphslam.graph.Graph\n", - " A graph consisting of the vertices and scan-matching edges from `g`\n", - "\n", - " \"\"\"\n", - " edges_odom = []\n", - " edges_scan = []\n", - " \n", - " for e in g._edges:\n", - " if abs(e.vertex_ids[1] - e.vertex_ids[0]) == 1:\n", - " edges_odom.append(e)\n", - " else:\n", - " edges_scan.append(e)\n", - "\n", - " g_odom = Graph(edges_odom, g._vertices)\n", - " g_scan = Graph(edges_scan, g._vertices)\n", - "\n", - " return g_odom, g_scan" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of odometry edges: 1227\n", - "Number of scan-matching edges: 256\n", - "\n", - "χ^2 error from odometry edges: 0.232\n", - "χ^2 error from scan-matching edges: 7191686.151\n" - ] - } - ], - "source": [ - "g_odom, g_scan = parse_edges(g)\n", - "\n", - "print(\"Number of odometry edges: {:4d}\".format(len(g_odom._edges)))\n", - "print(\"Number of scan-matching edges: {:4d}\".format(len(g_scan._edges)))\n", - "\n", - "print(\"\\nχ^2 error from odometry edges: {:11.3f}\".format(g_odom.calc_chi2()))\n", - "print(\"χ^2 error from scan-matching edges: {:11.3f}\".format(g_scan.calc_chi2()))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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yZfp370hhD5BKYf1hEiidfD04ZxyJwZUESmcPaMmkcsGJF7CoYjtevettcsoh\ntGyshKPVTEaN0+cwI/yuwHWx8j42IaEo4m/P0yOpkny25o9l6BDq6rBViJKQwPdh7lxtTikgSpUv\nFl9wAVvfp/Pg/pT3WTp0V5LDU1rYm+exT2JG+F1BOo1Y2gZfxWN8vuVuq+SzNRg6hHQalXAIlE1O\nbFY+/xp2mMNGkFy+3AEryn9b6AQ2Wfg6DVOm8sor2lKnI3MEG3oGRuB3EaU2+Ls9o23wlbHBN3Q0\nkb3/shNPBxSJN1/BIiRfiOBTaqlz3HFlI34F2KHPy+dMo2HfKoKLL0H22w+++U244IIuvhFDZ2AE\nflfgutgSNNngF/PZmpAKhk4glWKT7xYsdkJQkVgPm1nqXHMNjB8PW26JisXAtoklHfYfFuUIlgCC\nAFmyBLn2WiP0+wDtEvhKqeOVUm8rpUKl1J7N9l2klHpfKfWeUuqQ9lWzdyP7p8nrqDkQi5W9N+oc\nQ6eQTmPFdYwdRLARLfybx9W55hpYsgSefRauuAKVybDj1aOxHbvMIxhoUgEZei/tHeG/BRwHPFta\nqJTaETgJ2Ak4FLhJKWW381q9lvnzoeBmE4ZSdLgxFjqGziKVwvrZadoRKyoKofXgZ6lUk1VOLge5\nfPF7TT4Dxx3X+fU2dCrtEvgi8i8Rea+FXUcD94pIo4gsAN4H9mrPtXozS+50m6xyVBjoKIalFjoG\nQ2cwejRBvCKKja8Ft6jVB3VYtAgmn1AMmIZSqIEDterHxL/v9XSWDn9LYHHJ5yVR2SoopcYopV5R\nSr3y+eefd1J1upelVmXRKiceJzAqHUNXkEqRv35S0xjfBsjlWh5kZLO8eOxEfr5TlkeWpQljOmCa\nqqiARx9tu7C/4ALYbjuj7++hrNEOXyk1CxjUwq6LReSh9lZARKYAUwD23HPPvqfjyGY55MlxRauc\nceMIr53UpFs1GDqT5Io6Aorx90VANYup8/VTWWKHVLGH+DykHOr+nsEZotMrttXTVrwsDb+6kIqX\nIu3utdfqbsbMCnoUaxT4InLQOpz3I+CbJZ+HRGX9jnC2W2KVo2CedrqyjdOVoStIp5F4giDXEEVn\njSx1dtkFgPm3uzx/zyJ+LDqsgm35DHnfheNX42WbzYLrsuL7aWYuT/HCjVkum1NFBfVASXKX226D\nY44xz3cPorM8bR8G7lZK3QAMBrYDXuqka/Vo6qhkAFZT3lr/qBGEM5/R8U2MSsfQ2aRSNF4ziYrz\nzgSiFHeNjQR3TCP3l6kMDX22VLEonB86+1rzZzIS8LJ/mgULYPDoKmKhj8LhWjIc4rg4+FhQlnlL\nvlyKqqrSydiN0O8RtNcs81hnL11qAAAgAElEQVSl1BIgBTymlJoBICJvA/cB7wBPAmeJSNDeyvY6\nslk2njCuLNHJ4gG70PSXMCodQxewfoP29SiqdQTvgU+JhY3ECIhLYzGFYRjC5Mn6ixdcQDhkCMG+\nwwguvoSGfauYMWoasbAYOvnuMS6/m5UmlnTAtlG2TbD+RpF1kOioncYwocfQrhG+iDwAPNDKvquA\nq9pz/l6P62LldAwdQWciWnK/y1Ymjo6hK0mnCe04VtCoP4uw9xePRM9l0c6+Kd/u/ffzv6NGsdEj\nd+msXNEWx2fYMLBeciDnYzsO25ySbvLuLej8Y0Bu/yrCXCNKFHZn5HuIZh1lawzZbDF72OjR8Oab\nyNixetay3nqwYkXH16OXYYKndSZRHlslRfVN9rY32QcLscTEGjd0CSLwVXILNv36wybhbhMgKCyk\nKTdDkyqmvp4NH70bSsoFiCUddrp6NDB6VWGbSpUNXOw/TSIY+0sIA8Jzx2HtskvLA5vmgrtEaOd/\nMprPt03xn2lZNn5kGskKeH3X0SxbBj+5vYpY2Igoi0nbTuZf9i7c/G4aB51gJn/LbcTI6/sB1MqV\nsP76RuiLSI/Z9thjD+lLLH3ck3ocCVAijiPvnl8rK0hKHkskFhOpre3uKhr6IrW1knMSEoB87gyS\nFSQl0Mu1Ei3b6veJhIhtiySTItXV4jv6uNJjmo5Vau2e15oaCSxbBCSPEtl2W5Hx40WGDRMZMkTy\nvxkvH0+olbwVkwBLGmNJ+dN3a6UBp+ma9STk59RKfbOymxgrOaymejYSl5sYq6/T/B6bb30U4BVp\ng4w1I/xO5O3JLj8oUd+E/5iOg1bxUEg2bTB0JFOmIGecQcGtvdL/lACaFlQhUuHsthvcdFPZ6Dqe\nzZLbv4og5xMqm/jmA+HTT/V3LQu1Ns9rOo2VcMjXN+hIne+/D9de27Tbuu5aNotmGAqw8o1s+8Z0\nYuSaZiFxfH4cn048Vyxz8NltV1BvWkgYalWTChhxLKhH43rNAHRsoHy+/J7XW29tWrJPYoKndSJv\nfFSJRA5XYtss/GI9QqWDVJmwyIZOYfp0oKh3h3JhT+H91luXhVMAtNB/JsN/hp2OiCL89DMUkMci\nJzHthtvWcMmRXr9+0Leb6rNqvaS4bmBZDD5rBMTjTSokO+Fw4J9HYDvxptNaCYfUzaOxb56ss8VZ\nFqoiwTd+PRrLdVFjx6LGjtWxgWprUYUcAEaHr2nLNKCrtr6k0vl4uldU39i25O245LDFtx2RsWNF\nPK+7q2joi9TWrqrOGDRIPtpkxzKVjowd2/o5amokj1bHBMqSxVvuJfU4kleR+mctnt3c5NpV1UOl\naiLLEonHi+oiz9N1K/2PtFRWKK+pMf8lMSqdbueZy11+VFDfhAoloZ7ags4VaixzDO2l2YLn8uXw\nat0u7LXhZiSXf15UZwwdSuVL84BoAdayULvv3vp502mspEO+3icvNhUV6FDLEiK+j1oLy7LYL8Zo\n05Dp02GzzZC770EkJMDGPvpI1KBB5Rm2mi3+tlq2unJDqxiB3wnMuznL0jcWIXbUvLZN4AuQN85W\nvYXWzP7WsUy8LMHTLvkfpmnYPUVjo05p+9WTWQa/59Kwd5plO6Wor4dPH8iy/X9dZFialbumsG34\n35NZBr2rvVs/2TqF/XKWqpoqYoFP3nL46eAMH30ET0kVyeYer6++il2iPiGUordtK4JUZTIEt06D\nO+5g4w9ewSYkwEJhrRKaYY2MGaM3QJ11FvPOm8b2L9yBeugR7Ioof66hSzACv4MJbpnCTr/4JTsT\nYNsxOO10PhiwO1tec7ZeSDPOVl3HGoRxsFeKL76ABXdn2fQtl//tnuajoSmcV7MccJX2Jg1th9oT\nMtTXwy8frCIuPoHlMH6PDL4PN7xeRRyfnHIYuXmGXA7+XleFg4+Pw/B4hiCAmaEuC3EYToYXSLE3\nWTJEx052GEUGoFj2N4eqFsrOJkMal2p8bV4Z+hw9wGWTbSDxnI+SZh6vQYBd8tlCinHxWxshp1Ik\nXJfQymOFIfnoexLkV99ZrIlUip0Pc+GFPLYEaz1jMLQPI/A7kmwW+cVZxAoBafN5GDqUfz1aZ5yt\nOpIWBHn4fJblj2ihvXBwCv/ZLPtdVoUd+AQxh6sOyLBsGVz1YlEYH0QGoUSY4vDLSJgeFAnTMO/z\n2X0uToIoJlIAoc/2/3WJxbQlic5W7HPSIF2WqNPfVcrnopSLsiDxjM4gpZTP9Ye5vH5Yil0fd0k8\nrssty+e2n7gAVNzlY0Vlt49yEYHEncXj/jHWJffDNOo0B8n7xByHkbemddtUOVqYhyEiEi3Y6sAJ\nBZViCFitxcUvJbK0CRsasSWkEJpBGhvbJaRjB6XJX+mQ9xtRdJJjlqFFjMDvSFwXS4Ki5UEY0vjZ\nV3z2yjJCK6bn2MY6Z+2IhHvuh2k+3CLFl49l2f03VVh5n8B2OH/XDJ9/DncsrmJ9fOI4nBgJ7f1L\nhPaGr7p8I6nN+mKRMP79AS6JCkg8URSm/zzTJb9vGuuUojD9fSat6xIJ05jjcOa95WW243DCTauW\nHXr1qmU/vDjND1PA7ml4WpdbjsNOv4iOnV4s22FsVPbPYtmQUWktbLdpIaJl5PEabFxJ/hfnaEck\npUDCEksdVUx7uDoiSxtrwgTCmU+Vh1+YOFFb+KwLqRTqD5MIz/wlKgiQceNQ6zpjMKwVRuB3JOk0\nKhZD8kW7YfuPNzI6DLUp5umnly9Q9WeajdJXrtSqlaUPuCzdNc3LsRSxl7P8ZoZWo+RwGB0J8j0i\noS2Bz85fuGw0ABKLtXC3LJ9pJ7uoA9JYYxwk5xN3HH79aFpft0TwHnRlVDa7KEy3HKnrw1atC9MO\nK2sWkmCdvt/KAmcsmyUfKXaUSFn2KgvRs8+2jNJTKZgwATVnDvn6+ib7flm+XOvy19GXxF5aByrE\nlpCgwcc2s96uoS2mPF219QmzzNpayen0zxJalvayBQltW5uQ9XVaMJXLPevJx+fUyBu1ntxzj8i0\nMz2pt5KSw5aVKinVG3qyN9qMNYctK0jKPsqTqwfUSC4yD8wrW175UY28NtmToCKp27NgIuh5+r3d\nzGywJbO9tpb1dmqKbVdqChmABEp7fq/V/XreKt667fJc9TwJk/r3rseRZaOMqXJ7oI1mmd0u5Eu3\nPiHwPU8acCTf3PY4kejdD/RqBOXymZ688YbInOs8aYzpP3GDnZSxu3py7KByQb43nlxIURjlsOXB\nH9TI09U1Eii7qXPMXVHTfkHen/H0bxE0ew5z2BKi1u15HDiw3MZ/4MB21/HTY8dKPQn9PKyljb+h\nSFsFvlHpdDSuGyU4oSkSoSgFp57aM6esq7Fkkf3TLN0+Rd2jWbb+eRVWTi+AXneI1ptPfElbqFg4\njInULXtH6hYCn93/5zJgc0h8WlS3PHi2y4ZHpokdWdSHH31jWl93ji5TjkOsKt26ymNtbLX7K6kU\nL/3gbH74fDGcwdJNt2WjLxag1kalU0pdnVbjfPklDBzY/tAgqRSbf98l/0CeGAFho49lVDudihH4\nHU06jdgxgiDAAgIs7IpEz7E1LhHw9fXgHFaF8rUg/+uoDP/9L5z/hNab+zgcHgnyK0oWQK1nXbbb\nqMRCRfnUnuhiV6WxztZ685jjMObutL5mVVFHvvmJ6bXTXRtBvs58e5l2tiro7wPLIURpx6t1NR7o\n6PhP6TR25OQVhDbhe4tIZrPmN+8kjMDvBCRK/5DDYslme/CtK3/W+Q9wKyP1ZY+4LP5WmledFCsz\nWU7+W1GYT+VkTo8EeZDzWfBXlw03LJofKuVz3XAXf5806oqi1cpFM9L6/CULoN89J7ruTu1fcDR0\nDI077gZvzmyaaQ747D9YhCjbhkmTekZbR05eCy+bxpZP3UF86q3IfVNRJktW59AWvU9XbX1Ch19T\noosGvWjbSbrJr66rFf+Aavn85+PFjyclr2xpjCXl4gM9OXX7VXXnv7XKF0Ff3Wus+PGkBJYtQUVS\ncs92wAKooWfgedKoimGFRSnJFcIHW1bPMyBoFr9HqqvNc7UWYHT43UQ6rW3uA22P3yavxnVg+Q1T\n2Og3ZwBQOXsmAQobIcz7bPK6y7cHQoKi7vzhcS6bHJMmdkhxVP69SeXJLCwzIu87uC62FM2DESmu\nK4Uh9DRnp6b4PdrJK3xqFtacOSYfbgdjBH4nEITSFAZWVDv0pS2RzZJ7ymXx9Q+yA8V4KZZSiLKI\nJRzOfyS6VonufLMfpVe/CFqKEeS9Htk/TR4bK8r6hGURhEIM0fmVe1ouhkL8nosmwDOziK1DoDbD\nmjECv6NxXeLiF2OQKzpOX5rNIlVVqHqfbUtSGShA/ebXsPHGZhHUAMCLL8LuxfE9ohR5YlhWgJXo\nod7eqRSJiRPIp+eQ830UNrFCDH7zzHYIRuB3NF99VQytgF4j6ajRVP0TLvF6vaAaKFBHHwMrV8KI\nEU3RCMswwr3fsvwRtxjTCSAImccefOf477HJuT3Y2zuVIuZmeOT4aRz80R3YU25FTZ1qVDsdhMl4\n1dHMK5rCSeFdB4ymPvpnlof+tIg8NoGydVjZ8eNhxoyWhb2hX7NwRSWiLe6j51DYk1fY+OGp3Vux\ntpBK8d0jhuoOKwyKa2CGdmMEfkczYgRQmlJO4M0323XK+36VZZPjq/jRV7fixBX2GaebEY+hVcLn\ns4x66WydfAdQShGgiBGieonw3OrkNIHtkMfSjos9bZG5l2IEfkczZgzqmGOAyOFFBM46q+25QAtk\nsywdP5Hx+2WZO8ltivJohXmTMcuwWj7/p0s8SgbepFZEEWD1nmitqRTvnTmJEBvJhzoG/9r+hwyr\nYAR+ZzB8OFDU4zfFwG8jjW6Whn2r2PC6S5jwXBXpEZXYScckPze0iXc/a67OAZCe5XDVBr5TWYdF\niEVI2NAIEyYYod9OjMDvDOrqCFFNevw2TUmzWcKaiTxxaZZJx7jEQj2iT9o+h+xRpz0Pr7jCqHIM\nqyebZe+7i+ocmtQ5URqUnmaOuRoSh0RhF7BQEhLMnAVVVUbotwNjpdMZpNMEloMKG1FAGAr2atLC\niZcln65C5Xz2x+G1rSahGhzI+0UbfmNxY2gD4dMuMYoOV1qdYxEqhdXbZoeRbb516QTCWbN0Xt0G\nHzXbLToJGtYKM8LvJCQsTqZtBGm+WJbNIjUTee66LDed4KJyekRfYflcNKYOe7YZ0RvWnoVvfNWU\nmao3q3OaSKWwfj8BK5kgwKZRHG57oJL6Syeakf46YEb4nYHr6pAG6D9dgEJh6dCyoL1l99cj+u/h\n8FjlJIg7SKi9YjkgbUb0hrUnm2XIP24AimbBAtq7VnqXOqeMwkh/tsvTL1Yy6uFxxF/xkeudrguy\nNmoUPPGEXp+7887Ov14nYQR+J7AsXkkFFqESVMwmzIWoIMA6+2zqZs3F82B4rhiR8spxddhVLXjF\nGgxrg+uiJCzxASkKftWWpOU9mVQKlUpxxMSJBA9HEV4bfKzZLqqDvNib//+Wzciy9EGXAfNcBrww\nUx93111aXdZLhb4R+B1NNst6F52DIgfKRh1+OOrBR4ihY4Ns/I9aDkZb3AhgOw4Ukn0YQW9oB5+F\nlWwSWecUUKATlp92Wt94vqL4+UGDT6M4PDCzkp8wEVWYFTenmSAXgfl3ZVn5uMv/vpdm6ZcwODON\nnV++HVsC8pbDb9ebxPYr5zI6vJ0tCbAIgBJnyocf7rLb7WiMwO9opk3DzuvFWgkDnR1IKQLRCyY2\ngmXnUaefru3pzYje0BFks2w8YRxWwdkKLZxCFFZFRc9JwNNeogCA1myXux6sZOQz4wif8bXZ8qRJ\nWm2VTpP/fooFd2fZ6rQqrMAnsByu+sYkhnw+l9HB7cQIyN9jAyrK2hYleg8bufrrX2Jrly8UEFBM\nIgPAt77VDTfeMRiB38kEzz6HQspWx1Uspv+ARtAbOgrXxc43agMBSlQ6ttU7F2tXR6TeOS2ciLwc\nZWKrr4exZxKKIm85/Do2iSP86WxDIzFCCBu55FMtyBWFaLa6cyy0WYhCWRYxCbAkakGlsONxnRIy\njJLH3Hxzt916ezFWOh3N6NGoSF0DYGlfQaBket1T89saei+VlViEq6hzFPTexdo1YFelsWOqycFR\nSUiMgFjYyI25X3Iw2pQzH7lvaV9j3UIhConFUY6D2DYqkcAaewb2zZOxKhLayTGRgDPO0Cqh556D\nmhqYM6dX/3fNCL+jefNNwiAsurWDFvIihKCtcPrK9NrQY1j6fh0bRUlwoESd09ts79eGVIpgiy2x\nFy8ss0oSFEoCbbePxXtbHsTSA0eQ+vs4JPBRsRjq1FOxCv/D5sYSu+zSsgFFLxb0BYzA70CWzciy\n3hm/wI6mjNok08LefnuCf72HIkREyvWBBkMHsHBFJTthNS0wAlh9UZ1TypQp2IsXAsUwJiEwf8cj\n+fb7M5BAZ3bb6R8TdBuc2UZB3ocNKIzA70De+9009iQoG20EWNj/+lexEyjE1emjD5ShG8hm2f6W\ncU06aSgJ3NdH1TkATJ8OlPscWMB3zh0Ou4w3yX9awAj8DmTjAcX3Cgi+syP2e++hokWhAIWlLBPq\n1dCxuIXYS1JMvAMoy+q76hyAESNQM2eW+xxYFqquzgj3VmjXoq1S6jql1LtKqTeUUg8opTYu2XeR\nUup9pdR7SqlD2l/Vno/z89H4xPUDGI8TO+9cQhXTC0RW1Lfmczpc8pQpMLGZe3g2u2pZa+VtLTP0\neZbalYSoJmVO08LtiSf2baE3ZgzU1qL22ovQipPDxpcYwYeLzH+gNURknTegGohF768Brone7wi8\nDiSAbYAPAHtN59tjjz2kN/P5w57Uk5AAJZJIiNTWSqPlSB4lAUpCPcmWECSHkhy2rFRJ+ck2npzy\nHU9WqqTksKXeSsqv9vbkiCNEzvm+Ls9jS4OdlMsP9eSKw4pljbGkTB7lyZRTPWmwk5JXtuScpHj/\nz5MXXhB5+zZPPjuvRuqf9iQMo4p6nkhNjX4tpXl5a8cZeg6eJ42xpOSwJFCW5EueMYnH+89v53ny\n9rCxUk9CctgSJpP9595FBHhF2iCz26XSEZGZJR9fAH4UvT8auFdEGoEFSqn3gb2APt3tbjzPhchh\ng3wepk/XNr3RVLsUC8EiQOEzfD2XfA7iosMtEPp8e7GL25givUgnRbcJkMCn4gUXEZrKwrzPx/e4\nBAHY6LKc7/Pw+S4ukKEKBx//BocfkiGZhEfqdVlOOfxsqwxvb5Rix/9l+ctCXR7YDtP3m8SIOeOI\nBT4Sd3j2sgz576cYMACcV7Ns/i+XAUenSR6YwrJo0TW9XWWrK29OW885ZYrW+44Y0bolRm/DdbHy\nPjFCAtG2YVJwIupP60WpFDse6hI8m28Ku2D3l3tfCzpSh38a8Pfo/ZboDqDAkqhsFZRSY4AxAEOH\nDu3A6nQ9sYPSNF5qa6tf29Y6xmfnEDQ0FD35iGyGbRvQoRVG3ZrWO6oc8H1ijsNZ/0hzVgrIpsvK\nxz9efmzccbgyk0YEOMhBfB877jDyj2lOed4lMc3HFh2z5/K0i5+DxHO6Y1Dic/gGLvXfSnHAvGJW\nLQKfQc9Nxw6iDiTn89TvXK4mxd5ki53In3Unsl6zTuS0oRksC25dUCw7fZsMALfOryKOT145nLtz\nBseB6+ZWEQt1R3PbSRlWfDfFVh9nOXayLpe4w/wpGWTvFPZLWTZ6zWX5Hmk+3zaF9VKWXc+rIhb4\nhDGH6b/IUF8PP76tCjs65+/3z7DZp29y7jtn6LabOZPAimnnmoQOwKX2SbW9g+lBNG5QiULHvC8d\nWAig4vG+rcNvTjqNlXTI1/vkxebfMxaxQzrba37LrmCNAl8pNQsY1MKui0XkoeiYi4E8cNfaVkBE\npgBTAPbcc8/mA+Heh1LRv03BLrsQHHsc9j26WQRQAwZoZ45jjllVuGRaCKAWuZKv6VhVUqbSaXZO\npWBn4D7dMdiOw8FXpfV3q4plI6ekGdlCx3LgpBHIuDlNHcjPb01zxDaw6a3lncgVB7g0NoLzfBQM\nDp8jN3QJhWIHIj6HVLgoIF5S9v0VLvllNCV7kcDnk3tdrrorxYW4jMDHIiDX6HP7yeUzlvVxGEmG\nNC67R8cFOZ95f3ABPdspdF6bvunyg0ZdXljQtMI8FpBr8Kk50OWj7eCP71QRD33EcXhvcoaBh6fY\nfHOwXuyhHUE2izpPh1NQShEK5Z62/c0EOIqqKX+ZhvrLHWz3zK0EB0zVocZ70u/WnbRF77O6DTgF\nrapZr6TsIuCiks8zgNSaztXbdfhSUyM5bBEQsW2t/9522zLd/f8GDJHgqi7Ui7ekh2+PDt/zRJJJ\nfX8FPWkHloXPe7J8ucj8uzzJOUkJLFvyiaQ8fZUnLx9XI/mofQPLlndPrpHXJhePCyqS8r8nPfGf\naeE6tbX6d4m2IBaTwLLFjyfluuM8qd2m+Nv52HIhNQIi+8U8WYFeW2mwk3LLyZ7ccovIved68s7o\nGql71JMgWIe27ojfdNgwCaL7yYPksCRUJWtFhWewv1FTI6Glf8scluQOrO7z+nzaqMNvr7A/FHgH\n2KxZ+U6UL9rOpx8s2orniW/rRdrQcfRDNn58k7APQRqxJYcWTr32IWyrYGtPWUvlLXUYa3PO2lqR\n6mr92sK5w2RSQlv/NnOu82TyZJHHhxU7ghy2XKRqZG+KncAKkvJDy5MjKj1ZWdIxXHWEJ5NOjBbS\nsSUXT8rMyz155BGRZ6/x5LXja+Tt2zx56y2R997T29u3efL5eTXy8XRP5s8X+eADkVf+5MkHp9fI\nm1M8eeopkWeu1ou0BSOA5pvYtgSxuF7EteP6Xvsb0XMSKEtCkDxWn1/E7SqB/z6wGJgXbbeU7LsY\nbZ3zHjC8LefrEwLfSmiBn0gUH7Dx4/VIf79hTSNUH1vmHzLWWMGsLZ1pOdSGGU3uWU+Wjq+RQBVn\nGjMPqJF/7lneMdRsWCOXOavOGpp3FnvjCUiL5S2VXUjxnGHJjKXpvW1L7qhjpJGY5LDKO8b+hOeJ\nVFdLXq/USF717dlOlwj8jt56vcCvqdEPViQIVnnAIuER2rY0qITU4/T+0X5/oC0zjVbKSmcNC+72\nZPEvavSzET0jr59UI3feKeIdWa6uemVEjbz6o/Lnaf6YGnn3Dk/yiaSE0eh1lRF+MimNp41dVbXY\nH4naP4ct9Tiy6PCxffZ/1laBbzxtO5J0GpVwyDc0olCretRGC7DKdbEXLELdequ2gmnw+bhmGkP2\ncXvewqBhVa/NNi6kAzoFX7SQvnUqBVsDd+jFcctx+O45ab6bAr6VhlnF8j3OT+tzPlYs2+aU6Lzf\nia7z1VeEN05C5Xx9rXgcJk1iwb9hmyjjWp8OnrYmokXc3JRpqL/ewRaP3YpkpqKe7seLuG3pFbpq\n6/UjfBGR2lrxieup5Oqm0wU9o2VG+/2Otq5ZrO7YiMxBzQwFxo6VBityxIrF+qcOvzk1RRVcX1Xt\nYEb43URdHYoQmxB8v3XHl0LmHtfFnr8IdVtxtP/p6AsZxMdYxx0H11yDeFnUM27bHJPa6+zUXZTW\nB3pW3Tqa1uK8tFS+mpgwjW6WJd4iAmUTswDHAbSZq00Iovp28LS2kk5jVei0iDmxWfD0Irbtr/b5\nbekVumrrEyP8EksdKVjqtOE7hdF+TsXKdLJ32SPLFu6OH+LJHnuInLmbtgophFe45WRP7jzLk8aS\n8AqzrvBkdo0nfjwpgbIl7yQle4MnL9zoSa5QlkjKB3dqq5DPHvLkfxfpMAz5vK5a+HzHW+QEV9bI\nyownn3yirVPm3lRSx1hCGi1HAsvWC99j+67etV00hVSwJRcraSfPkwZVEt7DtJ3G86T+lL4begEz\nwu9GREUvqm2OLyWjfTV5Mnz0UZOD0FHxJ0iERUen4wa6/G3zFDu/4RKnGF5h4TQdcuHEkvAKsy5x\nAdi34MDk+zx0ni7bo8Sp6dZRzcIwTHTYlwwKmEXRW/aX22fYeGO46sXIAzbm8NA5GTbYAKomVmHn\nfcK4w5O/ztDQAEf+QXvA5m2HC/fM8OWXcPN/9PkEh2PJ8ALayeqKgvNUPsRGh56QxoDgllqC26Yy\n4zcZtvxRih2+ypJ80e27o/82Es4uhFQIEEHnR06l+Hh6loES+S8WXg2QSlHhugQqjy0BYUMDatq0\nfvcMGYHf0bguluS1x2M+3/ZYJtHUXX31FVx7LaC9QjcYMRzuv7/JM/akW9Kc1MwzNu44XDUrTX09\nWIc7SF57x55xe5ogAH7mEOZ9rLjDyD+lCQU4OyqLORx0aZofzXVJ3F/sWK480CUMITG7GIZh37zL\nioVFz9hc3ufVG1wADioIbN/n+RpddgzFGEDbLnHZYENIRGVK+dx4pMv8E1MMXphGLnMIo2xEIoLk\ncoBgI4R5H2+iiztRd0p5dBiFF67MsNPPU1T+u4epp7qAp1+vZBiKUFllC7ObvO4SK6Ty60+xdNpC\nOo0VtxE/QIkgd9yB6m+5pdsyDeiqra+odPKJaNHMXsdFs8huX8aPbzpnuxb52lK2Dp6xYTIpXz7m\nycJ7y71dP57uyZePFU0SV3u+lurjeVpF4ThN11l8nydzT2jdrj2PJXlly5IfHCOfXVUryy+u0eqo\nzmirFsrDUMT3Reoe9eTr32m12MqVIvX1IitmeZL7fcddO39wtQSRd20Yb+Zc5XnSaFQ6rTN2rG6b\nPraAi7HD7z7ePb9WGolL0NscX7rCg3ZtHKdW0ymFyaTMu9mTJ/avaXKuKV37yGHJCpJy1GaejN6u\nGHq6wU7K1Ud7csPxnjRYxTWQy4d7csGwkhDVKikn/58nP922WLZSJeXYQZ4MHixy6IC2OUo1L9vX\n9iSdKJatVEn58daenLp9eXjs3x7gyWXVXmRxY0ujnZQ/j/TktV1GFm3uC1up0PI8aVDNvL0NRSLb\nfB9bGpQj4Rk9YI2oA7oXUKgAACAASURBVJwJ2yrwjUqnE9jCqdMRM9dkqdPTaKuVSEeXtbU+Jfbv\nKp1m11SKXXcF9reRXNi0XiJAjBClfH76TZcVK0tCTwc+QcZlWQ7ssLgGUpF12ShRcpz4HGC5WDFw\nolDUiM9PBrtUfi/FIXNdnFejgHHK5/dpF9uGxKyiympitQsCiZnFsov3jVRlc6IyfI7YwCXXLDz2\npm+55PO6jjECcoHPp/e6DAmeAEqyWkG5nb3rEo9UOkEuMCGCmxPZ5r99/jS2z96BTLkVNW2qfq66\no52mTCE8YywqinOqttoKPvyw867Xll6hq7a+MsIXL1IzqBZUF4aOp7ZWq4lKR72W1WYv2LUO+Cay\nTiqwjigLRzYb4VdXl7dF9J08lvjEJbjF2OG3RP2lLQQ67ApqayVXVS0vjamVSw/2JNcsMZKAyA47\nrPVpMSqd7uX336yVFzeuNo4vXUVB7z92rG7z7gju1lVlI0eKDByoX1uitlZySsfS8WNmwNEiTWat\nloQd4aDWwu8U3FIry/apllkn1MpJJ4lcMLC2TO04nWOaop2uMlhZS4zA7068oj7WjPANXU5NjZ65\nFMIDV1WbZ7AFFv5uHdfamgn3ukc9ydmOBCjxLUdO/j9PfpkoF+4/p1aeoLpsNF+33V4S2Hb5ekwn\nj/DblcTc0AquixPpY6WhAaZN6+4aGfoT6TTKcRDL0h63mVlQVWUSezdj6HotrLU1J5uFiRMhm0UE\nPp6eJbdfmvC3F+P/MM1Rm2X5+xHTsAMfCyEW+vzg39M4yp8O0LSudN0PplN9y4iyrHcDf/0zrDlz\nUMOGQZQBjx12gHfe6bR7Nou2nUE6jcRsJB9N2O64A/qbva+h+ygE6ZswgfzMWcQIkX7qaLRa0mnC\nmPP/2zvzMCmqc3G/p6q7molLgHHBgEsUNagkbkHnRrF1zBiTeJ1IrknE4IYwBpOYxIxLTKI/tE24\nuQZNRBujRuIWFTUmbshI41LtLu4ad1TABUQCDFPdVd/vj1O9VM8MM8OsMOd9nn6mu6a7+nQt3/nO\nt5LPt2ArhaqujpT4aG6G+DdqUTmPvOVwzOeb+Panc2jA093bxOPIT1orcxMnwhbjJ6Cm6pbfChh6\n8gSYMkW/KPRVnjJFf2Dhwr76xcak01ssO6ZBl1cY7CVqDf2H60rgJEqmhXjcmHYqePlnacmB+CCB\nUpKzE5IPQ2PvoL54D/sgL8T2lrTdEDHLPPdfDbL4767OeVAVuQ/lDXd6GYxJp39JTJ5EjjgBSi/X\nBmuJWkPnOOss2HVX/benqKlBffNIIDQt5HLGvFjB7g9eEZbyAESw/RZsfGJBC0epfxYbwytgz/wi\nDhzxFkEsocumJBJ8+Q+T2P7YGliwAC66SP8trKKmTIH77y9p8gMAY9LpJUSgaMFTg6qVtAG04L79\ndjjgANhzz2LZBxH4z7wsq+7K0FKT5MOda9hs+ll8+b6wnEZYVoPf/75nxjFiRPGpuQpbo95+q9U2\nncVggQSRfAcBvtz8FDy0oHUpj67kl/QnnVkG9NWjX006I0Zo88uIET2yu/yF/RTna+h33jy2MRKh\n4aNkraqSI7Z05SC7kGWrJIclKRrlNUZHIzVGj+65wbhuGImCiDHptKYsryEITTctxOXqbRrFi1dJ\nUDDLFh7thcL2M5hM2y6w3XbIsmX6+bJl+EOria1crh04hSVwF52u9mFJWrB1bXxj0tm0yWb55NSz\n2eLjt0iceByf/9eNQEkrtBAcPE4bk2FIFSQyzdiAIJzNDFaNGQevlGXOHnBAjw4vQCEos9IsIwhg\n/vQsq17Zk2HxOg7IPcznaMYC4nbAyWcMhWTYWSyTgaeegiOPhOuv7+eRdw8j8IFg2TIUZTfoZyuY\nomZzGT8hQQsA/lXX8MHZf2JEbDlO80rUokVRT3tlA48ZM4iR1zewudE2LbJZZEGGd7+Y5Mknof6P\nB1OND4DMmMHQ0HhSFOCAshVDR1fzwvNQR9RM8Pn/LCFAaXuxUqg99+y5sWZ09UzbVM+Es84if+vt\nPL3TMVz2Tj1Xva1Ldfu2wys/mslXrj0DPA9VqD5aMNOcc05/j7zHMAIfsEaMQJYtK96EPjAxMZd4\ni1e6YX2P7S46HYs8hI4c5s1j1izdaOjEJ0/X5YGtGARCnBxWuL/Ay/HcHzO8dD18e/MMQ+uTg/em\n29gom8iX7FjDglSWY2bVEhePbXD4hBPCmjiagkZfEOYt9hBivofkA756wxm8t/kJSEG4F75j6VJw\n4uS9PGARq+yF3B2SSQIVI5AAKxYbtCvNZ484i73nzcAGxr09g59v/hgJpcuBx/DYe9TytvsUb2IY\ngQ+wdKmOwV2xAoBYVRWHzJwAP1mItGgNH2Vji9aUyrWz/Z67mn15pqTNB7nCAhrC9wQC9926kp9x\nKA4tBDMsFnz1TOSoevZemWGr7ybh8svh3ns3iWXjRk2ZgF+xAraor0XldQOYCdJEkgzfQyfVWZbH\npNplWPMV2oxaovDK8dcB+nqxbY+JE0HNGQLNzcXtEgjW7qMJXn4NJCD46RlYY8f2mNARCSeXijEO\nJnZ57nagdN/uYy3Cittau6vU6DdlOmPo76tHv8fht1WOt7w+S1WViFIRJ8+KZH2kPG8OWzxirZx2\nOexI/G4QOody2LqeR9n2geoY2tRZeruur5IPyxbPoqHoeM9hy8IjU/LKNaWia77jSIuV0LX4sWQp\nW8tyhrYuhlV4lBdFa2iQwHEkX3nu6eE67alNPHigs7Huja0d6b6z6bTQxNTS6QUKE0JjY+kiK1Q0\ntCyRsAjTupNKTRYKN7FfVhVPKm5wv+x5ACKbbdb7v6FwkVc2WxlEBI+68v60lFw/zZVDDhE5R6Ui\nAv7VPerFt2MShJU3P52Rlsw3UnL9IWn5/dBUZELIK1teOzElLT+PCpbiOa+vb7P42hu71EkuVBgE\nJI+SdTjindJDgsjddHvc5mZF69Xk/7uNY1xOY6MEw4cX700PW946ddOYAI3A70vaadQRWFYx1EuU\n0h2KyoVAPF58T0Tgb0ho6LhxesIZN05ERIJA5IPbXHnz1JQ8d6Urd94p8o+zXVlnlxpqPDiqotTu\npiT026heuPoBfTxuON2Vcw+NNib54WhXrpikO3cVtHfPToiPJTkVl8u3aIy8/5cHuXLnWa74Q1qX\nNM4puziJBx2EQ/qPuNKswqqNti1LdhsvzSR6rrS260ozuiGKbAoNUVxXVp2bkr9OdSWTiBYj80Ga\nVZVcfrwrc+fqFVtwURvVR6t0d7a1qkr+S7lywZGu5KZ3rwFJf2MEfn/juiLjx1d0YUKbf8aMKa4O\n3t69rqhxFB9dTcUeNy7yPc844+QQp3X3pbOJtgj8TG0RnWhGjuydY9HbuPrGXnG3K/Pni9z0k4IQ\n1Tf10dtEu0ytoUr+tkVJOw9sW147rEEerEvJVePSMmNYqpU5Z9GIOi2Ew/cXTSOVE0sqVXpf4Xx2\nYEpZNj0tLehyxkE8XtL4e8AE41+UKu3PsjZqk857t5Sq0K6hSm7csbHVSiqHLb+yUpFOY+vsKrmj\n0ZWXX9aruoKZdvUDrvx0XOl9wUZc2bazAt84bXuLmhpYsgQoOYpigNgKfvhDmDKFeRdkeevfO3Oy\nihNXodP3zDO7nor9zDOR7xmbe5rGr2VIPKqjECzLY+6PMti1SezjSo3P47vtjDz3XGk/u+zS7Z/d\n64Qhkav2TZKlhiVzsxx3dS0x8UjgcB7asfpdSp2rjh+VIbELOFm9zbZaOG7MM8izNn4e8r7NDg9e\ny87kORCH6ckmtv0ScPV1SOARcxy+csEEOOPhaNgetHb0JZNYQxzyzS3YBORRpcJc7bCtvZw8QoyA\nIA/KssgHYKGwuhmxs5xqtiLQgQZBAD0ZAdQXZLOsvDPDX95IsvLODOeHVWht2+MHU4dCdRquvlrf\nAyLEHIdf35Nkyt8zJNL6+hff4/EZGX4/A5rQoZjEHdZ8YxJ/PDpD8ER4rppb+GzmHKo35UidzswK\nffXYpDR8kVZZfEGZ4+7f15VpFoluOo8qNPziKqG9Tk1lzcIDO1Z0Ng9IB1bolGv5c1qyl5R6vBZW\nLeeUrVryypbXT07J8n+VdYpyHO0gTafFH1IlvrKKPW+bSciVVoPcvUND57T3zvYedV25qyYlc5io\nm9mj1m+ecV0JhoTaqErI2nHjxcPWq8Fuap0vT9p4NXxvYfR8Xz8+3dqEVqAds2qh//F7t7jy5DHR\nFe7ZpOQ320X9AOsK9fF7oilKH4Ix6QwAUinJlZtMLEuksVFW/DIlczYrmQx6Yum+ZqtRpe45hf11\nRkC5rjy0Z4M0k5DA6uOGLW2Mb90CV5b+NCUvzHblySmtOwSVC/c3Jqdk9QNudGILu10F6bQs/16D\neHaiKDAKTSgKUVW+ZUvzb1LttzHsBpmLdUmD4gTckbB1XXn7yAZpxok4+AOl9ES8gbx9btjkQ3V/\n8ugzXFcWn5aSm4dFzW6dvqbL9tOmb822xR9SJTec7srsnUsToo8qVsfc2CLmjMAfALx2ZroYclm4\n6X07JjlsaSYhftzpMSGTe8jV+9yAaIx3G/ogdC+8+dYtcOWVV0Qe+V/tQM6HNtZT93LlqK2idvYs\n4yI+hhW7jdN21nZWLasvSa83rPLJCSlZ/Hd3vfvoKYHoX5iKhOsGUHSot0tZCGVkxbah0TWudkLn\nsCRvbxwa66f3lPwv60hIPtZz94iItDkJBEO0E7fFSkiuMoBiIwlm6KzANzb8XmLlvVlG/eEMCFNw\nfBQWEPg+MQTLAuuUU2GHHXrEXhiLQb6QvSldS7DZYVKSlqsc8JuxfB/rzjs3LJ189myYO5cV8a3J\nL/mYd/efwAM7TcFbmKVxXpjGjsNJoZ39ADxstI31gOYMn98BEp/obZblsfv4L0CGYqLbsF+cAmPH\nRkpYfPizi3k0lmT2C+ew/wMXc36gP++Ix/jxYD3hQE7b4ff/RVIf5+3byKjs4aQb67AkUpVAwgQr\nAJ54Ao44QpfMbYvQ/p9b54XZuoEu25vPb1hJhEwGK+dhERCIguXLN+i39DqhX+YhK0n24gxnFu30\noCb33D0CtD7PNTWoB5tQmQxOMknw9a/DmjVAWXXR22/vueql/U1nZoW+emxKGv7f9y6zLRMNyYzY\ndHuqwXV5gs0GmAFWj63wA9TVdfjdLefryJh//1vk7XOi5pfyXp4Xbh61sz97rDbZ+EP08jpyLCq1\n7/LEmnAcH/3DlZt/Go3Y+O5IV/50XCmscr3Ht69wXfFjsWgCVlVVh5/JjGmQdaE5KAhNTxuknbuu\n+Amt4ft2fGBq+K4r+UTpPJ4/Mi35RM+a17rExImlc7UJavj9LuTLH5uKwH8u7cosGsSzEiK2Lfly\nm2wo8H8/Oi0n7FYyYaxVVfLfW7vy7erotu/v6MqkXV1ZWwgxs6rkV4e5cuG3XGkOHVpevEqavp+W\nFqvU3SiHkvwXRnX6Yg2qqiJjzGHJWmdL+WzErmE2qTa9TNvXle/t0Drks7JBc3E/h9W1byNf3yQW\n2uJzD7ny6qsiC1JRB165uSawbR1v3d4++5O6uqgzvXwibYfPzomadjp0+q6H936tQz57wgHcG7xw\nXFQZyF/YRTt9bzBxojajbb75RiHsRYxJp99Y25Rl9NRa9sDDjtlw8qlw2+3wyUcAxTqKw/zlHGpn\ncMLwQYXHD3fIYClwlpe2fXuLDH4e4oUww8Bj+PMZPA9igd6Wy3k8cPNyVnIk3+FOXbcFgSXvQycb\naqiDD4Z584o1YGwCbG8VQ5atKo5b+c38z9sz+GDkOBKUTC+zJmSwtpkAl8+LVogEYt+bUOyxuj4z\nSu6hLJ/cluH1kUnefjfJ986rJRZ4eDicGJqADiocK+Xx/WMhdpdTCpM8NNlqnwOC++9HHXEEPPww\nHHxw++acMrY8KknuDw5Bbh0KwUIIWjysDTDrbOEtx0J0M/NCo+4BcHxW3J3l/nMyPPhCNX9SDpby\nsBMOHJbs/3N4/fWbbj2rzswKffXYFDT8hd9o7QDN/aAiozUWa9+E0cVtQRh2tuJuV945sqG1wwk6\n31Cjrk5nIVaYIVqFfDY2tq2xF8wvEye2MsOUa2tr5rvy6gkp+ee5rpx+usipY6MrhsqSBU9NSMlL\nf9HOtVbHYCBp8z1Iy5/T4oX1lwpJe4Ftd+z4rSB4dMOd+b3FuzeXzndLrEpys9Kb7HnsKzAaft+z\n9PYsbzy4mANVDLHQmmd1NXLb7fjovpnqK1+BK64oaTBtab5d2KbCbcNqahg2DPx7Z2PpTroljjmm\ncz8g1D6t449HbrihVL43pLjPRYvaHs/YsdoxmEwiB9bw6T1ZtjykFpXz8GMOZ+3XxOLF8LelteyC\nx/Y4/LGqiWO3La10bNvjlBMgdpPW3m3HYb+Cs3WPdlYJmyDOquX4lJLpbND17J94QjdIefzxTu1H\nKVAb6MzvcbJZXro8Q/bvizmxLDGOlcs3qZrzAxkj8HuKbJZh361lkniouI065VTdJSuTwfY9HaFj\n2fC977WKEmgltLqxzcfGKmRWAkyc2PUIg+uv13X8b70N8VrC7kwUJwC1996Rt3sevHNTlp0m12Ll\nPfKWw3e2aGLvzzJMD00/Qd5jl/cyHLA1JJbpDEjb9njgvAzWoUmoLZlnnMmTYPKkjbdvaA+w5qtJ\nEnYM8f3ituKEG2ZWd4pMBnsANEDxH8mSO6SW3QOP0SqG5VSUJjb0Cd0S+Eqp6cDRQAB8BJwoIkuU\nUgq4FPgmsDbc3oWrdOPjoz/MoVrW6RsrQIeShTeWSjjkm9ehAoGVK3tvEOHNXdAKsW2YNm3D9nX9\n9VjTpuEfWku+xQOkNJHMnEnwf5eCnyevHOrsJr6W18K94Gc4ddcMua8l4QoH8XVY5Om3JfW+y4S7\nOjS5fhv/IOOJS7MsuznDZc8nmeCfxBTSxR4MxUl83307v8NkEok75HMt2Mpab4mH3iKfhzvPyFAf\nFEpbgDq5h8MtDZ2jM3af9h7AlmXPfwJcGT7/JnAv+vo8EHi8M/vbWG346xa4sg6n/USZilrcn1zc\ntm2727iu5K2y7E6lNiyJqpDI9IArL1/tyku710frtYehpoWEpnsOTsm8C9oIsyzbV4chpoORsuN8\n9dUiE3eOFvx665y0BJVhnaNGdflrnpvWf5E6n93nyhU7pmQyOimu1fVh6BHoCxu+iKwqe7kZpVX/\n0cCccCCPKaWGKqW2E5Gl3fm+gcqC8zMcXmhzpxScdFJUa1m0CCjZY18/52o+p17AEQ81xMF6sKnH\nkkqavvIzvv7sDK0NinRcLCubZe09Gd7YPslj1LD8X1l++k+dJKVwOJkmfstaxpSNX/f/FUQp7CEO\nh16QZOVKWPvdE7AsWH3MJD4dXsOyibM54IO5DDluQsdmrEGGuFn8Q2v1SgeHq2jiu9WZYvRTDI8v\nbrEcfv5zgv/9AyIBSinUIYd0+bvGbLMcVYjUaW7WkVt33NELvyrKqvuzxL9Ry2Q8Tok7xP88s+jj\nGeznv7/otg1fKXURMAn4DDg03DwSeK/sbe+H21oJfKXUFGAKwA477NDd4fQ5zQ9m+ejJxfgqppeq\njqNt9+VMmICaN6/4ctgeXyD+8tPY+OTWeVxzfIah9XCYlWH4Mclu3Qz7vXAtQGnyKc+uzGbJPZDh\nlRFJ5q+u4eO7svx6oRbuo3GYShN18bJQUeXxl+MyDN91ApxfCtks2pJFuLf5YKYfBgtIEidHjjhH\nz5nEXsxmNlP12xbO48Psm2y761DUwgw89dSgbeW4+oEsr16ZYVXTExzihSZAPG5uyLDNmGrULxSB\nbxFYDrfMq+Y7C6cTl0A7bUXghhtg5MioX6asLWP5tZN/OEvLvAzNn6tmqLIQ0f4AdeedOiu6q1VZ\nO0tY4fK+2YtLVUsDT1+Lxjnbv3S0BADmAy+28Ti64n3nABeEz/8FHFT2vyZg/46+a4NNOhMn6nA9\n2+5UYkuP4ZaSn/JxZ/3VJiszRsOQSi9WJedunS4u5ZutKpl3gauLgnXV7DFmTKtM1xVTG+XGG0Uu\n+Z/WyVK/H1oKIfUtW1b8MiX+I+0kSaXTIsOHtwrX/IjhMotoOOgsGuRxFa2Dk0cV65QU97GRFKbq\nFq4r0tAgK49rkL9+rXCeo20N1ylHfrZ56X8txGUyaZlFQ6SYV+G454cNl7dvdGXePJG7znGlxS5d\nO1PGurL77iK1n4ue7zfYKWoa6mJ4Z6dpbBRfWZJD6UYuPVgvytA+9HWmLbAD8GL4PA38oOx/rwHb\ndbSPDRL4bZQgbt5mVJ9cXCvPWn/RsZUNjdK8QzvtA8vs2MFFKZ0+H9rEZ9FQvFnziSothNsheNSV\nj36WklevdYulf8sFcpZxAiK/iUeF+6pzUl3LgBXRQr8ik3bthImy4ntRgf/UVxtk0c71rboRtcoP\nGD68u6dg4OG6sua8lDyfdmXumVHfjhf2Ly4/DnmU3Lldg9w4tqzYmmXJZ8c1iB93Wl3bhZj8tpra\n5LDlqp1Tcuyx0dIevmXLii+MiR77+vqe/+3pdGRC9wlLfBh/Ta/TJwIf2LXs+Y+B28Ln3yLqtH2i\nM/vbIIHfhtYZgDSTkNP2duWWr6flo33rxL+yZ+uIBI+6ct/OYVnhNjQYv8JRu94U7Yra3e8cWUo8\n8rBlxrCUzD7JlbenpsS/Mi3vNuikpYu+XSq5sIYqeZ0dIzdcAPLmV+rl2Wd1Nc0uCff2SKd1C8bN\nNitp6K6rHdWqLLnHdXVrP9CJZk5UeG3sGn4QiLzxN51AdtsvXJk2TeTCHdNhY3qrmECWL+tm5qMk\nb8WKE3OhV664rqz4fbQW0d2JslLQKFlk7S2f2sMlX9iXZctbU1KyZO56qn9Wlo2OxUrnoycEcGU/\n5LqKloPKkiBtkqr6gr4S+HND887zwD+BkeF2BVwOvAm80BlzjnRDw5dKQRLeJHOpj9xEv9slLX+d\n6sqrhzXI2hO60ezDdcWLh3VsrHZMOaNHR7NeO8p2LRe8ZSafXMyRBcPqpRmnaAooNO940h4Xqe2+\n+LSUeNvvWDoOlb1UezM6pqNonMLzujqt2W9kwn7JXFdePD4l1zW4cswxIt8arjNY86Hp4rSYjoQp\nHPsclty/c4PkbKd0PhIJkXRa8hem5LVjGuWt3erkr19Ly557SqipW8XPPjC6QbyYLtvrD1lPxrVI\n++e1jVLAPXX+P54cVWhy358oUh+9365joqxF/wZj0uldOivwlX7vwGD//feXp556qusfPP54uOkm\nCILiJkkkWD7qK1S/+UQxuuRxxrE3i0jgAeCpBHcfcRl7jVjOiL2q2dLrZATBxReTP/fXxPAR20ZN\nn97aGXXWWUihjg0QHDQee8bv1r/v0Pm25qtJnnoK8tfM4WuvX0ucQrlcilEyOi5boRCUZUEioWPZ\na2radeL1KIXvqK7e5CIvvIVZltyYYdHQJHd9XMPqB7LMef9Q4njkcJgwbAHH+XM4btWVkWtrf54i\nFuYqEI+jFi4EYO3/m8Ha15eQ3eMUZuWmkHsoy11rtbPcw+G8A5v40pfgpBt0/SDlOPpcQuvz2Bfn\ntj2yWVruz3Dt20lq/zaJ0fJG8ffr3G4LFbNQ++5DcEiSf/99Ebsunk+MgDw2S791KiP/awedaLeJ\nXCsDBaXU0yKyf4dv7Mys0FePbsfhhw6yosYd2pwLWvZbe9e3WmIXm0ejG4y3xKrkmYa0rJ7U0K4T\ndtG0TnYRamyUzz4/SlqwdQu9dtqyrW1y5YlLdUOQcqfqb52KhhhKhdqTimiSL46sk/d+3UNL5za0\nwE/vceXfJ6XkictcufZakasnF5zVVmift8SLV8nDM1x57kpXPjhdNzpZ3z67rW2uR3sNApFcTseA\nrzpXj8XzRHy/jc+l05Lff5x8PL5e5pzmyrR9S5p7C3G50mqQf9hRf8Rc6uWKCkf1mm/U6/OrlASW\nJa8c3Sg//alEKqKuoUom7uzK7V9Nda6l4gAi95ArOWzxQVqw5aEdo76zQmc3D1uu36JBmq0qCVTp\nvlqHI80kin6pgfgbN2Yw5ZFDKqNjymzJvtXaiZbDihStarES8uBFrnx2XziZ7L235LF0fftO9L3M\nXxgV2vKVr8i6dSL3/LrU2aetgmFvnZqK2t0LfW/Taf3XcXSXHrtKTouVony8eJV4C93o7xZpU5is\nbXLl/WnawXjrrSI3/7RUgrjZqpJjt3dbRXtUOgqDshu93Nm8hio5dIgrR29Tqlu/zqqSi//blT9P\nDLtdKVtyTpU8/WdXnn9e5K0b3FLLwbIx+1em5ZNfpOTxma5cconIhd+KlpAuRbiUxnggrcc9mXTY\nK1ZJM478TkXNEuuIFc2A0egiO7rNsuXDC9MSOI6ehJWSRUc2yrX/lS4qEGuokkMcV2Z/MSX5ttr0\n9XBLxd5i9bRGWTxktLxnjYqaKMePL9nwJ04smSCdKrmlurzTmCUv71Ani/ev107c8Fr565dS8vLV\nA3eC29gwAr89ylcBhUbfllV0MuVUPLIKyKNkFrrXaCunI3TcaMR1xbfsyGdvsCdGhGZe2fLuNxu6\n1nqvbPua80oao4ct/3SittQH9mssho82W7pZSEeCPIctN+yVkn/+V2nfvmXLJ78ItffwuBWiSvwh\nVbLsmAbxVek3/euglNy6b3SfF22ekl/ZrZtJVwro87ZNhxNiyW9RGOeFm0WP3Uuj6opCNa9seeCw\nlDQdnio6OPMgd27X0Epwv8uoyLn0QZ5NRMNJC0IrX7GqOpuUpIhOGP+MlxytvmVLbvoGREINJCoC\nDyIrmsRQWX5mO/4BVzc18S19vZ1mp3VwQ/hZz3bkD/HG4uRY9FEYNhgj8DtL4UItRBOk05FVgGcn\n5OZh0WiLcidxPu6Uwibbu4m32CJys6xKDJeHft+D5X7LnLx5p0pe3DwqtF5XoyNC96Yvp+Sumqgg\nX/LjlKy8t42Iz52gWAAAGJ1JREFUj44EVnkURlvvbWNb8Kj+7YFti5+okicvc+X5H5TGk1e2PLtt\nXatVhG/Z8p9z2xCihYm77Ds+2jnawesdRkXOYQCyZLfx0Qm8sGJzHB1xBFoZqKqStT9pFN/WETY5\np0r+cbYrr+9cEZUybtzGK9zboiLwoHV4qCU5OyG5yW2YPst+87rflp3bMJjCK2vwnsOS7D4NsvLs\njfAYDRCMwO8Olb4A19XL9za0HQ9bLhiSkp8d6EpLrEp8pUMrIxduRa5AJJyxpwRBuQ07HQ3xa7N+\nfVc0z66Ms7Ofb8sG35YQD1cRYlnrH2fF65Z4tIOXZyfEd5zSpF2IYEqndRJSff36J7O2vrPMRyTQ\nbv3/jRW/4rrNbbV1q3yKgtnLsx3JT2kn8q3s3AZVVfLS+IZIg3cPO7TvW5JXtgSjd9U1gzaSblMD\nASPwe5rCJDB+vMgee0gQi+llu6Pt0n/cJmqmuGNcSubPD+Pf+yMcsRM2/AEnnNoT4hsSy91Wa8HK\nibwnqDzOmwi5nMit+6WKdveg0Ce5zARaHjygnfdKfCfR9vGtMPlIVZU2BdpxeXxkfcSXFjlvRuh3\nis4K/E0jLLM/qAyPy2YJDqtFWnQ9+G/EmljXAk3o8DuJO7x9VRO77AL2/82AJUvglFN6r56JAbrY\nWtCgWTM/y80NGR57s5rLY2cQlzZCRaur4dln4ZprCHI5lEg0XDhmoy6/vP3ru/z+AWT8eMjno417\nAEaPhtdf742fuUkxOMMy+5tyR+oakae/G9X6C5E4EQ1mE9MMDRs3H99Vyt724lUdr67CVVOQSLTW\n+C2786updFqb84yGv0FgNPwBQDYLtboEbmDHWJHYjur/vFPUYgT4cMdxvHH94+y5Ksuw5zKbVAKT\nYSMim2XxnAyZOYs5bu1VusKlbUNbSYXtfJ45c5CrrgLfjyRkWU4cdfLJuopsR4mHc+bAY4/BJ5/A\nccd1vVvbIKWzGr4R+L1NeBH7V12N5ecAIgL/Dur5XxqLph/fdrjz9CZG/U8NY1dn2fKZjJkEDL2K\nuFm88bXYvodPjHhcsAJftx9s6mKvhtmz4fTTkVweqDDzWAoVi+nr2ZjYepTOCnzT07a3qanhoz/M\nYWs/V2o9WMCyOOj2Rva8I0PiOt34At9j0aUZZl6q7f95PIKYw0uXNrHXXhB/NGMmAEPPkM3y2T8y\nPHT9Yo70C03k0f2YN7T94JQpMHYsas4c5Jpr8L0cFoKFIIEgnqd7Q3ShEbuhB+mM3aevHhu9Db8N\n5l3gSpZSXLyAyNZbtw4DLAtJ/PguV577fmv7fyExqSVWpcsht1UqwGDoBP4jOow4VwiJtJ2ebz9Y\nsO87iYhdvvjcXLM9BiYss/+5+2hdc6eQpRkopZN62otVbicuPaiqkreOaChmkxayU+u20DVt8mF9\nks/u28gTfQx9QvYSVx4bWlcKhbTt3q1b77oi1dWtsppbknXmGu0hjMDvT1xXnv9aQ7EmTzFxqK6L\nF3gbscti63K59/3WlVv3a12ioFC7pjAJ/GeemQQMmtWrRc4+RJewyNO6Jn+v4rpSGWefw2qdpGjY\nIDor8I0Nv6fZaSfk3XfZK3xZjCu2bTj//K7ZRCubfTc1QSaDlUxyRE0NHAFS6yCehxVz2POUJHs/\nmiH+kfYH5Fo8UnUZFu8Mf3m7VpdZHuKguuqIM2zcZLO8ls5wzv1Jdl+mexbbBGBZcPjhXb8uN4Sa\nGnBd1Jw58MwzBE/oUtJ4no7HN9djn2AEfk+yxx7Iu+8CRB20sRj8+c/dv6grJ4CaGi28MxnsZJLj\na2ogC5RNAqN/mGR0JkNMwkmg2eOa4zJYh0JNS4Y9fpTE+lonxjV7NsydCxMmmGSxjYgVd2epOqqW\nXcTjBuWw+Bczic1ytKB1nL4R9gUK1282ixWGK+M4xeQrQ+9jBH5P8tprAJFGJTQ0dBx/3B3amARo\nakKFk8DJ4SQgtQ5Bi4dYDh/mqznzWh0G2nKTw1+Pb+KAM2rYZ10WtTDTOjpj9mxk6lT9fN48fB9i\np03p32YchvbJZgkWZPjnqiTPXZrhXAkjcCyP3auXF1eK/XbewmvUXDv9QGfsPn312Oht+GPGRLME\nd9yxv0dUotyGn0pJUNY0/VxVKk+cx25Vrja/f7T65uOMk+N3CX0FSvsKvIVu6x6nhr7HdSXvlMpM\nz9g1rc/nRlB737DhYGz4/cDLL6P22ENr+rvvDi+/3N8jKlGxElAJvayPOQ6/nJvk6NkZnDtDs886\nj6snZvj80fCtzTOsfXEJW5Xtqmr0FzgolyEuJV9BNnk2B8tD+g0zZujVjcmS7DvC9oNP37mYcZ7W\n6JXyOPPE5ahDjTZt0BiB39MMJCHfHhVL6qE1NYwbCtyvbf9YDu/8p5pfzaxlCM18ruyjSinGzmlk\nLCWHsbId9oq/CWtK5qzgkkuwwAj9PiB4NEs+WYud99iXGGLZiALbcaDQP9YIegNG4A9e1mP7jyeT\nTF+QgfNasIRI43Q1cmTxcwWHcSyZZO25lzMsc0PRUa3yeWTGDJYuhW2n1GM/nDEaZk8S+k+er07y\n6EUZTs1rrd6ywJrSjUxZwyaNEfiGEmWTgAX4qKiwB13QqvL92SxbL7ytJOzLPpP/2420/O1PxRLR\nq25voroaY2LoDtksfrIW8TxG4zBny5lI3EECD8txejdIwLBRYwS+oU1evvUFdhMtwovCfuLENk00\nq/48hy2kpXWtICDxpZ1JvLpU2/pzHrccNYcT1XU4okPy/HlNOIfUmIifThCkZ7N89lzeWvY59iuz\n06d+vpx4nbHTGzrGCHxDK9bMzzL6j9OwCbSgVwqmToUrrmjz/eref0Vf7703rF6NOuYYtq2vh1qt\njdoxh3FfhviTobPX85h+eIZPvwz/t0gnhqmESQxrk9mzUQ1T2QrYCvCVjVg2tuNg1yWNnd7QKYzA\nN5QIteznLnuCA8iXTDmxmDYTtIEccQSbf/q+fk64Epg1q1WGsMpkUMkk+wHUXld09lbXJ9lqYQY7\n8LDwyTd7PPSbDLueCtu/mTEaa4G5c4GSqcwS4ZWDprDzbycxBODii82xMnSIEfiDlTITyie71vD0\nn7McMr2WeLCOGspMOba93izhYOHDWJSVkEgkWr+3jRIRKnT2nhEmhgWHOfgtHnnlcNP8ai6dr0tD\nS8zhzdlN7HZCDdbjxuxTmFQDAhYuhEe+PocT/GuIKx9xHD46ZyZVrz7LlluCdeKkoo+l2JZw+fJB\nffwGPZ0J1u+rx0afeDWA8ceNE4nFxNttjLz7zQZpsRKSx5a1qkomk5Z7qRO/LLlKQKTQuHp9tNUs\nfEMoSwz7tDHVqjLot6t1opev7FLBrYFeEK6xUfzNNxc/kehe83rXlUBZkeS3PEqaSRQrsUpYjMwr\na6HZjCNnDU+HxfSssNG4JflElbz3m7Ss+F6DfHZcgwSPDtDjZ+g0mGqZhgK5MFM2KAoLIkKihVhx\nW6RufzzeOWFaV6ezODdU2FdSXhp6SJXc/WtX/vqlaGXQ27ZukJZYxQQwkGhsjBzzADZY6K/+VUry\nqKKwD0BW7zde94wt9I9FSU7FxA/fJ+GkcC91xePW3sSwTjny+OS0tJyf0r1lGxo634vWMCAwAt9Q\nxI/FItphREhY8VJd9HJhr1T/Nlhvoz9AUFUlvmWLF6uS20c0RCaA9BdT8pvfiMy/0JU15/W/1p//\nwsjI8QxAZMstu7yfz+5z5fYRDdKMI4GydJntxsZo05xEQgvodFo/L0wMjiPNl6UlGFIlgWWFk73V\n5sTQgr4OIhNULNbvx9HQOTor8E1P28HAAQcgTzwBlNnaHQdOPhn22QfOOANaWiAISp9pbBx4WbLl\noZtAcFgt0qL7ADeMbuK11+AB0UXh8srh8mOa2LZeF4Ub81EGq5B12ssEj2YJDhqPTT6yXVkWPPJI\n58eQzdL8tVri4qFiMezJJ0Vj7NsKZS00AofSeytt+NXV8JOfIC0terzKBhFsgpLjvUB9Pdxxx4Yd\nCEOfYXraGko8/jjqgAPgmWdg113hhz+MComxY7VAWLkSFi0auCWQK5y/1oOl0tDX1NSw7rcX40z3\nsMQH8fjPPzPcNlf3Bg7w8CyHv3y/ia2OqmHnD7Psd+1pqFdfxRo2DC64oPu/OZvFfzDDgr8uJhk2\n8AYI0IlsohSqC7XfP7wlQ3VY6RJBZ89WZkd35CBvb1vYdxbArpj0I0J/yZJOjdWwcWAE/mBhfQ2j\nN9YY7opxD/lGEv63VBTu1/OSNNyaIXFZ2CA+8Pjw7xluuBEe5mAsfABk2TKYOpVcDpx9x25YJFA2\ni9TWIs0eBxED2y6GtEog5HI+ECO+eLHWuDvYd0sLPDr7JY4uaN09XTe+8pyHk7566SW44YbS9lNO\n6bnvNPQ/nbH79NXD2PAN3WY9vYGlqkpys9KyYlxd1F8R2qwfY1yxUXzOqZK3b3Qld2bnSj6vOa/k\nVM6rih6xrit37xja4a0OyhSHjb/fHrZ3xJ7+4j4T5cWrXMlN7wP/RDqtHfD96cMxdAmM09ZgCClM\nAum0BFVVpabyFY8Xdq2POIIXMD7qxGxH6L95vSs3DdUCPa/aFuivn1yaEMS29XjaGGc+loh+Z/h3\nKVuFk5ElHjG56bC03HqryMd3uZKbXBZVM9BDVQ29ghH4BkMFq89LSb4sIikohJ6OGKG12XA1EISN\n4lduEY20kdGjW+0ze4lbXBX48UT74YyuK+tsLbCDWLxt7TmVikTPlAv8N9kpEk3VQlwmk9arhnCb\np+KSU3HxUXosRugPGjor8K1+tigZDH3C642zeTl1J0IpW1VZlnbWLl2qHbaFEtHTp2NdOpMPhuwC\nlBWEO+aY0g6zWd6fdjEvnz0Hh7A0cZBv7VgtUFPD0rNmEmAR5H3tJM1mo+9JJhE7Vhxj8btjMTa/\n8Bws2yqOPaZ8zhg5lzi5YnXSmDZGYSGoXAtzDp/D5Mlwd/1s/OFbIY4DRxzRMwfUsFFinLaGTZ6m\n78/msL9PLW2wQj0nkWjtCA2FdT5Zy26eh69sYiO302WhC2Gq2Sy5Q2oZkfOYSAzLscGnw4bcO22+\nnDxh+KPntY7Yqanh3wefwu6ZNBaix3n44XD++WxTUwNbA6efDr6PlUiw528mwI8z4Hl6YrCsSGjt\nmrWgrp7NNyn9dpk3D7XbbnDmmabMwiDECHzDJs2aLx9A8oUngbK6/vvvr+PL2xF2siCjI33wEcuG\nH/0Izjmn+P/Hfpdh/1zYGNwGdXInG44kk0jcIZ9rwVIWqrq61VuWbbcPu2ATtwJUIgHnn1/a55Qp\npRDawneNHQtz5ugwyn32gR//GHI5iMc5ZNYk6n53PrxRCrMUIHj9ddTUqQgWyomhTj7Z1NAfLHTG\n7tPRA/gF+lraKnytgMuAN4DngX07sx9jwzf0JB/uPK6VA1Sgw+iTx05JSwtx8ZUVccCume/KLfuk\ntO3c0rb+rjYG/ziVlhZi2h5f+VnXlXVWB3b+jqh02qbTEZ9FexnXpsH5xg191cRcKbU9UAcsLtt8\nJLBr+DgAuCL8azD0GcPfeQYoSyJSCq68cr0JVp/ek2Xs1Wdg4aNsC2bOhJoa3r81y/Bja/kOHkfH\nHOw/zUR92nWTyFZos06MAFm3Tic/FT6fyRALPG3yEaVNLl2lMr4+/K3q3HN1Yl0Q6OMQBAToY2Mh\n4Hl65WC0/E2annDa/hFohEizo6OBOeHk8xgwVCm1XQ98l8HQaWL77xvd8NWvdphNe9/ZmdAJG+h4\nmeXLueUWuPqHmaJz1hEP+9Pl2szTVQGZTEJMO2YRgWuuKTlvk0ny2PgoXZa6pxKtpkyBTz6BfB4e\nfRQuvBCVTmM1NGizkW136H8wbBp0S8NXSh0NfCAizykVqcAxEniv7PX74balbexjCjAFYIcddujO\ncAyGKOUlJfbdd/3ZxsAjf8iy8oXFiK1vC4nFeOAvi/njW1n23COJ9ZYDOa97wrG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Jzn1WrGAcfP/+FC+rDMEgj9mtG7NgS0tFjjqKnHXffaw3q89br56lpVNdVFSI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\nFTs2RGNhsDdQwXa9ICp46ZiyZb7fH++ePWdmZ1MQUK/5nme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ILDMMDI1YvWlrqUU4hRsj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\nxzSUft/UxKy4xGIYd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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "g_scan.plot(title=\"Scan-matching edges\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Optimization\n", - "\n", - "Initially, the pose estimates are consistent with the collected odometry measurements, and the odometry edges contribute almost zero towards the $\\chi^2$ error. However, there are large discrepancies between the scan-matching constraints and the initial pose estimates. This is not surprising, since small errors in odometry readings that are propagated over time can lead to large errors in the robot's trajectory. What makes Graph SLAM effective is that it allows incorporation of multiple different data sources into a single optimization problem." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Iteration chi^2 rel. change\n", - "--------- ----- -----------\n", - " 0 7191686.3825\n", - " 1 320031728.8624 43.500234\n", - " 2 125083004.3299 -0.609154\n", - " 3 338155.9074 -0.997297\n", - " 4 735.1344 -0.997826\n", - " 5 215.8405 -0.706393\n", - " 6 215.8405 -0.000000\n" - ] - } - ], - "source": [ - "g.optimize()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Graph_SLAM_optimization.gif](images/Graph_SLAM_optimization.gif)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+QwlrklvTY+kKDiCGkUoPMfvUmSSIMJAgXgdO5GmSgAF8bOzLPc553MVY0mm4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\nYCufSwnFn34SWfgPSyKmTxI1BHu2c2prj9V0Tzuysl+P4M7RiNJJKlu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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "g.plot(title=\"Optimized\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "χ^2 error from odometry edges: 142.189\n", - "χ^2 error from scan-matching edges: 73.652\n" - ] - } - ], - "source": [ - "print(\"\\nχ^2 error from odometry edges: {:7.3f}\".format(g_odom.calc_chi2()))\n", - "print(\"χ^2 error from scan-matching edges: {:7.3f}\".format(g_scan.calc_chi2()))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Wm4l+8IHIUUfpR+rEAbaeTZhm0Qyk3tCDryFDRP4T62Az4fXgG/y2Yl36ZnV1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\nPPQDW1yzVMP/7OjRkm00kmgI5lZkMPMO8ULnEcr7OzIYklYRcUxLXMO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kWku1JLBlLdUyAkd69hQ5q0dUPqFPXl1oqalpu0pZZQbtHWkLbO97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\nXvgUCWjQIb10hNcDHOIvddM6Jvn0CsVauqSGn8Gbb5K/qjT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z^A4^1Gkzc@7m9=)c4Rk@3ypT2D$RG=27Zj;fvjbcyLa1n+O@Z4PaY5ZEHIs!fuD&- zNBQaL)Ir1S;n+cpu$W6HfrcloTuMQgJ3^{rjVnTiZ0)N~(vMN@6~*GmUA8PXzD>5G z>wUjzxx9ut#+JNVt}_R%gMP9C`n-+}2MIAP%hTG>t^#>hhJ54n(_$B{QwN3KFa-JA z?<4c7fRat;lZ7> \bar{P_t}` then the Kalman gain will go to 0, +signaling a high trust in the process and little trust in the +measurement. + +The update is captured in the following. + +:math:`xUpdate = xEst + (K * y)` + +Of course, the covariance must also be updated as well to account for +the changing uncertainty. + +:math:`P_{t} = (I-K_tH_t)\bar{P_t}` + +.. code:: ipython3 + + def update(xEst, PEst, u, z, initP): + """ + Performs the update step of EKF SLAM + + :param xEst: nx1 the predicted pose of the system and the pose of the landmarks + :param PEst: nxn the predicted covariance + :param u: 2x1 the control function + :param z: the measurements read at new position + :param initP: 2x2 an identity matrix acting as the initial covariance + :returns: the updated state and covariance for the system + """ + for iz in range(len(z[:, 0])): # for each observation + minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark + + nLM = calc_n_LM(xEst) # number of landmarks we currently know about + + if minid == nLM: # Landmark is a NEW landmark + print("New LM") + # Extend state and covariance matrix + xAug = np.vstack((xEst, calc_LM_Pos(xEst, z[iz, :]))) + PAug = np.vstack((np.hstack((PEst, np.zeros((len(xEst), LM_SIZE)))), + np.hstack((np.zeros((LM_SIZE, len(xEst))), initP)))) + xEst = xAug + PEst = PAug + + lm = get_LM_Pos_from_state(xEst, minid) + y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid) + + K = (PEst @ H.T) @ np.linalg.inv(S) # Calculate Kalman Gain + xEst = xEst + (K @ y) + PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst + + xEst[2] = pi_2_pi(xEst[2]) + return xEst, PEst + + +.. code:: ipython3 + + def calc_innovation(lm, xEst, PEst, z, LMid): + """ + Calculates the innovation based on expected position and landmark position + + :param lm: landmark position + :param xEst: estimated position/state + :param PEst: estimated covariance + :param z: read measurements + :param LMid: landmark id + :returns: returns the innovation y, and the jacobian H, and S, used to calculate the Kalman Gain + """ + delta = lm - xEst[0:2] + q = (delta.T @ delta)[0, 0] + zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0] + zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]]) + # zp is the expected measurement based on xEst and the expected landmark position + + y = (z - zp).T # y = innovation + y[1] = pi_2_pi(y[1]) + + H = jacobH(q, delta, xEst, LMid + 1) + S = H @ PEst @ H.T + Cx[0:2, 0:2] + + return y, S, H + + def jacobH(q, delta, x, i): + """ + Calculates the jacobian of the measurement function + + :param q: the range from the system pose to the landmark + :param delta: the difference between a landmark position and the estimated system position + :param x: the state, including the estimated system position + :param i: landmark id + 1 + :returns: the jacobian H + """ + sq = math.sqrt(q) + G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]], + [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]]) + + G = G / q + nLM = calc_n_LM(x) + F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM)))) + F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))), + np.eye(2), np.zeros((2, 2 * nLM - 2 * i)))) + + F = np.vstack((F1, F2)) + + H = G @ F + + return H + +Observation Step +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The observation step described here is outside the main EKF SLAM process +and is primarily used as a method of driving the simulation. The +observations function is in charge of calculating how the poses of the +robots change and accumulate error over time, and the theoretical +measurements that are expected as a result of each measurement. + +Observations are based on the TRUE position of the robot. Error in dead +reckoning and control functions are passed along here as well. + +.. code:: ipython3 + + def observation(xTrue, xd, u, RFID): + """ + :param xTrue: the true pose of the system + :param xd: the current noisy estimate of the system + :param u: the current control input + :param RFID: the true position of the landmarks + + :returns: Computes the true position, observations, dead reckoning (noisy) position, + and noisy control function + """ + xTrue = motion_model(xTrue, u) + + # add noise to gps x-y + z = np.zeros((0, 3)) + + for i in range(len(RFID[:, 0])): # Test all beacons, only add the ones we can see (within MAX_RANGE) + + dx = RFID[i, 0] - xTrue[0, 0] + dy = RFID[i, 1] - xTrue[1, 0] + d = math.sqrt(dx**2 + dy**2) + angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) + if d <= MAX_RANGE: + dn = d + np.random.randn() * Qsim[0, 0] # add noise + anglen = angle + np.random.randn() * Qsim[1, 1] # add noise + zi = np.array([dn, anglen, i]) + z = np.vstack((z, zi)) + + # add noise to input + ud = np.array([[ + u[0, 0] + np.random.randn() * Rsim[0, 0], + u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T + + xd = motion_model(xd, ud) + return xTrue, z, xd, ud + +.. code:: ipython3 + + def calc_n_LM(x): + """ + Calculates the number of landmarks currently tracked in the state + :param x: the state + :returns: the number of landmarks n + """ + n = int((len(x) - STATE_SIZE) / LM_SIZE) + return n + + + def jacob_motion(x, u): + """ + Calculates the jacobian of motion model. + + :param x: The state, including the estimated position of the system + :param u: The control function + :returns: G: Jacobian + Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix + """ + + # [eye(3) [0 x y; 0 x y; 0 x y]] + Fx = np.hstack((np.eye(STATE_SIZE), np.zeros( + (STATE_SIZE, LM_SIZE * calc_n_LM(x))))) + + jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])], + [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])], + [0.0, 0.0, 0.0]],dtype=object) + + G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx + if calc_n_LM(x) > 0: + print(Fx.shape) + return G, Fx, + + + + +.. code:: ipython3 + + def calc_LM_Pos(x, z): + """ + Calculates the pose in the world coordinate frame of a landmark at the given measurement. + + :param x: [x; y; theta] + :param z: [range; bearing] + :returns: [x; y] for given measurement + """ + zp = np.zeros((2, 1)) + + zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1]) + zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1]) + #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1]) + #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1]) + + return zp + + + def get_LM_Pos_from_state(x, ind): + """ + Returns the position of a given landmark + + :param x: The state containing all landmark positions + :param ind: landmark id + :returns: The position of the landmark + """ + lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :] + + return lm + + + def search_correspond_LM_ID(xAug, PAug, zi): + """ + Landmark association with Mahalanobis distance. + + If this landmark is at least M_DIST_TH units away from all known landmarks, + it is a NEW landmark. + + :param xAug: The estimated state + :param PAug: The estimated covariance + :param zi: the read measurements of specific landmark + :returns: landmark id + """ + + nLM = calc_n_LM(xAug) + + mdist = [] + + for i in range(nLM): + lm = get_LM_Pos_from_state(xAug, i) + y, S, H = calc_innovation(lm, xAug, PAug, zi, i) + mdist.append(y.T @ np.linalg.inv(S) @ y) + + mdist.append(M_DIST_TH) # new landmark + + minid = mdist.index(min(mdist)) + + return minid + + def calc_input(): + v = 1.0 # [m/s] + yawrate = 0.1 # [rad/s] + u = np.array([[v, yawrate]]).T + return u + + def pi_2_pi(angle): + return (angle + math.pi) % (2 * math.pi) - math.pi + +.. code:: ipython3 + + def main(): + print(" start!!") + + time = 0.0 + + # RFID positions [x, y] + RFID = np.array([[10.0, -2.0], + [15.0, 10.0], + [3.0, 15.0], + [-5.0, 20.0]]) + + # State Vector [x y yaw v]' + xEst = np.zeros((STATE_SIZE, 1)) + xTrue = np.zeros((STATE_SIZE, 1)) + PEst = np.eye(STATE_SIZE) + + xDR = np.zeros((STATE_SIZE, 1)) # Dead reckoning + + # history + hxEst = xEst + hxTrue = xTrue + hxDR = xTrue + + while SIM_TIME >= time: + time += DT + u = calc_input() + + xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) + + xEst, PEst = ekf_slam(xEst, PEst, ud, z) + + x_state = xEst[0:STATE_SIZE] + + # store data history + hxEst = np.hstack((hxEst, x_state)) + hxDR = np.hstack((hxDR, xDR)) + hxTrue = np.hstack((hxTrue, xTrue)) + + if show_animation: # pragma: no cover + plt.cla() + + plt.plot(RFID[:, 0], RFID[:, 1], "*k") + plt.plot(xEst[0], xEst[1], ".r") + + # plot landmark + for i in range(calc_n_LM(xEst)): + plt.plot(xEst[STATE_SIZE + i * 2], + xEst[STATE_SIZE + i * 2 + 1], "xg") + + plt.plot(hxTrue[0, :], + hxTrue[1, :], "-b") + plt.plot(hxDR[0, :], + hxDR[1, :], "-k") + plt.plot(hxEst[0, :], + hxEst[1, :], "-r") + plt.axis("equal") + plt.grid(True) + plt.pause(0.001) + +.. code:: ipython3 + + %matplotlib notebook + main() + + +.. parsed-literal:: + + start!! + New LM + New LM + New LM + +.. image:: ekf_slam_files/ekf_slam_1_0.png + +References: +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- `PROBABILISTIC ROBOTICS`_ + +.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ + +.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif diff --git a/SLAM/EKFSLAM/animation.png b/docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png similarity index 100% rename from SLAM/EKFSLAM/animation.png rename to docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example.rst b/docs/modules/slam/graphSLAM_SE2_example.rst new file mode 100644 index 0000000000..bcc797ba3e --- /dev/null +++ b/docs/modules/slam/graphSLAM_SE2_example.rst @@ -0,0 +1,209 @@ +Graph SLAM for a real-world SE(2) dataset +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + from graphslam.graph import Graph + from graphslam.load import load_g2o_se2 + +Introduction +^^^^^^^^^^^^ + +For a complete derivation of the Graph SLAM algorithm, please see +`Graph SLAM Formulation`_. + +This notebook illustrates the iterative optimization of a real-world +:math:`SE(2)` dataset. The code can be found in the ``graphslam`` +folder. For simplicity, numerical differentiation is used in lieu of +analytic Jacobians. This code originated from the +`python-graphslam `__ +repo, which is a full-featured Graph SLAM solver. The dataset in this +example is used with permission from Luca Carlone and was downloaded +from his `website `__. + +The Dataset +^^^^^^^^^^^^ + +.. code:: ipython3 + + g = load_g2o_se2("data/input_INTEL.g2o") + + print("Number of edges: {}".format(len(g._edges))) + print("Number of vertices: {}".format(len(g._vertices))) + + +.. parsed-literal:: + + Number of edges: 1483 + Number of vertices: 1228 + + +.. code:: ipython3 + + g.plot(title=r"Original ($\chi^2 = {:.0f}$)".format(g.calc_chi2())) + + + +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png + + +Each edge in this dataset is a constraint that compares the measured +:math:`SE(2)` transformation between two poses to the expected +:math:`SE(2)` transformation between them, as computed using the current +pose estimates. These edges can be classified into two categories: + +1. Odometry edges constrain two consecutive vertices, and the + measurement for the :math:`SE(2)` transformation comes directly from + odometry data. +2. Scan-matching edges constrain two non-consecutive vertices. These + scan matches can be computed using, for example, 2-D LiDAR data or + landmarks; the details of how these constraints are determined is + beyond the scope of this example. This is often referred to as *loop + closure* in the Graph SLAM literature. + +We can easily parse out the two different types of edges present in this +dataset and plot them. + +.. code:: ipython3 + + def parse_edges(g): + """Split the graph `g` into two graphs, one with only odometry edges and the other with only scan-matching edges. + + Parameters + ---------- + g : graphslam.graph.Graph + The input graph + + Returns + ------- + g_odom : graphslam.graph.Graph + A graph consisting of the vertices and odometry edges from `g` + g_scan : graphslam.graph.Graph + A graph consisting of the vertices and scan-matching edges from `g` + + """ + edges_odom = [] + edges_scan = [] + + for e in g._edges: + if abs(e.vertex_ids[1] - e.vertex_ids[0]) == 1: + edges_odom.append(e) + else: + edges_scan.append(e) + + g_odom = Graph(edges_odom, g._vertices) + g_scan = Graph(edges_scan, g._vertices) + + return g_odom, g_scan + +.. code:: ipython3 + + g_odom, g_scan = parse_edges(g) + + print("Number of odometry edges: {:4d}".format(len(g_odom._edges))) + print("Number of scan-matching edges: {:4d}".format(len(g_scan._edges))) + + print("\nχ^2 error from odometry edges: {:11.3f}".format(g_odom.calc_chi2())) + print("χ^2 error from scan-matching edges: {:11.3f}".format(g_scan.calc_chi2())) + + +.. parsed-literal:: + + Number of odometry edges: 1227 + Number of scan-matching edges: 256 + + χ^2 error from odometry edges: 0.232 + χ^2 error from scan-matching edges: 7191686.151 + + +.. code:: ipython3 + + g_odom.plot(title="Odometry edges") + + + +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png + + +.. code:: ipython3 + + g_scan.plot(title="Scan-matching edges") + + + +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png + + +Optimization +^^^^^^^^^^^^ + +Initially, the pose estimates are consistent with the collected odometry +measurements, and the odometry edges contribute almost zero towards the +:math:`\chi^2` error. However, there are large discrepancies between the +scan-matching constraints and the initial pose estimates. This is not +surprising, since small errors in odometry readings that are propagated +over time can lead to large errors in the robot’s trajectory. What makes +Graph SLAM effective is that it allows incorporation of multiple +different data sources into a single optimization problem. + +.. code:: ipython3 + + g.optimize() + + +.. parsed-literal:: + + + Iteration chi^2 rel. change + --------- ----- ----------- + 0 7191686.3825 + 1 320031728.8624 43.500234 + 2 125083004.3299 -0.609154 + 3 338155.9074 -0.997297 + 4 735.1344 -0.997826 + 5 215.8405 -0.706393 + 6 215.8405 -0.000000 + + +.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif + :alt: Graph_SLAM_optimization.gif + +.. code:: ipython3 + + g.plot(title="Optimized") + + + +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png + + +.. code:: ipython3 + + print("\nχ^2 error from odometry edges: {:7.3f}".format(g_odom.calc_chi2())) + print("χ^2 error from scan-matching edges: {:7.3f}".format(g_scan.calc_chi2())) + + +.. parsed-literal:: + + + χ^2 error from odometry edges: 142.189 + χ^2 error from scan-matching edges: 73.652 + + +.. code:: ipython3 + + g_odom.plot(title="Odometry edges") + + + +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png + + +.. code:: ipython3 + + g_scan.plot(title="Scan-matching edges") + + + +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png + diff --git a/SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif b/docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif similarity index 100% rename from SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif rename to docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png new file mode 100644 index 0000000000000000000000000000000000000000..a3fca366e4ef37bceaac5cc1e45a987bbd2d8252 GIT binary patch literal 32748 zcmY(q1yEc~&@PMyhu{PWu((@rcZc8(!GkRBPVnIFkN}IjyZa)+-QC^o?)!c9-&_CG 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b/docs/modules/slam/graphSLAM_doc.rst @@ -0,0 +1,557 @@ + +Graph SLAM +~~~~~~~~~~~~ + +.. code:: ipython3 + + import copy + import math + import itertools + import numpy as np + import matplotlib.pyplot as plt + from graph_based_slam import calc_rotational_matrix, calc_jacobian, cal_observation_sigma, \ + calc_input, observation, motion_model, Edge, pi_2_pi + + %matplotlib inline + np.set_printoptions(precision=3, suppress=True) + np.random.seed(0) + +Introduction +^^^^^^^^^^^^ + +In contrast to the probabilistic approaches for solving SLAM, such as +EKF, UKF, particle filters, and so on, the graph technique formulates +the SLAM as an optimization problem. It is mostly used to solve the full +SLAM problem in an offline fashion, i.e. optimize all the poses of the +robot after the path has been traversed. However, some variants are +available that uses graph-based approaches to perform online estimation +or to solve for a subset of the poses. + +GraphSLAM uses the motion information as well as the observations of the +environment to create least square problem that can be solved using +standard optimization techniques. + +Minimal Example +^^^^^^^^^^^^^^^ + +The following example illustrates the main idea behind graphSLAM. A +simple case of a 1D robot is considered that can only move in 1 +direction. The robot is commanded to move forward with a control input +:math:`u_t=1`, however, the motion is not perfect and the measured +odometry will deviate from the true path. At each time step the robot can +observe its environment, for this simple case as well, there is only a +single landmark at coordinates :math:`x=3`. The measured observations +are the range between the robot and landmark. These measurements are +also subjected to noise. No bearing information is required since this +is a 1D problem. + +To solve this, graphSLAM creates what is called as the system +information matrix :math:`\Omega` also referred to as :math:`H` and the +information vector :math:`\xi` also known as :math:`b`. The entries are +created based on the information of the motion and the observation. + +.. code:: ipython3 + + R = 0.2 + Q = 0.2 + N = 3 + graphics_radius = 0.1 + + odom = np.empty((N,1)) + obs = np.empty((N,1)) + x_true = np.empty((N,1)) + + landmark = 3 + # Simulated readings of odometry and observations + x_true[0], odom[0], obs[0] = 0.0, 0.0, 2.9 + x_true[1], odom[1], obs[1] = 1.0, 1.5, 2.0 + x_true[2], odom[2], obs[2] = 2.0, 2.4, 1.0 + + hxDR = copy.deepcopy(odom) + # Visualization + plt.plot(landmark,0, '*k', markersize=30) + for i in range(N): + plt.plot(odom[i], 0, '.', markersize=50, alpha=0.8, color='steelblue') + plt.plot([odom[i], odom[i] + graphics_radius], + [0,0], 'r') + plt.text(odom[i], 0.02, "X_{}".format(i), fontsize=12) + plt.plot(obs[i]+odom[i],0,'.', markersize=25, color='brown') + plt.plot(x_true[i],0,'.g', markersize=20) + plt.grid() + plt.show() + + + # Defined as a function to facilitate iteration + def get_H_b(odom, obs): + """ + Create the information matrix and information vector. This implementation is + based on the concept of virtual measurement i.e. the observations of the + landmarks are converted into constraints (edges) between the nodes that + have observed this landmark. + """ + measure_constraints = {} + omegas = {} + zids = list(itertools.combinations(range(N),2)) + H = np.zeros((N,N)) + b = np.zeros((N,1)) + for (t1, t2) in zids: + x1 = odom[t1] + x2 = odom[t2] + z1 = obs[t1] + z2 = obs[t2] + + # Adding virtual measurement constraint + measure_constraints[(t1,t2)] = (x2-x1-z1+z2) + omegas[(t1,t2)] = (1 / (2*Q)) + + # populate system's information matrix and vector + H[t1,t1] += omegas[(t1,t2)] + H[t2,t2] += omegas[(t1,t2)] + H[t2,t1] -= omegas[(t1,t2)] + H[t1,t2] -= omegas[(t1,t2)] + + b[t1] += omegas[(t1,t2)] * measure_constraints[(t1,t2)] + b[t2] -= omegas[(t1,t2)] * measure_constraints[(t1,t2)] + + return H, b + + + H, b = get_H_b(odom, obs) + print("The determinant of H: ", np.linalg.det(H)) + H[0,0] += 1 # np.inf ? + print("The determinant of H after anchoring constraint: ", np.linalg.det(H)) + + for i in range(5): + H, b = get_H_b(odom, obs) + H[(0,0)] += 1 + + # Recover the posterior over the path + dx = np.linalg.inv(H) @ b + odom += dx + # repeat till convergence + print("Running graphSLAM ...") + print("Odometry values after optimzation: \n", odom) + + plt.figure() + plt.plot(x_true, np.zeros(x_true.shape), '.', markersize=20, label='Ground truth') + plt.plot(odom, np.zeros(x_true.shape), '.', markersize=20, label='Estimation') + plt.plot(hxDR, np.zeros(x_true.shape), '.', markersize=20, label='Odom') + plt.legend() + plt.grid() + plt.show() + + + +.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png + + +.. parsed-literal:: + + The determinant of H: 0.0 + The determinant of H after anchoring constraint: 18.750000000000007 + Running graphSLAM ... + Odometry values after optimzation: + [[-0. ] + [ 0.9] + [ 1.9]] + + + +.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png + + +In particular, the tasks are split into 2 parts, graph construction, and +graph optimization. ### Graph Construction + +Firstly the nodes are defined +:math:`\boldsymbol{x} = \boldsymbol{x}_{1:n}` such that each node is the +pose of the robot at time :math:`t_i` Secondly, the edges i.e. the +constraints, are constructed according to the following conditions: + +- robot moves from :math:`\boldsymbol{x}_i` to + :math:`\boldsymbol{x}_j`. This edge corresponds to the odometry + measurement. Relative motion constraints (Not included in the + previous minimal example). +- Measurement constraints, this can be done in two ways: + + - The information matrix is set in such a way that it includes the + landmarks in the map as well. Then the constraints can be entered + in a straightforward fashion between a node + :math:`\boldsymbol{x}_i` and some landmark :math:`m_k` + - Through creating a virtual measurement among all the node that + have observed the same landmark. More concretely, robot observes + the same landmark from :math:`\boldsymbol{x}_i` and + :math:`\boldsymbol{x}_j`. Relative measurement constraint. The + “virtual measurement” :math:`\boldsymbol{z}_{ij}`, which is the + estimated pose of :math:`\boldsymbol{x}_j` as seen from the node + :math:`\boldsymbol{x}_i`. The virtual measurement can then be + entered in the information matrix and vector in a similar fashion + to the motion constraints. + +An edge is fully characterized by the values of the error (entry to +information vector) and the local information matrix (entry to the +system’s information vector). The larger the local information matrix +(lower :math:`Q` or :math:`R`) the values that this edge will contribute +with. + +Important Notes: + +- The addition to the information matrix and vector are added to the + earlier values. +- In the case of 2D robot, the constraints will be non-linear, + therefore, a Jacobian of the error w.r.t the states is needed when + updated :math:`H` and :math:`b`. +- The anchoring constraint is needed in order to avoid having a + singular information matri. + +Graph Optimization +^^^^^^^^^^^^^^^^^^ + +The result from this formulation yields an overdetermined system of +equations. The goal after constructing the graph is to find: +:math:`x^*=\underset{x}{\mathrm{argmin}}~\underset{ij}\Sigma~f(e_{ij})`, +where :math:`f` is some error function that depends on the edges between +to related nodes :math:`i` and :math:`j`. The derivation in the references +arrive at the solution for :math:`x^* = H^{-1}b` + +Planar Example: +^^^^^^^^^^^^^^^ + +Now we will go through an example with a more realistic case of a 2D +robot with 3DoF, namely, :math:`[x, y, \theta]^T` + +.. code:: ipython3 + + # Simulation parameter + Qsim = np.diag([0.01, np.deg2rad(0.010)])**2 # error added to range and bearing + Rsim = np.diag([0.1, np.deg2rad(1.0)])**2 # error added to [v, w] + + DT = 2.0 # time tick [s] + SIM_TIME = 100.0 # simulation time [s] + MAX_RANGE = 30.0 # maximum observation range + STATE_SIZE = 3 # State size [x,y,yaw] + + # TODO: Why not use Qsim ? + # Covariance parameter of Graph Based SLAM + C_SIGMA1 = 0.1 + C_SIGMA2 = 0.1 + C_SIGMA3 = np.deg2rad(1.0) + + MAX_ITR = 20 # Maximum iteration during optimization + timesteps = 1 + + # consider only 2 landmarks for simplicity + # RFID positions [x, y, yaw] + RFID = np.array([[10.0, -2.0, 0.0], + # [15.0, 10.0, 0.0], + # [3.0, 15.0, 0.0], + # [-5.0, 20.0, 0.0], + # [-5.0, 5.0, 0.0] + ]) + + # State Vector [x y yaw v]' + xTrue = np.zeros((STATE_SIZE, 1)) + xDR = np.zeros((STATE_SIZE, 1)) # Dead reckoning + xTrue[2] = np.deg2rad(45) + xDR[2] = np.deg2rad(45) + # history initial values + hxTrue = xTrue + hxDR = xTrue + _, z, _, _ = observation(xTrue, xDR, np.array([[0,0]]).T, RFID) + hz = [z] + + for i in range(timesteps): + u = calc_input() + xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) + hxDR = np.hstack((hxDR, xDR)) + hxTrue = np.hstack((hxTrue, xTrue)) + hz.append(z) + + # visualize + graphics_radius = 0.3 + plt.plot(RFID[:, 0], RFID[:, 1], "*k", markersize=20) + plt.plot(hxDR[0, :], hxDR[1, :], '.', markersize=50, alpha=0.8, label='Odom') + plt.plot(hxTrue[0, :], hxTrue[1, :], '.', markersize=20, alpha=0.6, label='X_true') + + for i in range(hxDR.shape[1]): + x = hxDR[0, i] + y = hxDR[1, i] + yaw = hxDR[2, i] + plt.plot([x, x + graphics_radius * np.cos(yaw)], + [y, y + graphics_radius * np.sin(yaw)], 'r') + d = hz[i][:, 0] + angle = hz[i][:, 1] + plt.plot([x + d * np.cos(angle + yaw)], [y + d * np.sin(angle + yaw)], '.', + markersize=20, alpha=0.7) + plt.legend() + plt.grid() + plt.show() + + + +.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png + + +.. code:: ipython3 + + # Copy the data to have a consistent naming with the .py file + zlist = copy.deepcopy(hz) + x_opt = copy.deepcopy(hxDR) + xlist = copy.deepcopy(hxDR) + number_of_nodes = x_opt.shape[1] + n = number_of_nodes * STATE_SIZE + +After the data has been saved, the graph will be constructed by looking +at each pair for nodes. The virtual measurement is only created if two +nodes have observed the same landmark at different points in time. The +next cells are a walk through for a single iteration of graph +construction -> optimization -> estimate update. + +.. code:: ipython3 + + # get all the possible combination of the different node + zids = list(itertools.combinations(range(len(zlist)), 2)) + print("Node combinations: \n", zids) + + for i in range(xlist.shape[1]): + print("Node {} observed landmark with ID {}".format(i, zlist[i][0, 3])) + + +.. parsed-literal:: + + Node combinations: + [(0, 1)] + Node 0 observed landmark with ID 0.0 + Node 1 observed landmark with ID 0.0 + + +In the following code snippet the error based on the virtual measurement +between node 0 and 1 will be created. The equations for the error is as follows: +:math:`e_{ij}^x = x_j + d_j cos(\psi_j +\theta_j) - x_i - d_i cos(\psi_i + \theta_i)` + +:math:`e_{ij}^y = y_j + d_j sin(\psi_j + \theta_j) - y_i - d_i sin(\psi_i + \theta_i)` + +:math:`e_{ij}^x = \psi_j + \theta_j - \psi_i - \theta_i` + +Where :math:`[x_i, y_i, \psi_i]` is the pose for node :math:`i` and +similarly for node :math:`j`, :math:`d` is the measured distance at +nodes :math:`i` and :math:`j`, and :math:`\theta` is the measured +bearing to the landmark. The difference is visualized with the figure in +the next cell. + +In case of perfect motion and perfect measurement the error shall be +zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal +:math:`x_i + d_i cos(\psi_i + \theta_i)` + +.. code:: ipython3 + + # Initialize edges list + edges = [] + + # Go through all the different combinations + for (t1, t2) in zids: + x1, y1, yaw1 = xlist[0, t1], xlist[1, t1], xlist[2, t1] + x2, y2, yaw2 = xlist[0, t2], xlist[1, t2], xlist[2, t2] + + # All nodes have valid observation with ID=0, therefore, no data association condition + iz1 = 0 + iz2 = 0 + + d1 = zlist[t1][iz1, 0] + angle1, phi1 = zlist[t1][iz1, 1], zlist[t1][iz1, 2] + d2 = zlist[t2][iz2, 0] + angle2, phi2 = zlist[t2][iz2, 1], zlist[t2][iz2, 2] + + # find angle between observation and horizontal + tangle1 = pi_2_pi(yaw1 + angle1) + tangle2 = pi_2_pi(yaw2 + angle2) + + # project the observations + tmp1 = d1 * math.cos(tangle1) + tmp2 = d2 * math.cos(tangle2) + tmp3 = d1 * math.sin(tangle1) + tmp4 = d2 * math.sin(tangle2) + + edge = Edge() + print(y1,y2, tmp3, tmp4) + # calculate the error of the virtual measurement + # node 1 as seen from node 2 throught the observations 1,2 + edge.e[0, 0] = x2 - x1 - tmp1 + tmp2 + edge.e[1, 0] = y2 - y1 - tmp3 + tmp4 + edge.e[2, 0] = pi_2_pi(yaw2 - yaw1 - tangle1 + tangle2) + + edge.d1, edge.d2 = d1, d2 + edge.yaw1, edge.yaw2 = yaw1, yaw2 + edge.angle1, edge.angle2 = angle1, angle2 + edge.id1, edge.id2 = t1, t2 + + edges.append(edge) + + print("For nodes",(t1,t2)) + print("Added edge with errors: \n", edge.e) + + # Visualize measurement projections + plt.plot(RFID[0, 0], RFID[0, 1], "*k", markersize=20) + plt.plot([x1,x2],[y1,y2], '.', markersize=50, alpha=0.8) + plt.plot([x1, x1 + graphics_radius * np.cos(yaw1)], + [y1, y1 + graphics_radius * np.sin(yaw1)], 'r') + plt.plot([x2, x2 + graphics_radius * np.cos(yaw2)], + [y2, y2 + graphics_radius * np.sin(yaw2)], 'r') + + plt.plot([x1,x1+tmp1], [y1,y1], label="obs 1 x") + plt.plot([x2,x2+tmp2], [y2,y2], label="obs 2 x") + plt.plot([x1,x1], [y1,y1+tmp3], label="obs 1 y") + plt.plot([x2,x2], [y2,y2+tmp4], label="obs 2 y") + plt.plot(x1+tmp1, y1+tmp3, 'o') + plt.plot(x2+tmp2, y2+tmp4, 'o') + plt.legend() + plt.grid() + plt.show() + + +.. parsed-literal:: + + 0.0 1.427649841628278 -2.0126109674819155 -3.524048014922737 + For nodes (0, 1) + Added edge with errors: + [[-0.02 ] + [-0.084] + [ 0. ]] + + + +.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png + + +Since the constraints equations derived before are non-linear, +linearization is needed before we can insert them into the information +matrix and information vector. Two jacobians + +:math:`A = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_i}` as +:math:`\boldsymbol{x}_i` holds the three variabls x, y, and theta. +Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}` + +.. code:: ipython3 + + # Initialize the system information matrix and information vector + H = np.zeros((n, n)) + b = np.zeros((n, 1)) + x_opt = copy.deepcopy(hxDR) + + for edge in edges: + id1 = edge.id1 * STATE_SIZE + id2 = edge.id2 * STATE_SIZE + + t1 = edge.yaw1 + edge.angle1 + A = np.array([[-1.0, 0, edge.d1 * math.sin(t1)], + [0, -1.0, -edge.d1 * math.cos(t1)], + [0, 0, -1.0]]) + + t2 = edge.yaw2 + edge.angle2 + B = np.array([[1.0, 0, -edge.d2 * math.sin(t2)], + [0, 1.0, edge.d2 * math.cos(t2)], + [0, 0, 1.0]]) + + # TODO: use Qsim instead of sigma + sigma = np.diag([C_SIGMA1, C_SIGMA2, C_SIGMA3]) + Rt1 = calc_rotational_matrix(tangle1) + Rt2 = calc_rotational_matrix(tangle2) + edge.omega = np.linalg.inv(Rt1 @ sigma @ Rt1.T + Rt2 @ sigma @ Rt2.T) + + # Fill in entries in H and b + H[id1:id1 + STATE_SIZE, id1:id1 + STATE_SIZE] += A.T @ edge.omega @ A + H[id1:id1 + STATE_SIZE, id2:id2 + STATE_SIZE] += A.T @ edge.omega @ B + H[id2:id2 + STATE_SIZE, id1:id1 + STATE_SIZE] += B.T @ edge.omega @ A + H[id2:id2 + STATE_SIZE, id2:id2 + STATE_SIZE] += B.T @ edge.omega @ B + + b[id1:id1 + STATE_SIZE] += (A.T @ edge.omega @ edge.e) + b[id2:id2 + STATE_SIZE] += (B.T @ edge.omega @ edge.e) + + + print("The determinant of H: ", np.linalg.det(H)) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(H, extent=[0, n, 0, n]) + plt.subplot(1,2,2) + plt.imshow(b, extent=[0, 1, 0, n]) + plt.show() + + # Fix the origin, multiply by large number gives same results but better visualization + H[0:STATE_SIZE, 0:STATE_SIZE] += np.identity(STATE_SIZE) + print("The determinant of H after origin constraint: ", np.linalg.det(H)) + plt.figure() + plt.subplot(1,2,1) + plt.imshow(H, extent=[0, n, 0, n]) + plt.subplot(1,2,2) + plt.imshow(b, extent=[0, 1, 0, n]) + plt.show() + + +.. parsed-literal:: + + The determinant of H: 0.0 + The determinant of H after origin constraint: 716.1972439134893 + + + +.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png + + + +.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png + + +.. code:: ipython3 + + # Find the solution (first iteration) + dx = - np.linalg.inv(H) @ b + for i in range(number_of_nodes): + x_opt[0:3, i] += dx[i * 3:i * 3 + 3, 0] + print("dx: \n",dx) + print("ground truth: \n ",hxTrue) + print("Odom: \n", hxDR) + print("SLAM: \n", x_opt) + + # performance will improve with more iterations, nodes and landmarks. + print("\ngraphSLAM localization error: ", np.sum((x_opt - hxTrue) ** 2)) + print("Odom localization error: ", np.sum((hxDR - hxTrue) ** 2)) + + +.. parsed-literal:: + + dx: + [[-0. ] + [-0. ] + [ 0. ] + [ 0.02 ] + [ 0.084] + [-0. ]] + ground truth: + [[0. 1.414] + [0. 1.414] + [0.785 0.985]] + Odom: + [[0. 1.428] + [0. 1.428] + [0.785 0.976]] + SLAM: + [[-0. 1.448] + [-0. 1.512] + [ 0.785 0.976]] + + graphSLAM localization error: 0.010729072751057656 + Odom localization error: 0.0004460978857535104 + + +The references: +^^^^^^^^^^^^^^^ + +- http://robots.stanford.edu/papers/thrun.graphslam.pdf + +- http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf + +- http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf + +N.B. An additional step is required that uses the estimated path to +update the belief regarding the map. + diff --git a/docs/modules/slam/graphSLAM_formulation.rst b/docs/modules/slam/graphSLAM_formulation.rst new file mode 100644 index 0000000000..c673bdcbfb --- /dev/null +++ b/docs/modules/slam/graphSLAM_formulation.rst @@ -0,0 +1,216 @@ +.. _Graph SLAM Formulation: + +Graph SLAM Formulation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Author Jeff Irion + +Problem Formulation +^^^^^^^^^^^^^^^^^^^ + +Let a robot’s trajectory through its environment be represented by a +sequence of :math:`N` poses: +:math:`\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N`. Each pose lies +on a manifold: :math:`\mathbf{p}_i \in \mathcal{M}`. Simple examples of +manifolds used in Graph SLAM include 1-D, 2-D, and 3-D space, i.e., +:math:`\mathbb{R}`, :math:`\mathbb{R}^2`, and :math:`\mathbb{R}^3`. +These environments are *rectilinear*, meaning that there is no concept +of orientation. By contrast, in :math:`SE(2)` problem settings a robot’s +pose consists of its location in :math:`\mathbb{R}^2` and its +orientation :math:`\theta`. Similarly, in :math:`SE(3)` a robot’s pose +consists of its location in :math:`\mathbb{R}^3` and its orientation, +which can be represented via Euler angles, quaternions, or :math:`SO(3)` +rotation matrices. + +As the robot explores its environment, it collects a set of :math:`M` +measurements :math:`\mathcal{Z} = \{\mathbf{z}_j\}`. Examples of such +measurements include odometry, GPS, and IMU data. Given a set of poses +:math:`\mathbf{p}_1, \ldots, \mathbf{p}_N`, we can compute the estimated +measurement +:math:`\hat{\mathbf{z}}_j(\mathbf{p}_1, \ldots, \mathbf{p}_N)`. We can +then compute the *residual* +:math:`\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j)` for measurement +:math:`j`. The formula for the residual depends on the type of +measurement. As an example, let :math:`\mathbf{z}_1` be an odometry +measurement that was collected when the robot traveled from +:math:`\mathbf{p}_1` to :math:`\mathbf{p}_2`. The expected measurement +and the residual are computed as + +.. math:: + + \begin{aligned} + \hat{\mathbf{z}}_1(\mathbf{p}_1, \mathbf{p}_2) &= \mathbf{p}_2 \ominus \mathbf{p}_1 \\ + \mathbf{e}_1(\mathbf{z}_1, \hat{\mathbf{z}}_1) &= \mathbf{z}_1 \ominus \hat{\mathbf{z}}_1 = \mathbf{z}_1 \ominus (\mathbf{p}_2 \ominus \mathbf{p}_1),\end{aligned} + +where the :math:`\ominus` operator indicates inverse pose composition. +We model measurement :math:`\mathbf{z}_j` as having independent Gaussian +noise with zero mean and covariance matrix :math:`\Omega_j^{-1}`; we +refer to :math:`\Omega_j` as the *information matrix* for measurement +:math:`j`. That is, + +.. math:: + p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) = \eta_j \exp (-\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\mathsf{T}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) + :label: infomat + +where :math:`\eta_j` is the normalization constant. + +The objective of Graph SLAM is to find the maximum likelihood set of +poses given the measurements :math:`\mathcal{Z} = \{\mathbf{z}_j\}`; in +other words, we want to find + +.. math:: \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) + +Using Bayes’ rule, we can write this probability as + +.. math:: + \begin{aligned} + p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \frac{p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) p(\mathbf{p}_1, \ldots, \mathbf{p}_N) }{ p(\mathcal{Z}) } \notag \\ + &\propto p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) + \end{aligned} + :label: bayes + +since :math:`p(\mathcal{Z})` is a constant (albeit, an unknown constant) +and we assume that :math:`p(\mathbf{p}_1, \ldots, \mathbf{p}_N)` is +uniformly distributed `PROBABILISTIC ROBOTICS`_. Therefore, we +can use Eq. :eq:`infomat` and and Eq. :eq:`bayes` to simplify the Graph SLAM +optimization as follows: + +.. math:: + + \begin{aligned} + \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\ + &= \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\ + &= \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M \exp \left( -(\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right) \\ + &= \mathop{\mathrm{arg\,min}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j).\end{aligned} + +We define + +.. math:: \chi^2 := \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j), + +and this is what we seek to minimize. + +Dimensionality and Pose Representation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Before proceeding further, it is helpful to discuss the dimensionality +of the problem. We have: + +- A set of :math:`N` poses + :math:`\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N`, where each + pose lies on the manifold :math:`\mathcal{M}` + + - Each pose :math:`\mathbf{p}_i` is represented as a vector in (a + subset of) :math:`\mathbb{R}^d`. For example: + + - An :math:`SE(2)` pose is typically represented as + :math:`(x, y, \theta)`, and thus :math:`d = 3`. + + - An :math:`SE(3)` pose is typically represented as + :math:`(x, y, z, q_x, q_y, q_z, q_w)`, where :math:`(x, y, z)` + is a point in :math:`\mathbb{R}^3` and + :math:`(q_x, q_y, q_z, q_w)` is a *quaternion*, and so + :math:`d = 7`. For more information about :math:`SE(3)` + parameterization and pose transformations, see + [blanco2010tutorial]_. + + - We also need to be able to represent each pose compactly as a + vector in (a subset of) :math:`\mathbb{R}^c`. + + - Since an :math:`SE(2)` pose has three degrees of freedom, the + :math:`(x, y, \theta)` representation is again sufficient and + :math:`c=3`. + + - An :math:`SE(3)` pose only has six degrees of freedom, and we + can represent it compactly as :math:`(x, y, z, q_x, q_y, q_z)`, + and thus :math:`c=6`. + + - We use the :math:`\boxplus` operator to indicate pose composition + when one or both of the poses are represented compactly. The + output can be a pose in :math:`\mathcal{M}` or a vector in + :math:`\mathbb{R}^c`, as required by context. + +- A set of :math:`M` measurements + :math:`\mathcal{Z} = \{\mathbf{z}_1, \mathbf{z}_2, \ldots, \mathbf{z}_M\}` + + - Each measurement’s dimensionality can be unique, and we will use + :math:`\bullet` to denote a “wildcard” variable. + + - Measurement :math:`\mathbf{z}_j \in \mathbb{R}^\bullet` has an + associated information matrix + :math:`\Omega_j \in \mathbb{R}^{\bullet \times \bullet}` and + residual function + :math:`\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) = \mathbf{e}_j(\mathbf{z}_j, \mathbf{p}_1, \ldots, \mathbf{p}_N) \in \mathbb{R}^\bullet`. + + - A measurement could, in theory, constrain anywhere from 1 pose to + all :math:`N` poses. In practice, each measurement usually + constrains only 1 or 2 poses. + +Graph SLAM Algorithm +^^^^^^^^^^^^^^^^^^^^ + +The “Graph” in Graph SLAM refers to the fact that we view the problem as +a graph. The graph has a set :math:`\mathcal{V}` of :math:`N` vertices, +where each vertex :math:`v_i` has an associated pose +:math:`\mathbf{p}_i`. Similarly, the graph has a set :math:`\mathcal{E}` +of :math:`M` edges, where each edge :math:`e_j` has an associated +measurement :math:`\mathbf{z}_j`. In practice, the edges in this graph +are either unary (i.e., a loop) or binary. (Note: :math:`e_j` refers to +the edge in the graph associated with measurement :math:`\mathbf{z}_j`, +whereas :math:`\mathbf{e}_j` refers to the residual function associated +with :math:`\mathbf{z}_j`.) For more information about the Graph SLAM +algorithm, see [grisetti2010tutorial]_. + +We want to optimize + +.. math:: \chi^2 = \sum_{e_j \in \mathcal{E}} \mathbf{e}_j^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j. + +Let :math:`\mathbf{x}_i \in \mathbb{R}^c` be the compact representation +of pose :math:`\mathbf{p}_i \in \mathcal{M}`, and let + +.. math:: \mathbf{x} := \begin{bmatrix} \mathbf{x}_1 \\ \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \end{bmatrix} \in \mathbb{R}^{cN} + +We will solve this optimization problem iteratively. Let + +.. math:: \mathbf{x}^{k+1} := \mathbf{x}^k \boxplus \Delta \mathbf{x}^k = \begin{bmatrix} \mathbf{x}_1 \boxplus \Delta \mathbf{x}_1 \\ \mathbf{x}_2 \boxplus \Delta \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \boxplus \Delta \mathbf{x}_2 \end{bmatrix} + :label: update + +The :math:`\chi^2` error at iteration :math:`k+1` is + +.. math:: \chi_{k+1}^2 = \sum_{e_j \in \mathcal{E}} \underbrace{\left[ \mathbf{e}_j(\mathbf{x}^{k+1}) \right]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^{k+1})}_{\bullet \times 1}. + :label: chisq_at_kplusone + +We will linearize the residuals as: + +.. math:: + \begin{aligned} + \mathbf{e}_j(\mathbf{x}^{k+1}) &= \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \\ + &\approx \mathbf{e}_j(\mathbf{x}^{k}) + \frac{\partial}{\partial \Delta \mathbf{x}^k} \left[ \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \right] \Delta \mathbf{x}^k \\ + &= \mathbf{e}_j(\mathbf{x}^{k}) + \left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right) \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \Delta \mathbf{x}^k. + \end{aligned} + :label: linearization + +Plugging :eq:`linearization` into :eq:`chisq_at_kplusone`, we get: + +.. math:: + + \begin{aligned} + \chi_{k+1}^2 &\approx \ \ \ \ \ \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x}^k)]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^k)}_{\bullet \times 1} \notag \\ + &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\ + &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{(\Delta \mathbf{x}^k)^{\scriptstyle{\mathsf{T}}}}_{1 \times cN} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^{\scriptstyle{\mathsf{T}}}}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^{\scriptstyle{\mathsf{T}}}}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\ + &= \chi_k^2 + 2 \mathbf{b}^{\scriptstyle{\mathsf{T}}}\Delta \mathbf{x}^k + (\Delta \mathbf{x}^k)^{\scriptstyle{\mathsf{T}}}H \Delta \mathbf{x}^k, \notag\end{aligned} + +where + +.. math:: + + \begin{aligned} + \mathbf{b}^{\scriptstyle{\mathsf{T}}}&= \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \\ + H &= \sum_{e_j \in \mathcal{E}} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^{\scriptstyle{\mathsf{T}}}}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^{\scriptstyle{\mathsf{T}}}}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN}.\end{aligned} + +Using this notation, we obtain the optimal update as + +.. math:: \Delta \mathbf{x}^k = -H^{-1} \mathbf{b}. \label{eq:deltax} + +We apply this update to the poses via :eq:`update` and repeat until convergence. + + +.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ diff --git a/docs/modules/slam/slam.rst b/docs/modules/slam/slam_main.rst similarity index 75% rename from docs/modules/slam/slam.rst rename to docs/modules/slam/slam_main.rst index 981c45ada5..41210fee4e 100644 --- a/docs/modules/slam/slam.rst +++ b/docs/modules/slam/slam_main.rst @@ -21,32 +21,12 @@ Ref: - `Introduction to Mobile Robotics: Iterative Closest Point Algorithm`_ -EKF SLAM --------- -This is an Extended Kalman Filter based SLAM example. +.. include:: ekf_slam.rst -The blue line is ground truth, the black line is dead reckoning, the red -line is the estimated trajectory with EKF SLAM. - -The green crosses are estimated landmarks. - -|4| - -Ref: - -- `PROBABILISTIC ROBOTICS`_ .. include:: FastSLAM1.rst -|5| - -Ref: - -- `PROBABILISTIC ROBOTICS`_ - -- `SLAM simulations by Tim Bailey`_ - FastSLAM 2.0 ------------ @@ -56,7 +36,8 @@ The animation has the same meanings as one of FastSLAM 1.0. |6| -Ref: +References +~~~~~~~~~~ - `PROBABILISTIC ROBOTICS`_ @@ -77,6 +58,10 @@ The black stars are landmarks for graph edge generation. |7| +.. include:: graphSLAM_doc.rst +.. include:: graphSLAM_formulation.rst +.. include:: graphSLAM_SE2_example.rst + Ref: - `A Tutorial on Graph-Based SLAM`_ @@ -85,9 +70,12 @@ Ref: .. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ .. _SLAM simulations by Tim Bailey: http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm .. _A Tutorial on Graph-Based SLAM: http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf +.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf + +.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010) +.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43. .. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif -.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif .. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif .. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif .. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif From 413011ba9720f7ba9ff7f57653b7b28dd35d1643 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Nov 2021 07:07:28 +0900 Subject: [PATCH 372/909] Bump matplotlib from 3.4.3 to 3.5.0 (#575) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.3 to 3.5.0. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.3...v3.5.0) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index e11ba13b8d..84f663cb83 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.21.4 scipy == 1.7.2 pandas == 1.3.4 -matplotlib == 3.4.3 +matplotlib == 3.5.0 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test pytest-xdist == 2.4.0 # For unit test From 094a413dcd9ca7cbbc2db4b7c44f84294cd80714 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 25 Nov 2021 23:05:25 +0900 Subject: [PATCH 373/909] Add how to contribute docs (#573) * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * clean up SLAM docs * add contribution doc * add contribution doc * add contribution doc * add contribution doc * add contribution doc --- README.md | 20 ++--- docs/README.md | 3 + docs/_static/img/doc_ci.png | Bin 0 -> 51875 bytes docs/getting_started_main.rst | 53 +++++++++--- docs/how_to_contribute_main.rst | 129 +++++++++++++++++++++++++++++ docs/index_main.rst | 1 + docs/modules/introduction_main.rst | 3 +- 7 files changed, 187 insertions(+), 22 deletions(-) create mode 100644 docs/_static/img/doc_ci.png create mode 100644 docs/how_to_contribute_main.rst diff --git a/README.md b/README.md index 024c600cb1..195eb1bd4f 100644 --- a/README.md +++ b/README.md @@ -96,17 +96,17 @@ See this paper for more details: For running each sample code: -- Python 3.9.x - -- numpy - -- scipy - -- matplotlib - -- pandas +- [Python 3.9.x](https://www.python.org/) + +- [NumPy](https://numpy.org/) + +- [SciPy](https://scipy.org/) + +- [Matplotlib](https://matplotlib.org/) + +- [pandas](https://pandas.pydata.org/) -- [cvxpy](https://www.cvxpy.org/index.html) +- [cvxpy](https://www.cvxpy.org/) For development: diff --git a/docs/README.md b/docs/README.md index 728a81290e..5c4145e8e3 100644 --- a/docs/README.md +++ b/docs/README.md 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a/docs/getting_started_main.rst b/docs/getting_started_main.rst index 83565eb872..d907ab8669 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -1,4 +1,4 @@ -.. _getting_started: +.. _`getting started`: Getting Started =============== @@ -21,27 +21,60 @@ See this paper for more details: - PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703 +.. _`Requirements`: Requirements ------------- -- Python 3.9.x -- numpy -- scipy -- matplotlib -- pandas +- `Python 3.9.x`_ +- `NumPy`_ +- `SciPy`_ +- `Matplotlib`_ +- `pandas`_ - `cvxpy`_ -.. _cvxpy: http://www.cvxpy.org/en/latest/ +For development: + +- pytest (for unit tests) +- pytest-xdist (for parallel unit tests) +- mypy (for type check) +- sphinx (for document generation) +- pycodestyle (for code style check) + +.. _`Python 3.9.x`: https://www.python.org/ +.. _`NumPy`: https://numpy.org/ +.. _`SciPy`: https://scipy.org/ +.. _`Matplotlib`: https://matplotlib.org/ +.. _`pandas`: https://pandas.pydata.org/ +.. _`cvxpy`: https://www.cvxpy.org/ How to use ---------- -1. Install the required libraries. You can use environment.yml with - conda command. +1. Clone this repo and go into dir. + +.. code-block:: + + >$ git clone https://github.com/AtsushiSakai/PythonRobotics.git + + >$ cd PythonRobotics + + +2. Install the required libraries. + +using conda : + +.. code-block:: + + >$ conda env create -f environment.yml + +using pip : + +.. code-block:: + + >$ pip install -r requirements.txt -2. Clone this repo. 3. Execute python script in each directory. diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst new file mode 100644 index 0000000000..e00078541a --- /dev/null +++ b/docs/how_to_contribute_main.rst @@ -0,0 +1,129 @@ +How to contribute +================= + +This document describes how to contribute this project. + +Adding a new algorithm example +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +This is a step by step manual to add a new algorithm example. + +Step 1: Choose an algorithm to implement +----------------------------------------- + +Before choosing an algorithm, please check the :ref:`getting started` doc to +understand this project's philosophy and setup your development environment. + +If an algorithm is widely used and successful, let's create an issue to +propose it for our community. + +If some people agree by thumbs up or posting positive comments, let go to next step. + +It is OK to just create an issue to propose adding an algorithm, someone might implement it. + +In that case, please share any papers or documentations to implement it. + + +Step 2: Implement the algorithm with matplotlib based animation +---------------------------------------------------------------- + +When you implement an algorithm, please keep the following items in mind. + +1. Use only Python. Other language code is not acceptable. + +2. This project only accept codes for python 3.9 or higher. + +3. Use matplotlib based animation to show how the algorithm works. + +4. Only use current :ref:`Requirements` libraries, not adding new dependencies. + +5. Keep simple your code. The main goal is to make it easy for users to understand the algorithm, not for practical usage. + + +Step 3: Add a unittest +---------------------- +If you add a new algorithm sample code, please add a unit test file under `tests dir`_. + +This sample test code might help you : `test_a_star.py`_. + +At the least, try to run the example code without animation in the unit test. + + +.. _`how to write doc`: + +Step 4: Write a document about the algorithm +---------------------------------------------- +Please add a document to describe the algorithm details, mathematical backgrounds and show graphs and animation gif. + +This project is using `Sphinx`_ as a document builder, all documentations are written by `reStructuredText`_. + +You can add a new rst file under the subdirectory in `doc modules dir`_ and the top rst file can include it. + +Please check other documents for details. + +You can build the doc locally based on `doc README`_. + + +.. _`submit a pull request`: + +Step 5: Submit a pull request and fix codes based on review +------------------------------------------------------------ + +Let's submit a pull request when your code, test, and doc are ready. + +At first, please fix all CI errors before code review. + +You can check your PR doc from the CI panel. + +After the "ci/circleci: build_doc" CI is succeeded, +you can access you PR doc with clicking the [Details] of the "ci/circleci: build_doc artifact" CI. + +.. image:: /_static/img/doc_ci.png + +After that, I will start the review. + +Note that this is my hobby project; I appreciate your patience during the review process. + + +Reporting and fixing a defect +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Reporting and fixing a defect is also great contribution. + +When you report an issue, please provide these information: + +- A clear and concise description of what the bug is. +- A clear and concise description of what you expected to happen. +- Screenshots to help explain your problem if applicable. +- OS version +- Python version +- Each library versions + +If you want to fix any bug, you can find reported issues in `bug labeled issues`_. + +If you fix a bug of existing codes, please add a test function +in the test code to show the issue was solved. + +This doc `submit a pull request`_ can be helpful to submit a pull request. + + +Adding missed documentations for existing examples +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Adding the missed documentations for existing examples is also great contribution. + +If you check the `Python Robotics Docs`_, you can notice that some of the examples +only have a simulation gif or short overview descriptions, +but no detailed algorithm or mathematical description. + +This doc `how to write doc`_ can be helpful to write documents. + +.. _`Python Robotics Docs`: https://pythonrobotics.readthedocs.io/en/latest/ +.. _`bug labeled issues`: https://github.com/AtsushiSakai/PythonRobotics/issues?q=is%3Aissue+is%3Aopen+label%3Abug +.. _`tests dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/tests +.. _`test_a_star.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_a_star.py +.. _`Sphinx`: https://www.sphinx-doc.org/ +.. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html +.. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules +.. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md + diff --git a/docs/index_main.rst b/docs/index_main.rst index 978c216f11..7cdbddc71f 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -43,6 +43,7 @@ See this paper for more details: modules/arm_navigation/arm_navigation modules/aerial_navigation/aerial_navigation modules/appendix/appendix + how_to_contribute Indices and tables diff --git a/docs/modules/introduction_main.rst b/docs/modules/introduction_main.rst index e9fce33443..60cb085a29 100644 --- a/docs/modules/introduction_main.rst +++ b/docs/modules/introduction_main.rst @@ -2,5 +2,4 @@ Introduction ============ -Ref ---- +TBD \ No newline at end of file From 9d759d13c6481d001da72470a2eab67a740490f1 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 26 Nov 2021 21:46:14 +0900 Subject: [PATCH 374/909] update docs (#578) * update docs * update docs --- .github/ISSUE_TEMPLATE/bug_report.md | 3 ++- .github/pull_request_template.md | 13 +++++++------ README.md | 12 +++++++----- docs/how_to_contribute_main.rst | 28 ++++++++++++++++++++++++++++ 4 files changed, 44 insertions(+), 12 deletions(-) diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index a643b49571..4f13bd074f 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -17,5 +17,6 @@ A clear and concise description of what you expected to happen. If applicable, add screenshots to help explain your problem. **Desktop (please complete the following information):** - - Python version (This repo only supports Python 3.7.x or higher). + - Python version (This repo only supports Python 3.9.x or higher). - Each library version + - OS version diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index fb412d571c..c541063156 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,11 +1,7 @@ + +#### CheckList +-[] Did you add an unittest for your new example or defect fix? +-[] Did you add documents for your new example? +-[] All CIs are green? (You can check it after submitting) diff --git a/README.md b/README.md index 195eb1bd4f..f297b6fc12 100644 --- a/README.md +++ b/README.md @@ -603,11 +603,13 @@ If this project helps your robotics project, please let me know with creating an Your robot's video, which is using PythonRobotics, is very welcome!! -This is a list of other user's comment and references:[users\_comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md) +This is a list of user's comment and references:[users\_comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md) # Contribution -Any contribution is welcome!! +Any contribution is welcome!! + +Please check this document:[How to contribute](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#) # Citing @@ -615,7 +617,7 @@ If you use this project's code for your academic work, we encourage you to cite If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly. -# Support +# Supporting this project If you or your company would like to support this project, please consider: @@ -627,9 +629,9 @@ If you or your company would like to support this project, please consider: If you would like to support us in some other way, please contact with creating an issue. -# Sponsors +## Sponsors -## [JetBrains](https://www.jetbrains.com/) +### [JetBrains](https://www.jetbrains.com/) They are providing a free license of their IDEs for this OSS development. diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst index e00078541a..c761fd9928 100644 --- a/docs/how_to_contribute_main.rst +++ b/docs/how_to_contribute_main.rst @@ -48,6 +48,8 @@ This sample test code might help you : `test_a_star.py`_. At the least, try to run the example code without animation in the unit test. +If you want to run the test suites locally, you can use the `runtests.sh` script by just executing it. + .. _`how to write doc`: @@ -118,6 +120,27 @@ but no detailed algorithm or mathematical description. This doc `how to write doc`_ can be helpful to write documents. +Supporting this project +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Supporting this project financially is also a great contribution!!. + +If you or your company would like to support this project, please consider: + +- `Sponsor @AtsushiSakai on GitHub Sponsors`_ + +- `Become a backer or sponsor on Patreon`_ + +- `One-time donation via PayPal`_ + +If you would like to support us in some other way, please contact with creating an issue. + +Sponsors +--------- + +1. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development. + + .. _`Python Robotics Docs`: https://pythonrobotics.readthedocs.io/en/latest/ .. _`bug labeled issues`: https://github.com/AtsushiSakai/PythonRobotics/issues?q=is%3Aissue+is%3Aopen+label%3Abug .. _`tests dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/tests @@ -126,4 +149,9 @@ This doc `how to write doc`_ can be helpful to write documents. .. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html .. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md +.. _`JetBrains`: https://www.jetbrains.com/ +.. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai +.. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma +.. _`One-time donation via PayPal`: https://www.paypal.me/myenigmapay/ + From eb1fec748f9f5464e036b9d555b624a4d9c01c9f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 26 Nov 2021 23:14:23 +0900 Subject: [PATCH 375/909] Clean up Path planning docs (#579) * update docs * update docs --- .../quintic_polynomials_planner.ipynb | 143 ------------------ PathPlanning/RRTStar/Figure_1.png | Bin 119836 -> 0 bytes PathPlanning/RRTStar/rrt_star.ipynb | 74 --------- .../path_planning/path_planning_main.rst | 35 +---- .../quintic_polynomials_planner.rst | 106 +++++++++++++ docs/modules/path_planning/rrt_star.rst | 25 ++- 6 files changed, 120 insertions(+), 263 deletions(-) delete mode 100644 PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb delete mode 100644 PathPlanning/RRTStar/Figure_1.png delete mode 100644 PathPlanning/RRTStar/rrt_star.ipynb create mode 100644 docs/modules/path_planning/quintic_polynomials_planner.rst diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb deleted file mode 100644 index f094ae2c15..0000000000 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb +++ /dev/null @@ -1,143 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quintic polynomials planner" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quintic polynomials for one dimensional robot motion\n", - "\n", - "We assume a one-dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n", - "\n", - "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(1)\n", - "\n", - "$a_0, a_1. a_2, a_3, a_4, a_5$ are parameters of the quintic polynomial.\n", - "\n", - "It is assumed that terminal states (start and end) are known as boundary conditions.\n", - "\n", - "Start position, velocity, and acceleration are $x_s, v_s, a_s$ respectively.\n", - "\n", - "End position, velocity, and acceleration are $x_e, v_e, a_e$ respectively.\n", - "\n", - "So, when time is 0.\n", - "\n", - "$x(0) = a_0 = x_s$ -- (2)\n", - "\n", - "Then, differentiating the equation (1) with t, \n", - "\n", - "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4$ -- (3)\n", - "\n", - "So, when time is 0,\n", - "\n", - "$x'(0) = a_1 = v_s$ -- (4)\n", - "\n", - "Then, differentiating the equation (3) with t again, \n", - "\n", - "$x''(t) = 2a_2+6a_3t+12a_4t^2$ -- (5)\n", - "\n", - "So, when time is 0,\n", - "\n", - "$x''(0) = 2a_2 = a_s$ -- (6)\n", - "\n", - "so, we can calculate $a_0$, $a_1$, $a_2$ with eq. (2), (4), (6) and boundary conditions.\n", - "\n", - "$a_3, a_4, a_5$ are still unknown in eq(1).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We assume that the end time for a maneuver is $T$, we can get these equations from eq (1), (3), (5):\n", - "\n", - "$x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e$ -- (7)\n", - "\n", - "$x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e$ -- (8)\n", - "\n", - "$x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e$ -- (9)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From eq (7), (8), (9), we can calculate $a_3, a_4, a_5$ to solve the linear equations.\n", - "\n", - "$Ax=b$\n", - "\n", - "$\\begin{bmatrix} T^3 & T^4 & T^5 \\\\ 3T^2 & 4T^3 & 5T^4 \\\\ 6T & 12T^2 & 20T^3 \\end{bmatrix}\n", - "\\begin{bmatrix} a_3\\\\ a_4\\\\ a_5\\end{bmatrix}=\\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\\\ v_e-v_s-a_sT\\\\ a_e-a_s\\end{bmatrix}$\n", - "\n", - "We can get all unknown parameters now" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quintic polynomials for two dimensional robot motion (x-y)\n", - "\n", - "If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane.\n", - "\n", - "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(10)\n", - "\n", - "$y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5$ --(11)\n", - "\n", - "It is assumed that terminal states (start and end) are known as boundary conditions.\n", - "\n", - "Start position, orientation, velocity, and acceleration are $x_s, y_s, \\theta_s, v_s, a_s$ respectively.\n", - "\n", - "End position, orientation, velocity, and acceleration are $x_e, y_e. \\theta_e, v_e, a_e$ respectively.\n", - "\n", - "Each velocity and acceleration boundary condition can be calculated with each orientation.\n", - "\n", - "$v_{xs}=v_scos(\\theta_s), v_{ys}=v_ssin(\\theta_s)$\n", - "\n", - "$v_{xe}=v_ecos(\\theta_e), v_{ye}=v_esin(\\theta_e)$\n" - ] - }, - { - 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zT1{3uZ^;2oXVe)0MbF6C1-jD5d0lLu>FX47^YG-AmPX&Vwzd-Y^z@8bzLAlY{RLEg zrS&x9kHSJKVPTRIjj|x|+|G+p(NWWB5@1~bb@ueIW;Cy|5~IcaD5!5^Eh`%+A<*8} z(0nBk*q4#+GA-sXg9>S2UyeO~jt8zt_Qx9^%#<+)&v~# z2RuLY1?!JsZc0X#?r%" - ] - }, - "execution_count": 1, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename=\"Figure_1.png\",width=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Ref\n", - "\n", - "- [Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/pdf/1105.1186.pdf)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst index 60651dd5ff..50ed9bdbcf 100644 --- a/docs/modules/path_planning/path_planning_main.rst +++ b/docs/modules/path_planning/path_planning_main.rst @@ -91,6 +91,9 @@ Ref: - `Optimal rough terrain trajectory generation for wheeled mobile robots `__ + + + State Lattice Planning ---------------------- @@ -179,27 +182,8 @@ This is a simple path planning code with Rapidly-Exploring Random Trees Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. -.. _rrt*: - -RRT\* -~~~~~ - -|10| - -This is a path planning code with RRT\* - -Black circles are obstacles, green line is a searched tree, red crosses -are start and goal positions. - .. include:: rrt_star.rst -Ref: - -- `Incremental Sampling-based Algorithms for Optimal Motion - Planning `__ - -- `Sampling-based Algorithms for Optimal Motion - Planning `__ RRT with dubins path ~~~~~~~~~~~~~~~~~~~~ @@ -367,20 +351,9 @@ Ref: Autonomous Vehicles `__ -Quintic polynomials planning ----------------------------- -Motion planning with quintic polynomials. - -|2| - -It can calculate 2D path, velocity, and acceleration profile based on -quintic polynomials. - -Ref: +.. include:: quintic_polynomials_planner.rst -- `Local Path Planning And Motion Control For Agv In - Positioning `__ Dubins path planning -------------------- diff --git a/docs/modules/path_planning/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner.rst new file mode 100644 index 0000000000..2fab8a51ab --- /dev/null +++ b/docs/modules/path_planning/quintic_polynomials_planner.rst @@ -0,0 +1,106 @@ + +Quintic polynomials planning +---------------------------- + +Motion planning with quintic polynomials. + +|2| + +It can calculate 2D path, velocity, and acceleration profile based on +quintic polynomials. + + + +Quintic polynomials for one dimensional robot motion +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We assume a one-dimensional robot motion :math:`x(t)` at time :math:`t` is +formulated as a quintic polynomials based on time as follows: + +.. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5 + :label: eq1 + +:math:`a_0, a_1. a_2, a_3, a_4, a_5` are parameters of the quintic polynomial. + +It is assumed that terminal states (start and end) are known as boundary conditions. + +Start position, velocity, and acceleration are :math:`x_s, v_s, a_s` respectively. + +End position, velocity, and acceleration are :math:`x_e, v_e, a_e` respectively. + +So, when time is 0. + +.. math:: x(0) = a_0 = x_s + :label: eq2 + +Then, differentiating the equation :eq:`eq1` with t, + +.. math:: x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4 + :label: eq3 + +So, when time is 0, + +.. math:: x'(0) = a_1 = v_s + :label: eq4 + +Then, differentiating the equation :eq:`eq3` with t again, + +.. math:: x''(t) = 2a_2+6a_3t+12a_4t^2 + :label: eq5 + +So, when time is 0, + +.. math:: x''(0) = 2a_2 = a_s + :label: eq6 + +so, we can calculate :math:`a_0, a_1, a_2` with eq. :eq:`eq2`, :eq:`eq4`, :eq:`eq6` and boundary conditions. + +:math:`a_3, a_4, a_5` are still unknown in eq :eq:`eq1`. + +We assume that the end time for a maneuver is :math:`T`, we can get these equations from eq :eq:`eq1`, :eq:`eq3`, :eq:`eq5`: + +.. math:: x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e + :label: eq7 + +.. math:: x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e + :label: eq8 + +.. math:: x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e + :label: eq9 + +From eq :eq:`eq7`, :eq:`eq8`, :eq:`eq9`, we can calculate :math:`a_3, a_4, a_5` to solve the linear equations: :math:`Ax=b` + +.. math:: \begin{bmatrix} T^3 & T^4 & T^5 \\ 3T^2 & 4T^3 & 5T^4 \\ 6T & 12T^2 & 20T^3 \end{bmatrix}\begin{bmatrix} a_3\\ a_4\\ a_5\end{bmatrix}=\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\ v_e-v_s-a_sT\\ a_e-a_s\end{bmatrix} + +We can get all unknown parameters now. + +Quintic polynomials for two dimensional robot motion (x-y) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane. + +.. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5 + :label: eq10 + +.. math:: y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5 + :label: eq11 + +It is assumed that terminal states (start and end) are known as boundary conditions. + +Start position, orientation, velocity, and acceleration are :math:`x_s, y_s, \theta_s, v_s, a_s` respectively. + +End position, orientation, velocity, and acceleration are :math:`x_e, y_e. \theta_e, v_e, a_e` respectively. + +Each velocity and acceleration boundary condition can be calculated with each orientation. + +:math:`v_{xs}=v_scos(\theta_s), v_{ys}=v_ssin(\theta_s)` + +:math:`v_{xe}=v_ecos(\theta_e), v_{ye}=v_esin(\theta_e)` + +References: +~~~~~~~~~~~ + +- `Local Path Planning And Motion Control For Agv In + Positioning `__ + + diff --git a/docs/modules/path_planning/rrt_star.rst b/docs/modules/path_planning/rrt_star.rst index 5b8d62e7e1..cf136d529f 100644 --- a/docs/modules/path_planning/rrt_star.rst +++ b/docs/modules/path_planning/rrt_star.rst @@ -1,27 +1,22 @@ +RRT\* +~~~~~ -Simulation -^^^^^^^^^^ - -.. code-block:: ipython3 - - from IPython.display import Image - Image(filename="Figure_1.png",width=600) +.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif + :alt: gif +This is a path planning code with RRT\* +Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. +Simulation +^^^^^^^^^^ .. image:: rrt_star_files/rrt_star_1_0.png :width: 600px - -.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif - :alt: gif - - gif - Ref ^^^ +- `Sampling-based Algorithms for Optimal Motion Planning `__ +- `Incremental Sampling-based Algorithms for Optimal Motion Planning `__ -- `Sampling-based Algorithms for Optimal Motion - Planning `__ From 50ba79a3f53cb96c34002d54375be022a9bec5c9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 27 Nov 2021 20:10:45 +0900 Subject: [PATCH 376/909] Fix clean up other docs (#580) * update docs * update docs * update docs * update docs --- .../rocket_powered_landing/figure.png | Bin 688548 -> 0 bytes .../rocket_powered_landing.ipynb | 482 ------------------ .../Planar_Two_Link_IK.ipynb | 423 --------------- PathTracking/cgmres_nmpc/Figure_1.png | Bin 33823 -> 0 bytes PathTracking/cgmres_nmpc/Figure_2.png | Bin 30942 -> 0 bytes PathTracking/cgmres_nmpc/Figure_3.png | Bin 25270 -> 0 bytes PathTracking/cgmres_nmpc/Figure_4.png | Bin 19078 -> 0 bytes PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb | 208 -------- ...redictive_speed_and_steering_control.ipynb | 333 ------------ PathTracking/rear_wheel_feedback/Figure_2.png | Bin 39590 -> 0 bytes .../aerial_navigation_main.rst | 6 +- .../rocket_powered_landing.rst | 8 +- .../arm_navigation/arm_navigation_main.rst | 11 +- ...Two_Link_IK.rst => planar_two_link_ik.rst} | 5 +- .../Bezier_path_planning}/Figure_1.png | Bin .../Bezier_path_planning}/Figure_2.png | Bin .../path_planning/path_planning_main.rst | 5 +- ..._predictive_speed_and_steering_control.rst | 2 +- docs/modules/path_tracking/cgmres_nmpc.rst | 39 -- .../path_tracking/path_tracking_main.rst | 23 +- 20 files changed, 19 insertions(+), 1526 deletions(-) delete mode 100644 AerialNavigation/rocket_powered_landing/figure.png delete mode 100644 AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb delete mode 100644 ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb delete mode 100644 PathTracking/cgmres_nmpc/Figure_1.png delete mode 100644 PathTracking/cgmres_nmpc/Figure_2.png delete mode 100644 PathTracking/cgmres_nmpc/Figure_3.png delete mode 100644 PathTracking/cgmres_nmpc/Figure_4.png delete mode 100644 PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb delete mode 100644 PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb delete mode 100644 PathTracking/rear_wheel_feedback/Figure_2.png rename docs/modules/arm_navigation/{Planar_Two_Link_IK.rst => planar_two_link_ik.rst} (99%) rename {PathPlanning/BezierPath => docs/modules/path_planning/Bezier_path_planning}/Figure_1.png (100%) rename {PathPlanning/BezierPath => docs/modules/path_planning/Bezier_path_planning}/Figure_2.png (100%) diff --git a/AerialNavigation/rocket_powered_landing/figure.png b/AerialNavigation/rocket_powered_landing/figure.png deleted file mode 100644 index 0be109ed2b79f52191a0004c70390489cde54e3b..0000000000000000000000000000000000000000 GIT binary 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"metadata": {}, - "source": [ - "### Simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename=\"figure.png\",width=600)\n", - "from IPython.display import display, HTML\n", - "\n", - "display(HTML(data=\"\"\"\n", - "\n", - "\"\"\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Equation generation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import sympy as sp\n", - "import numpy as np\n", - "from IPython.display import display\n", - "sp.init_printing(use_latex='mathjax')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# parameters\n", - "# Angular moment of inertia\n", - "J_B = 1e-2 * np.diag([1., 1., 1.])\n", - "\n", - "# Gravity\n", - "g_I = np.array((-1, 0., 0.))\n", - "\n", - "# Fuel consumption\n", - "alpha_m = 0.01\n", - "\n", - "# Vector from thrust point to CoM\n", - "r_T_B = np.array([-1e-2, 0., 0.])\n", - "\n", - "\n", - "def dir_cosine(q):\n", - " return np.matrix([\n", - " [1 - 2 * (q[2] ** 2 + q[3] ** 2), 2 * (q[1] * q[2] +\n", - " q[0] * q[3]), 2 * (q[1] * q[3] - q[0] * q[2])],\n", - " [2 * (q[1] * q[2] - q[0] * q[3]), 1 - 2 *\n", - " (q[1] ** 2 + q[3] ** 2), 2 * (q[2] * q[3] + q[0] * q[1])],\n", - " [2 * (q[1] * q[3] + q[0] * q[2]), 2 * (q[2] * q[3] -\n", - " q[0] * q[1]), 1 - 2 * (q[1] ** 2 + q[2] ** 2)]\n", - " ])\n", - "\n", - "def omega(w):\n", - " return np.matrix([\n", - " [0, -w[0], -w[1], -w[2]],\n", - " [w[0], 0, w[2], -w[1]],\n", - " [w[1], -w[2], 0, w[0]],\n", - " [w[2], w[1], -w[0], 0],\n", - " ])\n", - "\n", - "def skew(v):\n", - " return np.matrix([\n", - " [0, -v[2], v[1]],\n", - " [v[2], 0, -v[0]],\n", - " [-v[1], v[0], 0]\n", - " ])\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "f = sp.zeros(14, 1)\n", - "\n", - "x = sp.Matrix(sp.symbols(\n", - " 'm rx ry rz vx vy vz q0 q1 q2 q3 wx wy wz', real=True))\n", - "u = sp.Matrix(sp.symbols('ux uy uz', real=True))\n", - "\n", - "g_I = sp.Matrix(g_I)\n", - "r_T_B = sp.Matrix(r_T_B)\n", - "J_B = sp.Matrix(J_B)\n", - "\n", - "C_B_I = dir_cosine(x[7:11, 0])\n", - "C_I_B = C_B_I.transpose()\n", - "\n", - "f[0, 0] = - alpha_m * u.norm()\n", - "f[1:4, 0] = x[4:7, 0]\n", - "f[4:7, 0] = 1 / x[0, 0] * C_I_B * u + g_I\n", - "f[7:11, 0] = 1 / 2 * omega(x[11:14, 0]) * x[7: 11, 0]\n", - "f[11:14, 0] = J_B ** -1 * \\\n", - " (skew(r_T_B) * u - skew(x[11:14, 0]) * J_B * x[11:14, 0])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$$\\left[\\begin{matrix}- 0.01 \\sqrt{ux^{2} + uy^{2} + uz^{2}}\\\\vx\\\\vy\\\\vz\\\\\\frac{- 1.0 m - ux \\left(2 q_{2}^{2} + 2 q_{3}^{2} - 1\\right) - 2 uy \\left(q_{0} q_{3} - q_{1} q_{2}\\right) + 2 uz \\left(q_{0} q_{2} + q_{1} q_{3}\\right)}{m}\\\\\\frac{2 ux \\left(q_{0} q_{3} + q_{1} q_{2}\\right) - uy \\left(2 q_{1}^{2} + 2 q_{3}^{2} - 1\\right) - 2 uz \\left(q_{0} q_{1} - q_{2} q_{3}\\right)}{m}\\\\\\frac{- 2 ux \\left(q_{0} q_{2} - q_{1} q_{3}\\right) + 2 uy \\left(q_{0} q_{1} + q_{2} q_{3}\\right) - uz \\left(2 q_{1}^{2} + 2 q_{2}^{2} - 1\\right)}{m}\\\\- 0.5 q_{1} wx - 0.5 q_{2} wy - 0.5 q_{3} wz\\\\0.5 q_{0} wx + 0.5 q_{2} wz - 0.5 q_{3} wy\\\\0.5 q_{0} wy - 0.5 q_{1} wz + 0.5 q_{3} wx\\\\0.5 q_{0} wz + 0.5 q_{1} wy - 0.5 q_{2} wx\\\\0\\\\1.0 uz\\\\- 1.0 uy\\end{matrix}\\right]$$" - ], - "text/plain": [ - "⎡ _________________ \n", - "⎢ ╱ 2 2 2 \n", - "⎢ -0.01⋅╲╱ ux + uy + uz \n", - "⎢ \n", - "⎢ vx \n", - "⎢ \n", - "⎢ vy \n", - "⎢ \n", - "⎢ vz \n", - "⎢ \n", - "⎢ ⎛ 2 2 ⎞ \n", - "⎢-1.0⋅m - ux⋅⎝2⋅q₂ + 2⋅q₃ - 1⎠ - 2⋅uy⋅(q₀⋅q₃ - q₁⋅q₂) + 2⋅uz⋅(q₀⋅q₂ + q₁⋅q₃)\n", - "⎢─────────────────────────────────────────────────────────────────────────────\n", - "⎢ m \n", - "⎢ \n", - "⎢ ⎛ 2 2 ⎞ \n", - "⎢ 2⋅ux⋅(q₀⋅q₃ + q₁⋅q₂) - uy⋅⎝2⋅q₁ + 2⋅q₃ - 1⎠ - 2⋅uz⋅(q₀⋅q₁ - q₂⋅q₃) \n", - "⎢ ──────────────────────────────────────────────────────────────────── \n", - "⎢ m \n", - "⎢ \n", - "⎢ ⎛ 2 2 ⎞ \n", - "⎢ -2⋅ux⋅(q₀⋅q₂ - q₁⋅q₃) + 2⋅uy⋅(q₀⋅q₁ + q₂⋅q₃) - uz⋅⎝2⋅q₁ + 2⋅q₂ - 1⎠ \n", - "⎢ ───────────────────────────────────────────────────────────────────── \n", - "⎢ m \n", - "⎢ \n", - "⎢ -0.5⋅q₁⋅wx - 0.5⋅q₂⋅wy - 0.5⋅q₃⋅wz \n", - "⎢ \n", - "⎢ 0.5⋅q₀⋅wx + 0.5⋅q₂⋅wz - 0.5⋅q₃⋅wy \n", - "⎢ \n", - "⎢ 0.5⋅q₀⋅wy - 0.5⋅q₁⋅wz + 0.5⋅q₃⋅wx \n", - "⎢ \n", - "⎢ 0.5⋅q₀⋅wz + 0.5⋅q₁⋅wy - 0.5⋅q₂⋅wx \n", - "⎢ \n", - "⎢ 0 \n", - "⎢ \n", - "⎢ 1.0⋅uz \n", - "⎢ \n", - "⎣ -1.0⋅uy \n", - "\n", - "⎤\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎥\n", - "⎦" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(sp.simplify(f)) # f" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$$\\left[\\begin{array}{cccccccccccccc}0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\\frac{ux \\left(2 q_{2}^{2} + 2 q_{3}^{2} - 1\\right) + 2 uy \\left(q_{0} q_{3} - q_{1} q_{2}\\right) - 2 uz \\left(q_{0} q_{2} + q_{1} q_{3}\\right)}{m^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{2 \\left(q_{2} uz - q_{3} uy\\right)}{m} & \\frac{2 \\left(q_{2} uy + q_{3} uz\\right)}{m} & \\frac{2 \\left(q_{0} uz + q_{1} uy - 2 q_{2} ux\\right)}{m} & \\frac{2 \\left(- q_{0} uy + q_{1} uz - 2 q_{3} ux\\right)}{m} & 0 & 0 & 0\\\\\\frac{- 2 ux \\left(q_{0} q_{3} + q_{1} q_{2}\\right) + uy \\left(2 q_{1}^{2} + 2 q_{3}^{2} - 1\\right) + 2 uz \\left(q_{0} q_{1} - q_{2} q_{3}\\right)}{m^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{2 \\left(- q_{1} uz + q_{3} ux\\right)}{m} & \\frac{2 \\left(- q_{0} uz - 2 q_{1} uy + q_{2} ux\\right)}{m} & \\frac{2 \\left(q_{1} ux + q_{3} uz\\right)}{m} & \\frac{2 \\left(q_{0} ux + q_{2} uz - 2 q_{3} uy\\right)}{m} & 0 & 0 & 0\\\\\\frac{2 ux \\left(q_{0} q_{2} - q_{1} q_{3}\\right) - 2 uy \\left(q_{0} q_{1} + q_{2} q_{3}\\right) + uz \\left(2 q_{1}^{2} + 2 q_{2}^{2} - 1\\right)}{m^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{2 \\left(q_{1} uy - q_{2} ux\\right)}{m} & \\frac{2 \\left(q_{0} uy - 2 q_{1} uz + q_{3} ux\\right)}{m} & \\frac{2 \\left(- q_{0} ux - 2 q_{2} uz + q_{3} uy\\right)}{m} & \\frac{2 \\left(q_{1} ux + q_{2} uy\\right)}{m} & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - 0.5 wx & - 0.5 wy & - 0.5 wz & - 0.5 q_{1} & - 0.5 q_{2} & - 0.5 q_{3}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.5 wx & 0 & 0.5 wz & - 0.5 wy & 0.5 q_{0} & - 0.5 q_{3} & 0.5 q_{2}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.5 wy & - 0.5 wz & 0 & 0.5 wx & 0.5 q_{3} & 0.5 q_{0} & - 0.5 q_{1}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.5 wz & 0.5 wy & - 0.5 wx & 0 & - 0.5 q_{2} & 0.5 q_{1} & 0.5 q_{0}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\end{array}\\right]$$" - ], - "text/plain": [ - "⎡ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ ⎛ 2 2 ⎞ \n", - "⎢ux⋅⎝2⋅q₂ + 2⋅q₃ - 1⎠ + 2⋅uy⋅(q₀⋅q₃ - q₁⋅q₂) - 2⋅uz⋅(q₀⋅q₂ + q₁⋅q₃) \n", - "⎢──────────────────────────────────────────────────────────────────── 0 0 \n", - "⎢ 2 \n", - "⎢ m \n", - "⎢ \n", - "⎢ ⎛ 2 2 ⎞ \n", - "⎢-2⋅ux⋅(q₀⋅q₃ + q₁⋅q₂) + uy⋅⎝2⋅q₁ + 2⋅q₃ - 1⎠ + 2⋅uz⋅(q₀⋅q₁ - q₂⋅q₃) \n", - "⎢───────────────────────────────────────────────────────────────────── 0 0 \n", - "⎢ 2 \n", - "⎢ m \n", - "⎢ \n", - "⎢ ⎛ 2 2 ⎞ \n", - "⎢2⋅ux⋅(q₀⋅q₂ - q₁⋅q₃) - 2⋅uy⋅(q₀⋅q₁ + q₂⋅q₃) + uz⋅⎝2⋅q₁ + 2⋅q₂ - 1⎠ \n", - "⎢──────────────────────────────────────────────────────────────────── 0 0 \n", - "⎢ 2 \n", - "⎢ m \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎢ 0 0 0 \n", - "⎢ \n", - "⎣ 0 0 0 \n", - "\n", - "0 0 0 0 0 0 0 \n", - " \n", - "0 1 0 0 0 0 0 \n", - " \n", - "0 0 1 0 0 0 0 \n", - " \n", - "0 0 0 1 0 0 0 \n", - " \n", - " \n", - " 2⋅(q₂⋅uz - q₃⋅uy) 2⋅(q₂⋅uy + q₃⋅uz) 2⋅(q₀⋅uz + q₁⋅uy\n", - "0 0 0 0 ───────────────── ───────────────── ────────────────\n", - " m m m \n", - " \n", - " \n", - " \n", - " 2⋅(-q₁⋅uz + q₃⋅ux) 2⋅(-q₀⋅uz - 2⋅q₁⋅uy + q₂⋅ux) 2⋅(q₁⋅ux + \n", - "0 0 0 0 ────────────────── ──────────────────────────── ───────────\n", - " m m m \n", - " \n", - " \n", - " \n", - " 2⋅(q₁⋅uy - q₂⋅ux) 2⋅(q₀⋅uy - 2⋅q₁⋅uz + q₃⋅ux) 2⋅(-q₀⋅ux - 2⋅q₂\n", - "0 0 0 0 ───────────────── ─────────────────────────── ────────────────\n", - " m m m \n", - " \n", - " \n", - "0 0 0 0 0 -0.5⋅wx -0.5⋅w\n", - " \n", - "0 0 0 0 0.5⋅wx 0 0.5⋅w\n", - " \n", - "0 0 0 0 0.5⋅wy -0.5⋅wz 0 \n", - " \n", - "0 0 0 0 0.5⋅wz 0.5⋅wy -0.5⋅w\n", - " \n", - "0 0 0 0 0 0 0 \n", - " \n", - "0 0 0 0 0 0 0 \n", - " \n", - "0 0 0 0 0 0 0 \n", - "\n", - " 0 0 0 0 ⎤\n", - " ⎥\n", - " 0 0 0 0 ⎥\n", - " ⎥\n", - " 0 0 0 0 ⎥\n", - " ⎥\n", - " 0 0 0 0 ⎥\n", - " ⎥\n", - " ⎥\n", - " - 2⋅q₂⋅ux) 2⋅(-q₀⋅uy + q₁⋅uz - 2⋅q₃⋅ux) ⎥\n", - "─────────── ──────────────────────────── 0 0 0 ⎥\n", - " m ⎥\n", - " ⎥\n", - " ⎥\n", - " ⎥\n", - "q₃⋅uz) 2⋅(q₀⋅ux + q₂⋅uz - 2⋅q₃⋅uy) ⎥\n", - "────── ─────────────────────────── 0 0 0 ⎥\n", - " m ⎥\n", - " ⎥\n", - " ⎥\n", - " ⎥\n", - "⋅uz + q₃⋅uy) 2⋅(q₁⋅ux + q₂⋅uy) ⎥\n", - "──────────── ───────────────── 0 0 0 ⎥\n", - " m ⎥\n", - " ⎥\n", - " ⎥\n", - "y -0.5⋅wz -0.5⋅q₁ -0.5⋅q₂ -0.5⋅q₃⎥\n", - " ⎥\n", - "z -0.5⋅wy 0.5⋅q₀ -0.5⋅q₃ 0.5⋅q₂ ⎥\n", - " ⎥\n", - " 0.5⋅wx 0.5⋅q₃ 0.5⋅q₀ -0.5⋅q₁⎥\n", - " ⎥\n", - "x 0 -0.5⋅q₂ 0.5⋅q₁ 0.5⋅q₀ ⎥\n", - " ⎥\n", - " 0 0 0 0 ⎥\n", - " ⎥\n", - " 0 0 0 0 ⎥\n", - " ⎥\n", - " 0 0 0 0 ⎦" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(sp.simplify(f.jacobian(x)))# A " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$$\\left[\\begin{matrix}- \\frac{0.01 ux}{\\sqrt{ux^{2} + uy^{2} + uz^{2}}} & - \\frac{0.01 uy}{\\sqrt{ux^{2} + uy^{2} + uz^{2}}} & - \\frac{0.01 uz}{\\sqrt{ux^{2} + uy^{2} + uz^{2}}}\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\\\frac{- 2 q_{2}^{2} - 2 q_{3}^{2} + 1}{m} & \\frac{2 \\left(- q_{0} q_{3} + q_{1} q_{2}\\right)}{m} & \\frac{2 \\left(q_{0} q_{2} + q_{1} q_{3}\\right)}{m}\\\\\\frac{2 \\left(q_{0} q_{3} + q_{1} q_{2}\\right)}{m} & \\frac{- 2 q_{1}^{2} - 2 q_{3}^{2} + 1}{m} & \\frac{2 \\left(- q_{0} q_{1} + q_{2} q_{3}\\right)}{m}\\\\\\frac{2 \\left(- q_{0} q_{2} + q_{1} q_{3}\\right)}{m} & \\frac{2 \\left(q_{0} q_{1} + q_{2} q_{3}\\right)}{m} & \\frac{- 2 q_{1}^{2} - 2 q_{2}^{2} + 1}{m}\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 1.0\\\\0 & -1.0 & 0\\end{matrix}\\right]$$" - ], - "text/plain": [ - "⎡ -0.01⋅ux -0.01⋅uy -0.01⋅uz ⎤\n", - "⎢──────────────────── ──────────────────── ────────────────────⎥\n", - "⎢ _________________ _________________ _________________⎥\n", - "⎢ ╱ 2 2 2 ╱ 2 2 2 ╱ 2 2 2 ⎥\n", - "⎢╲╱ ux + uy + uz ╲╱ ux + uy + uz ╲╱ ux + uy + uz ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 2 2 ⎥\n", - "⎢- 2⋅q₂ - 2⋅q₃ + 1 2⋅(-q₀⋅q₃ + q₁⋅q₂) 2⋅(q₀⋅q₂ + q₁⋅q₃) ⎥\n", - "⎢─────────────────── ────────────────── ───────────────── ⎥\n", - "⎢ m m m ⎥\n", - "⎢ ⎥\n", - "⎢ 2 2 ⎥\n", - "⎢ 2⋅(q₀⋅q₃ + q₁⋅q₂) - 2⋅q₁ - 2⋅q₃ + 1 2⋅(-q₀⋅q₁ + q₂⋅q₃) ⎥\n", - "⎢ ───────────────── ─────────────────── ────────────────── ⎥\n", - "⎢ m m m ⎥\n", - "⎢ ⎥\n", - "⎢ 2 2 ⎥\n", - "⎢ 2⋅(-q₀⋅q₂ + q₁⋅q₃) 2⋅(q₀⋅q₁ + q₂⋅q₃) - 2⋅q₁ - 2⋅q₂ + 1 ⎥\n", - "⎢ ────────────────── ───────────────── ─────────────────── ⎥\n", - "⎢ m m m ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 0 ⎥\n", - "⎢ ⎥\n", - "⎢ 0 0 1.0 ⎥\n", - "⎢ ⎥\n", - "⎣ 0 -1.0 0 ⎦" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sp.simplify(f.jacobian(u)) # B" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ref\n", - "\n", - "- Python implementation of 'Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time' paper\n", - "by Michael Szmuk and Behçet Açıkmeşe.\n", - "\n", - "- inspired by EmbersArc/SuccessiveConvexificationFreeFinalTime: Implementation of \"Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time\" https://github.com/EmbersArc/SuccessiveConvexificationFreeFinalTime\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb b/ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb deleted file mode 100644 index a96f0ea2f4..0000000000 --- a/ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb +++ /dev/null @@ -1,423 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Two joint arm to point control\n", - "\n", - "![TwoJointArmToPointControl](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/two_joint_arm_to_point_control/animation.gif)\n", - "\n", - "This is two joint arm to a point control simulation.\n", - "\n", - "This is a interactive simulation.\n", - "\n", - "You can set the goal position of the end effector with left-click on the ploting area.\n", - "\n", - "\n", - "\n", - "### Inverse Kinematics for a Planar Two-Link Robotic Arm\n", - "\n", - "A classic problem with robotic arms is getting the end-effector, the mechanism at the end of the arm responsible for manipulating the environment, to where you need it to be. Maybe the end-effector is a gripper and maybe you want to pick up an object and maybe you know where that object is relative to the robot - but you cannot tell the end-effector where to go directly. Instead, you have to determine the joint angles that get the end-effector to where you want it to be. This problem is known as inverse kinematics.\n", - "\n", - "Credit for this solution goes to: https://robotacademy.net.au/lesson/inverse-kinematics-for-a-2-joint-robot-arm-using-geometry/\n", - "\n", - "First, let's define a class to make plotting our arm easier.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "from math import cos, sin\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "class TwoLinkArm:\n", - " def __init__(self, joint_angles=[0, 0]):\n", - " self.shoulder = np.array([0, 0])\n", - " self.link_lengths = [1, 1]\n", - " self.update_joints(joint_angles)\n", - " \n", - " def update_joints(self, joint_angles):\n", - " self.joint_angles = joint_angles\n", - " self.forward_kinematics()\n", - " \n", - " def forward_kinematics(self):\n", - " theta0 = self.joint_angles[0]\n", - " theta1 = self.joint_angles[1]\n", - " l0 = self.link_lengths[0]\n", - " l1 = self.link_lengths[1]\n", - " self.elbow = self.shoulder + np.array([l0*cos(theta0), l0*sin(theta0)])\n", - " self.wrist = self.elbow + np.array([l1*cos(theta0 + theta1), l1*sin(theta0 + theta1)])\n", - " \n", - " def plot(self):\n", - " plt.plot([self.shoulder[0], self.elbow[0]],\n", - " [self.shoulder[1], self.elbow[1]],\n", - " 'r-')\n", - " plt.plot([self.elbow[0], self.wrist[0]],\n", - " [self.elbow[1], self.wrist[1]],\n", - " 'r-')\n", - " plt.plot(self.shoulder[0], self.shoulder[1], 'ko')\n", - " plt.plot(self.elbow[0], self.elbow[1], 'ko')\n", - " plt.plot(self.wrist[0], self.wrist[1], 'ko')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also define a function to make it easier to draw an angle on our diagram." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from math import sqrt\n", - "\n", - "def transform_points(points, theta, origin):\n", - " T = np.array([[cos(theta), -sin(theta), origin[0]],\n", - " [sin(theta), cos(theta), origin[1]],\n", - " [0, 0, 1]])\n", - " return np.matmul(T, np.array(points))\n", - "\n", - "def draw_angle(angle, offset=0, origin=[0, 0], r=0.5, n_points=100):\n", - " x_start = r*cos(angle)\n", - " x_end = r\n", - " dx = (x_end - x_start)/(n_points-1)\n", - " coords = [[0 for _ in range(n_points)] for _ in range(3)]\n", - " x = x_start\n", - " for i in range(n_points-1):\n", - " y = sqrt(r**2 - x**2)\n", - " coords[0][i] = x\n", - " coords[1][i] = y\n", - " coords[2][i] = 1\n", - " x += dx\n", - " coords[0][-1] = r\n", - " coords[2][-1] = 1\n", - " coords = transform_points(coords, offset, origin)\n", - " plt.plot(coords[0], coords[1], 'k-')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Okay, we now have a TwoLinkArm class to help us draw the arm, which we'll do several times during our derivation. Notice there is a method called forward_kinematics - forward kinematics specifies the end-effector position given the joint angles and link lengths. Forward kinematics is easier than inverse kinematics." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "arm = TwoLinkArm()\n", - "\n", - "theta0 = 0.5\n", - "theta1 = 1\n", - "\n", - "arm.update_joints([theta0, theta1])\n", - "arm.plot()\n", - "\n", - "def label_diagram():\n", - " plt.plot([0, 0.5], [0, 0], 'k--')\n", - " plt.plot([arm.elbow[0], arm.elbow[0]+0.5*cos(theta0)],\n", - " [arm.elbow[1], arm.elbow[1]+0.5*sin(theta0)],\n", - " 'k--')\n", - " \n", - " draw_angle(theta0, r=0.25)\n", - " draw_angle(theta1, offset=theta0, origin=[arm.elbow[0], arm.elbow[1]], r=0.25)\n", - " \n", - " plt.annotate(\"$l_0$\", xy=(0.5, 0.4), size=15, color=\"r\")\n", - " plt.annotate(\"$l_1$\", xy=(0.8, 1), size=15, color=\"r\")\n", - " \n", - " plt.annotate(r\"$\\theta_0$\", xy=(0.35, 0.05), size=15)\n", - " plt.annotate(r\"$\\theta_1$\", xy=(1, 0.8), size=15)\n", - "\n", - "label_diagram()\n", - "\n", - "plt.annotate(\"Shoulder\", xy=(arm.shoulder[0], arm.shoulder[1]), xytext=(0.15, 0.5),\n", - " arrowprops=dict(facecolor='black', shrink=0.05))\n", - "plt.annotate(\"Elbow\", xy=(arm.elbow[0], arm.elbow[1]), xytext=(1.25, 0.25),\n", - " arrowprops=dict(facecolor='black', shrink=0.05))\n", - "plt.annotate(\"Wrist\", xy=(arm.wrist[0], arm.wrist[1]), xytext=(1, 1.75),\n", - " arrowprops=dict(facecolor='black', shrink=0.05))\n", - "\n", - "plt.axis(\"equal\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's common to name arm joints anatomically, hence the names shoulder, elbow, and wrist. In this example, the wrist is not itself a joint, but we can consider it to be our end-effector. If we constrain the shoulder to the origin, we can write the forward kinematics for the elbow and the wrist.\n", - "\n", - "$elbow_x = l_0\\cos(\\theta_0)$ \n", - "$elbow_y = l_0\\sin(\\theta_0)$ \n", - "\n", - "$wrist_x = elbow_x + l_1\\cos(\\theta_0+\\theta_1) = l_0\\cos(\\theta_0) + l_1\\cos(\\theta_0+\\theta_1)$ \n", - "$wrist_y = elbow_y + l_1\\sin(\\theta_0+\\theta_1) = l_0\\sin(\\theta_0) + l_1\\sin(\\theta_0+\\theta_1)$ \n", - "\n", - "Since the wrist is our end-effector, let's just call its coordinates $x$ and $y$. The forward kinematics for our end-effector is then\n", - "\n", - "$x = l_0\\cos(\\theta_0) + l_1\\cos(\\theta_0+\\theta_1)$ \n", - "$y = l_0\\sin(\\theta_0) + l_1\\sin(\\theta_0+\\theta_1)$ \n", - "\n", - "A first attempt to find the joint angles $\\theta_0$ and $\\theta_1$ that would get our end-effector to the desired coordinates $x$ and $y$ might be to try solving the forward kinematics for $\\theta_0$ and $\\theta_1$, but that would be the wrong move. An easier path involves going back to the geometry of the arm." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from math import pi\n", - "\n", - "arm.plot()\n", - "label_diagram()\n", - "\n", - "plt.plot([0, arm.wrist[0]],\n", - " [0, arm.wrist[1]],\n", - " 'k--')\n", - "\n", - "plt.plot([arm.wrist[0], arm.wrist[0]],\n", - " [0, arm.wrist[1]],\n", - " 'b--')\n", - "plt.plot([0, arm.wrist[0]],\n", - " [0, 0],\n", - " 'b--')\n", - "\n", - "plt.annotate(\"$x$\", xy=(0.6, 0.05), size=15, color=\"b\")\n", - "plt.annotate(\"$y$\", xy=(1, 0.2), size=15, color=\"b\")\n", - "plt.annotate(\"$r$\", xy=(0.45, 0.9), size=15)\n", - "plt.annotate(r\"$\\alpha$\", xy=(0.75, 0.6), size=15)\n", - "\n", - "alpha = pi-theta1\n", - "draw_angle(alpha, offset=theta0+theta1, origin=[arm.elbow[0], arm.elbow[1]], r=0.1)\n", - "\n", - "plt.axis(\"equal\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The distance from the end-effector to the robot base (shoulder joint) is $r$ and can be written in terms of the end-effector position using the Pythagorean Theorem.\n", - "\n", - "$r^2$ = $x^2 + y^2$\n", - "\n", - "Then, by the law of cosines, $r$2 can also be written as:\n", - "\n", - "$r^2$ = $l_0^2 + l_1^2 - 2l_0l_1\\cos(\\alpha)$\n", - "\n", - "Because $\\alpha$ can be written as $\\pi - \\theta_1$, we can relate the desired end-effector position to one of our joint angles, $\\theta_1$.\n", - "\n", - "$x^2 + y^2$ = $l_0^2 + l_1^2 - 2l_0l_1\\cos(\\alpha)$ \n", - " \n", - "$x^2 + y^2$ = $l_0^2 + l_1^2 - 2l_0l_1\\cos(\\pi-\\theta_1)$ \n", - " \n", - "$2l_0l_1\\cos(\\pi-\\theta_1) = l_0^2 + l_1^2 - x^2 - y^2$ \n", - " \n", - "$\\cos(\\pi-\\theta_1) = \\frac{l_0^2 + l_1^2 - x^2 - y^2}{2l_0l_1}$ \n", - "$~$ \n", - "$~$ \n", - "$\\cos(\\pi-\\theta_1) = -cos(\\theta_1)$ is a trigonometric identity, so we can also write\n", - "\n", - "$\\cos(\\theta_1) = \\frac{x^2 + y^2 - l_0^2 - l_1^2}{2l_0l_1}$ \n", - "\n", - "which leads us to an equation for $\\theta_1$ in terms of the link lengths and the desired end-effector position!\n", - "\n", - "$\\theta_1 = \\cos^{-1}(\\frac{x^2 + y^2 - l_0^2 - l_1^2}{2l_0l_1})$ \n", - "\n", - "This is actually one of two possible solutions for $\\theta_1$, but we'll ignore the other possibility for now. This solution will lead us to the \"arm-down\" configuration of the arm, which is what's shown in the diagram. Now we'll derive an equation for $\\theta_0$ that depends on this value of $\\theta_1$." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from math import atan2\n", - "\n", - "arm.plot()\n", - "plt.plot([0, arm.wrist[0]],\n", - " [0, arm.wrist[1]],\n", - " 'k--')\n", - "\n", - "p = 1 + cos(theta1)\n", - "plt.plot([arm.elbow[0], p*cos(theta0)],\n", - " [arm.elbow[1], p*sin(theta0)],\n", - " 'b--', linewidth=5)\n", - "plt.plot([arm.wrist[0], p*cos(theta0)],\n", - " [arm.wrist[1], p*sin(theta0)],\n", - " 'b--', linewidth=5)\n", - "\n", - "beta = atan2(arm.wrist[1], arm.wrist[0])-theta0\n", - "draw_angle(beta, offset=theta0, r=0.45)\n", - "\n", - "plt.annotate(r\"$\\beta$\", xy=(0.35, 0.35), size=15)\n", - "plt.annotate(\"$r$\", xy=(0.45, 0.9), size=15)\n", - "plt.annotate(r\"$l_1sin(\\theta_1)$\",xy=(1.25, 1.1), size=15, color=\"b\")\n", - "plt.annotate(r\"$l_1cos(\\theta_1)$\",xy=(1.1, 0.4), size=15, color=\"b\")\n", - "\n", - "label_diagram()\n", - "\n", - "plt.axis(\"equal\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider the angle between the displacement vector $r$ and the first link $l_0$; let's call it $\\beta$. If we extend the first link to include the component of the second link in the same direction as the first, we form a right triangle with components $l_0+l_1cos(\\theta_1)$ and $l_1sin(\\theta_1)$, allowing us to express $\\beta$ as\n", - " \n", - "$\\beta = \\tan^{-1}(\\frac{l_1\\sin(\\theta_1)}{l_0+l_1\\cos(\\theta_1)})$ \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have an expression for this angle $\\beta$ in terms of one of our arm's joint angles. Now, can we relate $\\beta$ to $\\theta_0$? Yes!" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "arm.plot()\n", - "label_diagram()\n", - "plt.plot([0, arm.wrist[0]],\n", - " [0, arm.wrist[1]],\n", - " 'k--')\n", - "\n", - "plt.plot([arm.wrist[0], arm.wrist[0]],\n", - " [0, arm.wrist[1]],\n", - " 'b--')\n", - "plt.plot([0, arm.wrist[0]],\n", - " [0, 0],\n", - " 'b--')\n", - "\n", - "gamma = atan2(arm.wrist[1], arm.wrist[0])\n", - "draw_angle(beta, offset=theta0, r=0.2)\n", - "draw_angle(gamma, r=0.6)\n", - "\n", - "plt.annotate(\"$x$\", xy=(0.7, 0.05), size=15, color=\"b\")\n", - "plt.annotate(\"$y$\", xy=(1, 0.2), size=15, color=\"b\")\n", - "plt.annotate(r\"$\\beta$\", xy=(0.2, 0.2), size=15)\n", - "plt.annotate(r\"$\\gamma$\", xy=(0.6, 0.2), size=15)\n", - "\n", - "plt.axis(\"equal\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Our first joint angle $\\theta_0$ added to $\\beta$ gives us the angle between the positive $x$-axis and the displacement vector $r$; let's call this angle $\\gamma$.\n", - "\n", - "$\\gamma = \\theta_0+\\beta$ \n", - "\n", - "$\\theta_0$, our remaining joint angle, can then be expressed as \n", - "\n", - "$\\theta_0 = \\gamma-\\beta$ \n", - "\n", - "We already know $\\beta$. $\\gamma$ is simply the inverse tangent of $\\frac{y}{x}$, so we have an equation of $\\theta_0$! \n", - "\n", - "$\\theta_0 = \\tan^{-1}(\\frac{y}{x})-\\tan^{-1}(\\frac{l_1\\sin(\\theta_1)}{l_0+l_1\\cos(\\theta_1)})$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have the inverse kinematics for a planar two-link robotic arm. If you're planning on implementing this in a programming language, it's best to use the atan2 function, which is included in most math libraries and correctly accounts for the signs of $y$ and $x$. Notice that $\\theta_1$ must be calculated before $\\theta_0$.\n", - "\n", - "$\\theta_1 = \\cos^{-1}(\\frac{x^2 + y^2 - l_0^2 - l_1^2}{2l_0l_1})$ \n", - "$\\theta_0 = atan2(y, x)-atan2(l_1\\sin(\\theta_1), l_0+l_1\\cos(\\theta_1))$" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/PathTracking/cgmres_nmpc/Figure_1.png b/PathTracking/cgmres_nmpc/Figure_1.png deleted file mode 100644 index c15112dcac161a5c262afe0d3e2c7b069a59ee05..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 33823 zcmeFZWn7ir);@aE-Q6vvq?DwxC?%!4M3e^U?hZi_ly0OE>23u9k?xZ2Za8y2``!O{ 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9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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79BIRkU/xiU+lV155BRMnTsTMmTNx/PhxZGRk4NChQxg+fDj27dvn7fKILmtvTgUWrN+LJX/JxNmyOoTptXhhziB8+/NpuHtcEsMfERH5HJ+YAXz77bexdetWzJ49GwAwZMgQHDhwAL/85S8xbdo02O12L1dI1NbZsjr89ouT+DarHAAQpFXj4SmpeOSm3ggN5HX8iIjId/lEADx27BiioqJajGm1Wrz++uuYN2+el6oial+NtRFrvz6DP+/Pg0uUoFEJuG98Ep68uR+iDTpvl0dERHRFPhEAW4e/S02dOrUHKyHqmMMl4q/78/DW12c81/KbMSgWL8wdhNQoHqtKRET+wycCIJGv+zarDC//6yRyyusBAANiDXhx3mBM7tfxf7wQERH5KgZAoss4W2bBy1+cws6m4/wiggPwbFp/3D02kSd3EBGR32IAJGpH6+P8tGoBi29MwZM394MxiCd4EBGRf2MAJLpEe8f5zRwci1/O4XF+REQkHwyARE1aH+c3MM59nN+kvjzOj4iI5IUBkBSvo+P8fjwuyXPrNiIiIjlhACTFau84v59OSsWTN/flhZyJiEjWGABJcTo6zu+FOYOQwuP8iIhIARgASVF4nB8REREDIClE6+P8IoMD8GzaANw9LpHH+RERkeIwAJKsVdU34u2vs/GX7/N5nB8REVETBkCSpUaniD/ty8XvvzkDs80JgMf5ERERNWMAJFmRJAlfnbiAV748hdxKKwBgcK9Q/GreINzYh8f5ERERAQyAJCPHi2rx8hcnsf9cFQAg2qDD8rQBuHNMAo/zIyIiugQDIPm9MrMNr3+VhU8OFkKSAJ1GhUem9MaSaX0QouOvOBERUWv8dCS/ZbY5sHFXDv6wJxcNDhcA4LaRJvxi1kDEhwV5uToiIiLfxQBIfsfmcOFP+3KxfmcOaqzuCzmPTgrDr+YNxuikcO8WR0RE5AcYAMlvOF0ithwsxNqvz6Ck1gYA6BcTguW3DMDMwbEQBB7nR0RE1BkMgOTzXKKEfx0txu+/OeO5g4fJGIhlM/vjztE8wYOIiOhqMQCSz3K6RHx+pBjr/n0W5yrcwS9Mr8WT0/vi/gnJCNSqvVwhERGRf2IAJJ/jcIn47FAR/vfbs8hrupafMUiLhyen4ieTUngHDyIiouvEAEg+w+oE3t9zHn/eX+A5xi8iOAAPT0nFAxNTeEkXIiKiLsJPVB9kc7hgb3R6u4wek19pxQff5WBTphqN4hkAQFSIDj+7KRX3jU9GMIMfERFRl+Inqw/66kQpnt9yFANCVRCSSjFjSC/oA+T1o2p0ikg/eQGbMvKx52wFJAkABPSPCcHDN/XG/BEmHuNHRETUTeSVKmRi/7lKNDhEHK5U4b83H0WQ9gSmD4zGnGG9cPPAGL8Ng5Ik4USxGf84XIQtB4tQVd/oWXZTv0gM0ZTh6XsmIiAgwItVEhERyZ9/JgmZ+5/bh+G/Rsdj3ed7kdUQjMLqBmw7Voptx0oRqFXh5oExfhMGJUnCyRIzvjhagi+OlXhO6gCA2FAdFo5JxH+NTUSvUC22bdvGa/kRERH1AN9ODwolCAKGxodifrKI2bMnI6usAV8cK8EXx4pRUNUyDE4f4A6D0wfG+MxJErUNDvznbAV2Z5djV3a554QOAJ6a7xydgGkDoqFRqwAADofDW+USEREpjm8kBi9ISUlBXl5ei7HnnnsOr7zyipcqap8gCBiWYMSwBCOemzUAx4vM+OJYCbYdK0F+lRVfHi/Fl8dLoVYJGGoKxbiUCIxLjcCoxDBEG3TdPqMmSRIKqhpwqKAaB/OqcaigBieKzXCJkmcdncYd+uYOd89a8qQOIiIi71L0J/Hq1avxyCOPeJ6HhIR4sZorax0GTxSb8a+jJfjyuHvX6pHCWhwprMUHe84DcF9CZUCsAQN7GZAaFYz4sCCYwoIQHx4Eg07T6XAoihIq6uwoqbWhpNaGwmorzlyow5kyC86U1cFia3vGct+YENzULxpTB0RjfGoET+ggIiLyIYoOgAaDAXFxcd4u45q4dxMbMTTeiOdnD0RRTQMyzlfhQG4VMs5XIae8DlX1jdh3rhL7zlW2eb1GJSA0SAtDoAaGQA3UKhVUAtAcCa2NLlhsTtQ3OmGxOVvM6LWmVQsYbDJidFIYRieFY0xyOExhQd3UOREREV0vRQfAV199Fb/5zW+QmJiIhQsXYvny5Zc9A9Vut8Nut3uem81mAO7j17r6GLbm7XV2uzHBGswdGoO5Q2MAuK8leLasHqcvWJB9oQ6F1Q0orm1AcY0N1VYHnKKEqvrGFmfiXo5KAKINOsSFBqKXMRB9ooPRLyYEfaODkRIVDJ1G1W79nXW1/fo79itv7Ff+lNaz3PqVSx/XQ5AkqeOpHRl76623MHr0aISHh+PAgQNYsWIFbrvtNnzwwQcdvmblypVYtWpVm/GPP/4Yer2+O8vtUo0u9103rC7A5gQaXAKaJ/iafxl0KkCnlhCoBgLVgCEAUPMEXSIikgGr1Yp7770XtbW1CA0N9XY5XiGrANhRQLtURkYGxo4d22Z8y5YtuOuuu1BRUYHIyMh2X9veDGBiYiIqKiq6/BfI4XAgPT0dM2fOhFYr/3vfsl95Y7/yprR+AeX1LLd+zWYzoqKiFB0AZbUL+Mknn8SPf/zjy66TkpLS7viECRMAAGfPnu0wAOp0Ouh0Os/z5uzc0NDQ5X8QDocDVqsVDQ0NcDrlf1s49itv7FfelNYvoLye5dZvQ0MDgIuf40okqwAYFRWFqKioa3rtoUOHAAC9evXq9GssFgsAIDEx8Zrek4iIiLzHYrHAaDR6uwyvkNUu4M7at28f9u/fj+nTp8NoNCIjIwNPP/00xo4di3/84x+d3o4oiiguLobBYOjy6+01714uKChQxPQ0+5U39itvSusXUF7PcutXkiRYLBaYTCaoVKorv0CGZDUD2Fk6nQ6bN2/GqlWrYLfbkZycjEceeQS/+MUvrmo7KpUKCQkJ3VSlW2hoqCz+2DqL/cob+5U3pfULKK9nOfWr1Jm/ZooMgKNHj8b+/fu9XQYRERGRVyhz3pOIiIhIwdQrV65c6e0iqH1qtRrTpk2DRqOMiVr2K2/sV96U1i+gvJ6V1q/cKfIkECIiIiIl4y5gIiIiIoVhACQiIiJSGAZAIiIiIoVhACQiIiJSGAZAH7R+/XqkpqYiMDAQY8aMwXfffeftkrrEmjVrMG7cOBgMBsTExGDBggXIyspqsY7dbsfSpUsRFRWF4OBgzJ8/H4WFhV6quGutWbMGgiBg2bJlnjG59VtUVIT7778fkZGR0Ov1GDlyJDIzMz3LJUnCypUrYTKZEBQUhGnTpuHEiRNerPjaOZ1O/OpXv0JqaiqCgoLQu3dvrF69GqIoetbx9353796NW2+9FSaTCYIgYOvWrS2Wd6a/6upqLFq0CEajEUajEYsWLUJNTU1PttFpl+vX4XDgueeew7BhwxAcHAyTyYQHHngAxcXFLbYhl35be/TRRyEIAtauXdti3J/6pZYYAH3M5s2bsWzZMrzwwgs4dOgQpkyZgtmzZyM/P9/bpV23Xbt24YknnsD+/fuRnp4Op9OJtLQ01NfXe9ZZtmwZPvvsM2zatAl79uxBXV0d5s2bB5fL5cXKr19GRgbee+89DB8+vMW4nPqtrq7GpEmToNVq8eWXX+LkyZN44403EBYW5lnntddew5tvvol169YhIyMDcXFxmDlzpue+2v7k1Vdfxbvvvot169bh1KlTeO211/D666/jnXfe8azj7/3W19djxIgRWLduXbvLO9Pfvffei8OHD2P79u3Yvn07Dh8+jEWLFvVUC1flcv1arVYcPHgQL774Ig4ePIhPP/0U2dnZmD9/fov15NLvpbZu3Yrvv/8eJpOpzTJ/6pdakcin3HDDDdKSJUtajA0cOFB6/vnnvVRR9ykrK5MASLt27ZIkSZJqamokrVYrbdq0ybNOUVGRpFKppO3bt3urzOtmsVikfv36Senp6dLUqVOlp556SpIk+fX73HPPSZMnT+5wuSiKUlxcnPTKK694xmw2m2Q0GqV33323J0rsUnPnzpUefPDBFmN33HGHdP/990uSJL9+AUifffaZ53ln+jt58qQEQNq/f79nnX379kkApNOnT/dc8degdb/tOXDggARAysvLkyRJnv0WFhZK8fHx0vHjx6Xk5GTprbfe8izz535JkhQ/A+hLu1sbGxuRmZmJtLS0FuNpaWnYu3evl6rqPrW1tQCAiIgIAEBmZiYcDkeL/k0mE4YOHerX/T/xxBOYO3cuZsyY0WJcbv1+/vnnGDt2LBYuXIiYmBiMGjUK77//vmf5+fPnUVpa2qJfnU6HqVOn+mW/kydPxjfffIPs7GwAwJEjR7Bnzx7MmTMHgPz6ba0z/e3btw9GoxHjx4/3rDNhwgQYjUZZ/BvU1tZCEATPLLfc+hVFEYsWLcLy5csxZMiQNsvl1q/SKPpy3s27W9evX49JkyZh48aNmD17Nk6ePImkpKQrvl4URRQXF8NgMEAQhOuup6SkBC6XCyEhITCbzZ5xo9GIoqKiFmP+TpIkLF26FBMmTEBSUhLMZjPOnTsHrVYLtVrdotfIyEjk5+f7Zf+ffPIJMjIysHPnTpjNZjidTtjtdln2m5OTg/Xr1+PJJ5/Eli1bkJmZiaVLl0IURdxzzz3IyckBAOj1+ha9hYeHo6CgwO/6feyxx1BWVoYBAwZArVbD5XLhpZdewty5c2E2m2XXL+DeDdpcd2f6y83NRWRkZJteIyMjkZub6/P/Bpf225rNZsPy5cuxcOFCAJBlv2+88QYAYPHixTCbzRBFETabzbOOP/crSRIsFgtMJhNUKmXOhSn6TiDjx4/H6NGjsWHDBs/YoEGDsGDBAqxZs+aKry8sLERiYmJ3lkhERETdpKCgAAkJCd4uwysUOwPYvLv1+eefbzF+NbtbDQYDAPcvUGhoaJfW53A4sGPHDqSlpUGr1Xbptn0R+5U39itvSusXUF7PcuvXbDYjMTHR8zmuRIoNgBUVFXC5XIiNjW0xHhsbi9LS0nZfY7fbYbfbPc+bz3QLCgpCUFBQl9an0Wig1+sRFBQkiz+2K2G/8ibXfiVJgigBTpcIpyh5vuxOF2xqPSpsgNohQWxaT5Qkz2skz3M0Lb/0+9bLmt+r/WWX1tNxrZd836qH9sdbvb6DjUkAXC4XTtQHA9m1UKvV7b7/pUfJCB0sENof9owLnVi35aaFK2xX6GAcbZ60fm+X04nz9mAcKLRCo1G38z5t3xtX8d6dqbPFpttZv6OeOvp39PTazrpOpxPloh5nqxzQaKQrvt+VauuovvbWjQjWIjJE1/7Gr5HD4Wh6v+s/fMtfKTYANmv9w5ckqcNfiDVr1mDVqlVtxnfs2AG9Xt8t9aWnp3fLdn0V+5W37urXKQJ2F2BzAfam7+0uwf28+UsEnKIAh+he3yE1PYqXPErCJd8DLhEQ4X50ARCbHyXAJQGidLkPDw1wcH+39Oub1MDZU94uooepgdNHvV1ED9IAxzKvvFoXm5XgwuzErj1azWq1dun2/JFiA2BUVBTUanWb2b6ysrI2s4LNVqxYgWeeecbzvHkKOS0trVt2Aaenp2PmzJmymjHpCPuVt6vpV5IkmG1OXDDbcMFsR5nFjpoGB2qsDlRbHaixNrZ83uBAo1O87DZ7mloQIECEVqOBWiVAEACVAKiE5u+FVt+7/2NUdclzoOVzQRCgUrWzDVx5Bsy9tUvH0cH45WeG3N+2ncGRRBGVlZWIioqC0FTfpTqefbx0vP2VJM+6V56p7Ghm82rWbW/b7dUmSRJqas0wGkMhCEK7vbQY6+C9W8+mtl1Xam/Vy8zWtvPeHazb3rY7qk2UAJutAYGBLfd2Xenn08GPtd31O6pt+JB+mDM5FV3Jl09Q6SmKDYABAQEYM2YM0tPTcfvtt3vG09PTcdttt7X7Gp1OB52u7TS0Vqvttg/x7ty2L2K/8uY+61mDCxYb8iqtyK+0Iq+qHgVVDSg125pCnw02x7UFOp1GhRCdBsHNXwFqBOs0CNFpEBSgRqBWBZ1GDZ2m6VGruvi9RtX03P19gEYFrVqARqWCWiVAq3Y/alQCNE0edEF5AAAgAElEQVTj7kehzXKn04lt27ZhzpxbFPHzdTgcTf2OVUS/wKU9T1REzxf7vUkW/cqhh+ul2AAIAM888wwWLVqEsWPHYuLEiXjvvfeQn5+PJUuWeLs0Ir/ndInIq7Iiq9SCk0U12J2lwu/P/geF1Q2wd2LGLkyvRawhEDGhOkQEByBcH4AwvRbh+gCEBwcgvOl7Y5AWoYFa6HVqaNXKvJwDEdHVUnQAvPvuu1FZWYnVq1ejpKQEQ4cOxbZt25CcnOzt0oj8iihKOFteh8P5NThUUINjRTXIvlDXatesCoD7tn9qlYCE8CAkReiRHKlHUoQevYxBiDMGekJfoLb9kwmIiOj6KToAAsDjjz+Oxx9/3NtlEPkVu9OFQ/k12JtTiR9yq3C0sBZ1dmeb9YK0avSPDUG/mBA4K/Mxf9o49I0xwhQWCA1n64iIvEbxAZCIrkwUJRwvrsV3ZyqwL6cSGblVbXbj6gPUGBZvxMikMIxMCMNgUygSw/VQqYSm44fyMKVvFI+9ISLyAQyARNQum8OFvTkVSD9Zhm9OXUCZxd5iebRBhxv7RGJ8aiRGJYWhX0wIZ/WIiPwEAyARedgcLuzMKsfnR4rw7elyNDhcnmXBAWpM7heFSX2jcGOfSPSJDlH0RVSJiPwZAyCRwrlECftyKvGPw0XYfqIUFtvFY/lMxkDMGByLGYNiMb53BHQanphBRCQHDIBEClVaa8PffijA5owCFNU0eMZ7GQMxf4QJt44wYYgplLN8REQyxABIpCCiKGFXdjn++n0+/n36guc+ssYgLeYN74X5I0wYlxIBlYqhj4hIzhgAiRSgodGFTw4W4g97zuN8Rb1n/IbUCNx7QxJmDY3jdfeIiBSEAZBIxsotdvxpXy7+sj8P1VYHACA0UIOFYxNxzw1J6BsT4t0CiYjIKxgAiWSotNaGDTvP4v9lFHjuxpEYEYSHJqVi4dhEBOv4p09EpGT8FCCSkZLaBmzYmYNNBwrQ6HIHv1FJYXhkSm/cMiQOah7bR0REYAAkkoUysw3rvj3bIvjdkBKBZTP7YWLvSJ7JS0RELTAAEvmxOrsT7+0+h/d3n/NctPmG1Agsm8HgR0REHWMAJPJDDpeITRkFePvrbFTUNQJw7+pdfssA3NgnysvVERGRr2MAJPIjkiTh61NlWLPtFM41Xc4lJVKP52YNxKyhcZzxIyKiTmEAJPIT58rrsOqfJ7EruxwAEBkcgKdm9MM9NyRBq1Z5uToiIvInDIBEPq7e7sS6b8/ig+/OweGSoFULeHhKbzw+rQ8MgVpvl0dERH6IAZDIR0mShH8dLcFvvziFUrMNADBtQDRemjcYvaN5AWciIrp2DIBEPuhceR1e+Ow49p2rBOC+iPOv5w3BjwbF8Dg/IiK6bgyARD6k0Sli464cvPPtWTQ6Reg0KjwxvS9+dlNv3quXiIi6DAMgkY/IzKvGik+PIvtCHQBgav9ovLxgKBIj9F6ujIiI5IYBkMjLLDYHXtuehb98nwdJcp/d+9KtgzF/hIm7e4mIqFswABJ5UfrJMqz+4rTnJI+7xiTghTmDEB4c4OXKiIhIzhgAibygsr4RH2WrcHjfYQBAcqQea24fhhv78i4eRETU/RgAiXrYl8dK8MLWY6iqV0GtEvCzm3rjqR/140keRETUYxgAiXpIdX0jXvr8BP55pBgA0CtIwvqfTMColEgvV0ZERErDAEjUA3acKMUvPzuOijo7VALw6JRU9LWfwdD4UG+XRkRECsQASNSNaqyNWPXPk/jsUBEAoG9MCN5YOAKD44KxbdsZL1dHRERKxQBI1E2+OXUBKz49hjKLe9bvZzf1wbIZ7mP9HA6Ht8sjIiIFU3m7AABYvHgxdu/e7e0yiLpEbYMDz/7tCB76vx9QZrGjd3QwPnnsRjw/eyBP9CAiIp/gEzOAFosFaWlpSExMxE9/+lP85Cc/QXx8vLfLIrpq32aVYcWWYyg12yAIwMOTU/Fs2gAGPyIi8ik+MQO4ZcsWFBUV4cknn8Tf//53pKSkYPbs2fjkk0+4q4z8gtnmwHOfHMVPP8pAqdmG1Khg/P3RiXhh7mCGPyIi8jk+EQABIDIyEk899RQOHTqEAwcOoG/fvli0aBFMJhOefvppnDnDA+bJN313phyz3tqNzT8UQBCAByelYtt/T8HYlAhvl0ZERNQunwmAzUpKSrBjxw7s2LEDarUac+bMwYkTJzB48GC89dZb3i6PyKPO7sSKT49h0YcHUFxrQ1KEHpsemYCXbh2MoADO+hERke/yiWMAHQ4HPv/8c3z00UfYsWMHhg8fjqeffhr33XcfDAYDAGDTpk147LHH8PTTT3u5WiLgP2cr8ItPjqKopgEA8JOJyXhu9kDoA3ziT4qIiOiyfOLTqlevXhBFEffccw8OHDiAkSNHtlnnlltuQVhYmBeqI7qo3u7EK1+exp/35wEAEsKD8Npdw3FjH97Dl4iI/IdPBMC33noLCxcuRGBgYIfrhIeH4/z58z1YFVFL+89VYvknR1BQ5Z71u39CElbMHoRgnU/8GREREXWaT3xyLVq0yNslEHXI2ujEa9uz8Me9uQCA+LAgvHrncEzux1k/IiLyTz4RAIl81Q+5Vfj5348gt9IKALjnhkT8cs4gGAK1Xq6MiIjo2jEAErXD5nDhjR1Z+GDPeUgSEBcaiFfvGo6p/aO9XRoREdF1YwAkauVgfjV+/vcjOFdeDwC4a0wCXpw3GMYgzvoREZE8MAASNbE5XFj79Rm8tzsHogTEGHRYc8cw/GhQrLdLIyIi6lIMgEQAjhbW4Nm/HcGZsjoAwO2j4vHrWwcjTB/g5cqIiIi6HgMgKVqjU8Q7/z6D9Ttz4BIlRIUE4Le3D8MtQ+K8XRoREVG3YQAkxTpcUINffHIE2Rfcs363jjBh1fwhiAjmrB8REcmbz90LuCfk5ubioYceQmpqKoKCgtCnTx/8+te/RmNjo7dLox5gbXTi5X+dxB3r/4PsC3WIDA7A+vtG4517RjH8ERGRIihyBvD06dMQRREbN25E3759cfz4cTzyyCOor6/H7373O2+XR91o79kKPP/pMeRXua/rd/uoeLw0bzDCGfyIiEhBFBkAZ82ahVmzZnme9+7dG1lZWdiwYQMDoEzVNjiwZtspbMooAACYjIH47e3DMH1gjJcrIyIi6nmKDIDtqa2tRUREhLfLoG6w40QpfrX1OMosdgDAognJ+MWsAbybBxERKRYDIICcnBy88847eOONNy67nt1uh91u9zw3m80AAIfDAYfD0aU1NW+vq7frq7qj3wtmG367LQtfnrgAAEiJ1ON/FgzBuJTwLn+vq8Wfr7yxX/lTWs9y61cufVwPQZIkydtFdJWVK1di1apVl10nIyMDY8eO9TwvLi7G1KlTMXXqVHzwwQfXtP2PP/4Yer3+2oqmLidKwJ5SAf8qUMHuEqCChJtNEm5JEBGg9nZ1RETkbVarFffeey9qa2sRGhrq7XK8QlYBsKKiAhUVFZddJyUlBYGBgQDc4W/69OkYP348/vjHP0KluvxJ0e3NACYmJqKioqLLf4EcDgfS09Mxc+ZMaLXy31XZVf0eK6rFS5+fwvFi9+zs8IRQrL51MIaYfOsPnD9feWO/8qe0nuXWr9lsRlRUlKIDoKx2AUdFRSEqKqpT6xYVFWH69OkYM2YMPvrooyuGPwDQ6XTQ6XRtxrVabbf9QXTntn3RtfZb2+DAGzuy8Of9eZAkwBCowXOzBuKeG5KgVgndUGnX4M9X3tiv/CmtZ7n0K4cerpesAmBnFRcXY9q0aUhKSsLvfvc7lJeXe5bFxfEOEP5EFCVsPVyE/9l2GhV17tnZ20fF45dzBiHa0DasExERkUID4I4dO3D27FmcPXsWCQkJLZbJaI+47B3Mr8bqf57E4YIaAEDvqGC8vGAobuzbuVlgIiIipVJkAFy8eDEWL17s7TLoGpXUNuDVL09j6+FiAEBwgBpP3NwXD01OhU7DszyIiIiuRJEBkPxTnd2JD747h427zqHB4YIgAHeNTsDyWwYgJjTQ2+URERH5DQZA8nk2hwt//T4f6789i8p69/2axyaH49e3DsGwBKOXqyMiIvI/DIDks5wuEVsOFuLtr8+guNYGAEiNCsazaf0xd1gvCILvnt1LRETkyxgAyefYnS58erAIG3flILfSCgDoZQzEUz/qhzvHJECrvvIle4iIiKhjDIDkM+wu4MP/5OKjvXm4YHZf0iVcr8UT0/vi/gnJCNTyBA8iIqKuwADoo5wu0dsl9JjzFfX4097z2JSpRoMrGwAQFxqIR27qjXtuSIQ+gL+mREREXYmfrD7oWGEtlvzlB8yIFjBbptcltDtd+PZ0Of76fR6+O9N8+z4BKZF6PD6tLxaMikeAhrt6iYiIugMDoA96d3cOimps+L8aNU58mIEnb+6Hm/pFQ+XDtzTrDFGU8P35KvzjcBG2HSuB2eYEAAgCMLVfFAaoLuCZeyZBpwvwcqVERETyxgDog95YOAJ9ovTYsPMsfsirweKPMpASqcctQ+Pwo4GxGJ5g9Jvj4ersTuw5U45/ny7Dv0+Xe27XBgCxoTosGBWP+8cnI86gxbZt2/w+5BIREfkDBkAfFKhVY+n0PoioyUKurjc+OViM3EorNu5yXwRZoxIw2BSKkYlhGNwrFAPiDOgfa0Cwzvs/TrPNgcy8avyQW4WM3Gocyq+Gw3VxN7ZBp8HsYXFYMCoe41MjoW4KfA6Hw1slExERKY73EwN1KFwH3DdnIH4+axC+PV2G9JMXsDenAhV1jThaWIujhbUt1k+K0DeFwRAkRwQjMUKPpEg94kIDPUGrqzQ0ulBUY8XZsnpklVqQdcGM0yUWnK+sR+vDFlMi9bh5YCxuHhiDcanhvF0bERGRlzEA+oEQnQa3jjDh1hEmSJKEwuoGHCqowdGCGpwutSDrggXlFjvyq6zIr7Ii/eSFFq/XqgWYwoIQHaJDRHAAIkN0iAwOgCFQA51GBZ1WDZ1GBa1aBZcoweES4Wx6tNicqLE2orbBgWqrAxfMNhRWN6Cq6Y4c7UmJ1GNsSgTGpYTjhtRIpEYFd/c/EREREV0FBkA/IwgCEiP0SIzQY/4Ik2e8ss6OrAsWZJVacLasDvlVVhRUWVFY3QCHS0JepRV5TRdV7ioGnQZJkXoMjAvFwDgDBvYyYGBcKKINui59HyIiIupaDIAyERmiw40hOtzYJ6rFuEuUUFLbgKLqBlTWN7q/6uyoqm9End0Ju1OE3SHC7nSh0SlCoxagUamgbXoMDdIgTB8AY5AWxiAtYgw6JITrER8eBGOQ1kvdEhER0fVgAJQ5tUpAQrgeCeF6b5dCREREPoIB8DpITWc7mM3mLt+2w+GA1WqF2WyGViv/mTb2K2/sV96U1i+gvJ7l1m/z57Yk05stdAYD4HWwWCwAgMTERC9XQkRERFfLYrHAaDR6uwyvECQlx9/rJIoiiouLYTAYIAhde5kVs9mMxMREFBQUIDQ0tEu37YvYr7yxX3lTWr+A8nqWW7+SJMFiscBkMkGlUuZtRzkDeB1UKhUSEhK69T1CQ0Nl8cfWWexX3tivvCmtX0B5PcupX6XO/DVTZuwlIiIiUjAGQCIiIiKFUa9cuXKlt4ug9qnVakybNg0ajTL21LNfeWO/8qa0fgHl9ay0fuWOJ4EQERERKQx3ARMREREpDAMgERERkcIwABIREREpDAMgERERkcIwAPqg9evXIzU1FYGBgRgzZgy+++47b5fUJdasWYNx48bBYDAgJiYGCxYsQFZWVot17HY7li5diqioKAQHB2P+/PkoLCz0UsVda82aNRAEAcuWLfOMya3foqIi3H///YiMjIRer8fIkSORmZnpWS5JElauXAmTyYSgoCBMmzYNJ06c8GLF187pdOJXv/oVUlNTERQUhN69e2P16tUQRdGzjr/3u3v3btx6660wmUwQBAFbt25tsbwz/VVXV2PRokUwGo0wGo1YtGgRampqerKNTrtcvw6HA8899xyGDRuG4OBgmEwmPPDAAyguLm6xDbn029qjjz4KQRCwdu3aFuP+1C+1xADoYzZv3oxly5bhhRdewKFDhzBlyhTMnj0b+fn53i7tuu3atQtPPPEE9u/fj/T0dDidTqSlpaG+vt6zzrJly/DZZ59h06ZN2LNnD+rq6jBv3jy4XC4vVn79MjIy8N5772H48OEtxuXUb3V1NSZNmgStVosvv/wSJ0+exBtvvIGwsDDPOq+99hrefPNNrFu3DhkZGYiLi8PMmTM999X2J6+++ireffddrFu3DqdOncJrr72G119/He+8845nHX/vt76+HiNGjMC6devaXd6Z/u69914cPnwY27dvx/bt23H48GEsWrSop1q4Kpfr12q14uDBg3jxxRdx8OBBfPrpp8jOzsb8+fNbrCeXfi+1detWfP/99zCZTG2W+VO/1IpEPuWGG26QlixZ0mJs4MCB0vPPP++lirpPWVmZBEDatWuXJEmSVFNTI2m1WmnTpk2edYqKiiSVSiVt377dW2VeN4vFIvXr109KT0+Xpk6dKj311FOSJMmv3+eee06aPHlyh8tFUZTi4uKkV155xTNms9kko9Eovfvuuz1RYpeaO3eu9OCDD7YYu+OOO6T7779fkiT59QtA+uyzzzzPO9PfyZMnJQDS/v37Pevs27dPAiCdPn2654q/Bq37bc+BAwckAFJeXp4kSfLst7CwUIqPj5eOHz8uJScnS2+99ZZnmT/3S5LEGUAf0tjYiMzMTKSlpbUYT0tLw969e71UVfepra0FAERERAAAMjMz4XA4WvRvMpkwdOhQv+7/iSeewNy5czFjxowW43Lr9/PPP8fYsWOxcOFCxMTEYNSoUXj//fc9y8+fP4/S0tIW/ep0OkydOtUv+508eTK++eYbZGdnAwCOHDmCPXv2YM6cOQDk129rnelv3759MBqNGD9+vGedCRMmwGg0yuLfoLa2FoIgeGa55davKIpYtGgRli9fjiFDhrRZLrd+lYaX874OoiiiuLgYBoMBgiBc9/ZKSkrgcrkQEhICs9nsGTcajSgqKmox5u8kScLSpUsxYcIEJCUlwWw249y5c9BqtVCr1S16jYyMRH5+vl/2/8knnyAjIwM7d+6E2WyG0+mE3W6XZb85OTlYv349nnzySWzZsgWZmZlYunQpRFHEPffcg5ycHACAXq9v0Vt4eDgKCgr8rt/HHnsMZWVlGDBgANRqNVwuF1566SXMnTsXZrNZdv0C7t2gzXV3pr/c3FxERka26TUyMhK5ubk+/29wab+t2Ww2LF++HAsXLgQAWfb7xhtvAAAWL14Ms9kMURRhs9k86/hzv5IkwWKxwGQyQaVS5lwY7wRyHQoLC5GYmOjtMoiIiOgaFBQUICEhwdtleAVnAK+DwWAA4P4FCg0N7dJtOxwO7NixA2lpadBqtV26bV/EfuWN/cqb0voFlNez3Po1m81ITEz0fI4rEQPgdWje7RsaGtrlAbCxsRG6QD1CQ0Nl8cd2JQ6HA3o9+5Ur9itvSusXUF7Pcu23Kw7f8ld+s+P7aq6NN23aNAiC0OZr7ty5nnUWL17cZvmECRN6opUr+v03ZzDiN99gW6Hf/HiIiIjIj/jFDGDztfHWr1+PSZMmYePGjZg9ezZOnjyJpKSkNut/+umnaGxs9DyvrKzEiBEjPAfrNps1axY++ugjz/OAgIDua+Iq6DQqNDhEVNu9XQkRERHJkV9MMb355pt46KGH8PDDD2PQoEFYu3YtEhMTsWHDhnbXj4iIQFxcnOcrPT0der2+TQDU6XQt1mu+HIm3mcKCAAA1duVOTRMREVH38fkA2BXXxvvwww/x4x//GMHBwS3Gd+7ciZiYGPTv3x+PPPIIysrKuqzu69EcAKsbr7AiERER0TXw+V3AFRUVcLlciI2NbTEeGxuL0tLSK77+wIEDOH78OD788MMW47Nnz8bChQuRnJyM8+fP48UXX8TNN9+MzMxM6HS6drdlt9tht1/cL9t8jSOHwwGHw3G1rXUoOtj9Y6mxAza7MlJg879fV/47+jL2K2/sV/6U1rPc+pVLH9fD568DWFxcjPj4eOzduxcTJ070jP/2t7/Fn//8Z5w+ffqyr3/00Uexd+9eHDt27LLrlZSUIDk5GZs2bcIdd9zR7jorV67EqlWr2ox//PHH0Ov1neimc0QJeHa/GiIErBrtRFj7eZSIiIiugdVqxb333ova2touv4qHv/D5GcCoqCio1eo2s31lZWVtZgVbs1qt2LRpE1avXn3F9+nVqxeSk5Nx5syZDtdZsWIFnnnmGc/z5usIpaWldfkv0OundqO41oZ+I27AuN5RXbptX+RwOJCeno6ZM2fK6hIDHWG/8sZ+5U9pPcutX1++S0lP8fkAGBAQgDFjxiA9PR233367Zzw9PR233XbbZV/7t7/9DXa7Hffff/8V36eyshIFBQXo1atXh+vodLp2dw9rtdou/4MwhQWiuNaGsnqnLP7YOqs7/i19GfuVN/Yrf0rrWS79yqGH6+XzJ4EAwDPPPIMPPvgAf/jDH3Dq1Ck8/fTTyM/Px5IlSwAADzzwAFasWNHmdR9++CEWLFiAyMjIFuN1dXX4+c9/jn379iE3Nxc7d+7ErbfeiqioqBYh05tMRveJIMW1DV6uhIiIiOTG52cAAeDuu+9GZWUlVq9ejZKSEgwdOhTbtm1DcnIyACA/P7/NzZyzs7OxZ88e7Nixo8321Go1jh07hj/96U+oqalBr169MH36dGzevNlnbgtjCgsEAJTU2LxcCREREcmNXwRAAHj88cfx+OOPt7ts586dbcb69++Pjs5vCQoKwldffdWV5XW5OKM7ABbXMgASERFR1/KLXcBKZGoKgCUMgERERNTFGAB9FAMgERERdRcGQB/VfAxgtdUBa6PTy9UQERGRnDAA+ihDoBaBavcxjMU8EYSIiIi6EAOgDwsLcD8W1/BSMERERNR1/OYsYCUK10kobRBQwmsByp7d6UKN1YFqayOsjS6oBAFqQUBIoAYxBh2CdfxTJSKirsNPFR8W3nTTkSLuApYNUZSQU16HjPOV2HZOhc0f/YDzldYrnuwTotOgT0wIBvcKxchEI6b0i4YpLKiHqiYiIrlhAPRh4QHNxwByBtBfSZKEnPJ67Moux+7schzMq4bF3nxSjwpAlWddlQCE6QOgD1BDkgBRklDb4IC10YU6uxNHCmpwpKAG/++Ae/2+MSG4ZUgs7hidgD7RIT3eGxER+S8GQB/WPAPIAOhfLDYH9uZUYld2OXZllaOo1c8vSKvGsPhQhDRW4pYJw9A31ojUqGCEBWmhUglttldnd6KkpgFZFyw4UWzG9+cqcbigBmfL6nC2rA7/+20ORiSGYfGNyZg33AStmof2EhHR5TEA+jDOAPoHSZJwrqIe/z5Vhn+fLkNGbhWc4sW70ASoVbghNQJT+0djYp9IDIwzQBJd2LZtG+aMir/iTclDdBr0izWgX6wB84abAAC1Vgd2nSnHPw4VYWd2OY4U1ODpzTV4bXsWHpqcivsnJCNQq+7WvomIyH8xAPowzwxgrQ2SJEEQ2s4OkXfYnS4cOF+Fb06V4dusMuRVWlssT40KxtT+0ZjaPxrje0dAH9DyT80huq7r/Y16LeaPMGH+CBMq6uzYnFGAj/6Ti5JaG17+4hQ++O48np7ZD3eOToCGM4JERNQKA6APMwYAggA0OkVU1jciKkTn7ZIUS5IkZF+ow96cCvznbCX25VSgvvFiiNOqBYxPjcTNA2Nw88AYpEQF91htUSE6PDG9Lx6ekorPDhbhnX+fRVFNA57bcgwffHcev1kwFBN6R/ZYPURE5PsYAH2YRgXEhOhwwWJHcU0DA2APEkUJ5yrqkJFbjb057sBXUdfYYp1ogw7TB0Tj5oGxmNwvCiFevlSLTqPGj29IwoJR8fjL/jys+/YszpTV4cfv7cedoxPwyzkDEcnfISIiAgOgz+sVFugJgMMTwrxdjmxV1tlxuKCmxZfF1vIWfIFaFcalRGBin0hM7huFoSZjuydteFugVo2Hp/TGwjGJeO2r0/j4QD62HCzEN6cvYNX8IbhtZLy3SyQiIi9jAPRxJmMgDhfU8lqAXcTmcOFsWR2ySi3IvmDB6abH9q7DF6hVYXh8GCb2icSNfSIxMikMOo3/nFhh1Gvx29uH4c4xCXjhs+M4VWLGU5sOI/3kBby8YCjC9AHeLpGIiLyk2wLgCy+8gGnTpmHSpEnQ6/XXvb3169fj9ddfR0lJCYYMGYK1a9diypQp7a77xz/+ET/96U/bjDc0NCAwMPCatuktvYzuekt4JvBVcYkScivrkV16MeRllVqQW1mPS07QbaFPdDBGJoZjVFIYRiaGYUCcQRaXVBmdFI5/PjkJ//ttDn7/7zP419ESZORW4fW7RuCm/tHeLo+IiLyg2wJgZmYm3nnnHdjtdowePRrTpk3D1KlTMXnyZISEXN1Fazdv3oxly5Zh/fr1mDRpEjZu3IjZs2fj5MmTSEpKavc1oaGhyMrKajF2afi7lm16Q3MALObt4NolSRLKLHacLrUgq9Tc9GjB2bI62J1iu68J02sxINaAAXFNX7EG9I8zIDTw8pdj8WcatQpPzeiHaQOi8fTfDuNceT0e+MMBPDatD56d2Z9nChMRKUy3BcDt27fD5XLhwIED2LVrF3bu3In169ejoaEBo0ePxv79+zu9rTfffBMPPfQQHn74YQDA2rVr8dVXX2HDhg1Ys2ZNu68RBAFxcXFduk1vMBndt/viLmDA4RJxusSC48W1yCq14HRT4KuxOtpdP0irRv/YEPRvFfaiDTrFXlJnRGIYvlg6Bf+z7RT+vD8PG3bmIDOvGu/cMwqxoYFX3gAREclCtx4DqFarMXHiRERERCA8PBwGgwFbt25FTk5Op7fR2NiIzI+K4gcAACAASURBVMxMPP/88y3G09LSsHfv3g5fV1dXh+TkZLhcLowcORK/+c1vMGrUqOvapjeYwppmABW4C7i4pgE/5FXjcH4NDhdU40Sxud1ZPZXgvu7ewLhQT9AbGGdAYrjeJ0/S8LagADV+s2AoxveOwPNbjuHA+SrMefs7vP3jUZjcL8rb5RERUQ/otgC4YcMG7Nq1C7t27YLL5cKUKVMwdepUvPjiixg+fHint1NRUQGXy4XY2NgW47GxsSgtLW33NQMHDsQf//hHDBs2DGazGW+//TYmTZqEI0eOoF+/fte0TQCw2+2w2+2e52azGQDgcDjgcLQ/C3WtmrcXHew+6aDcYkddgx06jTx31TkcDtQ5gH8eKcKBvFrsy6lCXpW1zXqhgRoMjQ/FoDgD+seGYECsAX2ig9u964XL5YTr+q633G2af75d/XtzNW4ZFI0Bj43H0k1HcbrUgkV/+B7PzuiHn01J6fIZUl/otyexX/lTWs9y61cufVyPbguATzzxBKKjo/Hss89iyZIlCA0Nva7ttf5AutydMSZMmIAJEyZ4nk+aNAmjR4/GO++8g9///vfXtE0AWLNmDVatWtVmfMeOHV1yokt7MvbsglZQwyEJ2Pz5dkTJbC9dhQ04WiXgaJUKuRY1pB9OeJYJkJAQDKQYJCSHuL+iA50QBBsglgElQF4JkOfF+q9Xenq6t0vAQ0nAp6IK+8pU+F36GXxzMAv39BGh64YTnn2h357Efv9/e/cdHlWd9n/8faZkkkkvpAwJSZBOaAJKFXAlAipiWwUMllVWEVdEXXXdgjyPuuqztlV0cdfy25WyruCiIhKVplIDoXdSSCchyaROppzfH0kGUoBgyiQz9+u6ciVzzne+575Thg/nzDnH/Xlaz+7Sb2Vl0x0MnqbdAuCqVavYvHkzK1as4I9//CNDhgxh4sSJTJw4kfHjx7f4RJCwsDC0Wm2TPXMFBQVN9uBdiEajYeTIkRw/frxVcz777LMsXLjQ+dhsNhMTE0NiYmKrA25jVquV5ORkEhMn89aJHZwqrKDXkKsZc0XXv6NDnrmaz/fk8MW+PI4VlDdY1zvclzFXhDK6ZwhXxQXj76YnZtT/fCdPnnzJewF3hBnA8p2nWfzlEfYUaajSB/LurKFEB/u0yfydrd/2Jv26P0/r2d36rT+C58naLQDOmDGDGTNmAFBaWsqWLVv4z3/+w80334yiKA0OpV6Ml5cXw4cPJzk5mVtuucW5PDk5mZtvvrlFc6iqSmpqKoMGDWrVnAaDAYOh6Z0U9Hp9u/1B6PV6YkONnCqsILu0psv+4VVb7Xx7OJ9Pd2Wx5fgZ56VYtBqFUT1DuK5fN5ScA8y+ZWyX7fHnaM/fncs1Z0xP+puCePhfKRzJK+PW97bxzuwrGXNF270vsDP12xGkX/fnaT27S7/u0ENrtetJIGfPnnWeAbxx40YOHDhAaGgoEyZMuKx5Fi5cSFJSEiNGjGD06NEsXbqUzMxMHnroIQDmzJlD9+7dnWfvPv/884waNYrevXtjNpt56623SE1N5Z133mnxnJ1JbKgvcIaMsxWuLuWyqKrK/uxSPt2VxZq9OZRWnXvPxVVxIdw+PJrEgREEGb2wWq2sXXvAhdUKgJFxIayZP45f/zOF/dmlJP1jB7+/oT/3jmn79wUKIYRwnXYLgIMHD+bQoUOEhIRwzTXX8OCDDzJx4kQSEhIue64777yToqIiFi9eTG5uLgkJCaxdu5bY2FgAMjMz0WjOnRxRUlLC3LlzycvLIzAwkGHDhrF582auuuqqFs/ZmfQIqX1/4elmTorojArLLXy+J5tPd2VxNL/MuTwq0Jvbrozm9uHRxIX5urBCcTGmIB8+fWg0z67az+o92Tz/xSEO55r5nxkJXepOKEIIIS6s3QLg3Llzf3bga868efOYN29es+s2btzY4PHrr7/O66+/3qo5O5P6AJhR1HkDoMVm5/vDBXy2O4uNR89gqzvG66XTcP3ASO4YHs3YXmFo5bIsXYK3XstrvxzCgKgAXvr6MP/elcXJMxW8e/eVhPu72ZlIQgjhgdotAM6fP7+9pvY4saG1ATCzqPKSZyp3JLtDJSWjmC/25jQ5xDskJog7hkdz02ATgUZ5r0VXpCgKD17Tkz6R/sxftpuUjGJufvtHliaNYFB0oKvLE0II0Qrt+h7ArKws1qxZQ2ZmJjU1NQ3Wvfbaa+25abcSU7cHsMxi42xFDaF+TU9E6QiqqpJVXEVKRjFbjhfy/ZF8is+7C0dkgDczhnXntiu70zvC3yU1irY3oU83/vvIWB74f7s4daaC29/7iVduH8zNQ7u7ujQhhBA/U7sFwO+++47p06cTHx/P0aNHSUhIID09HVVVufLKK9trs27JW6+lR4iRzLOVHM0rY0yv1gVAVVU5W1FDYXkNZytqqLLasDvAoao4HCoOFaqsdsqrrZRbbGSXVJNeWMGJM+WcKWt49naQUc+1/cK5ZVh3xlwhh3jdVc9ufnz+yFgeW76HDUfP8NiKVI7klfFkYl/5mQshRBfUbgHw2Wef5YknnmDx4sX4+/vz2WefER4ezuzZs5kyZUp7bdZt9Yv0J/NsJYfzyhjT6/Ivy5FeWMH6Q3lsOV7IoRwzRRU1l35SM3QahYHdA7kqLphr+0UwMi4YndY9704iGgrw1vP3e0by6jdHeW/TSd7deJKjeWW8eddQt71eoxBCuKt2C4CHDx9m+fLltRvR6aiqqsLPz4/Fixdz88038/DDD7fXpt1S/6gA1h/K53Buyy9eqaoq3x0u4B8/pLH1VFGDdYoCwUYvgo16fA06NIqCRqHus4K3lxZ/gw4/g46IQG/iQo3EhvoyICoAHy85E9RTaTUKz0ztR79If57+bB/fHyngliU/8f6cEcTLmd1CCNFltFsA9PX1dV7s2WQycfLkSQYOHAjU3t9XXJ7+UbV3GmlpANyXVcL/fHmInenFAGgUGHNFGL/oH86wHsH0jfCXICd+thnDuhMf5svcf+7iREE5N7/9A+/MvpLxvbu5ujQhhBAt0G4BcNSoUfz4448MGDCAG264gSeeeIL9+/ezatWqBvfpFS0zoC4AHs8vp9pqx1vffHiz2R289d1x/rrhBKoK3noN94yOY86YOLoHtc1tvYSA2jO9v5g/jl//K4U9mSXc88EOfjetP78aF99pzlQXQgjRvHYLgK+99hrl5bX3eV20aBHl5eWsXLmSXr16tegafaKhmBAfwv0NFJRZ2J1Z3OztuXJLq3hseSo70s8CcPNQE09P6YdJgp9oJ+EB3ix/cBS///wA/0nJ4n+/OsyRvDJeuEUuGi2EEJ1ZuwXAxYsXc//99wNgNBpZsmRJe23KIyiKwtheYazek83mY4VNAuB3h/N58tO9FFda8TPoeOGWBLlMh+gQ3notr94+mH6R/ry49jD/Scni5Jly/nb3cMID5KLRQgjRGbXb6ZtlZWUkJibSu3dvXnzxRXJyctprUx7j2n7hAPw3NRt73Z02qq12Fn9xiF99vIviSisJ3QP48tFxEv5Eh1IUhQfG9+Sj+64iwFvHnswSpr/9I3tPl7i6NCGEEM1otwD42WefkZ2dzfz58/n000+JjY1l6tSpfPrpp1it1ktPIJqYPCCCYKOe3NJq3vzuON8fyeemv/7ABz+mAXDf2Dg+e3iM3GdXuMw1fbrx3/nj6BXuR565ml/+bSsrdmSiqqqrSxNCCHGedr2AW2hoKI899hh79uxhx44d9OrVizlz5mAymXj88cc5fvx4e27e7XjrtSyc3AeAt747zv0f7eJ4QTlhfgb+cc8I/nTTQHnflXC5+DBfVs8bwy/6hWOxOXhm1X6e+uwAFrurKxNCCFGvQ67gm5uby/r161m/fj1arZZp06Zx8OBBBgwYICeEXKa7R8Xy1PV9CfMz0M3fwP1j41n/+DX8on+Eq0sTwsnfW8/7c0bw2ym1dwr5795c/m+flqN5Za4uTQghBO14EojVamXNmjV8+OGHrF+/nsGDB/P4448ze/Zs/P1r7xO7YsUKHn74YR5//PH2KsPtKIrCI5N68cikXq4uRYiL0mgU5k3sxci4EOYv202+2cJtf9vO89MHcufIGLlUjBBCuFC7BcCoqCgcDgczZ85kx44dDB06tMmY66+/nqCgoPYqQQjRCYyMC2HNvNHc+973HC6BZ1btZ/PxM7wwYxDBvl6uLk8IITxSux0Cfv3118nJyeGdd95pNvwBBAcHk5aW1qL5lixZQnx8PN7e3gwfPpwtW7ZccOz777/P+PHjCQ4OJjg4mOuuu44dO3Y0GHPvvfeiKEqDD7lAtRDtI8TXi7n9HDw5uTc6jcLa/XkkvrGZDUcLXF2aEEJ4pHYLgElJSXh7t801wFauXMmCBQt47rnn2LNnD+PHj2fq1KlkZmY2O37jxo3MnDmTDRs2sHXrVnr06EFiYiLZ2dkNxk2ZMoXc3Fznx9q1a9ukXiFEUxoFfn1NPKvnjaVXuB9nyizc9+FOfrd6PxUWm6vLE0IIj9IhJ4G01muvvcavfvUrHnjgAfr3788bb7xBTEwM7777brPjP/nkE+bNm8fQoUPp168f77//Pg6Hg++++67BOIPBQGRkpPMjJCSkI9oRwqMNig7ky0fHcf/YeACWbc9k2ltb+Omk3CNcCCE6Sru9B7Ct1NTUkJKSwjPPPNNgeWJiIj/99FOL5qisrMRqtTYJeBs3biQ8PJygoCAmTJjACy+8QHh4+AXnsVgsWCwW52Oz2QzUnvDS1tc2rJ/PU66ZKP26t8b9aoFnp/RmYp8Qnl51kIyiSma9v51bh5l4Zkofgo1d+72Bnv7z9QSe1rO79esufbSGonbyK7Tm5OTQvXt3fvzxR8aMGeNc/uKLL/Lxxx9z9OjRS87xyCOP8M0333DgwAHnYemVK1fi5+dHbGwsaWlp/OEPf8Bms5GSkoLBYGh2nkWLFvH88883Wb5s2TKMRuPP7FAIz1Zlgy8zNfyYr6Ci4KtTuSXOwYgwFTlRWAjRHiorK5k1axalpaUEBAS4uhyX6PR7AOs1vmSEqqotuozEK6+8wvLly9m4cWOD9yTeeeedzq8TEhIYMWIEsbGxfPXVV9x6663NzvXss8+ycOFC52Oz2UxMTAyJiYlt/gtktVpJTk5m8uTJ6PX6Np27M5J+3dul+r0N2JNZwu//e4hjBeX864SWE/Zgfje1H/2j/Du+4FaSn6/787Se3a3f+iN4nqzTB8CwsDC0Wi15eXkNlhcUFBARcfGLH//f//0fL774It9++y2DBw++6NioqChiY2MvencSg8HQ7N5BvV7fbn8Q7Tl3ZyT9ureL9XvVFd348jfjeX/LKd767jjb0oq5+d2t3DUyhoWT+9LNv/k9852Z/Hzdn6f17C79ukMPrdXpTwLx8vJi+PDhJCcnN1ienJzc4JBwY6+++ir/8z//w7p16xgxYsQlt1NUVMTp06eJiopqdc1CiJ/HS6fhkUm9+HbhBG4YHIWqwvIdp5n0fxt5Z8MJOVtYCCHaSKcPgAALFy7k73//Ox988AGHDx/m8ccfJzMzk4ceegiAOXPm8OyzzzrHv/LKK/z+97/ngw8+IC4ujry8PPLy8igvLwegvLycJ598kq1bt5Kens7GjRu56aabCAsL45ZbbnFJj0KIc2JCjLwz60o+fWg0g6MDKbfYePWbo1zzygb+vuUU1Va5sbAQQrRGpz8EDLXv1ysqKmLx4sXk5uaSkJDA2rVriY2NBSAzMxON5lyWXbJkCTU1Ndx+++0N5vnTn/7EokWL0Gq17N+/n//3//4fJSUlREVFMWnSJFauXOm8TZ0QwvVGxoXw+byxfJ6azRvfHifzbCX/+9Vh/rb5FPMmXsGdI2MwenWJlzEhhOhUuswr57x585g3b16z6zZu3NjgcXp6+kXn8vHx4ZtvvmmjyoQQ7UmjUbj1ymhuGmJi1e4s3vruBNklVTz/xSHe/O44d18dy5wxsYT7t82F54UQwhN0iUPAQgih12q4c2QPNjw5kRduSSA21EhJpZW3N5xg3J838NSne9mXVUInv7KVEEJ0Cl1mD6AQQkDtiSKzr47lrpE9SD6Ux9LNp9idWcKnKVl8mpLFgKgAZl4Vw83DuhPgLWf6CSFEcyQACiG6JK1GYUpCFFMSokjJOMvHP2Ww7kAeh3LN/OG/B3lh7WEmD4jkxsFRTOjTDW+91tUlCyFEpyEBUAjR5Q2PDWF4bAjFFTWs2pPNih2ZHC8o54u9OXyxNwc/g47JAyKYNiiKsb1C5cQRIYTHk1dBIYTbCPb14lfj4rl/bBx7s0r5cm8OX+3PJbe0mtV7slm9JxsvrYare4YwoU83JvYN54puvi26q5AQQrgTCYBCCLejKApDY4IYGhPE76b1Z8/pYr7Ym8u3h/PJKq5iy/FCthwv5H+/Oky4v4GR8SFcFRfCiLhg+kUGoNVIIBRCuDcJgEIIt6bRKM5DxH+6aQAnz1Sw8WgBm46dYfupsxSUWfhqXy5f7csFwN+gY4ApgIGmQAaaAhhgCqBXuB96rVw0QQjhPiQACiE8hqIo9Ar3o1e4Hw+M70m11U7q6RJ2pZ9lR3oxuzOKKbPY2J52lu1pZ53P89JqiAszEh/mS3yYHz3DfInv5kuPECNhfgbZYyiE6HIkAAohPJa3XsuonqGM6hkKgM3u4HhBOQdzzBzMKeVgjpnDOWbKLDaO5ZdzLL8cyG8wh06jEBHgTVSgN5GB3piCfAg16sgsUPA5eoZuAT6E+HoRZPQiwFsn7zcUQnQKEgCFEKKOTquhf1QA/aMCuH14NAAOh0p2SRWnCitIO1NOWmEFpworOHWmgtzSKmx167NLqhrNpuWTk3sazq9R8PfWYfTS4WfQYTRoaz97afE16PD10uHjpUWvVfDSatHrFLy0Grx0GvRaDV5aDXqdBi+tgl6rQaMoKApoFKXuo/aQd/3XSv2yuvXOsecdzVaoDaT1ubQ+np7LqRdarzgf22w2Cqsh42wlXjp9o+c3nO/8ANx4WxeqhUusP7+W5uZzfmo8bwuee6E+bDYHNgdY7Q7QOC5diwR/0clIABRCiIvQaBRiQozEhBiZ0Kdbg3U2u4Mz5RZyS6vJLakmt7SK3NJqCsxVHM/IQecbRHGlleLKGipr7Ngcat1jq4u6aU86/mfPD64uooPpeGL7tz/72S0Nss7xNFlwsYeXfP7565s+t2lgtdm0PLf7+9qxl9z2hbfVovHnff3rCT2Ze80VTeoRrSMBUAghfiadVkNUoA9RgT7Q49xyq9XK2rVZTJs2Cr2+do9YtdVOcWUNZdU2Kiw2Kix2KmpsVNbYKLfYqbTULq+2OaixOaixO7DaHFjttV/X2NTar+uWWe0OVMChqjgcdZ9VFYda+7WqnrfMAWrdOnvdulq1X9Q/rl9cfzu9c4+bX17/hQMVm82GTqtr/nmNttPcHI3HXKoWd9C410s35+rmFbDbOnyrlTX2Dt+mJ5AAKIQQHcBbr60Li66upO3VBt61TJt2vTPwdhRnQGxpiD0vRDXOWxcKrM3NabNa+WZ9MomTJ6OrO+zd0hDbbD1NntOotkbhr+n6xr1cfPzF52r6XJvNxsaNG5k4cSJana7R+kbjLzF/4xGX6iXE16v5wkWrSAAUQgjRZTkPmzY5Ytm+77mzasGogwAffYeHXlewWq1084HYUKNH9OsJJAC2Qv3/esxmc5vPbbVaqaysxGw2e8Qfm/Tr3qRf9+Zp/YLn9exu/db/u91476UnkQDYCmVlZQDExMS4uBIhhBBCXK6ysjICA93wfRktoKieHH9byeFwkJOTg7+/f5uf4m82m4mJieH06dMEBAS06dydkfTr3qRf9+Zp/YLn9exu/aqqSllZGSaTCY3GM+/yI3sAW0Gj0RAdHd2u2wgICHCLP7aWkn7dm/Tr3jytX/C8nt2pX0/d81fPM2OvEEIIIYQHkwAohBBCCOFhtIsWLVrk6iJE87RaLRMnTkSn84wj9dKve5N+3Zun9Que17On9evu5CQQIYQQQggPI4eAhRBCCCE8jARAIYQQQggPIwFQCCGEEMLDSAAUQgghhPAwEgA7oSVLlhAfH4+3tzfDhw9ny5Ytri6pTbz00kuMHDkSf39/wsPDmTFjBkePHm0wxmKx8OijjxIWFoavry/Tp08nKyvLRRW3rZdeeglFUViwYIFzmbv1m52dzd13301oaChGo5GhQ4eSkpLiXK+qKosWLcJkMuHj48PEiRM5ePCgCyv++Ww2G7///e+Jj4/Hx8eHnj17snjxYhwOh3NMV+938+bN3HTTTZhMJhRF4fPPP2+wviX9FRcXk5SURGBgIIGBgSQlJVFSUtKRbbTYxfq1Wq08/fTTDBo0CF9fX0wmE3PmzCEnJ6fBHO7Sb2O//vWvURSFN954o8HyrtSvaEgCYCezcuVKFixYwHPPPceePXsYP348U6dOJTMz09WltdqmTZt45JFH2LZtG8nJydhsNhITE6moqHCOWbBgAatXr2bFihX88MMPlJeXc+ONN2K3211Yeevt3LmTpUuXMnjw4AbL3anf4uJixo4di16v5+uvv+bQoUP85S9/ISgoyDnmlVde4bXXXuPtt99m586dREZGMnnyZOd9tbuSl19+mffee4+3336bw4cP88orr/Dqq6/y17/+1Tmmq/dbUVHBkCFDePvtt5td35L+Zs2aRWpqKuvWrWPdunWkpqaSlJTUUS1clov1W1lZye7du/nDH/7A7t27WbVqFceOHWP69OkNxrlLv+f7/PPP2b59OyaTqcm6rtSvaEQVncpVV12lPvTQQw2W9evXT33mmWdcVFH7KSgoUAF106ZNqqqqaklJiarX69UVK1Y4x2RnZ6sajUZdt26dq8pstbKyMrV3795qcnKyOmHCBPWxxx5TVdX9+n366afVcePGXXC9w+FQIyMj1T//+c/OZdXV1WpgYKD63nvvdUSJbeqGG25Q77///gbLbr31VvXuu+9WVdX9+gXU1atXOx+3pL9Dhw6pgLpt2zbnmK1bt6qAeuTIkY4r/mdo3G9zduzYoQJqRkaGqqru2W9WVpbavXt39cCBA2psbKz6+uuvO9d15X6FqsoewE6kpqaGlJQUEhMTGyxPTEzkp59+clFV7ae0tBSAkJAQAFJSUrBarQ36N5lMJCQkdOn+H3nkEW644Qauu+66Bsvdrd81a9YwYsQI7rjjDsLDwxk2bBjvv/++c31aWhp5eXkN+jUYDEyYMKFL9jtu3Di+++47jh07BsDevXv54YcfmDZtGuB+/TbWkv62bt1KYGAgV199tXPMqFGjCAwMdIvvQWlpKYqiOPdyu1u/DoeDpKQknnrqKQYOHNhkvbv162nkct6t4HA4yMnJwd/fH0VRWj1fbm4udrsdPz8/zGazc3lgYCDZ2dkNlnV1qqry6KOPMmrUKHr06IHZbObUqVPo9Xq0Wm2DXkNDQ8nMzOyS/f/nP/9h586dbNy4EbPZjM1mw2KxuGW/J0+eZMmSJcyfP5/PPvuMlJQUHn30URwOBzNnzuTkyZMAGI3GBr0FBwdz+vTpLtfvww8/TEFBAX379kWr1WK32/njH//IDTfcgNlsdrt+ofYwaH3dLekvPT2d0NDQJr2GhoaSnp7e6b8H5/fbWHV1NU899RR33HEHgFv2+5e//AWAe++9F7PZjMPhoLq62jmmK/erqiplZWWYTCY0Gs/cFyZ3AmmFrKwsYmJiXF2GEEIIIX6G06dPEx0d7eoyXEL2ALaCv78/UPsLFBAQ0KZzW61W1q9fT2JiInq9vk3n7oykX/cm/bo3T+sXPK9nd+vXbDYTExPj/HfcE0kAbIX6w74BAQHtEgCNRiMBAQFu8cd2Ke3dr6qqnK2oIaekmsJyC2fKLZRWWqmxO7DYHFjtDjQKeOu0GPQafPRagn29CPMzEOZnoJu/gUCftqtLfr7uTfp1f57Ws7v22xZv3+qqJAAKt6OqKmmFFezKKGZPZgnH88s4caackkprq+YN9NETF2qkR6gvcaFG+kT4M9AUQFyoLxqN576ICCGE6HokAAq3UGGxseV4Id8ezmfj0QIKy2uajFEU6HbeHr1gox6DTouXToNeq8GhqlhsDixWO5U1ds5W1Dj3FpZV2yitsrI3q5S9WaUN5jV6aekX6c9AUyBXxgYxIjaE6GAfj/6fpRBCiM5NAqDoshwOla2nivj3rtOsO5CHxXbuDgwGnYYh0UFcGRvMAFMAvbr5ER/mi4+X9mdtq7LGRubZSjKKKskoqiCtsILDuWUcyTNTWWNnd2YJuzNL+Oe2DAAiAgyMiAthRGwwY3uF0TvcTwKhEEKITkMCoOhyqmrsrNiZyQc/pnH6bJVzeWyokev6R/CL/uGMiA3BS9d2p/YbvXT0iwygX2TD93raHSppheUczDGzP6uUXRnFHMguJd9s4at9uXy1LxeoDYTjenXjmj5hjO0VRqDBMy87IIQQonOQACi6jHKLjY9+TOODH9M5W1F7iNffoGP6UBN3joxhUPfADt/LptUo9Ar3p1e4PzcP7Q7UBtS9WSWkZBSz7VQRO9LOkm+28NnuLD7bXXuf3wFR/kRrNERnlTIsNlTeQyiEEKJDSQAUnZ7dofLprtP83/pjFJZbAIgJ8eHX11zBbVdG/+zDuu3Fx0vLqJ6hjOoZyiOTelFttZOSUczm42fYcqyQQ7lmDuWWcQgN6/+2nYgAA7/oH8HkARGM7hmKt75z9SOEEML9SAAUndq+rBKe/mw/h3NrrygfG2rk8ev6cOPgKHTarnEY1VuvZWyv2kO/z06FM2UWNh7J45MN+zheriffbGHZ9kyWbc/E6KXlmt7dmDookl/0j8DPIH+iQggh2p786yI6pWqrnde/Pcb7m0/hUCHAW8dj1/UhaVRsm763zxW6+RuYMdSEV04qv0icxK7MkplRNAAAIABJREFUUpIP5fPt4XzyzRbWHcxj3cE8DDoNE/t244bBJn7RLxxfCYNCCCHaiEf/i7J582ZeffVVUlJSyM3NZfXq1cyYMcPVZXm84/llPLJsN8fyywG4aYiJRTcNINTP4OLK2l5tyAtnYt9w/ndGAgeyzaw7mMva/XmkFVbwzcF8vjmYj0GnYVLfcKYNjpIwKIQQotU8+l+RiooKhgwZwn333cdtt93m6nIEsGpPNou+OEKV1U43fwMv3jKIyQMiXF1Wh1AUhUHRgQyKDuTJxL4czi3jq/05fLUvl/SiygZ7Bq/tF870ISYm9QuX9wwKIYS4bB4dAKdOncrUqVNdXYYAbHYH/z6l4cetBwEY3zuM1345lG7+7rfXryUURWGAKYABpgCeTOzLoVwza/fnOsPg1wfy+PpAHv4GHVMSIpkxrDujeoailbOJhRBCtIBHB0DROZirrcz71x5+zNegKLDwuj48MqmXXBqljqIoDDQFMtBUu2fwYI6ZL/bl8EVqDjml1XyaksWnKVl08zdw02ATM4aZXHJJHCGEEF2HBMDLYLFYsFgszsdmc+2ZqVarFau1dfeZbax+vraet7PJN1dz70cpnDhTgZdG5S93DGJKggm73Ybd7urq2k9rfr59w430va4XC6+9gpTMEtbsy2XdgXzOlFn44Mc0PvgxjbhQIzcNjuSmwVHEh/m2dfmXzVN+n+tJv+7P03p2t37dpY/WUFRVVV1dRGegKMolTwJZtGgRzz//fJPly5Ytw2g0tmd5bqm0Bv56UMuZaoVAvcqD/ezE+Lm6qq7J5oAjJQophQr7ixWsjnN7/2J8VYaHObgyTCXQy4VFCiFEJ1FZWcmsWbMoLS0lICDg0k9wQxIA67QkADa3BzAmJobCwsI2/wWyWq0kJyczefJk9Hp9m87dGeSZq0n6YBfpRZV0D/LmozlDObTzB7ftt7H2/PlWWGx8e7iAL/bl8cPJIuyO2j9xRYHR8SHcODiK6weEE+DTcd9nd/99bkz6dX+e1rO79Ws2mwkLC/PoACiHgC+DwWDAYGh6UoJer2+3P4j2nNtV8kqrmfNhSl3482HF3FFE+us5hHv2ezHt0W+QXs/tI2O5fWQsheUW1u7P5b+pOaRkFPPTqbP8dOosi744zIS+3Zg+xMR1/SM67G4q8vN1b57WL3hez+7Srzv00FoeHQDLy8s5ceKE83FaWhqpqamEhITQo0cPF1bmvvJKq7lr6dYG4S8mxCjvx2gnYX4G5oyOY87oOE6freS/qdms2ZvDsfxykg/lk3woH6OXlskDIpg+xMT43t26/IW2hRBCXJpHB8Bdu3YxadIk5+OFCxcCcM899/DRRx+5qCr3lVtaxcyl20gvqiQ62IflD9aGP9ExYkKMzL+2N/Ov7c2RPDNrUnNYszeHrOIq/puaw39Tcwgy6pmaEMlNQ0xcHS+XlRFCCHfl0QFw4sSJyFsgO4aEv86lX2QA/aYE8NT1fdlzuoQ1qTl8uS+XwnILy3ecZvmO04T7G7hxsInpQ00MiZbLygghhDvx6AAoOkZuaRV3Ld1GRl34WzF3FNHBEv46A0VRuLJHMFf2COYPNw5g26ki1qTm8PWBXArOu6xMbKiRm+rCYJ8If1eXLYQQopUkAIp2dX74iwmp3fMn4a9z0moUxvYKY2yvMBbPGMjmY4Ws2ZvDt4fyySiq5O0NJ3h7wwn6RPgxNSGKqYMi6RvhL3sGhRCiC5IAKNpNTkkVM9+X8NcVGXS1J4ZMHhBBZY2N5EP5fLE3h03HznAsv5xj+cd587vjxIf5MiUhkmkJUSR0D5AwKIQQXYQEQNEuGoe/FXNH0z3Ix9VliZ/B6KXj5qHduXlod0orrXx7OJ+vD+Sy+XghaYUVvLvxJO9uPEl0sA9TBkYydVAUw2KC5FZ+QgjRiUkAFG0up6T2sG/mWQl/7ibQqOe24dHcNjyacouN748UsO5ALhuOnCGruIq//5DG339IIyLAwJSBkVw3IIKr40ORKCiEEJ2LBEDRprJLas/2zTxbSY8QI8vnjpLw56b8DDqmDzExfYiJqho7m44V8PWBPL47XEC+2cLHWzP4eGsGfgYd43qFEmpRGFVRQ0SQXIBVCCFcTQKgaDONw9+KuaMwSfjzCD5eWqYkRDElIQqLzc6PJwr55kA+3x0poLDcwrqD+YCW5S9v5MoewfyifwTX9Q+nV7ifvG9QCCFcQAKgaBPZJVXctXQrp89WSfjzcAadlmv7RXBtvwgcDpV92aWsP5DLf3eeJLtSYVdGMbsyinl53RF6hBiZ1Lcb1/Tpxqieofga5CVJCCE6grzailY7P/zFhhpZ/qCEP1FLo1EYGhPEwEhf+tYcY+iYSWw+cZZvDxew9WQRmWcrnYeK9VqF4bHBjO/djQl9ujEgKkBOJBFCiHYiAVC0SlZxJTPf3ybhT7SIKciHpNFxJI2Oo8Ji44cThWw5fobNxwrJPFvJtlNn2XbqLK9+c5RQXy/G9Q5jfO9ujO0VSlSg/F4JIURbkQAofrbG4W/F3FHyj7RoMV+DjusHRnL9wEgA0gsrasPg8UK2niyiqKLGeY9igNhQI6PiQxl1RQhXx4fKfzSEEKIVJACKnyWruJK7lm4jq1jCn2gbcWG+xIX5kjQ6DqvdwZ7MEjYfO8OW42fYn11KRlElGUWVrNx1GoAeIUZG9QxhVM9Qru4ZKmebCyHEZZAAKC7b6bO1e/6yiquIC6291IuEP9GW9FoNV8WHcFV8CE9e3xdztZWU9GK2nSpi26ki9meXknm2ksyzlfx7VxYAUYHeXBkbXHdv4yAGmgLx0mlc3IkQQnROEgDFZWkc/lbMHU1koLeryxJuLsBbz6R+4UzqFw5AWbWVXRn1gfAsB7JLyS2t5qt9uXy1LxcAL52GQd0DGR5bGwiH9QgmIkB+V4UQAiQAistw+mztYd/sEgl/wrX8vfVM6hvOpL61gbCyxsbe06Xszixmd0YxuzOLKa60kpJRTEpGsfN53fwNDOoeSEL3QBJMAQyKDiQywFuuRSiE8DidIgDed9993H333Vx77bXyQtxJnR/+4sN8Wf7gKAl/otMweukYfUUoo68IBUBVVdKLKp1hcHdmCUfzzJwps/D9kQK+P1LgfG6or1dtIOweQIIpkL6R/sSG+qKVS9AIIdxYpwiARUVF3HDDDYSGhnLXXXeRlJTE0KFDXV2WqCPhT3Q1iqIQH+ZLfJgvtw2PBqCqxs6hXDMHc0rZn1XK/uxSjheUU1RRw6ZjZ9h07Izz+Qadhl7hfvSN8KdPpL/zsylQ9hYKIdxDpwiAa9asoaSkhH//+98sW7aMN954g759+3L33Xcza9Ys4uLiXF2ix5LwJ9yFj5eW4bHBDI8Ndi6rtto5klfGgexSDmSXcijXzLH8MqqtDg7mmDmYY24wh79BR68IP3qG+dGzm68zZMaF+uLjpe3oloQQ4mfrFAEQICgoiLlz5zJ37lyysrJYvnw5H3zwAX/84x+x2WyuLs8jnR/+eob5snzuKHkTvXAr3notQ2OCGBoT5FzmcKicLq7kaF4Zx/LLOFL3+dSZCsosNvZklrAns6TJXKZAb+LrQmGPYB8KzirE55YRF+5PgLe+I9sSQohL6jQBsJ7VamXXrl1s376d9PR0IiIiXF2SR8osqj3bV8Kf8DQajUJsqC+xob4k1l2kGqDG5iCtsIJj+WWkF1aQVlhBWlEFp85UUFplJae0mpzSan48UVT3DC1/P7oVgEAfPdHBPnUfRudnU5A3kQHeBBu95LZ3QogO1WkC4IYNG1i2bBmfffYZdrudW2+9lS+++IJrr722Xbe7ZMkSXn31VXJzcxk4cCBvvPEG48ePb9dtdnYS/oRoykunoW+kP30j/ZusK66o4VR9KCws51RBOQfT8yjHi7MVVkqraj8aH1Kup9cqhPt7ExFgIDLQu+5rbyIDDUT4exMe4E2orxeBPnoJikKINtEpAmB0dDRFRUVcf/31/O1vf+Omm27C27v9A8fKlStZsGABS5YsYezYsfztb39j6tSpHDp0iB49erT79jujzKJK7lq6lZzSanqG+bJi7ijCJfwJcVHBvl4M9/Vyvr/QarWydm0206ZNosahkF1SRVZxJVnFVXUftV9nF1dRVFGD1a6SXVJFdknVRbej1SgEG70I9fUixNeLED8vwny9CPE1EOJXuzzYWBsUA3x0BPro8fXSSWgUQjTRKQLgH//4R+644w6Cg4MvPbgNvfbaa/zqV7/igQceAOCNN97gm2++4d133+Wll17q0Fo6gwbhr5svKx6U8CdEa/kadPSJ8KdPRNM9h1B7aPlMuYW80moKzNXkmavJN1vO+7qaArOFMosNu0OlsNxCYbmlxdvXKLXXTQzw0RHgra8Nh+c99vfWY/TSYjRo8fXS4eNV+9lo0GKs/9pLi9FLh7deI2dBC+EmOkUAnDt3bodvs6amhpSUFJ555pkGyxMTE/npp5+afY7FYsFiOffCazbXHs6xWq1YrdY2ra9+vrae90IyzlaS9MEuckur6Rlm5J/3jSDYR9th2+/ofl1N+nVvl9OvAoT76gj39QOT3wXHWWwOiitrKCqv4WxlDWcrrJytqKG4otHjyhrM1TZKq6xY7SoOFechaLj4HsZL1qqA0UuLt06LQafBoNPgpdPgpVWoLNOyMn8nBr0WQ916r7oxBp0GL60GvU6DXqOg1SroNBp0GgWtRjn3WdvcsvO+1mjQahSUulo0dWFUoygoSu33UlP3hUZpNE6pDcMKSqPn1vamAqp6rldVVVGdX4OK6lyvUvuzLaiC43ml6HS6ujGNnuscrzZaf94yFRx14x11gxxq7c+tfoxDrf/c3LL655ybRz1vnUPFuez8eUA99xz1XM3NL1Ox2ewcy1Y49f1xFI3WuQ31vHk4r0aHeuE6zl+Gc2z9dhv26lBhysAIpgxs2/MBPOW16GI6RQB0hcLCQux2e5OTTCIiIsjLy2v2OS+99BLPP/98k+Xr16/HaDS2S53JycntMu/5Cqvhrwe1lNQoRPio3BtrZteW79p9u83piH47E+nXvbVnv3ogou4DPRBU93EeqwMqbVBlgyo7VNmU2sf2umU2hWoHWOxQYweLA2rsSt3nuuUOqHHUpiRVhQqLnQqLvZmKFE6WFTez3J3pIHW7q4voQFrITOvwrdqLs3FkqJceeBkqKyvbdL6uyGMDYL3GhzNUVb3gIY5nn32WhQsXOh+bzWZiYmJITEwkICCgTeuyWq0kJyczefJk9Pr2u4RExtlK7v7HTkpqLPQM8+Vf94+gm7+h3bZ3IR3Vb2ch/bo3d+vX7lCpstqpqrFTUWPDYnVgsZ37qLLUsHN3Kn0HJGBXFWrsdevqxtU/rrHZsTvAZndgc6jYHep5n88tsztUrPbz1zsaLHPuSYIme6rq99I592Jxbu+U2mi5WrcXqv4Vv/6lv/7fAOX8ZXXLz//XwW63odfpQTk3tn7Eubnqn39uL2X9NpTz9lRq6ibQNNh7WT+m4TKNpna+2ufUrjt/j6fSzDwa5dy6+j2lGuXcNs4ta7i3VKOAolFQHQ5yc3KIie6OVqtpMk997fXLLlRHfT/nP9/5fVBo8D2pf/6Q6MAGl2pqC/VH8DyZxwbAsLAwtFptk719BQUFF7z0jMFgwGBoGo70en27vci359zphRUkfbCLPLOFK7rVnu0b7u/a9/y1Z7+dkfTr3tylXz3gbYALvUvbarVizdjDtOExbtFvS9Se6LOWadOu94iea/vNYtq0QW7Rrzv00FoaVxfgKl5eXgwfPrzJIZrk5GTGjBnjoqo6TnphBTPf30ZuaTW9wv06RfgTQgghRMfw2D2AAAsXLiQpKYkRI0YwevRoli5dSmZmJg899JCrS2tX6YUV3LV0G3nm2vC37MGrJfwJIYQQHsSjA+Cdd95JUVERixcvJjc3l4SEBNauXUtsbKyrS2s3jcPf8gdHueQ9f0IIIYRwHY8OgADz5s1j3rx5ri6jQ6QVVjCzLvz1DvdjmYQ/IYQQwiN57HsAPY2EPyGEEELU8/g9gJ4grbCCu5ZuJd9skfAnhBBCCAmA7q5x+Fs+dxRhfhL+hBBCCE8mAdCNnTpTzsz3t5FvttAnonbPn4Q/IYQQQkgAdFOnzpRz19JtFJRZ6BvhzycPXi3hTwghhBCAnATiliT8CSGEEOJiJAC6mZONwt8yCX9CCCGEaEQCoBs5eaacmY3CX6iEPyGEEEI0IgHQTZwf/vpFSvgTQgghxIXJSSBuoP6w75m68PfJAxL+hBBCCHFhsgewiztRIOFPCCGEEJdH9gB2YScKaq/zd8Z52HcUIb5eri5LCCGEEJ2cBMAuqn7PX2G5hD8hhBBCXB4JgF3QiYIy7lq6ncJyC/2jAvjkgasl/AkhhBCixeQ9gF2MhD8hhBBCtJbsAexCjueXMfP9c+Fv2QNXEyzhTwghhBCXSfYAdhHnh78BEv6EEEII0QqyB7ALqA1/2ygsr2FA3WFfCX9CCCGE+LkkAHZyxwvKmfPhLgrLaxhoCuBfv5LwJ4QQQojW8dhDwC+88AJjxozBaDQSFBTk6nKaVVQN936U4gx/sudPCCGEEG3BYwNgTU0Nd9xxBw8//LCrS2mWqqr884SWgjILfSNq7/ARZJTwJ4QQQojW89hDwM8//zwAH330kWsLuYA1e3NJK1Mwemn54L6REv6EEEII0WY8dg9gZ3amzMILXx8F4KFr4uke5OPiioQQQgjhTjx2D+DPYbFYsFgszsdmsxkAq9WK1Wpts+08v+YAxZVWuhtV5lzdvU3n7qzqe/SEXkH6dXfSr/vztJ7drV936aM1FFVVVVcX0VYWLVrkPLR7ITt37mTEiBHOxx999BELFiygpKTkZ8+/bNkyjEbj5Rd8AQVV8O9TGm6Jc9Ddt82mFUIIIQRQWVnJrFmzKC0tJSAgwNXluIRbBcDCwkIKCwsvOiYuLg5vb2/n48sJgM3tAYyJiaGwsLDNf4GsVivJyclMnjwZvV7fpnN3RtKve5N+3Zun9Que17O79Ws2mwkLC/PoAOhWh4DDwsIICwtrt/kNBgMGg8H5uD47V1VVtfkfhNVqpbKykqqqKmw2W5vO3RlJv+5N+nVvntYveF7P7tZvVVUVcO7fcU/kVgHwcmRmZnL27FkyMzOx2+2kpqYC0KtXL/z8/Fo0R1lZGQAxMTHtVqcQQggh2kdZWRmBgYGuLsMl3OoQ8OW49957+fjjj5ss37BhAxMnTmzRHA6Hg5ycHPz9/VEUpU3rqz+8fPr0aY/YPS39ujfp1715Wr/geT27W7+qqlJWVobJZEKj8cwLonjsHsCPPvqo1dcA1Gg0REdHt01BFxAQEOAWf2wtJf26N+nXvXlav+B5PbtTv56656+eZ8ZeIYQQQggPJgFQCCGEEMLDaBctWrTI1UWI5mm1WiZOnIhO5xlH6qVf9yb9ujdP6xc8r2dP69fdeexJIEIIIYQQnkoOAQshhBBCeBgJgEIIIYQQHkYCoBBCCCGEh5EAKIQQQgjhYSQAdkJLliwhPj4eb29vhg8fzpYtW1xdUpt46aWXGDlyJP7+/oSHhzNjxgyOHj3aYIzFYuHRRx8lLCwMX19fpk+fTlZWlosqblsvvfQSiqKwYMEC5zJ36zc7O5u7776b0NBQjEYjQ4cOJSUlxbleVVUWLVqEyWTCx8eHiRMncvDgQRdW/PPZbDZ+//vfEx8fj4+PDz179mTx4sU4HA7nmK7e7+bNm7npppswmUwoisLnn3/eYH1L+isuLiYpKYnAwEACAwNJSkqipKSkI9tosYv1a7Vaefrppxk0aBC+vr6YTCbmzJlDTk5Ogzncpd/Gfv3rX6MoCm+88UaD5V2pX9GQBMBOZuXKlSxYsIDnnnuOPXv2MH78eKZOnUpmZqarS2u1TZs28cgjj7Bt2zaSk5Ox2WwkJiZSUVHhHLNgwQJWr17NihUr+OGHHygvL+fGG2/Ebre7sPLW27lzJ0uXLmXw4MENlrtTv8XFxYwdOxa9Xs/XX3/NoUOH+Mtf/kJQUJBzzCuvvMJrr73G22+/zc6dO4mMjGTy5MnO+2p3JS+//DLvvfceb7/9NocPH+aVV17h1Vdf5a9//atzTFfvt6KigiFDhvD22283u74l/c2aNYvU1FTWrVvHunXrSE1NJSkpqaNauCwX67eyspLdu3fzhz/8gd27d7Nq1SqOHTvG9OnTG4xzl37P9/nnn7N9+3ZMJlOTdV2pX9GIKjqVq666Sn3ooYcaLOvXr5/6zDPPuKii9lNQUKAC6qZNm1RVVdWSkhJVr9erK1ascI7Jzs5WNRqNum7dOleV2WplZWVq79691eTkZHXChAnqY489pqqq+/X79NNPq+PGjbvgeofDoUZGRqp//vOfncuqq6vVwMBA9b333uuIEtvUDTfcoN5///0Nlt16663q3Xffraqq+/ULqKtXr3Y+bkl/hw4dUgF127ZtzjFbt25VAfXIkSMdV/zP0Ljf5uzYsUMF1IyMDFVV3bPfrKwstXv37uqBAwfU2NhY9fXXX3eu68r9ClWVPYCdSE1NDSkpKSQmJjZYnpiYyE8//eSiqtpPaWkpACEhIQCkpKRgtVob9G8ymUhISOjS/T/yyCPccMMNXHfddQ2Wu1u/a9asYcSIEdxxxx2Eh4czbNgw3n//fef6tLQ08vLyGvRrMBiYMGFCl+x33LhxfPfddxw7dgyAvXv38sMPPzBt2jTA/fptrCX9bd26lcDAQK6++mrnmFGjRhEYGOgW34PS0lIURXHu5Xa3fh0OB0lJSTz11FMMHDiwyXp369fTyOW8O5HCwkLsdjsRERENlkdERJCXl+eiqtqHqqosXLiQcePGkZCQAEBeXh5eXl4EBwc3GNuV+1+xYgW7d+9m586dTda5W7+nTp3i3XffZeHChfzud79jx44d/OY3v8FgMDBnzhxnT839fmdkZLii5FZ5+umnKS0tpV+/fmi1Wux2Oy+88AIzZ84EcLt+G2tJf3l5eYSHhzd5bnh4eJf8HT9fdXU1zzzzDLNmzSIgIABwv35ffvlldDodv/nNb5pd7279ehoJgJ2QoigNHquq2mRZVzd//nz27dvHDz/8cMmxXbX/06dP89hjj7F+/Xq8vb1b/Lyu2q/D4WDEiBG8+OKLAAwbNoyDBw/y7rvvMmfOHOc4d/n9XrlyJf/6179YtmwZAwcOJDU1lQULFmAymbjnnnuc49yl3wu5VH/N9drVvwdWq5W77roLh8PBkiVLGqxzl35TUlJ488032b1790Vrd5d+PZEcAu5EwsLC0Gq1Tf7nVFBQ0OR/2V3Zo48+ypo1a9iwYQPR0dHO5ZGRkdTU1FBcXNxgfFftPyUlhYKCAoYPH45Op0On07Fp0ybeeustdDodERERbtVvVFQUAwYMaLCsf//+zhOYIiMjAdzm9/upp57imWee4a677mLQoEEkJSXx+OOP89JLLwHu129jLekvMjKS/Pz8Js89c+ZMl/0eWK1WfvnLX5KWlkZycrJz7x+4V79btmyhoKCAHj16OF+/MjIyeOKJJ4iLiwPcq19PJAGwE/Hy8mL48OEkJyc3WJ6cnMyYMWNcVFXbUVWV+fPns2rVKr7//nvi4+MbrB8+fDh6vb5B/7m5uRw4cKBL9v+LX/yC/fv3k5qa6vwYMWIEs2fPdn7tTv2OHTu2yWV9jh07RmxsLADx8fFERkY26LempoZNmzZ1yX4rKyvRaBq+hGq1WudlYNyt38Za0t/o0aMpLS1lx44dzjHbt2+ntLS0S34P6sPf8ePH+fbbbwkNDW2w3p36TUpKYt++fQ1ev0wmE0899RTffPMN4F79eiQXnXwiLmDFihWqXq9X//GPf6iHDh1SFyxYoPr6+qrp6emuLq3VHn74YTUwMFDduHGjmpub6/yorKx0jnnooYfU6Oho9dtvv1V3796tXnvtteqQIUNUm83mwsrbzvlnAauqe/W7Y8cOVafTqS+88IJ6/Phx9ZNPPlGNRqP6r3/9yznmz3/+sxoYGKiuWrVK3b9/vzpz5kw1KipKNZvNLqz857nnnnvU7t27q19++aWalpamrlq1Sg0LC1N/+9vfOsd09X7LysrUPXv2qHv27FEB9bXXXlP37NnjPOu1Jf1NmTJFHTx4sLp161Z169at6qBBg9Qbb7zRVS1d1MX6tVqt6vTp09Xo6Gg1NTW1wWuYxWJxzuEu/Tan8VnAqtq1+hUNSQDshN555x01NjZW9fLyUq+88krnZVK6OqDZjw8//NA5pqqqSp0/f74aEhKi+vj4qDfeeKOamZnpuqLbWOMA6G79fvHFF2pCQoJqMBjUfv36qUuXLm2w3uFwqH/605/UyMhI1WAwqNdcc426f/9+F1XbOmazWX3sscfUHj16qN7e3mrPnj3V5557rkEY6Or9btiwodm/2XvuuUdV1Zb1V1RUpM6ePVv19/dX/f391dmzZ6vFxcUu6ObSLtZvWlraBV/DNmzY4JzDXfptTnMBsCv1KxpSVFVVO2JPoxBCCCGE6BzkPYBCCCGEEB5GAqAQQgghhIeRACiEEEII4WEkAAohhBBCeBgJgEIIIYQQHkYCoBBCCCGEh5EAKIQQQgjhYSQACiGEEEJ4GAmAQgi3sHHjRhRFoaSkpMO3rSgKiqIQFBTUovH1tSqKwowZM9q5OiGEaEoCoBCiy5k4cSILFixosGzMmDHk5uYSGBjokpo+/PBDjh071qKx9bX+8pe/bOeqhBCieRIAhRBuwcvLi8jISBRFccn2g4KCCA8Pb9HY+lp9fHzauSohhGieBEAhRJdy7733smnTJt58803nYdT09PQmh4A/+ugjgoKC+PLLL+nbty9Go5Hbb7+diooKPv74Y+Li4ggODubRRx/Fbrc756+pqeG3v/0t3bt3x9fXl6uvvpqNGzcZpwtmAAADTElEQVRedp179+5l0qRJ+Pv7ExAQwPDhw9m1a1dbfRuEEKJVdK4uQAghLsebb77JsWPHSEhIYPHixQB069aN9PT0JmMrKyt56623WLFiBWVlZdx6663ceuutBAUFsXbtWk6dOsVtt93GuHHjuPPOOwG47777SE9PZ8WKFZhMJlavXs2UKVPYv38/vXv3bnGds2fPZtiwYbz77rtotVpSU1PR6/Vt8j0QQojWkgAohOhSAgMD8fLywmg0EhkZedGxVquVd999lyuuuAKA22+/nX/+85/k5+fj5+fHgAEDmDRpEhs2bODOO+/k5MmTLF++nKysLEwmEwBPPvkk69at48MPP+TFF19scZ2ZmZk89dRT9OvXD+CywqMQQrQ3CYBCCLdlNBqd4Q8gIiKCuLg4/Pz8GiwrKCgAYPfu3aiqSp8+fRrMY7FYCA0NvaxtL1y4kAceeIB//vOfXHfdddxxxx0NahFCCFeSACiEcFuND7kqitLsMofDAYDD4UCr1ZKSkoJWq20w7vzQ2BKLFi1i1qxZfPXVV3z99df86U9/YsWKFdxyyy0/oxMhhGhbEgCFEF2Ol5dXgxM32sqwYcOw2+0UFBQwfvz4Vs/Xp08f+vTpw+OPP87MmTP58MMPJQAKIToFOQtYCNHlxMXFsX37dtLT0yksLHTuwWutPn36MHv2bObMmcOqVatIS0tj586dvPzyy6xdu7bF81RVVTF//nw2btxIRkYGP/74Izt37qR///5tUqcQQrSWBEAhRJfz5JNPotVqGTBgAN26dSMzM7PN5v7www+ZM2cOTzzxBH379mX69Ols376dmJiYFs+h1WopKipizpw59OnTh1/+8pdMnTqV559/vs3qFEKI1lBUVVVdXYQQQnRliqKwevXqy76t27333ktJSQmff/55O1UmhBDNkz2AQgjRBmbOnEl0dHSLxm7ZsgU/Pz8++eSTdq5KCCGaJ3sAhRCilU6cOAHUHvqNj4+/5Piqqiqys7OB2rOLL3U9QyGEaGsSAIUQQgghPIwcAhZCCCGE8DASAIUQQgghPIw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\n", 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(filename=\"Figure_3.png\",width=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mathematical Formulation\n", - "\n", - "Motion model is\n", - "\n", - "$$\\dot{x}=vcos\\theta$$\n", - "\n", - "$$\\dot{y}=vsin\\theta$$\n", - "\n", - "$$\\dot{\\theta}=\\frac{v}{WB}sin(u_{\\delta})$$ (tan is not good for optimization)\n", - "\n", - "$$\\dot{v}=u_a$$\n", - "\n", - "Cost function is \n", - "\n", - "$$J=\\frac{1}{2}(u_a^2+u_{\\delta}^2)-\\phi_a d_a-\\phi_\\delta d_\\delta$$\n", - "\n", - "Input constraints are\n", - "\n", - "$$|u_a| \\leq u_{a,max}$$\n", - "\n", - "$$|u_\\delta| \\leq u_{\\delta,max}$$\n", - "\n", - "So, Hamiltonian　is\n", - "\n", - "$$J=\\frac{1}{2}(u_a^2+u_{\\delta}^2)-\\phi_a d_a-\\phi_\\delta d_\\delta\\\\ +\\lambda_1vcos\\theta+\\lambda_2vsin\\theta+\\lambda_3\\frac{v}{WB}sin(u_{\\delta})+\\lambda_4u_a\\\\ \n", - "+\\rho_1(u_a^2+d_a^2+u_{a,max}^2)+\\rho_2(u_\\delta^2+d_\\delta^2+u_{\\delta,max}^2)$$\n", - "\n", - "Partial differential equations of the Hamiltonian are:\n", - "\n", - "$\\begin{equation*}\n", - "\\frac{\\partial H}{\\partial \\bf{x}}=\\\\ \n", - "\\begin{bmatrix}\n", - "\\frac{\\partial H}{\\partial x}= 0&\\\\\n", - "\\frac{\\partial H}{\\partial y}= 0&\\\\\n", - "\\frac{\\partial H}{\\partial \\theta}= -\\lambda_1vsin\\theta+\\lambda_2vcos\\theta&\\\\\n", - "\\frac{\\partial H}{\\partial v}=-\\lambda_1cos\\theta+\\lambda_2sin\\theta+\\lambda_3\\frac{1}{WB}sin(u_{\\delta})&\\\\\n", - "\\end{bmatrix}\n", - "\\\\\n", - "\\end{equation*}$\n", - "\n", - "\n", - "$\\begin{equation*}\n", - "\\frac{\\partial H}{\\partial \\bf{u}}=\\\\ \n", - "\\begin{bmatrix}\n", - "\\frac{\\partial H}{\\partial u_a}= u_a+\\lambda_4+2\\rho_1u_a&\\\\\n", - "\\frac{\\partial H}{\\partial u_\\delta}= u_\\delta+\\lambda_3\\frac{v}{WB}cos(u_{\\delta})+2\\rho_2u_\\delta&\\\\\n", - "\\frac{\\partial H}{\\partial d_a}= -\\phi_a+2\\rho_1d_a&\\\\\n", - "\\frac{\\partial H}{\\partial d_\\delta}=-\\phi_\\delta+2\\rho_2d_\\delta&\\\\\n", - "\\end{bmatrix}\n", - "\\\\\n", - "\\end{equation*}$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ref\n", - "\n", - "- [Shunichi09/nonlinear\\_control: Implementing the nonlinear model predictive control, sliding mode control](https://github.com/Shunichi09/nonlinear_control)\n", - "\n", - "- [非線形モデル予測制御におけるCGMRES法をpythonで実装する \\- Qiita](https://qiita.com/MENDY/items/4108190a579395053924)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb b/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb deleted file mode 100644 index 244f63c8c1..0000000000 --- a/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb +++ /dev/null @@ -1,333 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "## Model predictive speed and steering control\n", - "\n", - "![Model predictive speed and steering control](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif?raw=true)\n" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "code:\n", - "\n", - "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py)\n", - "\n", - "This is a path tracking simulation using model predictive control (MPC).\n", - "\n", - "The MPC controller controls vehicle speed and steering base on linealized model.\n", - "\n", - "This code uses cvxpy as an optimization modeling tool.\n", - "\n", - "- [Welcome to CVXPY 1\\.0 — CVXPY 1\\.0\\.6 documentation](http://www.cvxpy.org/)" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### MPC modeling\n", - "\n", - "State vector is:\n", - "\n", - "$$ z = [x, y, v,\\phi]$$\n", - "\n", - "x: x-position, y:y-position, v:velocity, φ: yaw angle\n", - "\n", - "Input vector is:\n", - "\n", - "$$ u = [a, \\delta]$$\n", - "\n", - "a: accellation, δ: steering angle\n", - "\n" - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "The MPC cotroller minimize this cost function for path tracking:\n", - "\n", - "$$min\\ Q_f(z_{T,ref}-z_{T})^2+Q\\Sigma({z_{t,ref}-z_{t}})^2+R\\Sigma{u_t}^2+R_d\\Sigma({u_{t+1}-u_{t}})^2$$\n", - "\n", - "z_ref come from target path and speed." - ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "subject to:\n", - "\n", - "- Linearlied vehicle model\n", - "\n", - "$$z_{t+1}=Az_t+Bu+C$$\n", - "\n", - "- Maximum steering speed\n", - "\n", - "$$|u_{t+1}-u_{t}|komzEF&R+zkC>- zUk5MnP9l;jNZ{d(Wc&?$Mz;T?=>&mb8a_W^3Iy{lAduG(Nl{@H_tgCb4;__>r=F9k zv@eE;Vpuv@>?1l@_&U8ly;QPe?1}Tg%j(z5#K!1lMH$!<$A2q@4~!4+f0&xbz2bkN zs32-Dj5lLw>gzWzD*0aJ@`jH*+GD_^?rNakEjXF;DqW0)C{jESO9Y7`uy+K<^~Ha0 zMccnaXrJF1etruo>yoY=doBjpxv4|bw4K74AJW5W3+4{T1(+K$rT=ebW85fz2UrBkEd zx5Pm~_8)E=8p2Rm61u#+4AMzyN{EeZzdV?;SZd+l+}sS(O%pNP?P(joB3~!)CxMGG 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https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif -.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif -.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif -.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif -.. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif From 049709fd8b38817842fd1d93e02a1c745c0b8dca Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 27 Nov 2021 22:13:55 +0900 Subject: [PATCH 377/909] fix #581 (#582) * update docs * update docs --- SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb | 682 ------------------ docs/how_to_contribute_main.rst | 2 +- .../Figure_1.png | Bin .../Figure_2.png | Bin .../path_planning/bspline_path}/Figure_1.png | Bin .../closed_loop_rrt_star_car}/Figure_1.png | Bin .../closed_loop_rrt_star_car}/Figure_3.png | Bin .../closed_loop_rrt_star_car}/Figure_4.png | Bin .../closed_loop_rrt_star_car}/Figure_5.png | Bin .../path_planning/cubic_spline}/Figure_1.png | Bin .../path_planning/cubic_spline}/Figure_2.png | Bin .../path_planning/cubic_spline}/Figure_3.png | Bin .../lookuptable.png | Bin .../path_planning/path_planning_main.rst | 92 +-- .../quintic_polynomials_planner.rst | 2 +- .../modules/path_planning/rrt}/figure_1.png | Bin .../rrt_star_reeds_shepp}/figure_1.png | Bin .../state_lattice_pathplanner}/Figure_1.png | Bin .../state_lattice_pathplanner}/Figure_2.png | Bin .../state_lattice_pathplanner}/Figure_3.png | Bin .../state_lattice_pathplanner}/Figure_4.png | Bin .../state_lattice_pathplanner}/Figure_5.png | Bin .../state_lattice_pathplanner}/Figure_6.png | Bin .../path_tracking/path_tracking_main.rst | 2 +- 24 files changed, 32 insertions(+), 748 deletions(-) delete mode 100644 SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb rename docs/modules/path_planning/{Bezier_path_planning => bezier_path_planning}/Figure_1.png (100%) rename docs/modules/path_planning/{Bezier_path_planning => bezier_path_planning}/Figure_2.png (100%) rename {PathPlanning/BSplinePath => docs/modules/path_planning/bspline_path}/Figure_1.png (100%) rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_1.png (100%) rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_3.png (100%) rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_4.png (100%) rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_5.png (100%) rename {PathPlanning/CubicSpline => docs/modules/path_planning/cubic_spline}/Figure_1.png (100%) rename {PathPlanning/CubicSpline => docs/modules/path_planning/cubic_spline}/Figure_2.png (100%) rename {PathPlanning/CubicSpline => docs/modules/path_planning/cubic_spline}/Figure_3.png (100%) rename {PathPlanning/ModelPredictiveTrajectoryGenerator => docs/modules/path_planning/model_predictive_trajectry_generator}/lookuptable.png (100%) rename {PathPlanning/RRT => docs/modules/path_planning/rrt}/figure_1.png (100%) rename {PathPlanning/RRTStarReedsShepp => docs/modules/path_planning/rrt_star_reeds_shepp}/figure_1.png (100%) rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_1.png (100%) rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_2.png (100%) rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_3.png (100%) rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_4.png (100%) rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_5.png (100%) rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_6.png (100%) diff --git a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb deleted file mode 100644 index 53364e3276..0000000000 --- a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb +++ /dev/null @@ -1,682 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [], - "source": [ - "import copy\n", - "import math\n", - "import itertools\n", - "import numpy as np \n", - "import matplotlib.pyplot as plt\n", - "from graph_based_slam import calc_rotational_matrix, calc_jacobian, cal_observation_sigma, \\\n", - " calc_input, observation, motion_model, Edge, pi_2_pi\n", - "\n", - "%matplotlib inline\n", - "np.set_printoptions(precision=3, suppress=True)\n", - "np.random.seed(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction\n", - "\n", - "In contrast to the probabilistic approaches for solving SLAM, such as EKF, UKF, particle filters, and so on, the graph technique formulates the SLAM as an optimization problem. It is mostly used to solve the full SLAM problem in an offline fashion, i.e. optimize all the poses of the robot after the path has been traversed. However, some variants are availble that uses graph-based approaches to perform online estimation or to solve for a subset of the poses.\n", - "\n", - "GraphSLAM uses the motion information as well as the observations of the environment to create least square problem that can be solved using standard optimization techniques.\n", - "\n", - "### Minimal Example\n", - "The following example illustrates the main idea behind graphSLAM. \n", - "A simple case of a 1D robot is considered that can only move in 1 direction. The robot is commanded to move forward with a control input $u_t=1$, however, the motion is not perfect and the measured odometry will deviate from the true path. At each timestep the robot can observe its environment, for this simple case as well, there is only a single landmark at coordinates $x=3$. The measured observations are the range between the robot and landmark. These measurements are also subjected to noise. No bearing information is required since this is a 1D problem.\n", - "\n", - "To solve this, graphSLAM creates what is called as the system information matrix $\\Omega$ also referred to as $H$ and the information vector $\\xi$ also known as $b$. The entries are created based on the information of the motion and the observation." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "text": [ - "The determinant of H: 0.0\n", - "The determinant of H after anchoring constraint: 18.750000000000007\n", - "Running graphSLAM ...\n", - "Odometry values after optimzation: \n", - " [[-0. ]\n", - " [ 0.9]\n", - " [ 1.9]]\n" - ], - "output_type": "stream" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "R = 0.2\n", - "Q = 0.2\n", - "N = 3\n", - "graphics_radius = 0.1\n", - "\n", - "odom = np.empty((N,1))\n", - "obs = np.empty((N,1))\n", - "x_true = np.empty((N,1))\n", - "\n", - "landmark = 3\n", - "# Simulated readings of odometry and observations\n", - "x_true[0], odom[0], obs[0] = 0.0, 0.0, 2.9\n", - "x_true[1], odom[1], obs[1] = 1.0, 1.5, 2.0\n", - "x_true[2], odom[2], obs[2] = 2.0, 2.4, 1.0\n", - "\n", - "hxDR = copy.deepcopy(odom)\n", - "# Visualization\n", - "plt.plot(landmark,0, '*k', markersize=30)\n", - "for i in range(N):\n", - " plt.plot(odom[i], 0, '.', markersize=50, alpha=0.8, color='steelblue')\n", - " plt.plot([odom[i], odom[i] + graphics_radius],\n", - " [0,0], 'r')\n", - " plt.text(odom[i], 0.02, \"X_{}\".format(i), fontsize=12)\n", - " plt.plot(obs[i]+odom[i],0,'.', markersize=25, color='brown')\n", - " plt.plot(x_true[i],0,'.g', markersize=20)\n", - "plt.grid() \n", - "plt.show()\n", - "\n", - "\n", - "# Defined as a function to facilitate iteration\n", - "def get_H_b(odom, obs):\n", - " \"\"\"\n", - " Create the information matrix and information vector. This implementation is \n", - " based on the concept of virtual measurement i.e. the observations of the\n", - " landmarks are converted into constraints (edges) between the nodes that\n", - " have observed this landmark.\n", - " \"\"\"\n", - " measure_constraints = {}\n", - " omegas = {}\n", - " zids = list(itertools.combinations(range(N),2))\n", - " H = np.zeros((N,N))\n", - " b = np.zeros((N,1))\n", - " for (t1, t2) in zids:\n", - " x1 = odom[t1]\n", - " x2 = odom[t2]\n", - " z1 = obs[t1]\n", - " z2 = obs[t2]\n", - " \n", - " # Adding virtual measurement constraint\n", - " measure_constraints[(t1,t2)] = (x2-x1-z1+z2)\n", - " omegas[(t1,t2)] = (1 / (2*Q))\n", - " \n", - " # populate system's information matrix and vector\n", - " H[t1,t1] += omegas[(t1,t2)]\n", - " H[t2,t2] += omegas[(t1,t2)]\n", - " H[t2,t1] -= omegas[(t1,t2)]\n", - " H[t1,t2] -= omegas[(t1,t2)]\n", - "\n", - " b[t1] += omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n", - " b[t2] -= omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n", - "\n", - " return H, b\n", - "\n", - "\n", - "H, b = get_H_b(odom, obs)\n", - "print(\"The determinant of H: \", np.linalg.det(H))\n", - "H[0,0] += 1 # np.inf ?\n", - "print(\"The determinant of H after anchoring constraint: \", np.linalg.det(H))\n", - "\n", - "for i in range(5):\n", - " H, b = get_H_b(odom, obs)\n", - " H[(0,0)] += 1\n", - " \n", - " # Recover the posterior over the path\n", - " dx = np.linalg.inv(H) @ b\n", - " odom += dx\n", - " # repeat till convergence\n", - "print(\"Running graphSLAM ...\") \n", - "print(\"Odometry values after optimzation: \\n\", odom)\n", - "\n", - "plt.figure()\n", - "plt.plot(x_true, np.zeros(x_true.shape), '.', markersize=20, label='Ground truth')\n", - "plt.plot(odom, np.zeros(x_true.shape), '.', markersize=20, label='Estimation')\n", - "plt.plot(hxDR, np.zeros(x_true.shape), '.', markersize=20, label='Odom')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In particular, the tasks are split into 2 parts, graph construction, and graph optimization. \n", - "### Graph Construction\n", - "\n", - "Firstly the nodes are defined $\\boldsymbol{x} = \\boldsymbol{x}_{1:n}$ such that each node is the pose of the robot at time $t_i$\n", - "Secondly, the edges i.e. the constraints, are constructed according to the following conditions:\n", - "\n", - "* robot moves from $\\boldsymbol{x}_i$ to $\\boldsymbol{x}_j$. This edge corresponds to the odometry measurement. Relative motion constraints (Not included in the previous minimal example).\n", - "* Measurement constraints, this can be done in two ways:\n", - " * The information matrix is set in such a way that it includes the landmarks in the map as well. Then the constraints can be entered in a straightforward fashion between a node $\\boldsymbol{x}_i$ and some landmark $m_k$\n", - " * Through creating a virtual measurement among all the node that have observed the same landmark. More concretely, robot observes the same landmark from $\\boldsymbol{x}_i$ and $\\boldsymbol{x}_j$. Relative measurement constraint. The \"virtual measurement\" $\\boldsymbol{z}_{ij}$, which is the estimated pose of $\\boldsymbol{x}_j$ as seen from the node $\\boldsymbol{x}_i$. The virtual measurement can then be entered in the information matrix and vector in a similar fashion to the motion constraints.\n", - "\n", - "An edge is fully characterized by the values of the error (entry to information vector) and the local information matrix (entry to the system's information vector). The larger the local information matrix (lower $Q$ or $R$) the values that this edge will contribute with.\n", - "\n", - "Important Notes:\n", - "\n", - "* The addition to the information matrix and vector are added to the earlier values.\n", - "* In the case of 2D robot, the constraints will be non-linear, therefore, a Jacobian of the error w.r.t the states is needed when updated $H$ and $b$.\n", - "* The anchoring constraint is needed in order to avoid having a singular information matri.\n", - "\n", - "\n", - "### Graph Optimization\n", - "\n", - "The result from this formulation yields an overdetermined system of equations.\n", - "The goal after constructing the graph is to find: $x^* = \\underset{x}{\\mathrm{argmin}} ~ \\underset{ij} \\Sigma ~ f(e_{ij}) $, where $f$ is some error function that depends on the edges between to related nodes $i$ and $j$. The derivation in the references arrive at the solution for $x^* = H^{-1}b$ \n", - "\n", - "\n", - "### Planar Example:\n", - "\n", - "Now we will go through an example with a more realistic case of a 2D robot with 3DoF, namely, $[x, y, \\theta]^T$" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "data": { - "text/plain": "
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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Simulation parameter\n", - "Qsim = np.diag([0.01, np.deg2rad(0.010)])**2 # error added to range and bearing\n", - "Rsim = np.diag([0.1, np.deg2rad(1.0)])**2 # error added to [v, w]\n", - "\n", - "DT = 2.0 # time tick [s]\n", - "SIM_TIME = 100.0 # simulation time [s]\n", - "MAX_RANGE = 30.0 # maximum observation range\n", - "STATE_SIZE = 3 # State size [x,y,yaw]\n", - "\n", - "# TODO: Why not use Qsim ? \n", - "# Covariance parameter of Graph Based SLAM\n", - "C_SIGMA1 = 0.1\n", - "C_SIGMA2 = 0.1\n", - "C_SIGMA3 = np.deg2rad(1.0)\n", - "\n", - "MAX_ITR = 20 # Maximum iteration during optimization\n", - "timesteps = 1\n", - "\n", - "# consider only 2 landmarks for simplicity\n", - "# RFID positions [x, y, yaw]\n", - "RFID = np.array([[10.0, -2.0, 0.0],\n", - "# [15.0, 10.0, 0.0],\n", - "# [3.0, 15.0, 0.0],\n", - "# [-5.0, 20.0, 0.0],\n", - "# [-5.0, 5.0, 0.0]\n", - " ])\n", - "\n", - "# State Vector [x y yaw v]'\n", - "xTrue = np.zeros((STATE_SIZE, 1))\n", - "xDR = np.zeros((STATE_SIZE, 1)) # Dead reckoning\n", - "xTrue[2] = np.deg2rad(45)\n", - "xDR[2] = np.deg2rad(45)\n", - "# history initial values\n", - "hxTrue = xTrue\n", - "hxDR = xTrue\n", - "_, z, _, _ = observation(xTrue, xDR, np.array([[0,0]]).T, RFID)\n", - "hz = [z]\n", - "\n", - "for i in range(timesteps):\n", - " u = calc_input()\n", - " xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)\n", - " hxDR = np.hstack((hxDR, xDR))\n", - " hxTrue = np.hstack((hxTrue, xTrue))\n", - " hz.append(z)\n", - "\n", - "# visualize\n", - "graphics_radius = 0.3\n", - "plt.plot(RFID[:, 0], RFID[:, 1], \"*k\", markersize=20)\n", - "plt.plot(hxDR[0, :], hxDR[1, :], '.', markersize=50, alpha=0.8, label='Odom')\n", - "plt.plot(hxTrue[0, :], hxTrue[1, :], '.', markersize=20, alpha=0.6, label='X_true')\n", - "\n", - "for i in range(hxDR.shape[1]):\n", - " x = hxDR[0, i]\n", - " y = hxDR[1, i]\n", - " yaw = hxDR[2, i]\n", - " plt.plot([x, x + graphics_radius * np.cos(yaw)],\n", - " [y, y + graphics_radius * np.sin(yaw)], 'r')\n", - " d = hz[i][:, 0]\n", - " angle = hz[i][:, 1]\n", - " plt.plot([x + d * np.cos(angle + yaw)], [y + d * np.sin(angle + yaw)], '.',\n", - " markersize=20, alpha=0.7)\n", - " plt.legend()\n", - "plt.grid() \n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [], - "source": [ - "# Copy the data to have a consistent naming with the .py file\n", - "zlist = copy.deepcopy(hz)\n", - "x_opt = copy.deepcopy(hxDR)\n", - "xlist = copy.deepcopy(hxDR)\n", - "number_of_nodes = x_opt.shape[1]\n", - "n = number_of_nodes * STATE_SIZE" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After the data has been saved, the graph will be constructed by looking at each pair for nodes. The virtual measurement is only created if two nodes have observed the same landmark at different points in time. The next cells are a walk through for a single iteration of graph construction -> optimization -> estimate update. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "name": "stdout", - "text": [ - "Node combinations: \n", - " [(0, 1)]\n", - "Node 0 observed landmark with ID 0.0\n", - "Node 1 observed landmark with ID 0.0\n" - ], - "output_type": "stream" - } - ], - "source": [ - "# get all the possible combination of the different node\n", - "zids = list(itertools.combinations(range(len(zlist)), 2))\n", - "print(\"Node combinations: \\n\", zids)\n", - "\n", - "for i in range(xlist.shape[1]):\n", - " print(\"Node {} observed landmark with ID {}\".format(i, zlist[i][0, 3]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the following code snippet the error based on the virtual measurement between node 0 and 1 will be created.\n", - "The equations for the error is as follows: \n", - "$e_{ij}^x = x_j + d_j cos(\\psi_j + \\theta_j) - x_i - d_i cos(\\psi_i + \\theta_i) $\n", - "\n", - "$e_{ij}^y = y_j + d_j sin(\\psi_j + \\theta_j) - y_i - d_i sin(\\psi_i + \\theta_i) $\n", - "\n", - "$e_{ij}^x = \\psi_j + \\theta_j - \\psi_i - \\theta_i $\n", - "\n", - "Where $[x_i, y_i, \\psi_i]$ is the pose for node $i$ and similarly for node $j$, $d$ is the measured distance at nodes $i$ and $j$, and $\\theta$ is the measured bearing to the landmark. The difference is visualized with the figure in the next cell.\n", - "\n", - "In case of perfect motion and perfect measurement the error shall be zero since $ x_j + d_j cos(\\psi_j + \\theta_j)$ should equal $x_i + d_i cos(\\psi_i + \\theta_i)$" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "name": "stdout", - "text": [ - "0.0 1.427649841628278 -2.0126109674819155 -3.524048014922737\n", - "For nodes (0, 1)\n", - "Added edge with errors: \n", - " [[-0.02 ]\n", - " [-0.084]\n", - " [ 0. ]]\n" - ], - "output_type": "stream" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Initialize edges list\n", - "edges = []\n", - "\n", - "# Go through all the different combinations\n", - "for (t1, t2) in zids:\n", - " x1, y1, yaw1 = xlist[0, t1], xlist[1, t1], xlist[2, t1]\n", - " x2, y2, yaw2 = xlist[0, t2], xlist[1, t2], xlist[2, t2]\n", - " \n", - " # All nodes have valid observation with ID=0, therefore, no data association condition\n", - " iz1 = 0\n", - " iz2 = 0\n", - " \n", - " d1 = zlist[t1][iz1, 0]\n", - " angle1, phi1 = zlist[t1][iz1, 1], zlist[t1][iz1, 2]\n", - " d2 = zlist[t2][iz2, 0]\n", - " angle2, phi2 = zlist[t2][iz2, 1], zlist[t2][iz2, 2]\n", - " \n", - " # find angle between observation and horizontal \n", - " tangle1 = pi_2_pi(yaw1 + angle1)\n", - " tangle2 = pi_2_pi(yaw2 + angle2)\n", - " \n", - " # project the observations \n", - " tmp1 = d1 * math.cos(tangle1)\n", - " tmp2 = d2 * math.cos(tangle2)\n", - " tmp3 = d1 * math.sin(tangle1)\n", - " tmp4 = d2 * math.sin(tangle2)\n", - " \n", - " edge = Edge()\n", - " print(y1,y2, tmp3, tmp4)\n", - " # calculate the error of the virtual measurement\n", - " # node 1 as seen from node 2 throught the observations 1,2\n", - " edge.e[0, 0] = x2 - x1 - tmp1 + tmp2\n", - " edge.e[1, 0] = y2 - y1 - tmp3 + tmp4\n", - " edge.e[2, 0] = pi_2_pi(yaw2 - yaw1 - tangle1 + tangle2)\n", - "\n", - " edge.d1, edge.d2 = d1, d2\n", - " edge.yaw1, edge.yaw2 = yaw1, yaw2\n", - " edge.angle1, edge.angle2 = angle1, angle2\n", - " edge.id1, edge.id2 = t1, t2\n", - " \n", - " edges.append(edge)\n", - " \n", - " print(\"For nodes\",(t1,t2))\n", - " print(\"Added edge with errors: \\n\", edge.e)\n", - " \n", - " # Visualize measurement projections\n", - " plt.plot(RFID[0, 0], RFID[0, 1], \"*k\", markersize=20)\n", - " plt.plot([x1,x2],[y1,y2], '.', markersize=50, alpha=0.8)\n", - " plt.plot([x1, x1 + graphics_radius * np.cos(yaw1)],\n", - " [y1, y1 + graphics_radius * np.sin(yaw1)], 'r')\n", - " plt.plot([x2, x2 + graphics_radius * np.cos(yaw2)],\n", - " [y2, y2 + graphics_radius * np.sin(yaw2)], 'r')\n", - " \n", - " plt.plot([x1,x1+tmp1], [y1,y1], label=\"obs 1 x\")\n", - " plt.plot([x2,x2+tmp2], [y2,y2], label=\"obs 2 x\")\n", - " plt.plot([x1,x1], [y1,y1+tmp3], label=\"obs 1 y\")\n", - " plt.plot([x2,x2], [y2,y2+tmp4], label=\"obs 2 y\")\n", - " plt.plot(x1+tmp1, y1+tmp3, 'o')\n", - " plt.plot(x2+tmp2, y2+tmp4, 'o')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the constraints equations derived before are non-linear, linearization is needed before we can insert them into the information matrix and information vector. Two jacobians \n", - "\n", - "$A = \\frac{\\partial e_{ij}}{\\partial \\boldsymbol{x}_i}$ as $\\boldsymbol{x}_i$ holds the three variabls x, y, and theta.\n", - "Similarly, \n", - "$B = \\frac{\\partial e_{ij}}{\\partial \\boldsymbol{x}_j}$ " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "name": "stdout", - "text": [ - "The determinant of H: 0.0\n", - "The determinant of H after origin constraint: 716.1972439134893\n" - ], - "output_type": "stream" - }, - { - "data": { - "text/plain": "
", - "image/png": 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H7kwAUuA0E0AKhBmAFAgzAG+ZT8uebD9m+5jtSu95I8wASJqXy56ekLSh6saEGYAp82rZU0TslvRa1e0JMwBTzrfsqdVQLbNGmAFIgTADMKWnlz0RZgCm9PSyJ8IMgKTJZU+SppY9HZT0VIdlT0XZ/rKk70laa3vc9sdn3J7lTAAyYGYGIAXCDEAKhBmAFAgzACkQZgBSIMwApECYAUjh/wGp9kxGxr36PwAAAABJRU5ErkJggg==\n" 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H7kwAUuA0E0AKhBmAFAgzAG+ZT8uebD9m+5jtSu95I8wASJqXy56ekLSh6saEGYAp82rZU0TslvRa1e0JMwBTzrfsqdVQLbNGmAFIgTADMKWnlz0RZgCm9PSyJ8IMgKTJZU+SppY9HZT0VIdlT0XZ/rKk70laa3vc9sdn3J7lTAAyYGYGIAXCDEAKhBmAFAgzACkQZgBSIMwApECYAUjh/wGp9kxGxr36PwAAAABJRU5ErkJggg==\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Initialize the system information matrix and information vector\n", - "H = np.zeros((n, n))\n", - "b = np.zeros((n, 1))\n", - "x_opt = copy.deepcopy(hxDR)\n", - "\n", - "for edge in edges:\n", - " id1 = edge.id1 * STATE_SIZE\n", - " id2 = edge.id2 * STATE_SIZE\n", - " \n", - " t1 = edge.yaw1 + edge.angle1\n", - " A = np.array([[-1.0, 0, edge.d1 * math.sin(t1)],\n", - " [0, -1.0, -edge.d1 * math.cos(t1)],\n", - " [0, 0, -1.0]])\n", - "\n", - " t2 = edge.yaw2 + edge.angle2\n", - " B = np.array([[1.0, 0, -edge.d2 * math.sin(t2)],\n", - " [0, 1.0, edge.d2 * math.cos(t2)],\n", - " [0, 0, 1.0]])\n", - " \n", - " # TODO: use Qsim instead of sigma\n", - " sigma = np.diag([C_SIGMA1, C_SIGMA2, C_SIGMA3])\n", - " Rt1 = calc_rotational_matrix(tangle1)\n", - " Rt2 = calc_rotational_matrix(tangle2)\n", - " edge.omega = np.linalg.inv(Rt1 @ sigma @ Rt1.T + Rt2 @ sigma @ Rt2.T)\n", - "\n", - " # Fill in entries in H and b\n", - " H[id1:id1 + STATE_SIZE, id1:id1 + STATE_SIZE] += A.T @ edge.omega @ A\n", - " H[id1:id1 + STATE_SIZE, id2:id2 + STATE_SIZE] += A.T @ edge.omega @ B\n", - " H[id2:id2 + STATE_SIZE, id1:id1 + STATE_SIZE] += B.T @ edge.omega @ A\n", - " H[id2:id2 + STATE_SIZE, id2:id2 + STATE_SIZE] += B.T @ edge.omega @ B\n", - "\n", - " b[id1:id1 + STATE_SIZE] += (A.T @ edge.omega @ edge.e)\n", - " b[id2:id2 + STATE_SIZE] += (B.T @ edge.omega @ edge.e)\n", - " \n", - "\n", - "print(\"The determinant of H: \", np.linalg.det(H))\n", - "plt.figure()\n", - "plt.subplot(1,2,1)\n", - "plt.imshow(H, extent=[0, n, 0, n])\n", - "plt.subplot(1,2,2)\n", - "plt.imshow(b, extent=[0, 1, 0, n])\n", - "plt.show() \n", - "\n", - "# Fix the origin, multiply by large number gives same results but better visualization\n", - "H[0:STATE_SIZE, 0:STATE_SIZE] += np.identity(STATE_SIZE)\n", - "print(\"The determinant of H after origin constraint: \", np.linalg.det(H)) \n", - "plt.figure()\n", - "plt.subplot(1,2,1)\n", - "plt.imshow(H, extent=[0, n, 0, n])\n", - "plt.subplot(1,2,2)\n", - "plt.imshow(b, extent=[0, 1, 0, n])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [ - { - "name": "stdout", - "text": [ - "dx: \n", - " [[-0. ]\n", - " [-0. ]\n", - " [ 0. ]\n", - " [ 0.02 ]\n", - " [ 0.084]\n", - " [-0. ]]\n", - "ground truth: \n", - " [[0. 1.414]\n", - " [0. 1.414]\n", - " [0.785 0.985]]\n", - "Odom: \n", - " [[0. 1.428]\n", - " [0. 1.428]\n", - " [0.785 0.976]]\n", - "SLAM: \n", - " [[-0. 1.448]\n", - " [-0. 1.512]\n", - " [ 0.785 0.976]]\n", - "\n", - "graphSLAM localization error: 0.010729072751057656\n", - "Odom localization error: 0.0004460978857535104\n" - ], - "output_type": "stream" - } - ], - "source": [ - "# Find the solution (first iteration)\n", - "dx = - np.linalg.inv(H) @ b\n", - "for i in range(number_of_nodes):\n", - " x_opt[0:3, i] += dx[i * 3:i * 3 + 3, 0]\n", - "print(\"dx: \\n\",dx)\n", - "print(\"ground truth: \\n \",hxTrue)\n", - "print(\"Odom: \\n\", hxDR)\n", - "print(\"SLAM: \\n\", x_opt)\n", - "\n", - "# performance will improve with more iterations, nodes and landmarks.\n", - "print(\"\\ngraphSLAM localization error: \", np.sum((x_opt - hxTrue) ** 2))\n", - "print(\"Odom localization error: \", np.sum((hxDR - hxTrue) ** 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The references:\n", - "\n", - "\n", - "* http://robots.stanford.edu/papers/thrun.graphslam.pdf\n", - "\n", - "* http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf\n", - "\n", - "* http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf\n", - "\n", - "N.B.\n", - "An additional step is required that uses the estimated path to update the belief regarding the map." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "pycharm": { - "is_executing": false - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.5" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "source": [], - "metadata": { - "collapsed": false - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst index c761fd9928..a4ac3c819b 100644 --- a/docs/how_to_contribute_main.rst +++ b/docs/how_to_contribute_main.rst @@ -1,4 +1,4 @@ -How to contribute +How To Contribute ================= This document describes how to contribute this project. diff --git a/docs/modules/path_planning/Bezier_path_planning/Figure_1.png b/docs/modules/path_planning/bezier_path_planning/Figure_1.png similarity index 100% rename from docs/modules/path_planning/Bezier_path_planning/Figure_1.png rename to docs/modules/path_planning/bezier_path_planning/Figure_1.png diff --git a/docs/modules/path_planning/Bezier_path_planning/Figure_2.png b/docs/modules/path_planning/bezier_path_planning/Figure_2.png similarity index 100% rename from docs/modules/path_planning/Bezier_path_planning/Figure_2.png rename to docs/modules/path_planning/bezier_path_planning/Figure_2.png diff --git a/PathPlanning/BSplinePath/Figure_1.png b/docs/modules/path_planning/bspline_path/Figure_1.png similarity index 100% rename from PathPlanning/BSplinePath/Figure_1.png rename to docs/modules/path_planning/bspline_path/Figure_1.png diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_1.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_1.png similarity index 100% rename from PathPlanning/ClosedLoopRRTStar/Figure_1.png rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_1.png diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_3.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_3.png similarity index 100% rename from PathPlanning/ClosedLoopRRTStar/Figure_3.png rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_3.png diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_4.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_4.png similarity index 100% rename from PathPlanning/ClosedLoopRRTStar/Figure_4.png rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_4.png diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_5.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_5.png similarity index 100% rename from PathPlanning/ClosedLoopRRTStar/Figure_5.png rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_5.png diff --git a/PathPlanning/CubicSpline/Figure_1.png b/docs/modules/path_planning/cubic_spline/Figure_1.png similarity index 100% rename from PathPlanning/CubicSpline/Figure_1.png rename to docs/modules/path_planning/cubic_spline/Figure_1.png diff --git a/PathPlanning/CubicSpline/Figure_2.png b/docs/modules/path_planning/cubic_spline/Figure_2.png similarity index 100% rename from PathPlanning/CubicSpline/Figure_2.png rename to docs/modules/path_planning/cubic_spline/Figure_2.png diff --git a/PathPlanning/CubicSpline/Figure_3.png b/docs/modules/path_planning/cubic_spline/Figure_3.png similarity index 100% rename from PathPlanning/CubicSpline/Figure_3.png rename to docs/modules/path_planning/cubic_spline/Figure_3.png diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png b/docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png similarity index 100% rename from PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png rename to docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst index d284c4e06d..8033e1a875 100644 --- a/docs/modules/path_planning/path_planning_main.rst +++ b/docs/modules/path_planning/path_planning_main.rst @@ -11,7 +11,7 @@ This is a 2D navigation sample code with Dynamic Window Approach. - `The Dynamic Window Approach to Collision Avoidance `__ -|DWA| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif Grid based search ----------------- @@ -22,7 +22,7 @@ Dijkstra algorithm This is a 2D grid based shortest path planning with Dijkstra's algorithm. -|Dijkstra| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif In the animation, cyan points are searched nodes. @@ -33,7 +33,7 @@ A\* algorithm This is a 2D grid based shortest path planning with A star algorithm. -|astar| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif In the animation, cyan points are searched nodes. @@ -46,7 +46,7 @@ D\* algorithm This is a 2D grid based shortest path planning with D star algorithm. -|dstar| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm. @@ -59,7 +59,7 @@ Potential Field algorithm This is a 2D grid based path planning with Potential Field algorithm. -|PotentialField| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif In the animation, the blue heat map shows potential value on each grid. @@ -79,12 +79,12 @@ This algorithm is used for state lattice planner. Path optimization sample ~~~~~~~~~~~~~~~~~~~~~~~~ -|4| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif Lookup table generation sample ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -|5| +.. image:: model_predictive_trajectry_generator/lookuptable.png Ref: @@ -92,8 +92,6 @@ Ref: robots `__ - - State Lattice Planning ---------------------- @@ -114,24 +112,24 @@ Ref: Uniform polar sampling ~~~~~~~~~~~~~~~~~~~~~~ -|6| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif Biased polar sampling ~~~~~~~~~~~~~~~~~~~~~ -|7| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif Lane sampling ~~~~~~~~~~~~~ -|8| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif .. _probabilistic-road-map-(prm)-planning: Probabilistic Road-Map (PRM) planning ------------------------------------- -|PRM| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif This PRM planner uses Dijkstra method for graph search. @@ -151,7 +149,7 @@ Ref: Voronoi Road-Map planning ------------------------- -|VRM| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif This Voronoi road-map planner uses Dijkstra method for graph search. @@ -174,7 +172,7 @@ Rapidly-Exploring Random Trees (RRT) Basic RRT ~~~~~~~~~ -|9| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif This is a simple path planning code with Rapidly-Exploring Random Trees (RRT) @@ -188,7 +186,7 @@ are start and goal positions. RRT with dubins path ~~~~~~~~~~~~~~~~~~~~ -|PythonRobotics| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif Path planning for a car robot with RRT and dubins path planner. @@ -197,7 +195,7 @@ Path planning for a car robot with RRT and dubins path planner. RRT\* with dubins path ~~~~~~~~~~~~~~~~~~~~~~ -|AtsushiSakai/PythonRobotics| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif Path planning for a car robot with RRT\* and dubins path planner. @@ -206,7 +204,7 @@ Path planning for a car robot with RRT\* and dubins path planner. RRT\* with reeds-sheep path ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -|11| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif Path planning for a car robot with RRT\* and reeds sheep path planner. @@ -215,7 +213,7 @@ Path planning for a car robot with RRT\* and reeds sheep path planner. Informed RRT\* ~~~~~~~~~~~~~~ -|irrt| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif This is a path planning code with Informed RRT*. @@ -232,7 +230,7 @@ Ref: Batch Informed RRT\* ~~~~~~~~~~~~~~~~~~~~ -|irrt2| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif This is a path planning code with Batch Informed RRT*. @@ -249,7 +247,7 @@ Closed Loop RRT\* A vehicle model based path planning with closed loop RRT*. -|CLRRT| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif In this code, pure-pursuit algorithm is used for steering control, @@ -275,7 +273,7 @@ This is a path planning simulation with LQR-RRT*. A double integrator motion model is used for LQR local planner. -|LQRRRT| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif Ref: @@ -297,14 +295,14 @@ with cubic spline. Heading angle of each point can be also calculated analytically. -|12| -|13| -|14| +.. image:: cubic_spline/Figure_1.png +.. image:: cubic_spline/Figure_2.png +.. image:: cubic_spline/Figure_3.png B-Spline planning ----------------- -|B-Spline| +.. image:: bspline_path/Figure_1.png This is a path planning with B-Spline curse. @@ -321,7 +319,7 @@ Ref: Eta^3 Spline path planning -------------------------- -|eta3| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif This is a path planning with Eta^3 spline. @@ -360,7 +358,7 @@ Dubins path planning A sample code for Dubins path planning. -|dubins| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True Ref: @@ -372,7 +370,7 @@ Reeds Shepp planning A sample code with Reeds Shepp path planning. -|RSPlanning| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true Ref: @@ -390,12 +388,12 @@ LQR based path planning A sample code using LQR based path planning for double integrator model. -|RSPlanning2| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true Optimal Trajectory in a Frenet Frame ------------------------------------ -|15| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif This is optimal trajectory generation in a Frenet Frame. @@ -412,35 +410,3 @@ Ref: - `Optimal trajectory generation for dynamic street scenarios in a Frenet Frame `__ -.. |DWA| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif -.. |Dijkstra| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif -.. |astar| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif -.. |dstar| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif -.. |PotentialField| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif -.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif -.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png?raw=True -.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif -.. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif -.. |8| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif -.. |PRM| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif -.. |VRM| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif -.. |9| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif -.. |10| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif -.. |PythonRobotics| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif -.. |AtsushiSakai/PythonRobotics| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif -.. |11| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif -.. |irrt| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif -.. |irrt2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif -.. |CLRRT| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif -.. |LQRRRT| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif -.. |12| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_1.png?raw=True -.. |13| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_2.png?raw=True -.. |14| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_3.png?raw=True -.. |B-Spline| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BSplinePath/Figure_1.png?raw=True -.. |eta3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif -.. |Bezier1| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_1.png?raw=True -.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif -.. |dubins| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True -.. |RSPlanning| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true -.. |RSPlanning2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true -.. |15| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif diff --git a/docs/modules/path_planning/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner.rst index 2fab8a51ab..2bced07034 100644 --- a/docs/modules/path_planning/quintic_polynomials_planner.rst +++ b/docs/modules/path_planning/quintic_polynomials_planner.rst @@ -4,7 +4,7 @@ Quintic polynomials planning Motion planning with quintic polynomials. -|2| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials. diff --git a/PathPlanning/RRT/figure_1.png b/docs/modules/path_planning/rrt/figure_1.png similarity index 100% rename from PathPlanning/RRT/figure_1.png rename to docs/modules/path_planning/rrt/figure_1.png diff --git a/PathPlanning/RRTStarReedsShepp/figure_1.png b/docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png similarity index 100% rename from PathPlanning/RRTStarReedsShepp/figure_1.png rename to docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png diff --git a/PathPlanning/StateLatticePlanner/Figure_1.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png similarity index 100% rename from PathPlanning/StateLatticePlanner/Figure_1.png rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png diff --git a/PathPlanning/StateLatticePlanner/Figure_2.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png similarity index 100% rename from PathPlanning/StateLatticePlanner/Figure_2.png rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png diff --git a/PathPlanning/StateLatticePlanner/Figure_3.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png similarity index 100% rename from PathPlanning/StateLatticePlanner/Figure_3.png rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png diff --git a/PathPlanning/StateLatticePlanner/Figure_4.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png similarity index 100% rename from PathPlanning/StateLatticePlanner/Figure_4.png rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png diff --git a/PathPlanning/StateLatticePlanner/Figure_5.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png similarity index 100% rename from PathPlanning/StateLatticePlanner/Figure_5.png rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png diff --git a/PathPlanning/StateLatticePlanner/Figure_6.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png similarity index 100% rename from PathPlanning/StateLatticePlanner/Figure_6.png rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst index 3f86da0b07..725e4f04b4 100644 --- a/docs/modules/path_tracking/path_tracking_main.rst +++ b/docs/modules/path_tracking/path_tracking_main.rst @@ -1,6 +1,6 @@ .. _path_tracking: -Path tracking +Path Tracking ============= move to a pose control From f8f10a3ec8421bd9fd2b826716fbff97f5c2f32f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 28 Nov 2021 08:13:28 +0900 Subject: [PATCH 378/909] Adding all gifs to the doc (#584) * update docs * update docs --- docs/index_main.rst | 1 + .../aerial_navigation/aerial_navigation_main.rst | 10 ++-------- .../drone_3d_trajectory_following.rst | 6 ++++++ .../rocket_powered_landing.rst | 0 docs/modules/bipedal/bipedal_main.rst | 7 +++++++ .../bipedal/bipedal_planner/bipedal_planner.rst | 6 ++++++ 6 files changed, 22 insertions(+), 8 deletions(-) create mode 100644 docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst rename docs/modules/aerial_navigation/{ => rocket_powered_landing}/rocket_powered_landing.rst (100%) create mode 100644 docs/modules/bipedal/bipedal_main.rst create mode 100644 docs/modules/bipedal/bipedal_planner/bipedal_planner.rst diff --git a/docs/index_main.rst b/docs/index_main.rst index 7cdbddc71f..128a50a58e 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -42,6 +42,7 @@ See this paper for more details: modules/path_tracking/path_tracking modules/arm_navigation/arm_navigation modules/aerial_navigation/aerial_navigation + modules/bipedal/bipedal modules/appendix/appendix how_to_contribute diff --git a/docs/modules/aerial_navigation/aerial_navigation_main.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst index e072707acc..ea6c4c8e30 100644 --- a/docs/modules/aerial_navigation/aerial_navigation_main.rst +++ b/docs/modules/aerial_navigation/aerial_navigation_main.rst @@ -3,12 +3,6 @@ Aerial Navigation ================= -Drone 3d trajectory following ------------------------------ - -This is a 3d trajectory following simulation for a quadrotor. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif - -.. include:: rocket_powered_landing.rst +.. include:: drone_3d_trajectory_following/drone_3d_trajectory_following.rst +.. include:: rocket_powered_landing/rocket_powered_landing.rst diff --git a/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst b/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst new file mode 100644 index 0000000000..c3bdc33cdc --- /dev/null +++ b/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst @@ -0,0 +1,6 @@ +Drone 3d trajectory following +----------------------------- + +This is a 3d trajectory following simulation for a quadrotor. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif diff --git a/docs/modules/aerial_navigation/rocket_powered_landing.rst b/docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst similarity index 100% rename from docs/modules/aerial_navigation/rocket_powered_landing.rst rename to docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst diff --git a/docs/modules/bipedal/bipedal_main.rst b/docs/modules/bipedal/bipedal_main.rst new file mode 100644 index 0000000000..bab316b0d9 --- /dev/null +++ b/docs/modules/bipedal/bipedal_main.rst @@ -0,0 +1,7 @@ +.. _bipedal: + +Bipedal +================= + +.. include:: bipedal_planner/bipedal_planner.rst + diff --git a/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst b/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst new file mode 100644 index 0000000000..2ee5971e7a --- /dev/null +++ b/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst @@ -0,0 +1,6 @@ +Bipedal Planner +----------------------------- + +Bipedal Walking with modifying designated footsteps + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Bipedal/bipedal_planner/animation.gif From c99716d6920300b1ae0f49652f06db97d0621d63 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 28 Nov 2021 16:00:02 +0900 Subject: [PATCH 379/909] Adding all gifs to the doc 2 (#585) * update docs * update docs * update docs --- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .../inverted_pendulum_mpc_control.py | 0 {InvertedPendulumCart => Control}/__init__.py | 0 docs/index_main.rst | 1 + docs/modules/control/control_main.rst | 7 ++ .../inverted_pendulum_mpc_control.rst | 6 ++ docs/modules/introduction_main.rst | 39 +++++++- .../ensamble_kalman_filter_localization.rst | 7 ++ .../extended_kalman_filter_localization.rst | 1 - .../histogram_filter_localization.rst | 22 +++++ .../localization/localization_main.rst | 88 +----------------- .../particle_filter_localization.rst | 37 ++++++++ .../unscented_kalman_filter_localization.rst | 13 +++ .../mapping/circle_fitting/circle_fitting.rst | 13 +++ .../gaussian_grid_map/gaussian_grid_map.rst | 6 ++ .../k_means_object_clustering.rst | 6 ++ .../grid_map_example.png | Bin .../lidar_to_grid_map_tutorial.rst | 17 ++-- .../lidar_to_grid_map_tutorial_12_0.png | Bin .../lidar_to_grid_map_tutorial_14_1.png | Bin .../lidar_to_grid_map_tutorial_5_0.png | Bin .../lidar_to_grid_map_tutorial_7_0.png | Bin .../lidar_to_grid_map_tutorial_8_0.png | Bin docs/modules/mapping/mapping_main.rst | 51 ++-------- .../ray_casting_grid_map.rst | 6 ++ .../rectangle_fitting/rectangle_fitting.rst | 7 ++ tests/test_inverted_pendulum_mpc_control.py | 2 +- 28 files changed, 193 insertions(+), 140 deletions(-) rename {InvertedPendulumCart => Control}/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py (100%) rename {InvertedPendulumCart => Control}/__init__.py (100%) create mode 100644 docs/modules/control/control_main.rst create mode 100644 docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst create mode 100644 docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst rename docs/modules/localization/{ => extended_kalman_filter_localization_files}/extended_kalman_filter_localization.rst (99%) create mode 100644 docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst create mode 100644 docs/modules/localization/particle_filter_localization/particle_filter_localization.rst create mode 100644 docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst create mode 100644 docs/modules/mapping/circle_fitting/circle_fitting.rst create mode 100644 docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst create mode 100644 docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/grid_map_example.png (100%) rename docs/modules/mapping/{ => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial.rst (90%) rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_12_0.png (100%) rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_14_1.png (100%) rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_5_0.png (100%) rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_7_0.png (100%) rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_8_0.png (100%) create mode 100644 docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst create mode 100644 docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 4e2e57f8e4..ef773ff71a 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -41,7 +41,7 @@ jobs: mypy -p AerialNavigation mypy -p ArmNavigation mypy -p Bipedal - mypy -p InvertedPendulumCart + mypy -p Control mypy -p Localization mypy -p Mapping mypy -p PathPlanning diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 9bfb4b6fd3..f01d85e62b 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -48,7 +48,7 @@ jobs: mypy -p AerialNavigation mypy -p ArmNavigation mypy -p Bipedal - mypy -p InvertedPendulumCart + mypy -p Control mypy -p Localization mypy -p Mapping mypy -p PathPlanning diff --git a/InvertedPendulumCart/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py b/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py similarity index 100% rename from InvertedPendulumCart/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py rename to Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py diff --git a/InvertedPendulumCart/__init__.py b/Control/__init__.py similarity index 100% rename from InvertedPendulumCart/__init__.py rename to Control/__init__.py diff --git a/docs/index_main.rst b/docs/index_main.rst index 128a50a58e..79980c2bd9 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -43,6 +43,7 @@ See this paper for more details: modules/arm_navigation/arm_navigation modules/aerial_navigation/aerial_navigation modules/bipedal/bipedal + modules/control/control modules/appendix/appendix how_to_contribute diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst new file mode 100644 index 0000000000..7e13ab1eb6 --- /dev/null +++ b/docs/modules/control/control_main.rst @@ -0,0 +1,7 @@ +.. _control: + +Control +================= + +.. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst + diff --git a/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst b/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst new file mode 100644 index 0000000000..328f9f6b24 --- /dev/null +++ b/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst @@ -0,0 +1,6 @@ +Inverted Pendulum MPC Control +----------------------------- + +Bipedal Walking with modifying designated footsteps + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif diff --git a/docs/modules/introduction_main.rst b/docs/modules/introduction_main.rst index 60cb085a29..f9fb487297 100644 --- a/docs/modules/introduction_main.rst +++ b/docs/modules/introduction_main.rst @@ -2,4 +2,41 @@ Introduction ============ -TBD \ No newline at end of file +TBD + +Definition Of Robotics +---------------------- + +TBD + +History Of Robotics +---------------------- + +TBD + +Application Of Robotics +------------------------ + +TBD + +Software for Robotics +---------------------- + +TBD + +Software for Robotics +---------------------- + +TBD + +Python for Robotics +---------------------- + +TBD + +Learning Robotics Algorithms +---------------------------- + +TBD + + diff --git a/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst b/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst new file mode 100644 index 0000000000..2543d1186a --- /dev/null +++ b/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst @@ -0,0 +1,7 @@ +Ensamble Kalman Filter Localization +----------------------------------- + +.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/ensamble_kalman_filter/animation.gif + +This is a sensor fusion localization with Ensamble Kalman Filter(EnKF). + diff --git a/docs/modules/localization/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst similarity index 99% rename from docs/modules/localization/extended_kalman_filter_localization.rst rename to docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst index 507ea972a4..9777258e3e 100644 --- a/docs/modules/localization/extended_kalman_filter_localization.rst +++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst @@ -8,7 +8,6 @@ Extended Kalman Filter Localization .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif - :alt: EKF This is a sensor fusion localization with Extended Kalman Filter(EKF). diff --git a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst new file mode 100644 index 0000000000..afe689b9a5 --- /dev/null +++ b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst @@ -0,0 +1,22 @@ +Histogram filter localization +----------------------------- + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif + +This is a 2D localization example with Histogram filter. + +The red cross is true position, black points are RFID positions. + +The blue grid shows a position probability of histogram filter. + +In this simulation, x,y are unknown, yaw is known. + +The filter integrates speed input and range observations from RFID for +localization. + +Initial position is not needed. + +References: +~~~~~~~~~~~ + +- `PROBABILISTIC ROBOTICS`_ diff --git a/docs/modules/localization/localization_main.rst b/docs/modules/localization/localization_main.rst index c665eba694..b1769df163 100644 --- a/docs/modules/localization/localization_main.rst +++ b/docs/modules/localization/localization_main.rst @@ -3,89 +3,11 @@ Localization ============ -.. include:: extended_kalman_filter_localization.rst - - -Unscented Kalman Filter localization ------------------------------------- - -|2| - -This is a sensor fusion localization with Unscented Kalman Filter(UKF). - -The lines and points are same meaning of the EKF simulation. - -References: -~~~~~~~~~~~ - -- `Discriminatively Trained Unscented Kalman Filter for Mobile Robot - Localization`_ - -Particle filter localization ----------------------------- - -|3| - -This is a sensor fusion localization with Particle Filter(PF). - -The blue line is true trajectory, the black line is dead reckoning -trajectory, - -and the red line is estimated trajectory with PF. - -It is assumed that the robot can measure a distance from landmarks -(RFID). - -This measurements are used for PF localization. - -How to calculate covariance matrix from particles -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The covariance matrix :math:`\Xi` from particle information is calculated by the following equation: - -.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k) - -- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`. - -- :math:`w^i` is the weight of the :math:`i` th particle. - -- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle. - -- :math:`\mu_j` is the :math:`j` th mean state of particles. - -References: -~~~~~~~~~~~ - -- `PROBABILISTIC ROBOTICS`_ -- `Improving the particle filter in high dimensions using conjugate artificial process noise`_ - -Histogram filter localization ------------------------------ - -|4| - -This is a 2D localization example with Histogram filter. - -The red cross is true position, black points are RFID positions. - -The blue grid shows a position probability of histogram filter. - -In this simulation, x,y are unknown, yaw is known. - -The filter integrates speed input and range observations from RFID for -localization. - -Initial position is not needed. - -References: -~~~~~~~~~~~ - -- `PROBABILISTIC ROBOTICS`_ +.. include:: extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst +.. include:: ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst +.. include:: unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst +.. include:: histogram_filter_localization/histogram_filter_localization.rst +.. include:: particle_filter_localization/particle_filter_localization.rst .. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ -.. _Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization: https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization -.. _Improving the particle filter in high dimensions using conjugate artificial process noise: https://arxiv.org/pdf/1801.07000.pdf -.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif -.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif -.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif diff --git a/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst b/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst new file mode 100644 index 0000000000..55b44e166b --- /dev/null +++ b/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst @@ -0,0 +1,37 @@ +Particle filter localization +---------------------------- + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif + +This is a sensor fusion localization with Particle Filter(PF). + +The blue line is true trajectory, the black line is dead reckoning +trajectory, + +and the red line is estimated trajectory with PF. + +It is assumed that the robot can measure a distance from landmarks +(RFID). + +This measurements are used for PF localization. + +How to calculate covariance matrix from particles +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The covariance matrix :math:`\Xi` from particle information is calculated by the following equation: + +.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k) + +- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`. + +- :math:`w^i` is the weight of the :math:`i` th particle. + +- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle. + +- :math:`\mu_j` is the :math:`j` th mean state of particles. + +References: +~~~~~~~~~~~ + +- `PROBABILISTIC ROBOTICS`_ +- `Improving the particle filter in high dimensions using conjugate artificial process noise `_ diff --git a/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst b/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst new file mode 100644 index 0000000000..bb6b5b664b --- /dev/null +++ b/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst @@ -0,0 +1,13 @@ +Unscented Kalman Filter localization +------------------------------------ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif + +This is a sensor fusion localization with Unscented Kalman Filter(UKF). + +The lines and points are same meaning of the EKF simulation. + +References: +~~~~~~~~~~~ + +- `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization `_ diff --git a/docs/modules/mapping/circle_fitting/circle_fitting.rst b/docs/modules/mapping/circle_fitting/circle_fitting.rst new file mode 100644 index 0000000000..1892d1f8f7 --- /dev/null +++ b/docs/modules/mapping/circle_fitting/circle_fitting.rst @@ -0,0 +1,13 @@ +Object shape recognition using circle fitting +--------------------------------------------- + +This is an object shape recognition using circle fitting. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/circle_fitting/animation.gif + +The blue circle is the true object shape. + +The red crosses are observations from a ranging sensor. + +The red circle is the estimated object shape using circle fitting. + diff --git a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst new file mode 100644 index 0000000000..f78f2c95d8 --- /dev/null +++ b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst @@ -0,0 +1,6 @@ +Gaussian grid map +----------------- + +This is a 2D Gaussian grid mapping example. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif diff --git a/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst b/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst new file mode 100644 index 0000000000..e098ca5409 --- /dev/null +++ b/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst @@ -0,0 +1,6 @@ +k-means object clustering +------------------------- + +This is a 2D object clustering with k-means algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/grid_map_example.png similarity index 100% rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png rename to docs/modules/mapping/lidar_to_grid_map_tutorial/grid_map_example.png diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial.rst b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst similarity index 90% rename from docs/modules/mapping/lidar_to_grid_map_tutorial.rst rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst index 64dba26d4f..2e9470ff6c 100644 --- a/docs/modules/mapping/lidar_to_grid_map_tutorial.rst +++ b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst @@ -1,3 +1,7 @@ +Lidar to grid map +-------------------- + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif This simple tutorial shows how to read LIDAR (range) measurements from a file and convert it to occupancy grid. @@ -12,8 +16,7 @@ a ``numpy array``, and numbers close to 1 means the cell is occupied free (*marked with green*). The grid has the ability to represent unknown (unobserved) areas, which are close to 0.5. -.. figure:: lidar_to_grid_map_tutorial_files/grid_map_example.png - :alt: Example +.. figure:: lidar_to_grid_map_tutorial/grid_map_example.png In order to construct the grid map from the measurement we need to discretise the values. But, first let’s need to ``import`` some @@ -65,7 +68,7 @@ From the distances and the angles it is easy to determine the ``x`` and -.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png +.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png The ``lidar_to_grid_map.py`` contains handy functions which can used to @@ -86,7 +89,7 @@ map. Let’s see how this works. -.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png +.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png .. code:: ipython3 @@ -103,7 +106,7 @@ map. Let’s see how this works. -.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png +.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png To fill empty areas, a queue-based algorithm can be used that can be @@ -160,7 +163,7 @@ from a center point (e.g. (10, 20)) with zeros: -.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png +.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png Let’s use this flood fill on real data: @@ -191,5 +194,5 @@ Let’s use this flood fill on real data: -.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png +.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png similarity index 100% rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png similarity index 100% rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png similarity index 100% rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png similarity index 100% rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png similarity index 100% rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst index ccdd3198b1..7d2c4cfe67 100644 --- a/docs/modules/mapping/mapping_main.rst +++ b/docs/modules/mapping/mapping_main.rst @@ -3,49 +3,10 @@ Mapping ======= -Gaussian grid map ------------------ +.. include:: gaussian_grid_map/gaussian_grid_map.rst +.. include:: ray_casting_grid_map/ray_casting_grid_map.rst +.. include:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst +.. include:: k_means_object_clustering/k_means_object_clustering.rst +.. include:: circle_fitting/circle_fitting.rst +.. include:: rectangle_fitting/rectangle_fitting.rst -This is a 2D Gaussian grid mapping example. - -|2| - -Ray casting grid map --------------------- - -This is a 2D ray casting grid mapping example. - -|3| - -Lidar to grid map --------------------- - -|6| - -.. include:: lidar_to_grid_map_tutorial.rst - -k-means object clustering -------------------------- - -This is a 2D object clustering with k-means algorithm. - -|4| - -Object shape recognition using circle fitting ---------------------------------------------- - -This is an object shape recognition using circle fitting. - -|5| - -The blue circle is the true object shape. - -The red crosses are observations from a ranging sensor. - -The red circle is the estimated object shape using circle fitting. - -.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif -.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif -.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif -.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/circle_fitting/animation.gif -.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif diff --git a/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst b/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst new file mode 100644 index 0000000000..cc5a1a1c5b --- /dev/null +++ b/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst @@ -0,0 +1,6 @@ +Ray casting grid map +-------------------- + +This is a 2D ray casting grid mapping example. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif \ No newline at end of file diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst new file mode 100644 index 0000000000..c0949ac4c3 --- /dev/null +++ b/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst @@ -0,0 +1,7 @@ +Object shape recognition using rectangle fitting +------------------------------------------------ + +This is an object shape recognition using rectangle fitting. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/rectangle_fitting/animation.gif + diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py index d88d4d7915..9519b45ec9 100644 --- a/tests/test_inverted_pendulum_mpc_control.py +++ b/tests/test_inverted_pendulum_mpc_control.py @@ -2,7 +2,7 @@ import sys if 'cvxpy' in sys.modules: # pragma: no cover - from InvertedPendulumCart.InvertedPendulumMPCControl \ + from Control.InvertedPendulumMPCControl \ import inverted_pendulum_mpc_control as m def test1(): From d183a00a1c03c448ba8ae7ab75f3b9bd2331be9a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 29 Nov 2021 00:01:06 +0900 Subject: [PATCH 380/909] Adding all gifs to the doc (#586) * update docs * update docs * update docs * update docs --- Control/move_to_pose/__init__.py | 0 .../move_to_pose/move_to_pose.py | 0 docs/modules/control/control_main.rst | 1 + .../move_to_a_pose_control.rst | 11 ++ .../{ => cgmres_nmpc}/cgmres_nmpc.rst | 8 +- .../cgmres_nmpc_1_0.png | Bin .../cgmres_nmpc_2_0.png | Bin .../cgmres_nmpc_3_0.png | Bin .../cgmres_nmpc_4_0.png | Bin .../lqr_speed_and_steering_control.rst | 13 +++ .../lqr_steering_control.rst | 14 +++ ...predictive_speed_and_steering_control.rst} | 0 .../path_tracking/path_tracking_main.rst | 97 ++---------------- .../pure_pursuit_tracking.rst | 16 +++ .../rear_wheel_feedback_control.rst | 12 +++ .../stanley_control/stanley_control.rst | 16 +++ .../slam/{ => FastSLAM1}/FastSLAM1.rst | 22 ++-- .../FastSLAM1_12_0.png | Bin .../FastSLAM1_12_1.png | Bin .../FastSLAM1_1_0.png | Bin docs/modules/slam/FastSLAM2/FastSLAM2.rst | 16 +++ docs/modules/slam/{ => ekf_slam}/ekf_slam.rst | 8 +- .../ekf_slam_1_0.png | Bin .../Graph_SLAM_optimization.gif | Bin 114966 -> 0 bytes .../graphSLAM_SE2_example.rst | 15 ++- .../graphSLAM_SE2_example_13_0.png | Bin .../graphSLAM_SE2_example_15_0.png | Bin .../graphSLAM_SE2_example_16_0.png | Bin .../graphSLAM_SE2_example_4_0.png | Bin .../graphSLAM_SE2_example_8_0.png | Bin .../graphSLAM_SE2_example_9_0.png | Bin .../slam/{ => graph_slam}/graphSLAM_doc.rst | 21 ++-- .../graphSLAM_formulation.rst | 4 +- docs/modules/slam/graph_slam/graph_slam.rst | 24 +++++ .../iterative_closest_point_matching.rst | 16 +++ docs/modules/slam/slam_main.rst | 78 +------------- tests/test_move_to_pose.py | 2 +- 37 files changed, 186 insertions(+), 208 deletions(-) create mode 100644 Control/move_to_pose/__init__.py rename {PathTracking => Control}/move_to_pose/move_to_pose.py (100%) create mode 100644 docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst rename docs/modules/path_tracking/{ => cgmres_nmpc}/cgmres_nmpc.rst (91%) rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_1_0.png (100%) rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_2_0.png (100%) rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_3_0.png (100%) rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_4_0.png (100%) create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst create mode 100644 docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst rename docs/modules/path_tracking/{Model_predictive_speed_and_steering_control.rst => model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst} (100%) create mode 100644 docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst create mode 100644 docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst create mode 100644 docs/modules/path_tracking/stanley_control/stanley_control.rst rename docs/modules/slam/{ => FastSLAM1}/FastSLAM1.rst (97%) rename docs/modules/slam/{FastSLAM1_files => FastSLAM1}/FastSLAM1_12_0.png (100%) rename docs/modules/slam/{FastSLAM1_files => FastSLAM1}/FastSLAM1_12_1.png (100%) rename docs/modules/slam/{FastSLAM1_files => FastSLAM1}/FastSLAM1_1_0.png (100%) create mode 100644 docs/modules/slam/FastSLAM2/FastSLAM2.rst rename docs/modules/slam/{ => ekf_slam}/ekf_slam.rst (98%) rename docs/modules/slam/{ekf_slam_files => ekf_slam}/ekf_slam_1_0.png (100%) delete mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example.rst (90%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png (100%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png (100%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png (100%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png (100%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png (100%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png (100%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_doc.rst (95%) rename docs/modules/slam/{ => graph_slam}/graphSLAM_formulation.rst (97%) create mode 100644 docs/modules/slam/graph_slam/graph_slam.rst create mode 100644 docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst diff --git a/Control/move_to_pose/__init__.py b/Control/move_to_pose/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/PathTracking/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py similarity index 100% rename from PathTracking/move_to_pose/move_to_pose.py rename to Control/move_to_pose/move_to_pose.py diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst index 7e13ab1eb6..9eeba434d2 100644 --- a/docs/modules/control/control_main.rst +++ b/docs/modules/control/control_main.rst @@ -4,4 +4,5 @@ Control ================= .. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst +.. include:: move_to_a_pose_control/move_to_a_pose_control.rst diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst new file mode 100644 index 0000000000..c5bf15d970 --- /dev/null +++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst @@ -0,0 +1,11 @@ +Move to a pose control +---------------------- + +This is a simulation of moving to a pose control + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif + +Ref: + +- `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink + p102 `__ diff --git a/docs/modules/path_tracking/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst similarity index 91% rename from docs/modules/path_tracking/cgmres_nmpc.rst rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst index 6f6a102618..5092fa8e2f 100644 --- a/docs/modules/path_tracking/cgmres_nmpc.rst +++ b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst @@ -2,16 +2,16 @@ Nonlinear Model Predictive Control with C-GMRES ----------------------------------------------- -.. image:: cgmres_nmpc_files/cgmres_nmpc_1_0.png +.. image:: cgmres_nmpc/cgmres_nmpc_1_0.png :width: 600px -.. image:: cgmres_nmpc_files/cgmres_nmpc_2_0.png +.. image:: cgmres_nmpc/cgmres_nmpc_2_0.png :width: 600px -.. image:: cgmres_nmpc_files/cgmres_nmpc_3_0.png +.. image:: cgmres_nmpc/cgmres_nmpc_3_0.png :width: 600px -.. image:: cgmres_nmpc_files/cgmres_nmpc_4_0.png +.. image:: cgmres_nmpc/cgmres_nmpc_4_0.png :width: 600px .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_1_0.png similarity index 100% rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_1_0.png diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_2_0.png similarity index 100% rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_2_0.png diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_3_0.png similarity index 100% rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_3_0.png diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_4_0.png similarity index 100% rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_4_0.png diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst new file mode 100644 index 0000000000..68ea9c88b2 --- /dev/null +++ b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst @@ -0,0 +1,13 @@ +.. _linearquadratic-regulator-(lqr)-speed-and-steering-control: + +Linear–quadratic regulator (LQR) speed and steering control +----------------------------------------------------------- + +Path tracking simulation with LQR speed and steering control. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif + +References: +~~~~~~~~~~~ + +- `Towards fully autonomous driving: Systems and algorithms `__ diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst new file mode 100644 index 0000000000..bf6d6b5854 --- /dev/null +++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst @@ -0,0 +1,14 @@ +.. _linearquadratic-regulator-(lqr)-steering-control: + +Linear–quadratic regulator (LQR) steering control +------------------------------------------------- + +Path tracking simulation with LQR steering control and PID speed +control. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif + +References: +~~~~~~~~~~~ +- `ApolloAuto/apollo: An open autonomous driving platform `_ + diff --git a/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst similarity index 100% rename from docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst rename to docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst index 725e4f04b4..2a572ef6df 100644 --- a/docs/modules/path_tracking/path_tracking_main.rst +++ b/docs/modules/path_tracking/path_tracking_main.rst @@ -3,94 +3,11 @@ Path Tracking ============= -move to a pose control ----------------------- - -This is a simulation of moving to a pose control - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif - -Ref: - -- `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink - p102 `__ - -Pure pursuit tracking ---------------------- - -Path tracking simulation with pure pursuit steering control and PID -speed control. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif - -The red line is a target course, the green cross means the target point -for pure pursuit control, the blue line is the tracking. - -Ref: - -- `A Survey of Motion Planning and Control Techniques for Self-driving - Urban Vehicles `__ - -Stanley control ---------------- - -Path tracking simulation with Stanley steering control and PID speed -control. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif - -Ref: - -- `Stanley: The robot that won the DARPA grand - challenge `__ - -- `Automatic Steering Methods for Autonomous Automobile Path - Tracking `__ - -Rear wheel feedback control ---------------------------- - -Path tracking simulation with rear wheel feedback steering control and -PID speed control. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif - -Ref: - -- `A Survey of Motion Planning and Control Techniques for Self-driving - Urban Vehicles `__ - -.. _linearquadratic-regulator-(lqr)-steering-control: - -Linear–quadratic regulator (LQR) steering control -------------------------------------------------- - -Path tracking simulation with LQR steering control and PID speed -control. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif - -Ref: - -- `ApolloAuto/apollo: An open autonomous driving - platform `__ - -.. _linearquadratic-regulator-(lqr)-speed-and-steering-control: - -Linear–quadratic regulator (LQR) speed and steering control ------------------------------------------------------------ - -Path tracking simulation with LQR speed and steering control. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif - -Ref: - -- `Towards fully autonomous driving: Systems and algorithms - IEEE - Conference - Publication `__ - -.. include:: Model_predictive_speed_and_steering_control.rst - -.. include:: cgmres_nmpc.rst +.. include:: pure_pursuit_tracking/pure_pursuit_tracking.rst +.. include:: stanley_control/stanley_control.rst +.. include:: rear_wheel_feedback_control/rear_wheel_feedback_control.rst +.. include:: lqr_steering_control/lqr_steering_control.rst +.. include:: lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst +.. include:: model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst +.. include:: cgmres_nmpc/cgmres_nmpc.rst diff --git a/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst b/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst new file mode 100644 index 0000000000..5c7bcef85f --- /dev/null +++ b/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst @@ -0,0 +1,16 @@ +Pure pursuit tracking +--------------------- + +Path tracking simulation with pure pursuit steering control and PID +speed control. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif + +The red line is a target course, the green cross means the target point +for pure pursuit control, the blue line is the tracking. + +References: +~~~~~~~~~~~ + +- `A Survey of Motion Planning and Control Techniques for Self-driving + Urban Vehicles `_ diff --git a/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst b/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst new file mode 100644 index 0000000000..70875fdc6c --- /dev/null +++ b/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst @@ -0,0 +1,12 @@ +Rear wheel feedback control +--------------------------- + +Path tracking simulation with rear wheel feedback steering control and +PID speed control. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif + +References: +~~~~~~~~~~~ +- `A Survey of Motion Planning and Control Techniques for Self-driving + Urban Vehicles `__ diff --git a/docs/modules/path_tracking/stanley_control/stanley_control.rst b/docs/modules/path_tracking/stanley_control/stanley_control.rst new file mode 100644 index 0000000000..fe325b0102 --- /dev/null +++ b/docs/modules/path_tracking/stanley_control/stanley_control.rst @@ -0,0 +1,16 @@ +Stanley control +--------------- + +Path tracking simulation with Stanley steering control and PID speed +control. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif + +References: +~~~~~~~~~~~ + +- `Stanley: The robot that won the DARPA grand + challenge `_ + +- `Automatic Steering Methods for Autonomous Automobile Path + Tracking `_ diff --git a/docs/modules/slam/FastSLAM1.rst b/docs/modules/slam/FastSLAM1/FastSLAM1.rst similarity index 97% rename from docs/modules/slam/FastSLAM1.rst rename to docs/modules/slam/FastSLAM1/FastSLAM1.rst index 6e26aef72f..f1e9dfd8aa 100644 --- a/docs/modules/slam/FastSLAM1.rst +++ b/docs/modules/slam/FastSLAM1/FastSLAM1.rst @@ -2,8 +2,7 @@ FastSLAM1.0 ----------- -.. image:: FastSLAM1_files/FastSLAM1_1_0.png - :width: 600px +.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif @@ -12,6 +11,9 @@ Simulation This is a feature based SLAM example using FastSLAM 1.0. +.. image:: FastSLAM1/FastSLAM1_1_0.png + :width: 600px + The blue line is ground truth, the black line is dead reckoning, the red line is the estimated trajectory with FastSLAM. @@ -20,8 +22,6 @@ The red points are particles of FastSLAM. Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM. -.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif - :alt: gif Introduction ~~~~~~~~~~~~ @@ -527,19 +527,13 @@ indices -.. image:: FastSLAM1_files/FastSLAM1_12_0.png - - - -.. image:: FastSLAM1_files/FastSLAM1_12_1.png +.. image:: FastSLAM1/FastSLAM1_12_0.png +.. image:: FastSLAM1/FastSLAM1_12_1.png References ~~~~~~~~~~ -- `PROBABILISTIC ROBOTICS`_ - -- `FastSLAM Lecture`_ +- `PROBABILISTIC ROBOTICS `_ -.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ -.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf +- `FastSLAM Lecture `_ diff --git a/docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png b/docs/modules/slam/FastSLAM1/FastSLAM1_12_0.png similarity index 100% rename from docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png rename to docs/modules/slam/FastSLAM1/FastSLAM1_12_0.png diff --git a/docs/modules/slam/FastSLAM1_files/FastSLAM1_12_1.png b/docs/modules/slam/FastSLAM1/FastSLAM1_12_1.png similarity index 100% rename from docs/modules/slam/FastSLAM1_files/FastSLAM1_12_1.png rename to docs/modules/slam/FastSLAM1/FastSLAM1_12_1.png diff --git a/docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png b/docs/modules/slam/FastSLAM1/FastSLAM1_1_0.png similarity index 100% rename from docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png rename to docs/modules/slam/FastSLAM1/FastSLAM1_1_0.png diff --git a/docs/modules/slam/FastSLAM2/FastSLAM2.rst b/docs/modules/slam/FastSLAM2/FastSLAM2.rst new file mode 100644 index 0000000000..9e79b496a3 --- /dev/null +++ b/docs/modules/slam/FastSLAM2/FastSLAM2.rst @@ -0,0 +1,16 @@ +FastSLAM 2.0 +------------ + +This is a feature based SLAM example using FastSLAM 2.0. + +The animation has the same meanings as one of FastSLAM 1.0. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif + +References +~~~~~~~~~~ + +- `PROBABILISTIC ROBOTICS `_ + +- `SLAM simulations by Tim Bailey `_ + diff --git a/docs/modules/slam/ekf_slam.rst b/docs/modules/slam/ekf_slam/ekf_slam.rst similarity index 98% rename from docs/modules/slam/ekf_slam.rst rename to docs/modules/slam/ekf_slam/ekf_slam.rst index df520fbdaf..a79c2737ae 100644 --- a/docs/modules/slam/ekf_slam.rst +++ b/docs/modules/slam/ekf_slam/ekf_slam.rst @@ -8,7 +8,7 @@ line is the estimated trajectory with EKF SLAM. The green crosses are estimated landmarks. -|4| +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif Simulation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -581,13 +581,11 @@ reckoning and control functions are passed along here as well. New LM New LM -.. image:: ekf_slam_files/ekf_slam_1_0.png +.. image:: ekf_slam/ekf_slam_1_0.png References: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -- `PROBABILISTIC ROBOTICS`_ +- `PROBABILISTIC ROBOTICS `_ -.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ -.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif diff --git a/docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png b/docs/modules/slam/ekf_slam/ekf_slam_1_0.png similarity index 100% rename from docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png rename to docs/modules/slam/ekf_slam/ekf_slam_1_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif b/docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif deleted file mode 100644 index 2fabeaafc9cd4ad4b71bbf451637bf2560f1be5e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 114966 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b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst similarity index 90% rename from docs/modules/slam/graphSLAM_SE2_example.rst rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst index bcc797ba3e..ef5d05bf22 100644 --- a/docs/modules/slam/graphSLAM_SE2_example.rst +++ b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst @@ -44,7 +44,7 @@ The Dataset -.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png +.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png Each edge in this dataset is a constraint that compares the measured @@ -122,7 +122,7 @@ dataset and plot them. -.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png +.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png .. code:: ipython3 @@ -131,7 +131,7 @@ dataset and plot them. -.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png +.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png Optimization @@ -165,8 +165,7 @@ different data sources into a single optimization problem. 6 215.8405 -0.000000 -.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif - :alt: Graph_SLAM_optimization.gif +.. figure:: graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif .. code:: ipython3 @@ -174,7 +173,7 @@ different data sources into a single optimization problem. -.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png +.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png .. code:: ipython3 @@ -196,7 +195,7 @@ different data sources into a single optimization problem. -.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png +.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png .. code:: ipython3 @@ -205,5 +204,5 @@ different data sources into a single optimization problem. -.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png +.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png similarity index 100% rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png similarity index 100% rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png similarity index 100% rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png similarity index 100% rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png similarity index 100% rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png similarity index 100% rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png diff --git a/docs/modules/slam/graphSLAM_doc.rst b/docs/modules/slam/graph_slam/graphSLAM_doc.rst similarity index 95% rename from docs/modules/slam/graphSLAM_doc.rst rename to docs/modules/slam/graph_slam/graphSLAM_doc.rst index b17c3b18a2..4367f770c3 100644 --- a/docs/modules/slam/graphSLAM_doc.rst +++ b/docs/modules/slam/graph_slam/graphSLAM_doc.rst @@ -142,7 +142,7 @@ created based on the information of the motion and the observation. -.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png +.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png .. parsed-literal:: @@ -157,7 +157,7 @@ created based on the information of the motion and the observation. -.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png +.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png In particular, the tasks are split into 2 parts, graph construction, and @@ -289,7 +289,7 @@ robot with 3DoF, namely, :math:`[x, y, \theta]^T` -.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png +.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png .. code:: ipython3 @@ -420,7 +420,7 @@ zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal -.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png +.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png Since the constraints equations derived before are non-linear, @@ -494,12 +494,9 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}` -.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png - - - -.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png +.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png +.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png .. code:: ipython3 @@ -546,11 +543,11 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}` The references: ^^^^^^^^^^^^^^^ -- http://robots.stanford.edu/papers/thrun.graphslam.pdf +- `The GraphSLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures `_ -- http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf +- `Introduction to Mobile Robotics Graph-Based SLAM `_ -- http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf +- `A Tutorial on Graph-Based SLAM `_ N.B. An additional step is required that uses the estimated path to update the belief regarding the map. diff --git a/docs/modules/slam/graphSLAM_formulation.rst b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst similarity index 97% rename from docs/modules/slam/graphSLAM_formulation.rst rename to docs/modules/slam/graph_slam/graphSLAM_formulation.rst index c673bdcbfb..43f007e7bf 100644 --- a/docs/modules/slam/graphSLAM_formulation.rst +++ b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst @@ -213,4 +213,6 @@ Using this notation, we obtain the optimal update as We apply this update to the poses via :eq:`update` and repeat until convergence. -.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ +.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010) +.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43. + diff --git a/docs/modules/slam/graph_slam/graph_slam.rst b/docs/modules/slam/graph_slam/graph_slam.rst new file mode 100644 index 0000000000..a1248a01cc --- /dev/null +++ b/docs/modules/slam/graph_slam/graph_slam.rst @@ -0,0 +1,24 @@ +Graph based SLAM +---------------- + +This is a graph based SLAM example. + +The blue line is ground truth. + +The black line is dead reckoning. + +The red line is the estimated trajectory with Graph based SLAM. + +The black stars are landmarks for graph edge generation. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif + +.. include:: graph_slam/graphSLAM_doc.rst +.. include:: graph_slam/graphSLAM_formulation.rst +.. include:: graph_slam/graphSLAM_SE2_example.rst + +References: +~~~~~~~~~~~ + +- `A Tutorial on Graph-Based SLAM `_ + diff --git a/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst b/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst new file mode 100644 index 0000000000..a30b1fc99b --- /dev/null +++ b/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst @@ -0,0 +1,16 @@ +.. _iterative-closest-point-(icp)-matching: + +Iterative Closest Point (ICP) Matching +-------------------------------------- + +This is a 2D ICP matching example with singular value decomposition. + +It can calculate a rotation matrix and a translation vector between +points to points. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif + +References +~~~~~~~~~~ + +- `Introduction to Mobile Robotics: Iterative Closest Point Algorithm `_ diff --git a/docs/modules/slam/slam_main.rst b/docs/modules/slam/slam_main.rst index 41210fee4e..f4d10feb9a 100644 --- a/docs/modules/slam/slam_main.rst +++ b/docs/modules/slam/slam_main.rst @@ -5,77 +5,9 @@ SLAM Simultaneous Localization and Mapping(SLAM) examples -.. _iterative-closest-point-(icp)-matching: +.. include:: iterative_closest_point_matching/iterative_closest_point_matching.rst +.. include:: ekf_slam/ekf_slam.rst +.. include:: FastSLAM1/FastSLAM1.rst +.. include:: FastSLAM2/FastSLAM2.rst +.. include:: graph_slam/graph_slam.rst -Iterative Closest Point (ICP) Matching --------------------------------------- - -This is a 2D ICP matching example with singular value decomposition. - -It can calculate a rotation matrix and a translation vector between -points to points. - -|3| - -Ref: - -- `Introduction to Mobile Robotics: Iterative Closest Point Algorithm`_ - - -.. include:: ekf_slam.rst - - -.. include:: FastSLAM1.rst - -FastSLAM 2.0 ------------- - -This is a feature based SLAM example using FastSLAM 2.0. - -The animation has the same meanings as one of FastSLAM 1.0. - -|6| - -References -~~~~~~~~~~ - -- `PROBABILISTIC ROBOTICS`_ - -- `SLAM simulations by Tim Bailey`_ - -Graph based SLAM ----------------- - -This is a graph based SLAM example. - -The blue line is ground truth. - -The black line is dead reckoning. - -The red line is the estimated trajectory with Graph based SLAM. - -The black stars are landmarks for graph edge generation. - -|7| - -.. include:: graphSLAM_doc.rst -.. include:: graphSLAM_formulation.rst -.. include:: graphSLAM_SE2_example.rst - -Ref: - -- `A Tutorial on Graph-Based SLAM`_ - -.. _`Introduction to Mobile Robotics: Iterative Closest Point Algorithm`: https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf -.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ -.. _SLAM simulations by Tim Bailey: http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm -.. _A Tutorial on Graph-Based SLAM: http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf -.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf - -.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010) -.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43. - -.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif -.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif -.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif -.. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py index c6a6b8bc76..05cfa269d7 100644 --- a/tests/test_move_to_pose.py +++ b/tests/test_move_to_pose.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from PathTracking.move_to_pose import move_to_pose as m +from Control.move_to_pose import move_to_pose as m def test_1(): From 4b9d05dde18daaa113a30aaef857265f2c94846b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Nov 2021 07:18:13 +0900 Subject: [PATCH 381/909] Bump scipy from 1.7.2 to 1.7.3 (#587) Bumps [scipy](https://github.com/scipy/scipy) from 1.7.2 to 1.7.3. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.7.2...v1.7.3) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 84f663cb83..63bdc966cb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.21.4 -scipy == 1.7.2 +scipy == 1.7.3 pandas == 1.3.4 matplotlib == 3.5.0 cvxpy == 1.1.17 From 2c077225a7728a18152b2cf63fffc329069af989 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Nov 2021 07:18:43 +0900 Subject: [PATCH 382/909] Bump ipython from 7.29.0 to 7.30.0 (#588) Bumps [ipython](https://github.com/ipython/ipython) from 7.29.0 to 7.30.0. - [Release notes](https://github.com/ipython/ipython/releases) - [Commits](https://github.com/ipython/ipython/compare/7.29.0...7.30.0) --- updated-dependencies: - dependency-name: ipython dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 63bdc966cb..2a0135cf44 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,4 +7,4 @@ pytest == 6.2.5 # For unit test pytest-xdist == 2.4.0 # For unit test sphinx < 4.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.29.0 # For sphinx documentation +IPython == 7.30.0 # For sphinx documentation From 4d60c3c0b152eca35a6a244819d7d6e7664dfd05 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 1 Dec 2021 23:00:39 +0900 Subject: [PATCH 383/909] update docs (#589) * update docs * update docs --- .../Figure_1.png | Bin .../Figure_2.png | Bin .../path_planning/bezier_path/bezier_path.rst | 20 + .../bspline_path/bspline_path.rst | 14 + .../path_planning/bugplanner/bugplanner.rst | 8 + .../coverage_path/coverage_path.rst | 34 ++ .../cubic_spline/cubic_spline.rst | 13 + .../path_planning/dubins_path/dubins_path.rst | 11 + .../dynamic_window_approach.rst | 9 + .../path_planning/eta3_spline/eta3_spline.rst | 13 + .../frenet_frame_path/frenet_frame_path.rst | 20 + .../grid_base_search/grid_base_search.rst | 93 ++++ .../path_planning/hybridastar/hybridastar.rst | 6 + .../path_planning/lqr_path/lqr_path.rst | 6 + .../lookup_table.png} | Bin .../model_predictive_trajectory_generator.rst | 22 + .../path_planning/path_planning_main.rst | 426 +----------------- .../path_planning/prm_planner/prm_planner.rst | 19 + .../quintic_polynomials_planner.rst | 0 .../reeds_shepp_path/reeds_shepp_path.rst | 17 + docs/modules/path_planning/rrt/rrt.rst | 118 +++++ .../path_planning/{ => rrt}/rrt_star.rst | 3 +- .../rrt_star}/rrt_star_1_0.png | Bin .../rrt_star_reeds_shepp/figure_1.png | Bin .../Figure_1.png | Bin .../Figure_2.png | Bin .../Figure_3.png | Bin .../Figure_4.png | Bin .../Figure_5.png | Bin .../Figure_6.png | Bin .../state_lattice_planner.rst | 33 ++ .../path_planning/vrm_planner/vrm_planner.rst | 17 + 32 files changed, 493 insertions(+), 409 deletions(-) rename docs/modules/path_planning/{bezier_path_planning => bezier_path}/Figure_1.png (100%) rename docs/modules/path_planning/{bezier_path_planning => bezier_path}/Figure_2.png (100%) create mode 100644 docs/modules/path_planning/bezier_path/bezier_path.rst create mode 100644 docs/modules/path_planning/bspline_path/bspline_path.rst create mode 100644 docs/modules/path_planning/bugplanner/bugplanner.rst create mode 100644 docs/modules/path_planning/coverage_path/coverage_path.rst create mode 100644 docs/modules/path_planning/cubic_spline/cubic_spline.rst create mode 100644 docs/modules/path_planning/dubins_path/dubins_path.rst create mode 100644 docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst create mode 100644 docs/modules/path_planning/eta3_spline/eta3_spline.rst create mode 100644 docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst create mode 100644 docs/modules/path_planning/grid_base_search/grid_base_search.rst create mode 100644 docs/modules/path_planning/hybridastar/hybridastar.rst create mode 100644 docs/modules/path_planning/lqr_path/lqr_path.rst rename docs/modules/path_planning/{model_predictive_trajectry_generator/lookuptable.png => model_predictive_trajectory_generator/lookup_table.png} (100%) create mode 100644 docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst create mode 100644 docs/modules/path_planning/prm_planner/prm_planner.rst rename docs/modules/path_planning/{ => quintic_polynomials_planner}/quintic_polynomials_planner.rst (100%) create mode 100644 docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst create mode 100644 docs/modules/path_planning/rrt/rrt.rst rename docs/modules/path_planning/{ => rrt}/rrt_star.rst (90%) rename docs/modules/path_planning/{rrt_star_files => rrt/rrt_star}/rrt_star_1_0.png (100%) rename docs/modules/path_planning/{ => rrt}/rrt_star_reeds_shepp/figure_1.png (100%) rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_1.png (100%) rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_2.png (100%) rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_3.png (100%) rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_4.png (100%) rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_5.png (100%) rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_6.png (100%) create mode 100644 docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst create mode 100644 docs/modules/path_planning/vrm_planner/vrm_planner.rst diff --git a/docs/modules/path_planning/bezier_path_planning/Figure_1.png b/docs/modules/path_planning/bezier_path/Figure_1.png similarity index 100% rename from docs/modules/path_planning/bezier_path_planning/Figure_1.png rename to docs/modules/path_planning/bezier_path/Figure_1.png diff --git a/docs/modules/path_planning/bezier_path_planning/Figure_2.png b/docs/modules/path_planning/bezier_path/Figure_2.png similarity index 100% rename from docs/modules/path_planning/bezier_path_planning/Figure_2.png rename to docs/modules/path_planning/bezier_path/Figure_2.png diff --git a/docs/modules/path_planning/bezier_path/bezier_path.rst b/docs/modules/path_planning/bezier_path/bezier_path.rst new file mode 100644 index 0000000000..c630d54345 --- /dev/null +++ b/docs/modules/path_planning/bezier_path/bezier_path.rst @@ -0,0 +1,20 @@ +Bezier path planning +-------------------- + +A sample code of Bezier path planning. + +It is based on 4 control points Beizer path. + +.. image:: Bezier_path/Figure_1.png + +If you change the offset distance from start and end point, + +You can get different Beizer course: + +.. image:: Bezier_path/Figure_2.png + +Ref: + +- `Continuous Curvature Path Generation Based on Bezier Curves for + Autonomous + Vehicles `__ diff --git a/docs/modules/path_planning/bspline_path/bspline_path.rst b/docs/modules/path_planning/bspline_path/bspline_path.rst new file mode 100644 index 0000000000..a8043122fa --- /dev/null +++ b/docs/modules/path_planning/bspline_path/bspline_path.rst @@ -0,0 +1,14 @@ +B-Spline planning +----------------- + +.. image:: bspline_path/Figure_1.png + +This is a path planning with B-Spline curse. + +If you input waypoints, it generates a smooth path with B-Spline curve. + +The final course should be on the first and last waypoints. + +Ref: + +- `B-spline - Wikipedia `__ diff --git a/docs/modules/path_planning/bugplanner/bugplanner.rst b/docs/modules/path_planning/bugplanner/bugplanner.rst new file mode 100644 index 0000000000..72cc46709f --- /dev/null +++ b/docs/modules/path_planning/bugplanner/bugplanner.rst @@ -0,0 +1,8 @@ +Bug planner +----------- + +This is a 2D planning with Bug algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif + +- `ECE452 Bug Algorithms `_ diff --git a/docs/modules/path_planning/coverage_path/coverage_path.rst b/docs/modules/path_planning/coverage_path/coverage_path.rst new file mode 100644 index 0000000000..828342a294 --- /dev/null +++ b/docs/modules/path_planning/coverage_path/coverage_path.rst @@ -0,0 +1,34 @@ +Coverage path planner +--------------------- + +Grid based sweep +~~~~~~~~~~~~~~~~ + +This is a 2D grid based sweep coverage path planner simulation: + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif + +Spiral Spanning Tree +~~~~~~~~~~~~~~~~~~~~ + +This is a 2D grid based spiral spanning tree coverage path planner simulation: + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation1.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation2.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation3.gif + +- `Spiral-STC: An On-Line Coverage Algorithm of Grid Environments by a Mobile Robot `_ + + +Wavefront path +~~~~~~~~~~~~~~ + +This is a 2D grid based wavefront coverage path planner simulation: + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation1.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif + +- `Planning paths of complete coverage of an unstructured environment by a mobile robot `_ + + diff --git a/docs/modules/path_planning/cubic_spline/cubic_spline.rst b/docs/modules/path_planning/cubic_spline/cubic_spline.rst new file mode 100644 index 0000000000..b1973856e8 --- /dev/null +++ b/docs/modules/path_planning/cubic_spline/cubic_spline.rst @@ -0,0 +1,13 @@ +Cubic spline planning +--------------------- + +A sample code for cubic path planning. + +This code generates a curvature continuous path based on x-y waypoints +with cubic spline. + +Heading angle of each point can be also calculated analytically. + +.. image:: cubic_spline/Figure_1.png +.. image:: cubic_spline/Figure_2.png +.. image:: cubic_spline/Figure_3.png diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path.rst new file mode 100644 index 0000000000..5e14820f8f --- /dev/null +++ b/docs/modules/path_planning/dubins_path/dubins_path.rst @@ -0,0 +1,11 @@ +Dubins path planning +-------------------- + +A sample code for Dubins path planning. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True + +Ref: + +- `Dubins path - + Wikipedia `__ diff --git a/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst new file mode 100644 index 0000000000..0ece6e0054 --- /dev/null +++ b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst @@ -0,0 +1,9 @@ +Dynamic Window Approach +----------------------- + +This is a 2D navigation sample code with Dynamic Window Approach. + +- `The Dynamic Window Approach to Collision + Avoidance `__ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif diff --git a/docs/modules/path_planning/eta3_spline/eta3_spline.rst b/docs/modules/path_planning/eta3_spline/eta3_spline.rst new file mode 100644 index 0000000000..ffc3cc6451 --- /dev/null +++ b/docs/modules/path_planning/eta3_spline/eta3_spline.rst @@ -0,0 +1,13 @@ +.. _eta^3-spline-path-planning: + +Eta^3 Spline path planning +-------------------------- + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif + +This is a path planning with Eta^3 spline. + +Ref: + +- `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile + Robots `__ diff --git a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst new file mode 100644 index 0000000000..e9d9041e0e --- /dev/null +++ b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst @@ -0,0 +1,20 @@ +Optimal Trajectory in a Frenet Frame +------------------------------------ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif + +This is optimal trajectory generation in a Frenet Frame. + +The cyan line is the target course and black crosses are obstacles. + +The red line is predicted path. + +Ref: + +- `Optimal Trajectory Generation for Dynamic Street Scenarios in a + Frenet + Frame `__ + +- `Optimal trajectory generation for dynamic street scenarios in a + Frenet Frame `__ + diff --git a/docs/modules/path_planning/grid_base_search/grid_base_search.rst b/docs/modules/path_planning/grid_base_search/grid_base_search.rst new file mode 100644 index 0000000000..ac4ea2f6fd --- /dev/null +++ b/docs/modules/path_planning/grid_base_search/grid_base_search.rst @@ -0,0 +1,93 @@ +Grid based search +----------------- + +Breadth First Search +~~~~~~~~~~~~~~~~~~~~ + +This is a 2D grid based path planning with Breadth first search algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BreadthFirstSearch/animation.gif + +In the animation, cyan points are searched nodes. + +Depth First Search +~~~~~~~~~~~~~~~~~~~~ + +This is a 2D grid based path planning with Depth first search algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DepthFirstSearch/animation.gif + +In the animation, cyan points are searched nodes. + +Dijkstra algorithm +~~~~~~~~~~~~~~~~~~ + +This is a 2D grid based shortest path planning with Dijkstra's algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif + +In the animation, cyan points are searched nodes. + +.. _a*-algorithm: + +A\* algorithm +~~~~~~~~~~~~~ + +This is a 2D grid based shortest path planning with A star algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif + +In the animation, cyan points are searched nodes. + +Its heuristic is 2D Euclid distance. + +Bidirectional A\* algorithm +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This is a 2D grid based shortest path planning with bidirectional A star algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BidirectionalAStar/animation.gif + +In the animation, cyan points are searched nodes. + +.. _D*-algorithm: + +D\* algorithm +~~~~~~~~~~~~~ + +This is a 2D grid based shortest path planning with D star algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif + +The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm. + +Ref: + +- `D* search Wikipedia `__ + +D\* lite algorithm +~~~~~~~~~~~~~~~~~~ + +This is a 2D grid based path planning and replanning with D star lite algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif + +Ref: + +- `Improved Fast Replanning for Robot Navigation in Unknown Terrain `_ + + +Potential Field algorithm +~~~~~~~~~~~~~~~~~~~~~~~~~ + +This is a 2D grid based path planning with Potential Field algorithm. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif + +In the animation, the blue heat map shows potential value on each grid. + +Ref: + +- `Robotic Motion Planning:Potential + Functions `__ + diff --git a/docs/modules/path_planning/hybridastar/hybridastar.rst b/docs/modules/path_planning/hybridastar/hybridastar.rst new file mode 100644 index 0000000000..a9876fa3d4 --- /dev/null +++ b/docs/modules/path_planning/hybridastar/hybridastar.rst @@ -0,0 +1,6 @@ +Hybrid a star +--------------------- + +This is a simple vehicle model based hybrid A\* path planner. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/HybridAStar/animation.gif diff --git a/docs/modules/path_planning/lqr_path/lqr_path.rst b/docs/modules/path_planning/lqr_path/lqr_path.rst new file mode 100644 index 0000000000..788442ff63 --- /dev/null +++ b/docs/modules/path_planning/lqr_path/lqr_path.rst @@ -0,0 +1,6 @@ +LQR based path planning +----------------------- + +A sample code using LQR based path planning for double integrator model. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true diff --git a/docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png b/docs/modules/path_planning/model_predictive_trajectory_generator/lookup_table.png similarity index 100% rename from docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png rename to docs/modules/path_planning/model_predictive_trajectory_generator/lookup_table.png diff --git a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst new file mode 100644 index 0000000000..07a7ee9b59 --- /dev/null +++ b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst @@ -0,0 +1,22 @@ +Model Predictive Trajectory Generator +------------------------------------- + +This is a path optimization sample on model predictive trajectory +generator. + +This algorithm is used for state lattice planner. + +Path optimization sample +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif + +Lookup table generation sample +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: model_predictive_trajectory_generator/lookup_table.png + +Ref: + +- `Optimal rough terrain trajectory generation for wheeled mobile + robots `__ diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst index 8033e1a875..61f8ea2113 100644 --- a/docs/modules/path_planning/path_planning_main.rst +++ b/docs/modules/path_planning/path_planning_main.rst @@ -3,410 +3,22 @@ Path Planning ============= -Dynamic Window Approach ------------------------ - -This is a 2D navigation sample code with Dynamic Window Approach. - -- `The Dynamic Window Approach to Collision - Avoidance `__ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif - -Grid based search ------------------ - -Dijkstra algorithm -~~~~~~~~~~~~~~~~~~ - -This is a 2D grid based shortest path planning with Dijkstra's -algorithm. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif - -In the animation, cyan points are searched nodes. - -.. _a*-algorithm: - -A\* algorithm -~~~~~~~~~~~~~ - -This is a 2D grid based shortest path planning with A star algorithm. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif - -In the animation, cyan points are searched nodes. - -Its heuristic is 2D Euclid distance. - -.. _D*-algorithm: - -D\* algorithm -~~~~~~~~~~~~~ - -This is a 2D grid based shortest path planning with D star algorithm. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif - -The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm. - -Ref: - -- `D* search Wikipedia `__ - -Potential Field algorithm -~~~~~~~~~~~~~~~~~~~~~~~~~ - -This is a 2D grid based path planning with Potential Field algorithm. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif - -In the animation, the blue heat map shows potential value on each grid. - -Ref: - -- `Robotic Motion Planning:Potential - Functions `__ - -Model Predictive Trajectory Generator -------------------------------------- - -This is a path optimization sample on model predictive trajectory -generator. - -This algorithm is used for state lattice planner. - -Path optimization sample -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif - -Lookup table generation sample -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: model_predictive_trajectry_generator/lookuptable.png - -Ref: - -- `Optimal rough terrain trajectory generation for wheeled mobile - robots `__ - - -State Lattice Planning ----------------------- - -This script is a path planning code with state lattice planning. - -This code uses the model predictive trajectory generator to solve -boundary problem. - -Ref: - -- `Optimal rough terrain trajectory generation for wheeled mobile - robots `__ - -- `State Space Sampling of Feasible Motions for High-Performance Mobile - Robot Navigation in Complex - Environments `__ - -Uniform polar sampling -~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif - -Biased polar sampling -~~~~~~~~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif - -Lane sampling -~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif - -.. _probabilistic-road-map-(prm)-planning: - -Probabilistic Road-Map (PRM) planning -------------------------------------- - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif - -This PRM planner uses Dijkstra method for graph search. - -In the animation, blue points are sampled points, - -Cyan crosses means searched points with Dijkstra method, - -The red line is the final path of PRM. - -Ref: - -- `Probabilistic roadmap - - Wikipedia `__ - -　　 - -Voronoi Road-Map planning -------------------------- - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif - -This Voronoi road-map planner uses Dijkstra method for graph search. - -In the animation, blue points are Voronoi points, - -Cyan crosses mean searched points with Dijkstra method, - -The red line is the final path of Vornoi Road-Map. - -Ref: - -- `Robotic Motion - Planning `__ - -.. _rapidly-exploring-random-trees-(rrt): - -Rapidly-Exploring Random Trees (RRT) ------------------------------------- - -Basic RRT -~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif - -This is a simple path planning code with Rapidly-Exploring Random Trees -(RRT) - -Black circles are obstacles, green line is a searched tree, red crosses -are start and goal positions. - -.. include:: rrt_star.rst - - -RRT with dubins path -~~~~~~~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif - -Path planning for a car robot with RRT and dubins path planner. - -.. _rrt*-with-dubins-path: - -RRT\* with dubins path -~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif - -Path planning for a car robot with RRT\* and dubins path planner. - -.. _rrt*-with-reeds-sheep-path: - -RRT\* with reeds-sheep path -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif - -Path planning for a car robot with RRT\* and reeds sheep path planner. - -.. _informed-rrt*: - -Informed RRT\* -~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif - -This is a path planning code with Informed RRT*. - -The cyan ellipse is the heuristic sampling domain of Informed RRT*. - -Ref: - -- `Informed RRT\*: Optimal Sampling-based Path Planning Focused via - Direct Sampling of an Admissible Ellipsoidal - Heuristic `__ - -.. _batch-informed-rrt*: - -Batch Informed RRT\* -~~~~~~~~~~~~~~~~~~~~ - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif - -This is a path planning code with Batch Informed RRT*. - -Ref: - -- `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the - Heuristically Guided Search of Implicit Random Geometric - Graphs `__ - -.. _closed-loop-rrt*: - -Closed Loop RRT\* -~~~~~~~~~~~~~~~~~ - -A vehicle model based path planning with closed loop RRT*. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif - -In this code, pure-pursuit algorithm is used for steering control, - -PID is used for speed control. - -Ref: - -- `Motion Planning in Complex Environments using Closed-loop - Prediction `__ - -- `Real-time Motion Planning with Applications to Autonomous Urban - Driving `__ - -- `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning - Using Closed-loop Prediction `__ - -.. _lqr-rrt*: - -LQR-RRT\* -~~~~~~~~~ - -This is a path planning simulation with LQR-RRT*. - -A double integrator motion model is used for LQR local planner. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif - -Ref: - -- `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically - Derived Extension - Heuristics `__ - -- `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion - planning of a simple pendulum in its phase - plot `__ - -Cubic spline planning ---------------------- - -A sample code for cubic path planning. - -This code generates a curvature continuous path based on x-y waypoints -with cubic spline. - -Heading angle of each point can be also calculated analytically. - -.. image:: cubic_spline/Figure_1.png -.. image:: cubic_spline/Figure_2.png -.. image:: cubic_spline/Figure_3.png - -B-Spline planning ------------------ - -.. image:: bspline_path/Figure_1.png - -This is a path planning with B-Spline curse. - -If you input waypoints, it generates a smooth path with B-Spline curve. - -The final course should be on the first and last waypoints. - -Ref: - -- `B-spline - Wikipedia `__ - -.. _eta^3-spline-path-planning: - -Eta^3 Spline path planning --------------------------- - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif - -This is a path planning with Eta^3 spline. - -Ref: - -- `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile - Robots `__ - -Bezier path planning --------------------- - -A sample code of Bezier path planning. - -It is based on 4 control points Beier path. - -.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_1.png?raw=True - -If you change the offset distance from start and end point, - -You can get different Beizer course: - -.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_2.png?raw=True - -Ref: - -- `Continuous Curvature Path Generation Based on Bezier Curves for - Autonomous - Vehicles `__ - - -.. include:: quintic_polynomials_planner.rst - - -Dubins path planning --------------------- - -A sample code for Dubins path planning. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True - -Ref: - -- `Dubins path - - Wikipedia `__ - -Reeds Shepp planning --------------------- - -A sample code with Reeds Shepp path planning. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true - -Ref: - -- `15.3.2 Reeds-Shepp - Curves `__ - -- `optimal paths for a car that goes both forwards and - backwards `__ - -- `ghliu/pyReedsShepp: Implementation of Reeds Shepp - curve. `__ - -LQR based path planning ------------------------ - -A sample code using LQR based path planning for double integrator model. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true - -Optimal Trajectory in a Frenet Frame ------------------------------------- - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif - -This is optimal trajectory generation in a Frenet Frame. - -The cyan line is the target course and black crosses are obstacles. - -The red line is predicted path. - -Ref: - -- `Optimal Trajectory Generation for Dynamic Street Scenarios in a - Frenet - Frame `__ - -- `Optimal trajectory generation for dynamic street scenarios in a - Frenet Frame `__ - +.. include:: dynamic_window_approach/dynamic_window_approach.rst +.. include:: bugplanner/bugplanner.rst +.. include:: grid_base_search/grid_base_search.rst +.. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst +.. include:: state_lattice_planner/state_lattice_planner.rst +.. include:: prm_planner/prm_planner.rst +.. include:: vrm_planner/vrm_planner.rst +.. include:: rrt/rrt.rst +.. include:: cubic_spline/cubic_spline.rst +.. include:: bspline_path/bspline_path.rst +.. include:: eta3_spline/eta3_spline.rst +.. include:: bezier_path/bezier_path.rst +.. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst +.. include:: dubins_path/dubins_path.rst +.. include:: reeds_shepp_path/reeds_shepp_path.rst +.. include:: lqr_path/lqr_path.rst +.. include:: hybridastar/hybridastar.rst +.. include:: frenet_frame_path/frenet_frame_path.rst +.. include:: coverage_path/coverage_path.rst diff --git a/docs/modules/path_planning/prm_planner/prm_planner.rst b/docs/modules/path_planning/prm_planner/prm_planner.rst new file mode 100644 index 0000000000..0628719176 --- /dev/null +++ b/docs/modules/path_planning/prm_planner/prm_planner.rst @@ -0,0 +1,19 @@ +.. _probabilistic-road-map-(prm)-planning: + +Probabilistic Road-Map (PRM) planning +------------------------------------- + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif + +This PRM planner uses Dijkstra method for graph search. + +In the animation, blue points are sampled points, + +Cyan crosses means searched points with Dijkstra method, + +The red line is the final path of PRM. + +Ref: + +- `Probabilistic roadmap - + Wikipedia `__ diff --git a/docs/modules/path_planning/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst similarity index 100% rename from docs/modules/path_planning/quintic_polynomials_planner.rst rename to docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst diff --git a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst new file mode 100644 index 0000000000..81e565888e --- /dev/null +++ b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst @@ -0,0 +1,17 @@ +Reeds Shepp planning +-------------------- + +A sample code with Reeds Shepp path planning. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true + +Ref: + +- `15.3.2 Reeds-Shepp + Curves `__ + +- `optimal paths for a car that goes both forwards and + backwards `__ + +- `ghliu/pyReedsShepp: Implementation of Reeds Shepp + curve. `__ diff --git a/docs/modules/path_planning/rrt/rrt.rst b/docs/modules/path_planning/rrt/rrt.rst new file mode 100644 index 0000000000..1afa17b144 --- /dev/null +++ b/docs/modules/path_planning/rrt/rrt.rst @@ -0,0 +1,118 @@ +.. _rapidly-exploring-random-trees-(rrt): + +Rapidly-Exploring Random Trees (RRT) +------------------------------------ + +Basic RRT +~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif + +This is a simple path planning code with Rapidly-Exploring Random Trees +(RRT) + +Black circles are obstacles, green line is a searched tree, red crosses +are start and goal positions. + +.. include:: rrt/rrt_star.rst + + +RRT with dubins path +~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif + +Path planning for a car robot with RRT and dubins path planner. + +.. _rrt*-with-dubins-path: + +RRT\* with dubins path +~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif + +Path planning for a car robot with RRT\* and dubins path planner. + +.. _rrt*-with-reeds-sheep-path: + +RRT\* with reeds-sheep path +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif + +Path planning for a car robot with RRT\* and reeds sheep path planner. + +.. _informed-rrt*: + +Informed RRT\* +~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif + +This is a path planning code with Informed RRT*. + +The cyan ellipse is the heuristic sampling domain of Informed RRT*. + +Ref: + +- `Informed RRT\*: Optimal Sampling-based Path Planning Focused via + Direct Sampling of an Admissible Ellipsoidal + Heuristic `__ + +.. _batch-informed-rrt*: + +Batch Informed RRT\* +~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif + +This is a path planning code with Batch Informed RRT*. + +Ref: + +- `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the + Heuristically Guided Search of Implicit Random Geometric + Graphs `__ + +.. _closed-loop-rrt*: + +Closed Loop RRT\* +~~~~~~~~~~~~~~~~~ + +A vehicle model based path planning with closed loop RRT*. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif + +In this code, pure-pursuit algorithm is used for steering control, + +PID is used for speed control. + +Ref: + +- `Motion Planning in Complex Environments using Closed-loop + Prediction `__ + +- `Real-time Motion Planning with Applications to Autonomous Urban + Driving `__ + +- `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning + Using Closed-loop Prediction `__ + +.. _lqr-rrt*: + +LQR-RRT\* +~~~~~~~~~ + +This is a path planning simulation with LQR-RRT*. + +A double integrator motion model is used for LQR local planner. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif + +Ref: + +- `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically + Derived Extension + Heuristics `__ + +- `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion planning of a simple pendulum in its phase plot `__ \ No newline at end of file diff --git a/docs/modules/path_planning/rrt_star.rst b/docs/modules/path_planning/rrt/rrt_star.rst similarity index 90% rename from docs/modules/path_planning/rrt_star.rst rename to docs/modules/path_planning/rrt/rrt_star.rst index cf136d529f..546e9bf6dd 100644 --- a/docs/modules/path_planning/rrt_star.rst +++ b/docs/modules/path_planning/rrt/rrt_star.rst @@ -2,7 +2,6 @@ RRT\* ~~~~~ .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif - :alt: gif This is a path planning code with RRT\* @@ -11,7 +10,7 @@ Black circles are obstacles, green line is a searched tree, red crosses are star Simulation ^^^^^^^^^^ -.. image:: rrt_star_files/rrt_star_1_0.png +.. image:: rrt/rrt_star/rrt_star_1_0.png :width: 600px diff --git a/docs/modules/path_planning/rrt_star_files/rrt_star_1_0.png b/docs/modules/path_planning/rrt/rrt_star/rrt_star_1_0.png similarity index 100% rename from docs/modules/path_planning/rrt_star_files/rrt_star_1_0.png rename to docs/modules/path_planning/rrt/rrt_star/rrt_star_1_0.png diff --git a/docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png b/docs/modules/path_planning/rrt/rrt_star_reeds_shepp/figure_1.png similarity index 100% rename from docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png rename to docs/modules/path_planning/rrt/rrt_star_reeds_shepp/figure_1.png diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png b/docs/modules/path_planning/state_lattice_planner/Figure_1.png similarity index 100% rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png rename to docs/modules/path_planning/state_lattice_planner/Figure_1.png diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png b/docs/modules/path_planning/state_lattice_planner/Figure_2.png similarity index 100% rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png rename to docs/modules/path_planning/state_lattice_planner/Figure_2.png diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png b/docs/modules/path_planning/state_lattice_planner/Figure_3.png similarity index 100% rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png rename to docs/modules/path_planning/state_lattice_planner/Figure_3.png diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png b/docs/modules/path_planning/state_lattice_planner/Figure_4.png similarity index 100% rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png rename to docs/modules/path_planning/state_lattice_planner/Figure_4.png diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png b/docs/modules/path_planning/state_lattice_planner/Figure_5.png similarity index 100% rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png rename to docs/modules/path_planning/state_lattice_planner/Figure_5.png diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png b/docs/modules/path_planning/state_lattice_planner/Figure_6.png similarity index 100% rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png rename to docs/modules/path_planning/state_lattice_planner/Figure_6.png diff --git a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst new file mode 100644 index 0000000000..3ef0c7eca2 --- /dev/null +++ b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst @@ -0,0 +1,33 @@ +State Lattice Planning +---------------------- + +This script is a path planning code with state lattice planning. + +This code uses the model predictive trajectory generator to solve +boundary problem. + + +Uniform polar sampling +~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif + +Biased polar sampling +~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif + +Lane sampling +~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif + +Ref: + +- `Optimal rough terrain trajectory generation for wheeled mobile + robots `__ + +- `State Space Sampling of Feasible Motions for High-Performance Mobile + Robot Navigation in Complex + Environments `__ + diff --git a/docs/modules/path_planning/vrm_planner/vrm_planner.rst b/docs/modules/path_planning/vrm_planner/vrm_planner.rst new file mode 100644 index 0000000000..92e729ab29 --- /dev/null +++ b/docs/modules/path_planning/vrm_planner/vrm_planner.rst @@ -0,0 +1,17 @@ +Voronoi Road-Map planning +------------------------- + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif + +This Voronoi road-map planner uses Dijkstra method for graph search. + +In the animation, blue points are Voronoi points, + +Cyan crosses mean searched points with Dijkstra method, + +The red line is the final path of Vornoi Road-Map. + +Ref: + +- `Robotic Motion Planning `__ + From c70add32a15ad7e662185fa816ba152d244be002 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 4 Dec 2021 20:56:54 +0900 Subject: [PATCH 384/909] add visibility road map path doc (#592) * add visibility road map path doc * add visibility road map path doc * add visibility road map path doc * add visibility road map path doc * add visibility road map path doc * add visibility road map path doc * add visibility road map path doc --- PathPlanning/VisibilityRoadMap/geometry.py | 3 +- .../VisibilityRoadMap/visibility_road_map.py | 21 +++--- .../grid_base_search/grid_base_search.rst | 2 + .../path_planning/path_planning_main.rst | 1 + .../visibility_road_map_planner/step0.png | Bin 0 -> 322560 bytes .../visibility_road_map_planner/step1.png | Bin 0 -> 289363 bytes .../visibility_road_map_planner/step2.png | Bin 0 -> 795677 bytes .../visibility_road_map_planner/step3.png | Bin 0 -> 791560 bytes .../visibility_road_map_planner.rst | 71 ++++++++++++++++++ 9 files changed, 87 insertions(+), 11 deletions(-) create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step0.png create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step1.png create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step2.png create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step3.png create mode 100644 docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst diff --git a/PathPlanning/VisibilityRoadMap/geometry.py b/PathPlanning/VisibilityRoadMap/geometry.py index c4d73837c9..b15cdb8a43 100644 --- a/PathPlanning/VisibilityRoadMap/geometry.py +++ b/PathPlanning/VisibilityRoadMap/geometry.py @@ -1,4 +1,5 @@ class Geometry: + class Point: def __init__(self, x, y): self.x = x @@ -40,4 +41,4 @@ def orientation(p, q, r): if (o4 == 0) and on_segment(p2, q1, q2): return True - return False \ No newline at end of file + return False diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py index a677804440..6458cd882c 100644 --- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py +++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py @@ -23,14 +23,14 @@ class VisibilityRoadMap: - def __init__(self, robot_radius, do_plot=False): - self.robot_radius = robot_radius + def __init__(self, expand_distance, do_plot=False): + self.expand_distance = expand_distance self.do_plot = do_plot def planning(self, start_x, start_y, goal_x, goal_y, obstacles): - nodes = self.generate_graph_node(start_x, start_y, goal_x, goal_y, - obstacles) + nodes = self.generate_visibility_nodes(start_x, start_y, + goal_x, goal_y, obstacles) road_map_info = self.generate_road_map_info(nodes, obstacles) @@ -48,7 +48,8 @@ def planning(self, start_x, start_y, goal_x, goal_y, obstacles): return rx, ry - def generate_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles): + def generate_visibility_nodes(self, start_x, start_y, goal_x, goal_y, + obstacles): # add start and goal as nodes nodes = [DijkstraSearch.Node(start_x, start_y), @@ -129,8 +130,8 @@ def calc_offset_xy(self, px, py, x, y, nx, ny): offset_vec = math.atan2(math.sin(p_vec) + math.sin(n_vec), math.cos(p_vec) + math.cos( n_vec)) + math.pi / 2.0 - offset_x = x + self.robot_radius * math.cos(offset_vec) - offset_y = y + self.robot_radius * math.sin(offset_vec) + offset_x = x + self.expand_distance * math.cos(offset_vec) + offset_y = y + self.expand_distance * math.sin(offset_vec) return offset_x, offset_y @staticmethod @@ -184,7 +185,7 @@ def main(): sx, sy = 10.0, 10.0 # [m] gx, gy = 50.0, 50.0 # [m] - robot_radius = 5.0 # [m] + expand_distance = 5.0 # [m] obstacles = [ ObstaclePolygon( @@ -209,8 +210,8 @@ def main(): plt.axis("equal") plt.pause(1.0) - rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning( - sx, sy, gx, gy, obstacles) + rx, ry = VisibilityRoadMap(expand_distance, do_plot=show_animation)\ + .planning(sx, sy, gx, gy, obstacles) if show_animation: # pragma: no cover plt.plot(rx, ry, "-r") diff --git a/docs/modules/path_planning/grid_base_search/grid_base_search.rst b/docs/modules/path_planning/grid_base_search/grid_base_search.rst index ac4ea2f6fd..1644ed00cc 100644 --- a/docs/modules/path_planning/grid_base_search/grid_base_search.rst +++ b/docs/modules/path_planning/grid_base_search/grid_base_search.rst @@ -19,6 +19,8 @@ This is a 2D grid based path planning with Depth first search algorithm. In the animation, cyan points are searched nodes. +.. _dijkstra: + Dijkstra algorithm ~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst index 61f8ea2113..c3c5c389ec 100644 --- a/docs/modules/path_planning/path_planning_main.rst +++ b/docs/modules/path_planning/path_planning_main.rst @@ -9,6 +9,7 @@ Path Planning .. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst .. include:: state_lattice_planner/state_lattice_planner.rst .. include:: prm_planner/prm_planner.rst +.. include:: visibility_road_map_planner/visibility_road_map_planner.rst .. include:: vrm_planner/vrm_planner.rst .. include:: rrt/rrt.rst .. include:: cubic_spline/cubic_spline.rst diff --git a/docs/modules/path_planning/visibility_road_map_planner/step0.png b/docs/modules/path_planning/visibility_road_map_planner/step0.png new file mode 100644 index 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Start point (Red point) +- 2. Goal point (Blue point) +- 3. Obstacle polygons (Black lines) + +.. image:: visibility_road_map_planner/step0.png + :width: 400px + + +Step1: Generate visibility nodes based on polygon obstacles +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The nodes are generated by expanded these polygons vertexes like the below figure: + +.. image:: visibility_road_map_planner/step1.png + :width: 400px + +Each polygon vertex is expanded outward from the vector of adjacent vertices. + +The start and goal point are included as nodes as well. + +Step2: Generate visibility graphs connecting the nodes. +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When connecting the nodes, the arc between two nodes is checked to collided or not to each obstacles. + +If the arc is collided, the graph is removed. + +The blue lines are generated visibility graphs in the figure: + +.. image:: visibility_road_map_planner/step2.png + :width: 400px + + +Step3: Search the shortest path in the graphs using Dijkstra algorithm +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The red line is searched path in the figure: + +.. image:: visibility_road_map_planner/step3.png + :width: 400px + +You can find the details of Dijkstra algorithm in :ref:`dijkstra`. + +References +^^^^^^^^^^ + +- `Visibility graph - Wikipedia `_ + + From d5aca66e1b61692e239e95c928884ede6f2c94cd Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Dec 2021 07:23:13 +0900 Subject: [PATCH 385/909] Bump ipython from 7.30.0 to 7.30.1 (#594) Bumps [ipython](https://github.com/ipython/ipython) from 7.30.0 to 7.30.1. - [Release notes](https://github.com/ipython/ipython/releases) - [Commits](https://github.com/ipython/ipython/compare/7.30.0...7.30.1) --- updated-dependencies: - dependency-name: ipython dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2a0135cf44..09bd9f50ad 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,4 +7,4 @@ pytest == 6.2.5 # For unit test pytest-xdist == 2.4.0 # For unit test sphinx < 4.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.30.0 # For sphinx documentation +IPython == 7.30.1 # For sphinx documentation From bf4e68245f4af09af65a579a9a8c7c3df0f644c9 Mon Sep 17 00:00:00 2001 From: Muhammad-Yazdian <93126501+Muhammad-Yazdian@users.noreply.github.com> Date: Sun, 12 Dec 2021 13:32:59 +0100 Subject: [PATCH 386/909] Add speed limitation of the robot (#595) * Added speed limitation of the robot * Removed leading underscores for global vars * Added unit test for robot speed limitation * Modified x/abs(x) to np.sign(x); fixed code style * Removed 'random' from test func header comment --- Control/move_to_pose/move_to_pose.py | 10 ++++++++++ tests/test_move_to_pose.py | 11 +++++++++++ 2 files changed, 21 insertions(+) diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py index 2ae0090dda..4548a6bb8a 100644 --- a/Control/move_to_pose/move_to_pose.py +++ b/Control/move_to_pose/move_to_pose.py @@ -19,6 +19,10 @@ Kp_beta = -3 dt = 0.01 +# Robot specifications +MAX_LINEAR_SPEED = 15 +MAX_ANGULAR_SPEED = 7 + show_animation = True @@ -63,6 +67,12 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal): if alpha > np.pi / 2 or alpha < -np.pi / 2: v = -v + if abs(v) > MAX_LINEAR_SPEED: + v = np.sign(v) * MAX_LINEAR_SPEED + + if abs(w) > MAX_ANGULAR_SPEED: + w = np.sign(w) * MAX_ANGULAR_SPEED + theta = theta + w * dt x = x + v * np.cos(theta) * dt y = y + v * np.sin(theta) * dt diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py index 05cfa269d7..8bc11a8d24 100644 --- a/tests/test_move_to_pose.py +++ b/tests/test_move_to_pose.py @@ -7,5 +7,16 @@ def test_1(): m.main() +def test_2(): + """ + This unit test tests the move_to_pose.py program for a MAX_LINEAR_SPEED and + MAX_ANGULAR_SPEED + """ + m.show_animation = False + m.MAX_LINEAR_SPEED = 11 + m.MAX_ANGULAR_SPEED = 5 + m.main() + + if __name__ == '__main__': conftest.run_this_test(__file__) From 6e4d43962a12913ffb2be5ceb35bee58f58178bc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Dec 2021 07:35:03 +0900 Subject: [PATCH 387/909] Bump pytest-xdist from 2.4.0 to 2.5.0 (#597) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 2.4.0 to 2.5.0. - [Release notes](https://github.com/pytest-dev/pytest-xdist/releases) - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v2.4.0...v2.5.0) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 09bd9f50ad..4763ee4253 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ pandas == 1.3.4 matplotlib == 3.5.0 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test -pytest-xdist == 2.4.0 # For unit test +pytest-xdist == 2.5.0 # For unit test sphinx < 4.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.30.1 # For sphinx documentation From 528050ec373ebcfb9c64419b46f4139202e983ad Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Dec 2021 08:06:05 +0900 Subject: [PATCH 388/909] Bump pandas from 1.3.4 to 1.3.5 (#598) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.4 to 1.3.5. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.4...v1.3.5) --- updated-dependencies: - dependency-name: pandas dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 4763ee4253..7069c53c93 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.21.4 scipy == 1.7.3 -pandas == 1.3.4 +pandas == 1.3.5 matplotlib == 3.5.0 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test From 54be632f716f9913a89a166b7d8a722e156da7d0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Dec 2021 08:49:57 +0900 Subject: [PATCH 389/909] Bump matplotlib from 3.5.0 to 3.5.1 (#599) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.0 to 3.5.1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.0...v3.5.1) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7069c53c93..407f161b60 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.21.4 scipy == 1.7.3 pandas == 1.3.5 -matplotlib == 3.5.0 +matplotlib == 3.5.1 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test From 196c756706bb4fa2610e70808fdaf59df36e1bb4 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 25 Dec 2021 13:53:27 +0900 Subject: [PATCH 390/909] try fix appveyor CI (#605) --- appveyor.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/appveyor.yml b/appveyor.yml index 16a5d7751d..e9535e4e3c 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -1,4 +1,4 @@ -os: Visual Studio 2019 +os: Visual Studio 2022 environment: global: From 4e1f644007b8da56e8926d77242c4ad479d0b701 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 25 Dec 2021 14:14:41 +0900 Subject: [PATCH 391/909] Bump numpy from 1.21.4 to 1.21.5 (#602) Bumps [numpy](https://github.com/numpy/numpy) from 1.21.4 to 1.21.5. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.21.4...v1.21.5) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 407f161b60..966b566000 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.21.4 +numpy == 1.21.5 scipy == 1.7.3 pandas == 1.3.5 matplotlib == 3.5.1 From 680ecdafb21a798eb47649175e0eba4e72ef356d Mon Sep 17 00:00:00 2001 From: Muhammad-Yazdian <93126501+Muhammad-Yazdian@users.noreply.github.com> Date: Sat, 25 Dec 2021 13:42:32 +0100 Subject: [PATCH 392/909] Add a Robot class for Move to Pose Algorithm (#596) * Added speed limitation of the robot * Removed leading underscores for global vars * Added unit test for robot speed limitation * Modified x/abs(x) to np.sign(x); fixed code style * Removed 'random' from test func header comment * Added Robot class for move to pose * Revert * Added Robot class for move to pose * Added a type annotation for Robot class * Fixed the annotaion comment * Moved instance varaible outside of the Robot class * Fixed code style Python 3.9 CI * Removed whitespaces from the last line * Applied PR #596 change requests * Fixed typos * Update Control/move_to_pose/move_to_pose_robot_class.py Co-authored-by: Atsushi Sakai * Moved PathFinderController class to move_to_pose * Fixed issue #600 * Added update_command() to PathFinderController * Removed trailing whitespaces * Updated move to pose doc * Added code and doc comments * Updated unit test * Removed trailing whitespaces * Removed more trailing whitespaces Co-authored-by: Atsushi Sakai --- Control/move_to_pose/move_to_pose.py | 105 ++++++-- Control/move_to_pose/move_to_pose_robot.py | 240 ++++++++++++++++++ .../move_to_a_pose_control.rst | 95 ++++++- tests/test_move_to_pose_robot.py | 14 + 4 files changed, 424 insertions(+), 30 deletions(-) create mode 100644 Control/move_to_pose/move_to_pose_robot.py create mode 100644 tests/test_move_to_pose_robot.py diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py index 4548a6bb8a..73604ccd7f 100644 --- a/Control/move_to_pose/move_to_pose.py +++ b/Control/move_to_pose/move_to_pose.py @@ -3,7 +3,8 @@ Move to specified pose Author: Daniel Ingram (daniel-s-ingram) - Atsushi Sakai(@Atsushi_twi) + Atsushi Sakai (@Atsushi_twi) + Seied Muhammad Yazdian (@Muhammad-Yazdian) P. I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7 @@ -13,10 +14,77 @@ import numpy as np from random import random + +class PathFinderController: + """ + Constructs an instantiate of the PathFinderController for navigating a + 3-DOF wheeled robot on a 2D plane + + Parameters + ---------- + Kp_rho : The linear velocity gain to translate the robot along a line + towards the goal + Kp_alpha : The angular velocity gain to rotate the robot towards the goal + Kp_beta : The offset angular velocity gain accounting for smooth merging to + the goal angle (i.e., it helps the robot heading to be parallel + to the target angle.) + """ + + def __init__(self, Kp_rho, Kp_alpha, Kp_beta): + self.Kp_rho = Kp_rho + self.Kp_alpha = Kp_alpha + self.Kp_beta = Kp_beta + + def calc_control_command(self, x_diff, y_diff, theta, theta_goal): + """ + Returns the control command for the linear and angular velocities as + well as the distance to goal + + Parameters + ---------- + x_diff : The position of target with respect to current robot position + in x direction + y_diff : The position of target with respect to current robot position + in y direction + theta : The current heading angle of robot with respect to x axis + theta_goal: The target angle of robot with respect to x axis + + Returns + ------- + rho : The distance between the robot and the goal position + v : Command linear velocity + w : Command angular velocity + """ + + # Description of local variables: + # - alpha is the angle to the goal relative to the heading of the robot + # - beta is the angle between the robot's position and the goal + # position plus the goal angle + # - Kp_rho*rho and Kp_alpha*alpha drive the robot along a line towards + # the goal + # - Kp_beta*beta rotates the line so that it is parallel to the goal + # angle + # + # Note: + # we restrict alpha and beta (angle differences) to the range + # [-pi, pi] to prevent unstable behavior e.g. difference going + # from 0 rad to 2*pi rad with slight turn + + rho = np.hypot(x_diff, y_diff) + alpha = (np.arctan2(y_diff, x_diff) + - theta + np.pi) % (2 * np.pi) - np.pi + beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi + v = self.Kp_rho * rho + w = self.Kp_alpha * alpha - controller.Kp_beta * beta + + if alpha > np.pi / 2 or alpha < -np.pi / 2: + v = -v + + return rho, v, w + + # simulation parameters -Kp_rho = 9 -Kp_alpha = 15 -Kp_beta = -3 +controller = PathFinderController(9, 15, 3) dt = 0.01 # Robot specifications @@ -27,14 +95,6 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal): - """ - rho is the distance between the robot and the goal position - alpha is the angle to the goal relative to the heading of the robot - beta is the angle between the robot's position and the goal position plus the goal angle - - Kp_rho*rho and Kp_alpha*alpha drive the robot along a line towards the goal - Kp_beta*beta rotates the line so that it is parallel to the goal angle - """ x = x_start y = y_start theta = theta_start @@ -52,20 +112,8 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal): x_diff = x_goal - x y_diff = y_goal - y - # Restrict alpha and beta (angle differences) to the range - # [-pi, pi] to prevent unstable behavior e.g. difference going - # from 0 rad to 2*pi rad with slight turn - - rho = np.hypot(x_diff, y_diff) - alpha = (np.arctan2(y_diff, x_diff) - - theta + np.pi) % (2 * np.pi) - np.pi - beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi - - v = Kp_rho * rho - w = Kp_alpha * alpha + Kp_beta * beta - - if alpha > np.pi / 2 or alpha < -np.pi / 2: - v = -v + rho, v, w = controller.calc_control_command( + x_diff, y_diff, theta, theta_goal) if abs(v) > MAX_LINEAR_SPEED: v = np.sign(v) * MAX_LINEAR_SPEED @@ -104,8 +152,9 @@ def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover plt.plot(x_traj, y_traj, 'b--') # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.xlim(0, 20) plt.ylim(0, 20) diff --git a/Control/move_to_pose/move_to_pose_robot.py b/Control/move_to_pose/move_to_pose_robot.py new file mode 100644 index 0000000000..d5c51caa12 --- /dev/null +++ b/Control/move_to_pose/move_to_pose_robot.py @@ -0,0 +1,240 @@ +""" + +Move to specified pose (with Robot class) + +Author: Daniel Ingram (daniel-s-ingram) + Atsushi Sakai (@Atsushi_twi) + Seied Muhammad Yazdian (@Muhammad-Yazdian) + +P.I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7 + +""" + +import matplotlib.pyplot as plt +import numpy as np +import copy +from move_to_pose import PathFinderController + +# Simulation parameters +TIME_DURATION = 1000 +TIME_STEP = 0.01 +AT_TARGET_ACCEPTANCE_THRESHOLD = 0.01 +SHOW_ANIMATION = True +PLOT_WINDOW_SIZE_X = 20 +PLOT_WINDOW_SIZE_Y = 20 +PLOT_FONT_SIZE = 8 + +simulation_running = True +all_robots_are_at_target = False + + +class Pose: + """2D pose""" + + def __init__(self, x, y, theta): + self.x = x + self.y = y + self.theta = theta + + +class Robot: + """ + Constructs an instantiate of the 3-DOF wheeled Robot navigating on a + 2D plane + + Parameters + ---------- + name : (string) + The name of the robot + color : (string) + The color of the robot + max_linear_speed : (float) + The maximum linear speed that the robot can go + max_angular_speed : (float) + The maximum angular speed that the robot can rotate about its vertical + axis + path_finder_controller : (PathFinderController) + A configurable controller to finds the path and calculates command + linear and angular velocities. + """ + + def __init__(self, name, color, max_linear_speed, max_angular_speed, + path_finder_controller): + self.name = name + self.color = color + self.MAX_LINEAR_SPEED = max_linear_speed + self.MAX_ANGULAR_SPEED = max_angular_speed + self.path_finder_controller = path_finder_controller + self.x_traj = [] + self.y_traj = [] + self.pose = Pose(0, 0, 0) + self.pose_start = Pose(0, 0, 0) + self.pose_target = Pose(0, 0, 0) + self.is_at_target = False + + def set_start_target_poses(self, pose_start, pose_target): + """ + Sets the start and target positions of the robot + + Parameters + ---------- + pose_start : (Pose) + Start postion of the robot (see the Pose class) + pose_target : (Pose) + Target postion of the robot (see the Pose class) + """ + self.pose_start = copy.copy(pose_start) + self.pose_target = pose_target + self.pose = pose_start + + def move(self, dt): + """ + Moves the robot for one time step increment + + Parameters + ---------- + dt : (float) + time step + """ + self.x_traj.append(self.pose.x) + self.y_traj.append(self.pose.y) + + rho, linear_velocity, angular_velocity = \ + self.path_finder_controller.calc_control_command( + self.pose_target.x - self.pose.x, + self.pose_target.y - self.pose.y, + self.pose.theta, self.pose_target.theta) + + if rho < AT_TARGET_ACCEPTANCE_THRESHOLD: + self.is_at_target = True + + if abs(linear_velocity) > self.MAX_LINEAR_SPEED: + linear_velocity = (np.sign(linear_velocity) + * self.MAX_LINEAR_SPEED) + + if abs(angular_velocity) > self.MAX_ANGULAR_SPEED: + angular_velocity = (np.sign(angular_velocity) + * self.MAX_ANGULAR_SPEED) + + self.pose.theta = self.pose.theta + angular_velocity * dt + self.pose.x = self.pose.x + linear_velocity * \ + np.cos(self.pose.theta) * dt + self.pose.y = self.pose.y + linear_velocity * \ + np.sin(self.pose.theta) * dt + + +def run_simulation(robots): + """Simulates all robots simultaneously""" + global all_robots_are_at_target + global simulation_running + + robot_names = [] + for instance in robots: + robot_names.append(instance.name) + + time = 0 + while simulation_running and time < TIME_DURATION: + time += TIME_STEP + robots_are_at_target = [] + + for instance in robots: + if not instance.is_at_target: + instance.move(TIME_STEP) + robots_are_at_target.append(instance.is_at_target) + + if all(robots_are_at_target): + simulation_running = False + + if SHOW_ANIMATION: + plt.cla() + plt.xlim(0, PLOT_WINDOW_SIZE_X) + plt.ylim(0, PLOT_WINDOW_SIZE_Y) + + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + + plt.text(0.3, PLOT_WINDOW_SIZE_Y - 1, + 'Time: {:.2f}'.format(time), + fontsize=PLOT_FONT_SIZE) + + plt.text(0.3, PLOT_WINDOW_SIZE_Y - 2, + 'Reached target: {} = '.format(robot_names) + + str(robots_are_at_target), + fontsize=PLOT_FONT_SIZE) + + for instance in robots: + plt.arrow(instance.pose_start.x, + instance.pose_start.y, + np.cos(instance.pose_start.theta), + np.sin(instance.pose_start.theta), + color='r', + width=0.1) + plt.arrow(instance.pose_target.x, + instance.pose_target.y, + np.cos(instance.pose_target.theta), + np.sin(instance.pose_target.theta), + color='g', + width=0.1) + plot_vehicle(instance.pose.x, + instance.pose.y, + instance.pose.theta, + instance.x_traj, + instance.y_traj, instance.color) + + plt.pause(TIME_STEP) + + +def plot_vehicle(x, y, theta, x_traj, y_traj, color): + # Corners of triangular vehicle when pointing to the right (0 radians) + p1_i = np.array([0.5, 0, 1]).T + p2_i = np.array([-0.5, 0.25, 1]).T + p3_i = np.array([-0.5, -0.25, 1]).T + + T = transformation_matrix(x, y, theta) + p1 = T @ p1_i + p2 = T @ p2_i + p3 = T @ p3_i + + plt.plot([p1[0], p2[0]], [p1[1], p2[1]], color+'-') + plt.plot([p2[0], p3[0]], [p2[1], p3[1]], color+'-') + plt.plot([p3[0], p1[0]], [p3[1], p1[1]], color+'-') + + plt.plot(x_traj, y_traj, color+'--') + + +def transformation_matrix(x, y, theta): + return np.array([ + [np.cos(theta), -np.sin(theta), x], + [np.sin(theta), np.cos(theta), y], + [0, 0, 1] + ]) + + +def main(): + pose_target = Pose(15, 15, -1) + + pose_start_1 = Pose(5, 2, 0) + pose_start_2 = Pose(5, 2, 0) + pose_start_3 = Pose(5, 2, 0) + + controller_1 = PathFinderController(5, 8, 2) + controller_2 = PathFinderController(5, 16, 4) + controller_3 = PathFinderController(10, 25, 6) + + robot_1 = Robot("Yellow Robot", "y", 12, 5, controller_1) + robot_2 = Robot("Black Robot", "k", 16, 5, controller_2) + robot_3 = Robot("Blue Robot", "b", 20, 5, controller_3) + + robot_1.set_start_target_poses(pose_start_1, pose_target) + robot_2.set_start_target_poses(pose_start_2, pose_target) + robot_3.set_start_target_poses(pose_start_3, pose_target) + + robots: list[Robot] = [robot_1, robot_2, robot_3] + + run_simulation(robots) + + +if __name__ == '__main__': + main() diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst index c5bf15d970..81c64f5485 100644 --- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst +++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst @@ -1,11 +1,102 @@ Move to a pose control ---------------------- -This is a simulation of moving to a pose control +This is a simulation of moving to a pose control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif -Ref: +PathFinderController class +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Constructor +~~~~~~~~~~~ + +.. code-block:: ipython3 + PathFinderController(Kp_rho, Kp_alpha, Kp_beta) + +Constructs an instantiate of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane. + +Parameters: +- Kp_rho: The linear velocity gain to translate the robot along a line towards the goal +- Kp_alpha: The angular velocity gain to rotate the robot towards the goal +- Kp_beta: The offset angular velocity gain accounting for smooth merging to the goal angle (i.e., it helps the robot heading to be parallel to the target angle.) + + +Member function(s) +~~~~~~~~~~~~~~~~~~ + +.. code-block:: ipython3 + calc_control_command(x_diff, y_diff, theta, theta_goal) + +Returns the control command for the linear and angular velocities as well as the distance to goal + +Parameters: +- x_diff : The position of target with respect to current robot position in x direction +- y_diff : The position of target with respect to current robot position in y direction +- theta : The current heading angle of robot with respect to x axis +- theta_goal : The target angle of robot with respect to x axis + +Returns: +- rho : The distance between the robot and the goal position +- v : Command linear velocity +- w : Command angular velocity + +Move to a Pose Robot (Class) +---------------------------- +This program (move_to_pose_robot.py) provides a Robot class to define different robots with different specifications. +Using this class, you can simulate different robots simultaneously and compare the effect of your parameter settings. + +.. image:: https://user-images.githubusercontent.com/93126501/145834505-a8df8311-5445-413f-a96f-00460d47991c.png + +Note: A gif animation will be added soon. + +Note: The robot Class is based on PathFinderController class in 'the move_to_pose.py'. + +Robot Class +~~~~~~~~~~~ + +Constructor +~~~~~~~~~~~ + +.. code-block:: ipython3 + Robot(name, color, max_linear_speed, max_angular_speed, path_finder_controller) + +Constructs an instantiate of the 3-DOF wheeled Robot navigating on a 2D plane + +Parameters: +- name : (string) The name of the robot +- color : (string) The color of the robot +- max_linear_speed : (float) The maximum linear speed that the robot can go +- max_angular_speed : (float) The maximum angular speed that the robot can rotate about its vertical axis +- path_finder_controller : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities. + +Member function(s) +~~~~~~~~~~~~~~~~~~ + +.. code-block:: ipython3 + set_start_target_poses(pose_start, pose_target) + +Sets the start and target positions of the robot. + +Parameters: +- pose_start : (Pose) Start postion of the robot (see the Pose class) +- pose_target : (Pose) Target postion of the robot (see the Pose class) + +.. code-block:: ipython3 + move(dt) + +Move the robot for one time step increment + +Parameters: +- dt: time increment + +See Also +-------- +- PathFinderController class + + +Ref: +---- - `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102 `__ diff --git a/tests/test_move_to_pose_robot.py b/tests/test_move_to_pose_robot.py new file mode 100644 index 0000000000..a93b44d198 --- /dev/null +++ b/tests/test_move_to_pose_robot.py @@ -0,0 +1,14 @@ +import conftest # Add root path to sys.path +from Control.move_to_pose import move_to_pose as m + + +def test_1(): + """ + This unit test tests the move_to_pose_robot.py program + """ + m.show_animation = False + m.main() + + +if __name__ == '__main__': + conftest.run_this_test(__file__) From 7034d5ff9eb19eff561f84c806ec591b2d0c8e26 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 25 Dec 2021 23:06:37 +0900 Subject: [PATCH 393/909] Add rng provide function for PRM planner (#607) * Add rng provide function * fix CI error * remove unused import --- .../probabilistic_road_map.py | 60 ++++++++++++------- tests/test_probabilistic_road_map.py | 3 +- 2 files changed, 41 insertions(+), 22 deletions(-) diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 8681420e2d..7eac40a886 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -6,11 +6,10 @@ """ -import random import math import numpy as np import matplotlib.pyplot as plt -from scipy.spatial import cKDTree +from scipy.spatial import KDTree # parameter N_SAMPLE = 500 # number of sample_points @@ -36,19 +35,35 @@ def __str__(self): str(self.cost) + "," + str(self.parent_index) -def prm_planning(sx, sy, gx, gy, ox, oy, rr): - - obstacle_kd_tree = cKDTree(np.vstack((ox, oy)).T) +def prm_planning(start_x, start_y, goal_x, goal_y, + obstacle_x_list, obstacle_y_list, robot_radius, *, rng=None): + """ + Run probabilistic road map planning + + :param start_x: + :param start_y: + :param goal_x: + :param goal_y: + :param obstacle_x_list: + :param obstacle_y_list: + :param robot_radius: + :param rng: + :return: + """ + obstacle_kd_tree = KDTree(np.vstack((obstacle_x_list, obstacle_y_list)).T) - sample_x, sample_y = sample_points(sx, sy, gx, gy, - rr, ox, oy, obstacle_kd_tree) + sample_x, sample_y = sample_points(start_x, start_y, goal_x, goal_y, + robot_radius, + obstacle_x_list, obstacle_y_list, + obstacle_kd_tree, rng) if show_animation: plt.plot(sample_x, sample_y, ".b") - road_map = generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree) + road_map = generate_road_map(sample_x, sample_y, + robot_radius, obstacle_kd_tree) rx, ry = dijkstra_planning( - sx, sy, gx, gy, road_map, sample_x, sample_y) + start_x, start_y, goal_x, goal_y, road_map, sample_x, sample_y) return rx, ry @@ -88,13 +103,13 @@ def generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree): sample_x: [m] x positions of sampled points sample_y: [m] y positions of sampled points - rr: Robot Radius[m] + robot_radius: Robot Radius[m] obstacle_kd_tree: KDTree object of obstacles """ road_map = [] n_sample = len(sample_x) - sample_kd_tree = cKDTree(np.vstack((sample_x, sample_y)).T) + sample_kd_tree = KDTree(np.vstack((sample_x, sample_y)).T) for (i, ix, iy) in zip(range(n_sample), sample_x, sample_y): @@ -122,11 +137,11 @@ def dijkstra_planning(sx, sy, gx, gy, road_map, sample_x, sample_y): """ s_x: start x position [m] s_y: start y position [m] - gx: goal x position [m] - gy: goal y position [m] - ox: x position list of Obstacles [m] - oy: y position list of Obstacles [m] - rr: robot radius [m] + goal_x: goal x position [m] + goal_y: goal y position [m] + obstacle_x_list: x position list of Obstacles [m] + obstacle_y_list: y position list of Obstacles [m] + robot_radius: robot radius [m] road_map: ??? [m] sample_x: ??? [m] sample_y: ??? [m] @@ -215,7 +230,7 @@ def plot_road_map(road_map, sample_x, sample_y): # pragma: no cover [sample_y[i], sample_y[ind]], "-k") -def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree): +def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree, rng): max_x = max(ox) max_y = max(oy) min_x = min(ox) @@ -223,9 +238,12 @@ def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree): sample_x, sample_y = [], [] + if rng is None: + rng = np.random.default_rng() + while len(sample_x) <= N_SAMPLE: - tx = (random.random() * (max_x - min_x)) + min_x - ty = (random.random() * (max_y - min_y)) + min_y + tx = (rng.random() * (max_x - min_x)) + min_x + ty = (rng.random() * (max_y - min_y)) + min_y dist, index = obstacle_kd_tree.query([tx, ty]) @@ -241,7 +259,7 @@ def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree): return sample_x, sample_y -def main(): +def main(rng=None): print(__file__ + " start!!") # start and goal position @@ -280,7 +298,7 @@ def main(): plt.grid(True) plt.axis("equal") - rx, ry = prm_planning(sx, sy, gx, gy, ox, oy, robot_size) + rx, ry = prm_planning(sx, sy, gx, gy, ox, oy, robot_size, rng=rng) assert rx, 'Cannot found path' diff --git a/tests/test_probabilistic_road_map.py b/tests/test_probabilistic_road_map.py index aa58e28bce..a830cc5e0c 100644 --- a/tests/test_probabilistic_road_map.py +++ b/tests/test_probabilistic_road_map.py @@ -1,10 +1,11 @@ import conftest # Add root path to sys.path +import numpy as np from PathPlanning.ProbabilisticRoadMap import probabilistic_road_map def test1(): probabilistic_road_map.show_animation = False - probabilistic_road_map.main() + probabilistic_road_map.main(rng=np.random.default_rng(1234)) if __name__ == '__main__': From 88671e4ba10072efc2099f24fdbdc4cf937479d8 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 26 Dec 2021 10:55:21 +0900 Subject: [PATCH 394/909] add doc policy (#609) --- docs/how_to_contribute_main.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst index a4ac3c819b..703143f6e2 100644 --- a/docs/how_to_contribute_main.rst +++ b/docs/how_to_contribute_main.rst @@ -65,6 +65,10 @@ Please check other documents for details. You can build the doc locally based on `doc README`_. +Note that the `reStructuredText`_ based doc should only focus on the mathematics and the algorithm of the example. + +Documentations related codes should be in the python script as the header comments of the script or docstrings of each function. + .. _`submit a pull request`: From ea7a52a835612961251770bae3990174a8a59615 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 26 Dec 2021 11:10:33 +0900 Subject: [PATCH 395/909] add requirements.txt under docs dir. (#611) --- docs/requirements.txt | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 docs/requirements.txt diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000000..5006af8a8e --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,3 @@ +sphinx < 4.0 # For sphinx documentation +sphinx_rtd_theme == 1.0.0 +IPython == 7.30.1 # For sphinx documentation From d08a044f75808d0c8eca5f76697e4d11359bd346 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Dec 2021 08:00:15 +0900 Subject: [PATCH 396/909] Update sphinx requirement from <4.0 to <5.0 (#612) Updates the requirements on [sphinx](https://github.com/sphinx-doc/sphinx) to permit the latest version. - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/4.x/CHANGES) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v0.1.61611...v4.3.2) --- updated-dependencies: - dependency-name: sphinx dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- docs/requirements.txt | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 5006af8a8e..3d564ac8d9 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,3 @@ -sphinx < 4.0 # For sphinx documentation +sphinx < 5.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.30.1 # For sphinx documentation diff --git a/requirements.txt b/requirements.txt index 966b566000..8a2591dc4a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,6 @@ matplotlib == 3.5.1 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test -sphinx < 4.0 # For sphinx documentation +sphinx < 5.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.30.1 # For sphinx documentation From 29bb0197f9d6c40eb3d1da791da00163ec0cff1c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 28 Dec 2021 08:29:45 +0900 Subject: [PATCH 397/909] Update requirements.txt (#613) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8a2591dc4a..debc39ea17 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,6 @@ matplotlib == 3.5.1 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test -sphinx < 5.0 # For sphinx documentation +sphinx == 4.3.1 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.30.1 # For sphinx documentation From a67ecab1eef7fc858823433727e58798b9f012f4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Jan 2022 06:51:32 +0900 Subject: [PATCH 398/909] Bump sphinx from 4.3.1 to 4.3.2 (#614) Bumps [sphinx](https://github.com/sphinx-doc/sphinx) from 4.3.1 to 4.3.2. - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/4.x/CHANGES) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v4.3.1...v4.3.2) --- updated-dependencies: - dependency-name: sphinx dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index debc39ea17..2636263e6c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,6 @@ matplotlib == 3.5.1 cvxpy == 1.1.17 pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test -sphinx == 4.3.1 # For sphinx documentation +sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.30.1 # For sphinx documentation From d24ae2ac1abe2d8bd70e1c8ffa6a6b05a960356e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Jan 2022 06:53:44 +0900 Subject: [PATCH 399/909] Bump numpy from 1.21.5 to 1.22.0 (#616) Bumps [numpy](https://github.com/numpy/numpy) from 1.21.5 to 1.22.0. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.21.5...v1.22.0) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 2636263e6c..3387ce5a61 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.21.5 +numpy == 1.22.0 scipy == 1.7.3 pandas == 1.3.5 matplotlib == 3.5.1 From 79f7107cd1543ff354049a8da311e718b430ff2e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Jan 2022 08:41:50 +0900 Subject: [PATCH 400/909] Bump cvxpy from 1.1.17 to 1.1.18 (#615) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.1.17 to 1.1.18. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.1.17...v1.1.18) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 3387ce5a61..396a80e9a3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.22.0 scipy == 1.7.3 pandas == 1.3.5 matplotlib == 3.5.1 -cvxpy == 1.1.17 +cvxpy == 1.1.18 pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test sphinx == 4.3.2 # For sphinx documentation From 0dfa274be3eaddb270b2bcee197f7d34acbc1363 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 6 Jan 2022 20:26:24 +0900 Subject: [PATCH 401/909] add diff style check test (#617) * add diff style check test * add diff style check test * add diff style check test * add diff style check test * add license * add license --- .github/workflows/Linux_CI.yml | 6 -- .github/workflows/MacOS_CI.yml | 13 +--- docs/how_to_contribute_main.rst | 3 + requirements.txt | 1 + tests/test_diff_codestyle.py | 115 ++++++++++++++++++++++++++++++++ 5 files changed, 120 insertions(+), 18 deletions(-) create mode 100644 tests/test_diff_codestyle.py diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index ef773ff71a..d8cad21172 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -32,10 +32,6 @@ jobs: run: pip install coverage - name: install mypy run: pip install mypy - - name: install pycodestyle - run: pip install pycodestyle - - name: install pytest-xdist - run: pip install pytest-xdist - name: mypy check run: | mypy -p AerialNavigation @@ -47,8 +43,6 @@ jobs: mypy -p PathPlanning mypy -p PathTracking mypy -p SLAM - - name: do diff style check - run: bash rundiffstylecheck.sh - name: do all unit tests run: bash runtests.sh diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index f01d85e62b..1921799157 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -39,10 +39,6 @@ jobs: run: pip install coverage - name: install mypy run: pip install mypy - - name: install pycodestyle - run: pip install pycodestyle - - name: install pytest-xdist - run: pip install pytest-xdist - name: mypy check run: | mypy -p AerialNavigation @@ -54,12 +50,5 @@ jobs: mypy -p PathPlanning mypy -p PathTracking mypy -p SLAM - - name: do diff style check - run: bash rundiffstylecheck.sh - - name: do all unit tests - run: bash runtests.sh - - - - + run: bash runtests.sh \ No newline at end of file diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst index 703143f6e2..4b39089e7b 100644 --- a/docs/how_to_contribute_main.rst +++ b/docs/how_to_contribute_main.rst @@ -50,6 +50,8 @@ At the least, try to run the example code without animation in the unit test. If you want to run the test suites locally, you can use the `runtests.sh` script by just executing it. +The `test_diff_codestyle.py`_ check code style for your PR's codes. + .. _`how to write doc`: @@ -153,6 +155,7 @@ Sponsors .. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html .. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md +.. _`test_diff_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_diff_codestyle.py .. _`JetBrains`: https://www.jetbrains.com/ .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma diff --git a/requirements.txt b/requirements.txt index 396a80e9a3..c0de8f226a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,7 @@ matplotlib == 3.5.1 cvxpy == 1.1.18 pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test +flake8 == 4.0.1 # For unit test sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.30.1 # For sphinx documentation diff --git a/tests/test_diff_codestyle.py b/tests/test_diff_codestyle.py new file mode 100644 index 0000000000..3dbe11a11a --- /dev/null +++ b/tests/test_diff_codestyle.py @@ -0,0 +1,115 @@ +""" +Diff based code style checker with flake8 + +This code come from: +https://github.com/scipy/scipy/blob/main/tools/lint_diff.py + +Scipy's licence: https://github.com/scipy/scipy/blob/main/LICENSE.txt +Copyright (c) 2001-2002 Enthought, Inc. 2003-2022, SciPy Developers. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions +are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +""" +import conftest +import subprocess + + +def rev_list(branch, num_commits): + """List commits in reverse chronological order. + Only the first `num_commits` are shown. + """ + res = subprocess.run( + [ + 'git', + 'rev-list', + '--max-count', + f'{num_commits}', + '--first-parent', + branch + ], + stdout=subprocess.PIPE, + encoding='utf-8', + ) + res.check_returncode() + return res.stdout.rstrip('\n').split('\n') + + +def find_branch_point(branch): + """Find when the current branch split off from the given branch. + It is based off of this Stackoverflow post: + https://stackoverflow.com/questions/1527234/finding-a-branch-point-with-git#4991675 + """ + branch_commits = rev_list('HEAD', 1000) + main_commits = set(rev_list(branch, 1000)) + for branch_commit in branch_commits: + if branch_commit in main_commits: + return branch_commit + + # If a branch split off over 1000 commits ago we will fail to find + # the ancestor. + raise RuntimeError( + 'Failed to find a common ancestor in the last 1000 commits') + + +def find_diff(sha): + """Find the diff since the given sha.""" + files = ['*.py'] + res = subprocess.run( + ['git', 'diff', '--unified=0', sha, '--'] + files, + stdout=subprocess.PIPE, + encoding='utf-8' + ) + res.check_returncode() + return res.stdout + + +def run_flake8(diff): + """Run flake8 on the given diff.""" + res = subprocess.run( + ['flake8', '--diff'], + input=diff, + stdout=subprocess.PIPE, + encoding='utf-8', + ) + return res.returncode, res.stdout + + +def test(): + branch_commit = find_branch_point("origin/master") + diff = find_diff(branch_commit) + rc, errors = run_flake8(diff) + if errors: + print(errors) + else: + print("No lint errors found.") + assert rc == 0 + + +if __name__ == '__main__': + conftest.run_this_test(__file__) From c05a4fdada59fd97332417c4b99515118bfef45c Mon Sep 17 00:00:00 2001 From: Trung Kien Date: Sat, 8 Jan 2022 06:44:42 +0700 Subject: [PATCH 402/909] Fix ModuleNotFoundError when executing test in the tests folder and little improve MPC controller (#619) * Fix ModuleNotFoundError when executing test in the tests folder Signed-off-by: Trung Kien * Improve model_predictive_speed_and_steer_control - Fix typo - Using @ for matrix multiplication instead of * with have been deprecated in CVXPY1.1 - Fix missing conftest module in test file Signed-off-by: Trung Kien --- .../Eta3SplineTrajectory/eta3_spline_trajectory.py | 3 ++- .../lqr_speed_steer_control.py | 4 +++- PathTracking/lqr_steer_control/lqr_steer_control.py | 5 ++++- .../model_predictive_speed_and_steer_control.py | 11 +++++++---- PathTracking/stanley_controller/stanley_controller.py | 5 ++++- .../test_model_predictive_speed_and_steer_control.py | 1 + 6 files changed, 21 insertions(+), 8 deletions(-) diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py index 01a687679f..bd211046c7 100644 --- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py +++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py @@ -17,7 +17,8 @@ from matplotlib.collections import LineCollection import sys import os -sys.path.append(os.path.relpath("../Eta3SplinePath")) +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../Eta3SplinePath") try: from eta3_spline_path import Eta3Path, Eta3PathSegment diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py index bd2e54bf91..db5a755008 100644 --- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py +++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py @@ -7,12 +7,14 @@ """ import math import sys +import os import matplotlib.pyplot as plt import numpy as np import scipy.linalg as la -sys.path.append("../../PathPlanning/CubicSpline/") +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../../PathPlanning/CubicSpline/") try: import cubic_spline_planner diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py index 6f01e5a840..5596ea0fdf 100644 --- a/PathTracking/lqr_steer_control/lqr_steer_control.py +++ b/PathTracking/lqr_steer_control/lqr_steer_control.py @@ -10,7 +10,10 @@ import math import numpy as np import sys -sys.path.append("../../PathPlanning/CubicSpline/") +import os + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../../PathPlanning/CubicSpline/") try: import cubic_spline_planner diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py index 26e8e74f8b..ac8b883bd9 100644 --- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py @@ -10,7 +10,10 @@ import math import numpy as np import sys -sys.path.append("../../PathPlanning/CubicSpline/") +import os + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../../PathPlanning/CubicSpline/") try: import cubic_spline_planner @@ -175,9 +178,9 @@ def update_state(state, a, delta): state.yaw = state.yaw + state.v / WB * math.tan(delta) * DT state.v = state.v + a * DT - if state. v > MAX_SPEED: + if state.v > MAX_SPEED: state.v = MAX_SPEED - elif state. v < MIN_SPEED: + elif state.v < MIN_SPEED: state.v = MIN_SPEED return state @@ -272,7 +275,7 @@ def linear_mpc_control(xref, xbar, x0, dref): A, B, C = get_linear_model_matrix( xbar[2, t], xbar[3, t], dref[0, t]) - constraints += [x[:, t + 1] == A * x[:, t] + B * u[:, t] + C] + constraints += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t] + C] if t < (T - 1): cost += cvxpy.quad_form(u[:, t + 1] - u[:, t], Rd) diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index 2af3989fcc..3a67d6268e 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -12,7 +12,10 @@ import numpy as np import matplotlib.pyplot as plt import sys -sys.path.append("../../PathPlanning/CubicSpline/") +import os + +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + + "/../../PathPlanning/CubicSpline/") try: import cubic_spline_planner diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py index 9a50e48044..904cd2920e 100644 --- a/tests/test_model_predictive_speed_and_steer_control.py +++ b/tests/test_model_predictive_speed_and_steer_control.py @@ -1,4 +1,5 @@ import sys +import conftest if 'cvxpy' in sys.modules: # pragma: no cover From a13ef29dc4a28e48e31dd9d6c65cee40ac4de960 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 8 Jan 2022 17:06:29 +0900 Subject: [PATCH 403/909] enable MPC test (#620) * enable_mpc_test * enable_mpc_test * enable_mpc_test * enable_mpc_test * enable_mpc_test * enable_mpc_test * enable_mpc_test * enable_mpc_test --- .../rocket_powered_landing.py | 51 ++++++++++--------- .../probabilistic_road_map.py | 16 +++--- ...odel_predictive_speed_and_steer_control.py | 19 +++---- tests/test_probabilistic_road_map.py | 2 +- tests/test_rocket_powered_landing.py | 17 +++++-- 5 files changed, 58 insertions(+), 47 deletions(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index eb9d7ba4a7..37dca15b98 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -43,7 +43,7 @@ class Rocket_Model_6DoF: A 6 degree of freedom rocket landing problem. """ - def __init__(self): + def __init__(self, rng): """ A large r_scale for a small scale problem will ead to numerical problems as parameters become excessively small @@ -92,7 +92,7 @@ def __init__(self): # Vector from thrust point to CoM self.r_T_B = np.array([-1e-2, 0., 0.]) - self.set_random_initial_state() + self.set_random_initial_state(rng) self.x_init = np.concatenate( ((self.m_wet,), self.r_I_init, self.v_I_init, self.q_B_I_init, self.w_B_init)) @@ -102,29 +102,32 @@ def __init__(self): self.r_scale = np.linalg.norm(self.r_I_init) self.m_scale = self.m_wet - def set_random_initial_state(self): + def set_random_initial_state(self, rng): + if rng is None: + rng = np.random.default_rng() + self.r_I_init = np.array((0., 0., 0.)) - self.r_I_init[0] = np.random.uniform(3, 4) - self.r_I_init[1:3] = np.random.uniform(-2, 2, size=2) + self.r_I_init[0] = rng.uniform(3, 4) + self.r_I_init[1:3] = rng.uniform(-2, 2, size=2) self.v_I_init = np.array((0., 0., 0.)) - self.v_I_init[0] = np.random.uniform(-1, -0.5) - self.v_I_init[1:3] = np.random.uniform( - -0.5, -0.2, size=2) * self.r_I_init[1:3] + self.v_I_init[0] = rng.uniform(-1, -0.5) + self.v_I_init[1:3] = rng.uniform(-0.5, -0.2, + size=2) * self.r_I_init[1:3] self.q_B_I_init = self.euler_to_quat((0, - np.random.uniform(-30, 30), - np.random.uniform(-30, 30))) + rng.uniform(-30, 30), + rng.uniform(-30, 30))) self.w_B_init = np.deg2rad((0, - np.random.uniform(-20, 20), - np.random.uniform(-20, 20))) + rng.uniform(-20, 20), + rng.uniform(-20, 20))) def f_func(self, x, u): m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[ 2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13] ux, uy, uz = u[0], u[1], u[2] - return np.matrix([ + return np.array([ [-0.01 * np.sqrt(ux**2 + uy**2 + uz**2)], [vx], [vy], @@ -149,7 +152,7 @@ def A_func(self, x, u): 2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13] ux, uy, uz = u[0], u[1], u[2] - return np.matrix([ + return np.array([ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], @@ -177,7 +180,7 @@ def B_func(self, x, u): 2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13] ux, uy, uz = u[0], u[1], u[2] - return np.matrix([ + return np.array([ [-0.01 * ux / np.sqrt(ux**2 + uy**2 + uz**2), -0.01 * uy / np.sqrt(ux ** 2 + uy**2 + uz**2), -0.01 * uz / np.sqrt(ux**2 + uy**2 + uz**2)], @@ -219,14 +222,14 @@ def euler_to_quat(self, a): return q def skew(self, v): - return np.matrix([ + return np.array([ [0, -v[2], v[1]], [v[2], 0, -v[0]], [-v[1], v[0], 0] ]) def dir_cosine(self, q): - return np.matrix([ + return np.array([ [1 - 2 * (q[2] ** 2 + q[3] ** 2), 2 * (q[1] * q[2] + q[0] * q[3]), 2 * (q[1] * q[3] - q[0] * q[2])], [2 * (q[1] * q[2] - q[0] * q[3]), 1 - 2 @@ -236,7 +239,7 @@ def dir_cosine(self, q): ]) def omega(self, w): - return np.matrix([ + return np.array([ [0, -w[0], -w[1], -w[2]], [w[0], 0, w[2], -w[1]], [w[1], -w[2], 0, w[0]], @@ -304,7 +307,7 @@ def get_constraints(self, X_v, U_v, X_last_p, U_last_p): ] # linearized lower thrust constraint - rhs = [U_last_p[:, k] / cvxpy.norm(U_last_p[:, k]) * U_v[:, k] + rhs = [U_last_p[:, k] / cvxpy.norm(U_last_p[:, k]) @ U_v[:, k] for k in range(X_v.shape[1])] constraints += [ self.T_min <= cvxpy.vstack(rhs) @@ -460,11 +463,11 @@ def __init__(self, m, K): # x_t+1 = A_*x_t+B_*U_t+C_*U_T+1*S_*sigma+zbar+nu constraints += [ self.var['X'][:, k + 1] == - cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x)) * + cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x)) @ self.var['X'][:, k] + - cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u)) * + cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u)) @ self.var['U'][:, k] + - cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u)) * + cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u)) @ self.var['U'][:, k + 1] + self.par['S_bar'][:, k] * self.var['sigma'] + self.par['z_bar'][:, k] + @@ -606,9 +609,9 @@ def plot_animation(X, U): # pragma: no cover plt.pause(0.5) -def main(): +def main(rng=None): print("start!!") - m = Rocket_Model_6DoF() + m = Rocket_Model_6DoF(rng) # state and input list X = np.empty(shape=[m.n_x, K]) diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 7eac40a886..294c389023 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -40,14 +40,14 @@ def prm_planning(start_x, start_y, goal_x, goal_y, """ Run probabilistic road map planning - :param start_x: - :param start_y: - :param goal_x: - :param goal_y: - :param obstacle_x_list: - :param obstacle_y_list: - :param robot_radius: - :param rng: + :param start_x: start x position + :param start_y: start y position + :param goal_x: goal x position + :param goal_y: goal y position + :param obstacle_x_list: obstacle x positions + :param obstacle_y_list: obstacle y positions + :param robot_radius: robot radius + :param rng: (Optional) Random generator :return: """ obstacle_kd_tree = KDTree(np.vstack((obstacle_x_list, obstacle_y_list)).T) diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py index 904cd2920e..0ed520dcbe 100644 --- a/tests/test_model_predictive_speed_and_steer_control.py +++ b/tests/test_model_predictive_speed_and_steer_control.py @@ -1,18 +1,19 @@ import sys import conftest -if 'cvxpy' in sys.modules: # pragma: no cover +from PathTracking.model_predictive_speed_and_steer_control \ + import model_predictive_speed_and_steer_control as m - from PathTracking.model_predictive_speed_and_steer_control \ - import model_predictive_speed_and_steer_control as m - def test_1(): - m.show_animation = False - m.main() +def test_1(): + m.show_animation = False + m.main() + + +def test_2(): + m.show_animation = False + m.main2() - def test_2(): - m.show_animation = False - m.main2() if __name__ == '__main__': conftest.run_this_test(__file__) diff --git a/tests/test_probabilistic_road_map.py b/tests/test_probabilistic_road_map.py index a830cc5e0c..6c5eb540b1 100644 --- a/tests/test_probabilistic_road_map.py +++ b/tests/test_probabilistic_road_map.py @@ -5,7 +5,7 @@ def test1(): probabilistic_road_map.show_animation = False - probabilistic_road_map.main(rng=np.random.default_rng(1234)) + probabilistic_road_map.main(rng=np.random.default_rng(1233)) if __name__ == '__main__': diff --git a/tests/test_rocket_powered_landing.py b/tests/test_rocket_powered_landing.py index 7cf69567e1..52b812eeda 100644 --- a/tests/test_rocket_powered_landing.py +++ b/tests/test_rocket_powered_landing.py @@ -1,13 +1,20 @@ import conftest # Add root path to sys.path import sys +import numpy as np +from numpy.testing import suppress_warnings -if 'cvxpy' in sys.modules: # pragma: no cover +from AerialNavigation.rocket_powered_landing import rocket_powered_landing as m - from AerialNavigation.rocket_powered_landing import rocket_powered_landing as m - def test1(): - m.show_animation = False - m.main() +def test1(): + m.show_animation = False + with suppress_warnings() as sup: + sup.filter(UserWarning, + "You are solving a parameterized problem that is not DPP" + ) + sup.filter(UserWarning, + "Solution may be inaccurate") + m.main(rng=np.random.default_rng(1234)) if __name__ == '__main__': From 82d97cef940679d9739ab81ed3eedeec9d73ab1b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 9 Jan 2022 00:01:29 +0900 Subject: [PATCH 404/909] add mypy unit test (#621) * add mypy unit test * add mypy unit test * add mypy unit test --- .github/workflows/Linux_CI.yml | 15 -------- .github/workflows/MacOS_CI.yml | 15 -------- PathPlanning/DStarLite/d_star_lite.py | 14 ++++---- requirements.txt | 1 + tests/test_mypy_type_check.py | 50 +++++++++++++++++++++++++++ 5 files changed, 58 insertions(+), 37 deletions(-) create mode 100644 tests/test_mypy_type_check.py diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index d8cad21172..d0cdd2996c 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -28,21 +28,6 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install -r requirements.txt - - name: install coverage - run: pip install coverage - - name: install mypy - run: pip install mypy - - name: mypy check - run: | - mypy -p AerialNavigation - mypy -p ArmNavigation - mypy -p Bipedal - mypy -p Control - mypy -p Localization - mypy -p Mapping - mypy -p PathPlanning - mypy -p PathTracking - mypy -p SLAM - name: do all unit tests run: bash runtests.sh diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 1921799157..7fc1854386 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -35,20 +35,5 @@ jobs: python -m pip install --upgrade pip pip install numpy # cvxpy install workaround pip install -r requirements.txt - - name: install coverage - run: pip install coverage - - name: install mypy - run: pip install mypy - - name: mypy check - run: | - mypy -p AerialNavigation - mypy -p ArmNavigation - mypy -p Bipedal - mypy -p Control - mypy -p Localization - mypy -p Mapping - mypy -p PathPlanning - mypy -p PathTracking - mypy -p SLAM - name: do all unit tests run: bash runtests.sh \ No newline at end of file diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py index 603c053dab..c250e38f91 100644 --- a/PathPlanning/DStarLite/d_star_lite.py +++ b/PathPlanning/DStarLite/d_star_lite.py @@ -64,15 +64,15 @@ def __init__(self, ox: list, oy: list): for x, y in zip(ox, oy)] self.start = Node(0, 0) self.goal = Node(0, 0) - self.U = list() + self.U = list() # type: ignore self.km = 0.0 self.kold = 0.0 - self.rhs = list() - self.g = list() - self.detected_obstacles = list() + self.rhs = list() # type: ignore + self.g = list() # type: ignore + self.detected_obstacles = list() # type: ignore if show_animation: - self.detected_obstacles_for_plotting_x = list() - self.detected_obstacles_for_plotting_y = list() + self.detected_obstacles_for_plotting_x = list() # type: ignore + self.detected_obstacles_for_plotting_y = list() # type: ignore def create_grid(self, val: float): grid = list() @@ -248,7 +248,7 @@ def compare_paths(self, path1: list, path2: list): return False return True - def display_path(self, path: list, colour: str, alpha: int = 1): + def display_path(self, path: list, colour: str, alpha: float = 1.0): px = [(node.x + self.x_min_world) for node in path] py = [(node.y + self.y_min_world) for node in path] drawing = plt.plot(px, py, colour, alpha=alpha) diff --git a/requirements.txt b/requirements.txt index c0de8f226a..8b6a5ecbfe 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,7 @@ matplotlib == 3.5.1 cvxpy == 1.1.18 pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test +mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 diff --git a/tests/test_mypy_type_check.py b/tests/test_mypy_type_check.py new file mode 100644 index 0000000000..07afb40afd --- /dev/null +++ b/tests/test_mypy_type_check.py @@ -0,0 +1,50 @@ +import os +import subprocess + +import conftest + +SUBPACKAGE_LIST = [ + "AerialNavigation", + "ArmNavigation", + "Bipedal", + "Control", + "Localization", + "Mapping", + "PathPlanning", + "PathTracking", + "SLAM", +] + + +def run_mypy(dir_name, project_path, config_path): + res = subprocess.run( + ['mypy', + '--config-file', + config_path, + '-p', + dir_name], + cwd=project_path, + stdout=subprocess.PIPE, + encoding='utf-8') + return res.returncode, res.stdout + + +def test(): + project_dir_path = os.path.dirname( + os.path.dirname(os.path.abspath(__file__))) + print(f"{project_dir_path=}") + + config_path = os.path.join(project_dir_path, "mypy.ini") + print(f"{config_path=}") + + for sub_package_name in SUBPACKAGE_LIST: + rc, errors = run_mypy(sub_package_name, project_dir_path, config_path) + if errors: + print(errors) + else: + print("No lint errors found.") + assert rc == 0 + + +if __name__ == '__main__': + conftest.run_this_test(__file__) From e24e2b781d270c37a71ae49ff62e3a25b8d69af6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Jan 2022 07:20:58 +0900 Subject: [PATCH 405/909] Bump ipython from 7.30.1 to 7.31.0 (#624) Bumps [ipython](https://github.com/ipython/ipython) from 7.30.1 to 7.31.0. - [Release notes](https://github.com/ipython/ipython/releases) - [Commits](https://github.com/ipython/ipython/compare/7.30.1...7.31.0) --- updated-dependencies: - dependency-name: ipython dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- docs/requirements.txt | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 3d564ac8d9..9d921cb0fc 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,3 @@ sphinx < 5.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.30.1 # For sphinx documentation +IPython == 7.31.0 # For sphinx documentation diff --git a/requirements.txt b/requirements.txt index 8b6a5ecbfe..87d71b96a7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,4 @@ mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.30.1 # For sphinx documentation +IPython == 7.31.0 # For sphinx documentation From 727354c5397f3af857fd33ad6f333724a6892e5d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Jan 2022 12:48:04 +0900 Subject: [PATCH 406/909] Bump numpy from 1.22.0 to 1.22.1 (#626) Bumps [numpy](https://github.com/numpy/numpy) from 1.22.0 to 1.22.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.22.0...v1.22.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 87d71b96a7..e44291157d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.22.0 +numpy == 1.22.1 scipy == 1.7.3 pandas == 1.3.5 matplotlib == 3.5.1 From cc30a6bbe928c1c4043a157cfd54da953a7ec6ac Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Jan 2022 19:34:13 +0900 Subject: [PATCH 407/909] Bump ipython from 7.31.0 to 8.0.0 (#627) Bumps [ipython](https://github.com/ipython/ipython) from 7.31.0 to 8.0.0. - [Release notes](https://github.com/ipython/ipython/releases) - [Commits](https://github.com/ipython/ipython/compare/7.31.0...8.0.0) --- updated-dependencies: - dependency-name: ipython dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- docs/requirements.txt | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 9d921cb0fc..f38287f015 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,3 @@ sphinx < 5.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.31.0 # For sphinx documentation +IPython == 8.0.0 # For sphinx documentation diff --git a/requirements.txt b/requirements.txt index e44291157d..dbd81424ac 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,4 @@ mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.31.0 # For sphinx documentation +IPython == 8.0.0 # For sphinx documentation From 30dae5691f5639ca5ab0ad953d53e68d2d733f32 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Jan 2022 19:34:27 +0900 Subject: [PATCH 408/909] Bump sphinx from 4.3.2 to 4.4.0 (#625) Bumps [sphinx](https://github.com/sphinx-doc/sphinx) from 4.3.2 to 4.4.0. - [Release notes](https://github.com/sphinx-doc/sphinx/releases) - [Changelog](https://github.com/sphinx-doc/sphinx/blob/4.x/CHANGES) - [Commits](https://github.com/sphinx-doc/sphinx/compare/v4.3.2...v4.4.0) --- updated-dependencies: - dependency-name: sphinx dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index dbd81424ac..306ac1e1c1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,6 +7,6 @@ pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test -sphinx == 4.3.2 # For sphinx documentation +sphinx == 4.4.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 8.0.0 # For sphinx documentation From 88c7447d292685ec849a09b717db510807e7f19e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 18 Jan 2022 21:28:07 +0900 Subject: [PATCH 409/909] Update requirements.txt --- docs/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index f38287f015..b093728994 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,3 @@ -sphinx < 5.0 # For sphinx documentation +sphinx = 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 8.0.0 # For sphinx documentation +IPython == 7.30.1 # For sphinx documentation From d2645da2c7ff6ecbb4b62b7c6612712380ba91e1 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 18 Jan 2022 21:31:10 +0900 Subject: [PATCH 410/909] Update requirements.txt --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index b093728994..6614e6b8db 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,3 @@ -sphinx = 4.3.2 # For sphinx documentation +sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.30.1 # For sphinx documentation From 87a535ae752a50674e26db0aadd5005bbcef7f45 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 18 Jan 2022 21:39:47 +0900 Subject: [PATCH 411/909] Rename requirements.txt to doc_requirements.txt --- docs/{requirements.txt => doc_requirements.txt} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/{requirements.txt => doc_requirements.txt} (100%) diff --git a/docs/requirements.txt b/docs/doc_requirements.txt similarity index 100% rename from docs/requirements.txt rename to docs/doc_requirements.txt From 7c3dbb01e0d1fd64f6eb9c37799161930b9d664d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 22 Jan 2022 07:36:51 +0900 Subject: [PATCH 412/909] Bump ipython from 7.30.1 to 7.31.1 in /docs (#628) Bumps [ipython](https://github.com/ipython/ipython) from 7.30.1 to 7.31.1. - [Release notes](https://github.com/ipython/ipython/releases) - [Commits](https://github.com/ipython/ipython/compare/7.30.1...7.31.1) --- updated-dependencies: - dependency-name: ipython dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- docs/doc_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index 6614e6b8db..65670d346e 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -1,3 +1,3 @@ sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.30.1 # For sphinx documentation +IPython == 7.31.1 # For sphinx documentation From 1280ace8e03cf68b28cc551af15cd4f0ed518459 Mon Sep 17 00:00:00 2001 From: Muhammad-Yazdian <93126501+Muhammad-Yazdian@users.noreply.github.com> Date: Sat, 22 Jan 2022 00:12:59 +0100 Subject: [PATCH 413/909] Fix format to show contents properly (#608) * Fix format to show contents properly Lots of contents were not displayed correctly. I empty lines and :math: labels and fixed it. * Rewrote one subscript as _{rho} This is just a try to see if subscript renders properly. * Added a gif animation for move to pose robot * Added gif; Removed png * Added the into * Changed Param. Def. style to bold text * Added algorithm logic and math * Update docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst Co-authored-by: Atsushi Sakai * Update docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst Co-authored-by: Atsushi Sakai * Changed Eqs 1 and 2 color to black * Added cross-reference labels for Equations 1 and 2 Co-authored-by: Atsushi Sakai --- .../move_to_a_pose_control.rst | 108 ++++++++++++++---- 1 file changed, 84 insertions(+), 24 deletions(-) diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst index 81c64f5485..c8d844d053 100644 --- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst +++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst @@ -1,11 +1,23 @@ -Move to a pose control +Position Control of non-Holonomic Systems +----------------------------------------- + +This section explains the logic of a position controller for systems with constraint (non-Holonomic system). + +The position control of a 1-DOF (Degree of Freedom) system is quite straightforward. We only need to compute a position error and multiply it with a proportional gain to create a command. The actuator of the system takes this command and drive the system to the target position. This controller can be easily extended to higher dimensions (e.g., using Kp_x and Kp_y gains for a 2D position control). In these systems, the number of control commands is equal to the number of degrees of freedom (Holonomic system). + +To describe the configuration of a car on a 2D plane, we need three DOFs (i.e., x, y, and theta). But to drive a car we only need two control commands (theta_engine and theta_steering_wheel). This difference is because of a constraint between the x and y DOFs. The relationship between the delta_x and delta_y is governed by the theta_steering_wheel. + +Note that a car is normally a non-Holonomic system but if the road is slippery, the car turns into a Holonomic system and thus it needs three independent commands to be controlled. + +Move to a Pose Control ---------------------- -This is a simulation of moving to a pose control. +In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif + PathFinderController class ~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -13,45 +25,87 @@ Constructor ~~~~~~~~~~~ .. code-block:: ipython3 + PathFinderController(Kp_rho, Kp_alpha, Kp_beta) Constructs an instantiate of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane. Parameters: -- Kp_rho: The linear velocity gain to translate the robot along a line towards the goal -- Kp_alpha: The angular velocity gain to rotate the robot towards the goal -- Kp_beta: The offset angular velocity gain accounting for smooth merging to the goal angle (i.e., it helps the robot heading to be parallel to the target angle.) + +- | **Kp_rho** : The linear velocity gain to translate the robot along a line towards the goal +- | **Kp_alpha** : The angular velocity gain to rotate the robot towards the goal +- | **Kp_beta** : The offset angular velocity gain accounting for smooth merging to the goal angle (i.e., it helps the robot heading to be parallel to the target angle.) Member function(s) ~~~~~~~~~~~~~~~~~~ .. code-block:: ipython3 + calc_control_command(x_diff, y_diff, theta, theta_goal) Returns the control command for the linear and angular velocities as well as the distance to goal Parameters: -- x_diff : The position of target with respect to current robot position in x direction -- y_diff : The position of target with respect to current robot position in y direction -- theta : The current heading angle of robot with respect to x axis -- theta_goal : The target angle of robot with respect to x axis + +- | **x_diff** : The position of target with respect to current robot position in x direction +- | **y_diff** : The position of target with respect to current robot position in y direction +- | **theta** : The current heading angle of robot with respect to x axis +- | **theta_goal** : The target angle of robot with respect to x axis Returns: -- rho : The distance between the robot and the goal position -- v : Command linear velocity -- w : Command angular velocity + +- | **rho** : The distance between the robot and the goal position +- | **v** : Command linear velocity +- | **w** : Command angular velocity + +How does the Algorithm Work +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The distance between the robot and the goal position, :math:`\rho`, is computed as + +.. math:: + \rho = \sqrt{(x_{robot} - x_{target})^2 + (y_{robot} - y_{target})^2}. + +The distance :math:`\rho` is used to determine the robot speed. The idea is to slow down the robot as it gets closer to the target. + +.. math:: + v = K_P{_\rho} \times \rho\qquad + :label: eq1 + +Note that for your applications, you need to tune the speed gain, :math:`K_P{_\rho}` to a proper value. + +To turn the robot and align its heading, :math:`\theta`, toward the target position (not orientation), :math:`\rho \vec{u}`, we need to compute the angle difference :math:`\alpha`. + +.. math:: + \alpha = (\arctan2(y_{diff}, x_{diff}) - \theta + \pi) mod (2\pi) - \pi + +The term :math:`mod(2\pi)` is used to map the angle to :math:`[-\pi, \pi)` range. + +Lastly to correct the orientation of the robot, we need to compute the orientation error, :math:`\beta`, of the robot. + +.. math:: + \beta = (\theta_{goal} - \theta - \alpha + \pi) mod (2\pi) - \pi + +Note that to cancel out the effect of :math:`\alpha` when the robot is at the vicinity of the target, the term + +:math:`-\alpha` is included. + +The final angular speed command is given by + +.. math:: + \omega = K_P{_\alpha} \alpha - K_P{_\beta} \beta\qquad + :label: eq2 + +The linear and angular speeds (Equations :eq:`eq1` and :eq:`eq2`) are the output of the algorithm. Move to a Pose Robot (Class) ---------------------------- This program (move_to_pose_robot.py) provides a Robot class to define different robots with different specifications. Using this class, you can simulate different robots simultaneously and compare the effect of your parameter settings. -.. image:: https://user-images.githubusercontent.com/93126501/145834505-a8df8311-5445-413f-a96f-00460d47991c.png - -Note: A gif animation will be added soon. +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/move_to_pos_robot_class/animation.gif -Note: The robot Class is based on PathFinderController class in 'the move_to_pose.py'. +Note: The robot class is based on PathFinderController class in 'the move_to_pose.py'. Robot Class ~~~~~~~~~~~ @@ -60,36 +114,42 @@ Constructor ~~~~~~~~~~~ .. code-block:: ipython3 + Robot(name, color, max_linear_speed, max_angular_speed, path_finder_controller) Constructs an instantiate of the 3-DOF wheeled Robot navigating on a 2D plane Parameters: -- name : (string) The name of the robot -- color : (string) The color of the robot -- max_linear_speed : (float) The maximum linear speed that the robot can go -- max_angular_speed : (float) The maximum angular speed that the robot can rotate about its vertical axis -- path_finder_controller : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities. + +- | **name** : (string) The name of the robot +- | **color** : (string) The color of the robot +- | **max_linear_speed** : (float) The maximum linear speed that the robot can go +- | **max_angular_speed** : (float) The maximum angular speed that the robot can rotate about its vertical axis +- | **path_finder_controller** : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities. Member function(s) ~~~~~~~~~~~~~~~~~~ .. code-block:: ipython3 + set_start_target_poses(pose_start, pose_target) Sets the start and target positions of the robot. Parameters: -- pose_start : (Pose) Start postion of the robot (see the Pose class) -- pose_target : (Pose) Target postion of the robot (see the Pose class) + +- | **pose_start** : (Pose) Start postion of the robot (see the Pose class) +- | **pose_target** : (Pose) Target postion of the robot (see the Pose class) .. code-block:: ipython3 + move(dt) Move the robot for one time step increment Parameters: -- dt: time increment + +- | **dt** : time increment See Also -------- From 2878c4a84cdac66b0f0c471758809a385b83546c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 22 Jan 2022 18:57:04 +0900 Subject: [PATCH 414/909] Bump ipython from 8.0.0 to 8.0.1 (#629) Bumps [ipython](https://github.com/ipython/ipython) from 8.0.0 to 8.0.1. - [Release notes](https://github.com/ipython/ipython/releases) - [Commits](https://github.com/ipython/ipython/compare/8.0.0...8.0.1) --- updated-dependencies: - dependency-name: ipython dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- docs/doc_requirements.txt | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index 65670d346e..9319f7729f 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -1,3 +1,3 @@ sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.31.1 # For sphinx documentation +IPython == 8.0.1 # For sphinx documentation diff --git a/requirements.txt b/requirements.txt index 306ac1e1c1..557aab4368 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,4 @@ mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test sphinx == 4.4.0 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 8.0.0 # For sphinx documentation +IPython == 8.0.1 # For sphinx documentation From 058addd2ba90a16e2697333ba5be2f430944f417 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 22 Jan 2022 22:18:22 +0900 Subject: [PATCH 415/909] add requirements dir (#630) * add requirements dir * add requirements dir * add requirements dir --- .circleci/config.yml | 2 +- .github/dependabot.yml | 2 +- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 4 +- README.md | 4 +- appveyor.yml | 2 +- docs/doc_requirements.txt | 2 +- docs/getting_started_main.rst | 4 +- .../move_to_a_pose_control.rst | 39 +++++++++---------- .../environment.yml | 0 .../requirements.txt | 3 -- rundiffstylecheck.sh | 15 ------- ...odel_predictive_speed_and_steer_control.py | 3 +- tests/test_rocket_powered_landing.py | 1 - 14 files changed, 30 insertions(+), 53 deletions(-) rename environment.yml => requirements/environment.yml (100%) rename requirements.txt => requirements/requirements.txt (65%) delete mode 100755 rundiffstylecheck.sh diff --git a/.circleci/config.yml b/.circleci/config.yml index 4866240bd4..0a1962ad0b 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -14,7 +14,7 @@ jobs: command: | python -m venv venv . venv/bin/activate - pip install -r requirements.txt + pip install -r docs/doc_requirements.txt cd docs;make html - store_artifacts: path: docs/_build/html/ diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 9117f1216c..07f1bc80e0 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,7 +1,7 @@ version: 2 updates: - package-ecosystem: pip - directory: "/" + directory: "/requirements" schedule: interval: weekly time: "20:00" diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index d0cdd2996c..c706a50ed6 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -27,7 +27,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install -r requirements.txt + python -m pip install -r requirements/requirements.txt - name: do all unit tests run: bash runtests.sh diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 7fc1854386..4088c4900f 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -33,7 +33,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install numpy # cvxpy install workaround - pip install -r requirements.txt + #pip install numpy # cvxpy install workaround + pip install -r requirements/requirements.txt - name: do all unit tests run: bash runtests.sh \ No newline at end of file diff --git a/README.md b/README.md index f297b6fc12..9aff69ab72 100644 --- a/README.md +++ b/README.md @@ -141,11 +141,11 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation using conda : -> conda env create -f environment.yml +> conda env create -f requirements/environment.yml using pip : -> pip install -r requirements.txt +> pip install -r requirements/requirements.txt 3. Execute python script in each directory. diff --git a/appveyor.yml b/appveyor.yml index e9535e4e3c..c35e76ae4a 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -37,7 +37,7 @@ install: - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH% - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin - "python -m pip install --upgrade pip" - - "python -m pip install -r requirements.txt" + - "python -m pip install -r requirements/requirements.txt" - "python -m pip install pytest-xdist" # Check that we have the expected version and architecture for Python diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index 9319f7729f..65670d346e 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -1,3 +1,3 @@ sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 8.0.1 # For sphinx documentation +IPython == 7.31.1 # For sphinx documentation diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index d907ab8669..a7b9ff3607 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -67,13 +67,13 @@ using conda : .. code-block:: - >$ conda env create -f environment.yml + >$ conda env create -f requirements/environment.yml using pip : .. code-block:: - >$ pip install -r requirements.txt + >$ pip install -r requirements/requirements.txt 3. Execute python script in each directory. diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst index c8d844d053..8bb40f2201 100644 --- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst +++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst @@ -1,5 +1,12 @@ +Move to a Pose Control +---------------------- + +In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif + Position Control of non-Holonomic Systems ------------------------------------------ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This section explains the logic of a position controller for systems with constraint (non-Holonomic system). @@ -9,20 +16,11 @@ To describe the configuration of a car on a 2D plane, we need three DOFs (i.e., Note that a car is normally a non-Holonomic system but if the road is slippery, the car turns into a Holonomic system and thus it needs three independent commands to be controlled. -Move to a Pose Control ----------------------- - -In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif - - - PathFinderController class ~~~~~~~~~~~~~~~~~~~~~~~~~~ Constructor -~~~~~~~~~~~ +^^^^^^^^^^^ .. code-block:: ipython3 @@ -38,7 +36,7 @@ Parameters: Member function(s) -~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^ .. code-block:: ipython3 @@ -99,7 +97,8 @@ The final angular speed command is given by The linear and angular speeds (Equations :eq:`eq1` and :eq:`eq2`) are the output of the algorithm. Move to a Pose Robot (Class) ----------------------------- +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + This program (move_to_pose_robot.py) provides a Robot class to define different robots with different specifications. Using this class, you can simulate different robots simultaneously and compare the effect of your parameter settings. @@ -108,10 +107,10 @@ Using this class, you can simulate different robots simultaneously and compare t Note: The robot class is based on PathFinderController class in 'the move_to_pose.py'. Robot Class -~~~~~~~~~~~ +^^^^^^^^^^^^ Constructor -~~~~~~~~~~~ +^^^^^^^^^^^^ .. code-block:: ipython3 @@ -128,7 +127,7 @@ Parameters: - | **path_finder_controller** : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities. Member function(s) -~~~~~~~~~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^ .. code-block:: ipython3 @@ -151,12 +150,10 @@ Parameters: - | **dt** : time increment -See Also --------- -- PathFinderController class -Ref: ----- +References +~~~~~~~~~~~~ +- PathFinderController class - `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102 `__ diff --git a/environment.yml b/requirements/environment.yml similarity index 100% rename from environment.yml rename to requirements/environment.yml diff --git a/requirements.txt b/requirements/requirements.txt similarity index 65% rename from requirements.txt rename to requirements/requirements.txt index 557aab4368..f052ea51fc 100644 --- a/requirements.txt +++ b/requirements/requirements.txt @@ -7,6 +7,3 @@ pytest == 6.2.5 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test -sphinx == 4.4.0 # For sphinx documentation -sphinx_rtd_theme == 1.0.0 -IPython == 8.0.1 # For sphinx documentation diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh deleted file mode 100755 index 8a19842c6e..0000000000 --- a/rundiffstylecheck.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash -echo "$(basename $0) start!" -VERSION=v0.1.6 -wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip -unzip ${VERSION}.zip -./DiffSentinel*/starter.sh HEAD origin/master -check_result=$? -rm -rf ${VERSION}.zip DiffSentinel* -if [[ ${check_result} -ne 0 ]]; -then - echo "Error: Your changes contain pycodestyle errors." - exit 1 -fi -echo "$(basename $0) done!" -exit 0 diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py index 0ed520dcbe..9bc8ccf31c 100644 --- a/tests/test_model_predictive_speed_and_steer_control.py +++ b/tests/test_model_predictive_speed_and_steer_control.py @@ -1,5 +1,4 @@ -import sys -import conftest +import conftest # Add root path to sys.path from PathTracking.model_predictive_speed_and_steer_control \ import model_predictive_speed_and_steer_control as m diff --git a/tests/test_rocket_powered_landing.py b/tests/test_rocket_powered_landing.py index 52b812eeda..2f294c74cf 100644 --- a/tests/test_rocket_powered_landing.py +++ b/tests/test_rocket_powered_landing.py @@ -1,5 +1,4 @@ import conftest # Add root path to sys.path -import sys import numpy as np from numpy.testing import suppress_warnings From c066a9420e9373e85bc2214cbb44e3a2fd5cc593 Mon Sep 17 00:00:00 2001 From: Trung Kien Date: Sun, 23 Jan 2022 17:35:54 +0700 Subject: [PATCH 416/909] InvertedPendulumMPCControl: fixed deprecated waring about using * for matrix multiplication (#631) --- .../InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py b/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py index e7b3077740..3764f29558 100644 --- a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py +++ b/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py @@ -77,7 +77,7 @@ def mpc_control(x0): for t in range(T): cost += cvxpy.quad_form(x[:, t + 1], Q) cost += cvxpy.quad_form(u[:, t], R) - constr += [x[:, t + 1] == A * x[:, t] + B * u[:, t]] + constr += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t]] constr += [x[:, 0] == x0[:, 0]] prob = cvxpy.Problem(cvxpy.Minimize(cost), constr) From 550baa1e4929aa98e69d4da97205809943cce213 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 23 Jan 2022 20:07:34 +0900 Subject: [PATCH 417/909] fix duplicated math lables (#633) --- .../move_to_a_pose_control.rst | 6 ++-- .../quintic_polynomials_planner.rst | 34 +++++++++---------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst index 8bb40f2201..77ec682a30 100644 --- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst +++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst @@ -68,7 +68,7 @@ The distance :math:`\rho` is used to determine the robot speed. The idea is to s .. math:: v = K_P{_\rho} \times \rho\qquad - :label: eq1 + :label: move_to_a_pose_eq1 Note that for your applications, you need to tune the speed gain, :math:`K_P{_\rho}` to a proper value. @@ -92,9 +92,9 @@ The final angular speed command is given by .. math:: \omega = K_P{_\alpha} \alpha - K_P{_\beta} \beta\qquad - :label: eq2 + :label: move_to_a_pose_eq2 -The linear and angular speeds (Equations :eq:`eq1` and :eq:`eq2`) are the output of the algorithm. +The linear and angular speeds (Equations :eq:`move_to_a_pose_eq1` and :eq:`move_to_a_pose_eq2`) are the output of the algorithm. Move to a Pose Robot (Class) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst index 2bced07034..feec345bae 100644 --- a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst +++ b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst @@ -18,7 +18,7 @@ We assume a one-dimensional robot motion :math:`x(t)` at time :math:`t` is formulated as a quintic polynomials based on time as follows: .. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5 - :label: eq1 + :label: quintic_eq1 :math:`a_0, a_1. a_2, a_3, a_4, a_5` are parameters of the quintic polynomial. @@ -31,44 +31,44 @@ End position, velocity, and acceleration are :math:`x_e, v_e, a_e` respectively. So, when time is 0. .. math:: x(0) = a_0 = x_s - :label: eq2 + :label: quintic_eq2 -Then, differentiating the equation :eq:`eq1` with t, +Then, differentiating the equation :eq:`quintic_eq1` with t, .. math:: x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4 - :label: eq3 + :label: quintic_eq3 So, when time is 0, .. math:: x'(0) = a_1 = v_s - :label: eq4 + :label: quintic_eq4 -Then, differentiating the equation :eq:`eq3` with t again, +Then, differentiating the equation :eq:`quintic_eq3` with t again, .. math:: x''(t) = 2a_2+6a_3t+12a_4t^2 - :label: eq5 + :label: quintic_eq5 So, when time is 0, .. math:: x''(0) = 2a_2 = a_s - :label: eq6 + :label: quintic_eq6 -so, we can calculate :math:`a_0, a_1, a_2` with eq. :eq:`eq2`, :eq:`eq4`, :eq:`eq6` and boundary conditions. +so, we can calculate :math:`a_0, a_1, a_2` with eq. :eq:`quintic_eq2`, :eq:`quintic_eq4`, :eq:`quintic_eq6` and boundary conditions. -:math:`a_3, a_4, a_5` are still unknown in eq :eq:`eq1`. +:math:`a_3, a_4, a_5` are still unknown in eq :eq:`quintic_eq1`. -We assume that the end time for a maneuver is :math:`T`, we can get these equations from eq :eq:`eq1`, :eq:`eq3`, :eq:`eq5`: +We assume that the end time for a maneuver is :math:`T`, we can get these equations from eq :eq:`quintic_eq1`, :eq:`quintic_eq3`, :eq:`quintic_eq5`: .. math:: x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e - :label: eq7 + :label: quintic_eq7 .. math:: x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e - :label: eq8 + :label: quintic_eq8 .. math:: x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e - :label: eq9 + :label: quintic_eq9 -From eq :eq:`eq7`, :eq:`eq8`, :eq:`eq9`, we can calculate :math:`a_3, a_4, a_5` to solve the linear equations: :math:`Ax=b` +From eq :eq:`quintic_eq7`, :eq:`quintic_eq8`, :eq:`quintic_eq9`, we can calculate :math:`a_3, a_4, a_5` to solve the linear equations: :math:`Ax=b` .. math:: \begin{bmatrix} T^3 & T^4 & T^5 \\ 3T^2 & 4T^3 & 5T^4 \\ 6T & 12T^2 & 20T^3 \end{bmatrix}\begin{bmatrix} a_3\\ a_4\\ a_5\end{bmatrix}=\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\ v_e-v_s-a_sT\\ a_e-a_s\end{bmatrix} @@ -80,10 +80,10 @@ Quintic polynomials for two dimensional robot motion (x-y) If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane. .. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5 - :label: eq10 + :label: quintic_eq10 .. math:: y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5 - :label: eq11 + :label: quintic_eq11 It is assumed that terminal states (start and end) are known as boundary conditions. From 702551748cdfa64c2baa3debc6c84e05181e3416 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Jan 2022 07:26:33 +0900 Subject: [PATCH 418/909] Bump pandas from 1.3.5 to 1.4.0 in /requirements (#634) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.5 to 1.4.0. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.5...v1.4.0) --- updated-dependencies: - dependency-name: pandas dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f052ea51fc..90e98e4f5b 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.22.1 scipy == 1.7.3 -pandas == 1.3.5 +pandas == 1.4.0 matplotlib == 3.5.1 cvxpy == 1.1.18 pytest == 6.2.5 # For unit test From 2c245d9d81ecd9efeb53f567401c15731e34f359 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 27 Jan 2022 21:37:19 +0900 Subject: [PATCH 419/909] enhance histogram filter doc (#623) * enhance histogram filter doc * enhance histogram filter doc * enhance histogram filter doc * enhance histogram filter doc * enhance histogram filter doc * enhance histogram filter doc * Update docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst * Update docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst * fix duplicated math lables * fix duplicated math lables * fix duplicated math lables * update doc * update doc * update doc * update doc * update doc * update doc --- .../histogram_filter/histogram_filter.py | 15 +-- .../extended_kalman_filter_localization.rst | 4 +- .../histogram_filter_localization/1.png | Bin 0 -> 57183 bytes .../histogram_filter_localization/2.png | Bin 0 -> 198077 bytes .../histogram_filter_localization/3.png | Bin 0 -> 98927 bytes .../histogram_filter_localization/4.png | Bin 0 -> 51007 bytes .../histogram_filter_localization.rst | 102 +++++++++++++++++- .../gaussian_grid_map/gaussian_grid_map.rst | 2 + 8 files changed, 110 insertions(+), 13 deletions(-) create mode 100644 docs/modules/localization/histogram_filter_localization/1.png create mode 100644 docs/modules/localization/histogram_filter_localization/2.png create mode 100644 docs/modules/localization/histogram_filter_localization/3.png create mode 100644 docs/modules/localization/histogram_filter_localization/4.png diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py index 7f453d69dd..a4aeb419cf 100644 --- a/Localization/histogram_filter/histogram_filter.py +++ b/Localization/histogram_filter/histogram_filter.py @@ -71,7 +71,7 @@ def calc_gaussian_observation_pdf(grid_map, z, iz, ix, iy, std): d = math.hypot(x - z[iz, 1], y - z[iz, 2]) # likelihood - pdf = (1.0 - norm.cdf(abs(d - z[iz, 0]), 0.0, std)) + pdf = norm.pdf(d - z[iz, 0], 0.0, std) return pdf @@ -88,7 +88,7 @@ def observation_update(grid_map, z, std): return grid_map -def calc_input(): +def calc_control_input(): v = 1.0 # [m/s] yaw_rate = 0.1 # [rad/s] u = np.array([v, yaw_rate]).reshape(2, 1) @@ -113,6 +113,7 @@ def motion_model(x, u): def draw_heat_map(data, mx, my): max_value = max([max(i_data) for i_data in data]) + plt.grid(False) plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues")) plt.axis("equal") @@ -197,6 +198,7 @@ def motion_update(grid_map, u, yaw): grid_map.dx -= x_shift * grid_map.xy_resolution grid_map.dy -= y_shift * grid_map.xy_resolution + # Add motion noise grid_map.data = gaussian_filter(grid_map.data, sigma=MOTION_STD) return grid_map @@ -230,9 +232,9 @@ def main(): while SIM_TIME >= time: time += DT - print("Time:", time) + print(f"{time=:.1f}") - u = calc_input() + u = calc_control_input() yaw = xTrue[2, 0] # Orientation is known xTrue, z, ud = observation(xTrue, u, RF_ID) @@ -249,8 +251,9 @@ def main(): plt.plot(xTrue[0, :], xTrue[1, :], "xr") plt.plot(RF_ID[:, 0], RF_ID[:, 1], ".k") for i in range(z.shape[0]): - plt.plot([xTrue[0, :], z[i, 1]], [ - xTrue[1, :], z[i, 2]], "-k") + plt.plot([xTrue[0, 0], z[i, 1]], + [xTrue[1, 0], z[i, 2]], + "-k") plt.title("Time[s]:" + str(time)[0: 4]) plt.pause(0.1) diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst index 9777258e3e..d2cd52cf12 100644 --- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst +++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst @@ -120,10 +120,10 @@ Its Jacobian matrix is :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}` -Extented Kalman Filter +Extended Kalman Filter ~~~~~~~~~~~~~~~~~~~~~~ -Localization process using Extendted Kalman Filter:EKF is +Localization process using Extended Kalman Filter:EKF is === Predict === diff --git a/docs/modules/localization/histogram_filter_localization/1.png 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-0,0 +1,70 @@ +.. _point_cloud_sampling: + +Point cloud Sampling +---------------------- + +This sections explains point cloud sampling algorithms in PythonRobotics. + +Point clouds are two-dimensional and three-dimensional based data +acquired by external sensors like LIDAR, cameras, etc. +In general, Point Cloud data is very large in number of data. +So, if you process all the data, computation time might become an issue. + +Point cloud sampling is a technique for solving this computational complexity +issue by extracting only representative point data and thinning the point +cloud data without compromising the performance of processing using the point +cloud data. + +Voxel Point Sampling +~~~~~~~~~~~~~~~~~~~~~~~~ +.. figure:: voxel_point_sampling.png + +Voxel grid sampling is a method of reducing point cloud data by using the +`Voxel grids `_ which is regular grids +in three-dimensional space. + +This method determines which each point is in a grid, and replaces the point +clouds that are in the same Voxel with their average to reduce the number of +points. + +API +===== + +.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling + + +Farthest Point Sampling +~~~~~~~~~~~~~~~~~~~~~~~~~ +.. figure:: farthest_point_sampling.png + +Farthest Point Sampling is a point cloud sampling method by a specified +number of points so that the distance between points is as far from as +possible. + +This method is useful for machine learning and other situations where +you want to obtain a specified number of points from point cloud. + +API +===== + +.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling + +Poisson Disk Sampling +~~~~~~~~~~~~~~~~~~~~~~~~~ +.. figure:: poisson_disk_sampling.png + +Poisson disk sample is a point cloud sampling method by a specified +number of points so that the algorithm selects points where the distance +from selected points is greater than a certain distance. + +Although this method does not have good performance comparing the Farthest +distance sample where each point is distributed farther from each other, +this is suitable for real-time processing because of its fast computation time. + +API +===== + +.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling + + + diff --git a/docs/modules/mapping/point_cloud_sampling/poisson_disk_sampling.png b/docs/modules/mapping/point_cloud_sampling/poisson_disk_sampling.png new file mode 100644 index 0000000000000000000000000000000000000000..6863c9e0469014acbf371a911a67cb50d30da41d GIT binary patch literal 265198 zcmeFZbyQW~`aTK@3L+wnv~-trY`UbSTco=?HlVbKf`EW@N-A9&ln^QDknV;}cig$v z&pGG&JNJ(J=N)&9FJo-?+H0;k*Nk_*@jUM|LRDE7^B(a%BqStEc{!0Xhwq@=36q$H)Pi=&0LojDScTts~0T~+mUg1{T!AxQ~^@WA)|DU_*5 z&m^pn-y7M#q7h?wjB+cSOxGX?Us?*qZmuvq*cRE0HJqVZT1>4Nqnv?Z^M$yE#tdrc z+)m2LOyd*R6~B`y*Pqa9=#Ho>8}>7DL{=b00hnELUXb&V1z^u|~^s6%D(77{8YX{*8_9kn0vK%&7reln!t 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diff --git a/tests/test_point_cloud_sampling.py b/tests/test_point_cloud_sampling.py new file mode 100644 index 0000000000..8f6447c69c --- /dev/null +++ b/tests/test_point_cloud_sampling.py @@ -0,0 +1,15 @@ +import conftest # Add root path to sys.path +from Mapping.point_cloud_sampling import point_cloud_sampling as m + + +def test_1(capsys): + m.do_plot = False + m.main() + captured = capsys.readouterr() + assert "voxel_sampling_points.shape=(27, 3)" in captured.out + assert "farthest_sampling_points.shape=(20, 3)" in captured.out + assert "poisson_disk_points.shape=(20, 3)" in captured.out + + +if __name__ == '__main__': + conftest.run_this_test(__file__) From ad4067df891b2d65c22f4cbf75cc0d16f5584612 Mon Sep 17 00:00:00 2001 From: U S A M A H <39458672+usamah1@users.noreply.github.com> Date: Sun, 8 Jan 2023 13:04:51 +0000 Subject: [PATCH 513/909] Change 'Path is find!' to 'Path found!' (#773) Minor - correct grammatical error :) --- PathPlanning/AStar/a_star_searching_from_two_side.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/AStar/a_star_searching_from_two_side.py b/PathPlanning/AStar/a_star_searching_from_two_side.py index 0318970cc8..822f5ea500 100644 --- a/PathPlanning/AStar/a_star_searching_from_two_side.py +++ b/PathPlanning/AStar/a_star_searching_from_two_side.py @@ -267,7 +267,7 @@ def draw_control(org_closed, goal_closed, flag, start, end, bound, obstacle): if node_intersect: # a path is find path = get_path(org_closed, goal_closed, node_intersect[0]) stop_loop = 1 - print('Path is find!') + print('Path found!') if show_animation: # draw the path plt.plot(path[:, 0], path[:, 1], '-r') plt.title('Robot Arrived', size=20, loc='center') From 98d5851c4da12bdb7c500682e32cfb5277adc782 Mon Sep 17 00:00:00 2001 From: Kadir Haspalamutgil <31936408+kadirhas@users.noreply.github.com> Date: Sun, 8 Jan 2023 15:46:46 +0100 Subject: [PATCH 514/909] Utilize numpy to reduce calculation time (#767) * Utilize numpy to reduce calculation time Change-Id: I6e421a1c2524a3d8f8875121a1a6d2ed832c3150 * styling updates to follow flake8 Change-Id: I909d49b40e6792efcb796846c97a0d984471d3c9 * improves readabilty for d_star_lite Change-Id: Ic329c2140f2200b49feeb03c588d7a805b96cbdc * removes self.detected_obstacles Change-Id: I4a0f2a424f17f44de3b444cb9c2e8e715d761d34 * styling for flake8 Change-Id: Id55c5972cbe76a2f6a69527dbf770f9bdf059109 Co-authored-by: Kadir Haspalamutgil --- PathPlanning/DStarLite/d_star_lite.py | 95 +++++++++++++++++---------- 1 file changed, 62 insertions(+), 33 deletions(-) diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py index c250e38f91..86e777d7b7 100644 --- a/PathPlanning/DStarLite/d_star_lite.py +++ b/PathPlanning/DStarLite/d_star_lite.py @@ -12,6 +12,7 @@ import math import matplotlib.pyplot as plt import random +import numpy as np show_animation = True pause_time = 0.001 @@ -62,32 +63,40 @@ def __init__(self, ox: list, oy: list): self.y_max = int(abs(max(oy) - self.y_min_world)) self.obstacles = [Node(x - self.x_min_world, y - self.y_min_world) for x, y in zip(ox, oy)] + self.obstacles_xy = np.array( + [[obstacle.x, obstacle.y] for obstacle in self.obstacles] + ) self.start = Node(0, 0) self.goal = Node(0, 0) self.U = list() # type: ignore self.km = 0.0 self.kold = 0.0 - self.rhs = list() # type: ignore - self.g = list() # type: ignore - self.detected_obstacles = list() # type: ignore + self.rhs = self.create_grid(float("inf")) + self.g = self.create_grid(float("inf")) + self.detected_obstacles_xy = np.empty((0, 2)) + self.xy = np.empty((0, 2)) if show_animation: self.detected_obstacles_for_plotting_x = list() # type: ignore self.detected_obstacles_for_plotting_y = list() # type: ignore + self.initialized = False def create_grid(self, val: float): - grid = list() - for _ in range(0, self.x_max): - grid_row = list() - for _ in range(0, self.y_max): - grid_row.append(val) - grid.append(grid_row) - return grid + return np.full((self.x_max, self.y_max), val) def is_obstacle(self, node: Node): - return any([compare_coordinates(node, obstacle) - for obstacle in self.obstacles]) or \ - any([compare_coordinates(node, obstacle) - for obstacle in self.detected_obstacles]) + x = np.array([node.x]) + y = np.array([node.y]) + obstacle_x_equal = self.obstacles_xy[:, 0] == x + obstacle_y_equal = self.obstacles_xy[:, 1] == y + is_in_obstacles = (obstacle_x_equal & obstacle_y_equal).any() + + is_in_detected_obstacles = False + if self.detected_obstacles_xy.shape[0] > 0: + is_x_equal = self.detected_obstacles_xy[:, 0] == x + is_y_equal = self.detected_obstacles_xy[:, 1] == y + is_in_detected_obstacles = (is_x_equal & is_y_equal).any() + + return is_in_obstacles or is_in_detected_obstacles def c(self, node1: Node, node2: Node): if self.is_obstacle(node2): @@ -139,13 +148,16 @@ def initialize(self, start: Node, goal: Node): self.start.y = start.y - self.y_min_world self.goal.x = goal.x - self.x_min_world self.goal.y = goal.y - self.y_min_world - self.U = list() # Would normally be a priority queue - self.km = 0.0 - self.rhs = self.create_grid(math.inf) - self.g = self.create_grid(math.inf) - self.rhs[self.goal.x][self.goal.y] = 0 - self.U.append((self.goal, self.calculate_key(self.goal))) - self.detected_obstacles = list() + if not self.initialized: + self.initialized = True + print('Initializing') + self.U = list() # Would normally be a priority queue + self.km = 0.0 + self.rhs = self.create_grid(math.inf) + self.g = self.create_grid(math.inf) + self.rhs[self.goal.x][self.goal.y] = 0 + self.U.append((self.goal, self.calculate_key(self.goal))) + self.detected_obstacles_xy = np.empty((0, 2)) def update_vertex(self, u: Node): if not compare_coordinates(u, self.goal): @@ -167,26 +179,33 @@ def compare_keys(self, key_pair1: tuple[float, float], def compute_shortest_path(self): self.U.sort(key=lambda x: x[1]) - while (len(self.U) > 0 and - self.compare_keys(self.U[0][1], - self.calculate_key(self.start))) or \ - self.rhs[self.start.x][self.start.y] != \ - self.g[self.start.x][self.start.y]: + has_elements = len(self.U) > 0 + start_key_not_updated = self.compare_keys( + self.U[0][1], self.calculate_key(self.start) + ) + rhs_not_equal_to_g = self.rhs[self.start.x][self.start.y] != \ + self.g[self.start.x][self.start.y] + while has_elements and start_key_not_updated or rhs_not_equal_to_g: self.kold = self.U[0][1] u = self.U[0][0] self.U.pop(0) if self.compare_keys(self.kold, self.calculate_key(u)): self.U.append((u, self.calculate_key(u))) self.U.sort(key=lambda x: x[1]) - elif self.g[u.x][u.y] > self.rhs[u.x][u.y]: - self.g[u.x][u.y] = self.rhs[u.x][u.y] + elif (self.g[u.x, u.y] > self.rhs[u.x, u.y]).any(): + self.g[u.x, u.y] = self.rhs[u.x, u.y] for s in self.pred(u): self.update_vertex(s) else: - self.g[u.x][u.y] = math.inf + self.g[u.x, u.y] = math.inf for s in self.pred(u) + [u]: self.update_vertex(s) self.U.sort(key=lambda x: x[1]) + start_key_not_updated = self.compare_keys( + self.U[0][1], self.calculate_key(self.start) + ) + rhs_not_equal_to_g = self.rhs[self.start.x][self.start.y] != \ + self.g[self.start.x][self.start.y] def detect_changes(self): changed_vertices = list() @@ -196,7 +215,12 @@ def detect_changes(self): compare_coordinates(spoofed_obstacle, self.goal): continue changed_vertices.append(spoofed_obstacle) - self.detected_obstacles.append(spoofed_obstacle) + self.detected_obstacles_xy = np.concatenate( + ( + self.detected_obstacles_xy, + [[spoofed_obstacle.x, spoofed_obstacle.y]] + ) + ) if show_animation: self.detected_obstacles_for_plotting_x.append( spoofed_obstacle.x + self.x_min_world) @@ -210,14 +234,19 @@ def detect_changes(self): # Allows random generation of obstacles random.seed() if random.random() > 1 - p_create_random_obstacle: - x = random.randint(0, self.x_max) - y = random.randint(0, self.y_max) + x = random.randint(0, self.x_max - 1) + y = random.randint(0, self.y_max - 1) new_obs = Node(x, y) if compare_coordinates(new_obs, self.start) or \ compare_coordinates(new_obs, self.goal): return changed_vertices changed_vertices.append(Node(x, y)) - self.detected_obstacles.append(Node(x, y)) + self.detected_obstacles_xy = np.concatenate( + ( + self.detected_obstacles_xy, + [[x, y]] + ) + ) if show_animation: self.detected_obstacles_for_plotting_x.append(x + self.x_min_world) From 0f50d84ce6493ac192de9a743f6b51485dff0bd5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Jan 2023 07:04:10 +0900 Subject: [PATCH 515/909] Bump scipy from 1.9.3 to 1.10.0 in /requirements (#775) Bumps [scipy](https://github.com/scipy/scipy) from 1.9.3 to 1.10.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.9.3...v1.10.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9a990856f1..b613447ba7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.24.1 -scipy == 1.9.3 +scipy == 1.10.0 matplotlib == 3.6.2 cvxpy == 1.2.3 pytest == 7.2.0 # For unit test From c8b0698a88a7b72f7d3e0725136acb30f514ed52 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Jan 2023 07:05:36 +0900 Subject: [PATCH 516/909] Bump cvxpy from 1.2.3 to 1.3.0 in /requirements (#776) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.3 to 1.3.0. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.3...v1.3.0) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b613447ba7..3f44e47f21 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.24.1 scipy == 1.10.0 matplotlib == 3.6.2 -cvxpy == 1.2.3 +cvxpy == 1.3.0 pytest == 7.2.0 # For unit test pytest-xdist == 3.1.0 # For unit test mypy == 0.991 # For unit test From 5a16a113c703c9e9a88daf1c681ececb67f7f95c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 17 Jan 2023 07:28:31 +0900 Subject: [PATCH 517/909] Bump matplotlib from 3.6.2 to 3.6.3 in /requirements (#778) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.2 to 3.6.3. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.2...v3.6.3) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3f44e47f21..a2b9663b2d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.24.1 scipy == 1.10.0 -matplotlib == 3.6.2 +matplotlib == 3.6.3 cvxpy == 1.3.0 pytest == 7.2.0 # For unit test pytest-xdist == 3.1.0 # For unit test From 15ab19688b2f6c03ee91a853f1f8cc9def84d162 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 17 Jan 2023 07:28:49 +0900 Subject: [PATCH 518/909] Bump pytest from 7.2.0 to 7.2.1 in /requirements (#779) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.2.0 to 7.2.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.2.0...7.2.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a2b9663b2d..944c1ce217 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.24.1 scipy == 1.10.0 matplotlib == 3.6.3 cvxpy == 1.3.0 -pytest == 7.2.0 # For unit test +pytest == 7.2.1 # For unit test pytest-xdist == 3.1.0 # For unit test mypy == 0.991 # For unit test flake8 == 5.0.4 # For unit test From 489ee5c0e367a64f19b6cf3875ea6282e78bf889 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 26 Jan 2023 21:56:42 +0900 Subject: [PATCH 519/909] fix github scanning alerts (#784) --- .../two_joint_arm_to_point_control.py | 2 +- Bipedal/bipedal_planner/bipedal_planner.py | 1 - PathPlanning/HybridAStar/dynamic_programming_heuristic.py | 2 +- PathPlanning/InformedRRTStar/informed_rrt_star.py | 5 ++--- PathPlanning/LQRPlanner/lqr_planner.py | 2 +- PathPlanning/LQRRRTStar/lqr_rrt_star.py | 2 +- PathPlanning/RRT/rrt_with_pathsmoothing.py | 6 +++--- PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py | 6 +++--- 8 files changed, 12 insertions(+), 14 deletions(-) diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index 9f835c8b32..012a7f72f1 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -45,7 +45,7 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0): if x is not None and y is not None: x_prev = x y_prev = y - if np.sqrt(x**2 + y**2) > (l1 + l2): + if np.hypot(x, y) > (l1 + l2): theta2_goal = 0 else: theta2_goal = np.arccos( diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py index 8ca5ffcf78..c34357df67 100644 --- a/Bipedal/bipedal_planner/bipedal_planner.py +++ b/Bipedal/bipedal_planner/bipedal_planner.py @@ -47,7 +47,6 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False): return # set up plotter - com_trajectory_for_plot, ax = None, None if plot: fig = plt.figure() ax = Axes3D(fig) diff --git a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py index 8a78b15d5f..09bcd556a6 100644 --- a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py +++ b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py @@ -150,7 +150,7 @@ def calc_obstacle_map(ox, oy, resolution, vr): y = iy + min_y # print(x, y) for iox, ioy in zip(ox, oy): - d = math.sqrt((iox - x) ** 2 + (ioy - y) ** 2) + d = math.hypot(iox - x, ioy - y) if d <= vr / resolution: obstacle_map[ix][iy] = True break diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 25c50a0cd2..289e33fe4b 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -192,7 +192,7 @@ def get_path_len(path): @staticmethod def line_cost(node1, node2): - return math.sqrt((node1.x - node2.x) ** 2 + (node1.y - node2.y) ** 2) + return math.hypot(node1.x - node2.x, node1.y - node2.y) @staticmethod def get_nearest_list_index(nodes, rnd): @@ -222,8 +222,7 @@ def rewire(self, newNode, nearInds): for i in nearInds: nearNode = self.node_list[i] - d = math.sqrt((nearNode.x - newNode.x) ** 2 - + (nearNode.y - newNode.y) ** 2) + d = math.hypot(nearNode.x - newNode.x, nearNode.y - newNode.y) s_cost = newNode.cost + d diff --git a/PathPlanning/LQRPlanner/lqr_planner.py b/PathPlanning/LQRPlanner/lqr_planner.py index ba01526a2c..0f58f93ea3 100644 --- a/PathPlanning/LQRPlanner/lqr_planner.py +++ b/PathPlanning/LQRPlanner/lqr_planner.py @@ -47,7 +47,7 @@ def lqr_planning(self, sx, sy, gx, gy, show_animation=True): rx.append(x[0, 0] + gx) ry.append(x[1, 0] + gy) - d = math.sqrt((gx - rx[-1]) ** 2 + (gy - ry[-1]) ** 2) + d = math.hypot(gx - rx[-1], gy - ry[-1]) if d <= self.GOAL_DIST: found_path = True break diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py index e3f18a722a..0ed08123ea 100644 --- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py +++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py @@ -179,7 +179,7 @@ def sample_path(self, wx, wy, step): dx = np.diff(px) dy = np.diff(py) - clen = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)] + clen = [math.hypot(idx, idy) for (idx, idy) in zip(dx, dy)] return px, py, clen diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py index 616c73a697..2ed6a366d1 100644 --- a/PathPlanning/RRT/rrt_with_pathsmoothing.py +++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py @@ -23,7 +23,7 @@ def get_path_length(path): for i in range(len(path) - 1): dx = path[i + 1][0] - path[i][0] dy = path[i + 1][1] - path[i][1] - d = math.sqrt(dx * dx + dy * dy) + d = math.hypot(dx, dy) le += d return le @@ -36,7 +36,7 @@ def get_target_point(path, targetL): for i in range(len(path) - 1): dx = path[i + 1][0] - path[i][0] dy = path[i + 1][1] - path[i][1] - d = math.sqrt(dx * dx + dy * dy) + d = math.hypot(dx, dy) le += d if le >= targetL: ti = i - 1 @@ -67,7 +67,7 @@ def line_collision_check(first, second, obstacleList): return False for (ox, oy, size) in obstacleList: - d = abs(a * ox + b * oy + c) / (math.sqrt(a * a + b * b)) + d = abs(a * ox + b * oy + c) / (math.hypot(a, b)) if d <= size: return False diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index 5bc04c8ee3..1577d048d3 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -57,13 +57,13 @@ def straight_left_straight(x, y, phi): xd = - y / math.tan(phi) + x t = xd - math.tan(phi / 2.0) u = phi - v = math.sqrt((x - xd) ** 2 + y ** 2) - math.tan(phi / 2.0) + v = math.hypot(x - xd, y) - math.tan(phi / 2.0) return True, t, u, v elif y < 0.0 < phi < math.pi * 0.99: xd = - y / math.tan(phi) + x t = xd - math.tan(phi / 2.0) u = phi - v = -math.sqrt((x - xd) ** 2 + y ** 2) - math.tan(phi / 2.0) + v = -math.hypot(x - xd, y) - math.tan(phi / 2.0) return True, t, u, v return False, 0.0, 0.0, 0.0 @@ -103,7 +103,7 @@ def straight_curve_straight(x, y, phi, paths, step_size): def polar(x, y): - r = math.sqrt(x ** 2 + y ** 2) + r = math.hypot(x, y) theta = math.atan2(y, x) return r, theta From 732ed3ea6be5856b10727aac0e0f3724dad02d5a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 26 Jan 2023 22:46:00 +0900 Subject: [PATCH 520/909] clean up informed_rrt_star.py (#785) * clean up informed_rrt_star.py * clean up informed_rrt_star.py --- .../InformedRRTStar/informed_rrt_star.py | 261 +++++++++--------- 1 file changed, 126 insertions(+), 135 deletions(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 289e33fe4b..4f0ec31988 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -11,6 +11,7 @@ """ import sys import pathlib + sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) import copy @@ -27,18 +28,17 @@ class InformedRRTStar: - def __init__(self, start, goal, - obstacleList, randArea, - expandDis=0.5, goalSampleRate=10, maxIter=200): + def __init__(self, start, goal, obstacle_list, rand_area, expand_dis=0.5, + goal_sample_rate=10, max_iter=200): self.start = Node(start[0], start[1]) self.goal = Node(goal[0], goal[1]) - self.min_rand = randArea[0] - self.max_rand = randArea[1] - self.expand_dis = expandDis - self.goal_sample_rate = goalSampleRate - self.max_iter = maxIter - self.obstacle_list = obstacleList + self.min_rand = rand_area[0] + self.max_rand = rand_area[1] + self.expand_dis = expand_dis + self.goal_sample_rate = goal_sample_rate + self.max_iter = max_iter + self.obstacle_list = obstacle_list self.node_list = None def informed_rrt_star_search(self, animation=True): @@ -46,110 +46,109 @@ def informed_rrt_star_search(self, animation=True): self.node_list = [self.start] # max length we expect to find in our 'informed' sample space, # starts as infinite - cBest = float('inf') - solutionSet = set() + c_best = float('inf') + solution_set = set() path = None # Computing the sampling space - cMin = math.sqrt(pow(self.start.x - self.goal.x, 2) - + pow(self.start.y - self.goal.y, 2)) - xCenter = np.array([[(self.start.x + self.goal.x) / 2.0], - [(self.start.y + self.goal.y) / 2.0], [0]]) - a1 = np.array([[(self.goal.x - self.start.x) / cMin], - [(self.goal.y - self.start.y) / cMin], [0]]) + c_min = math.hypot(self.start.x - self.goal.x, + self.start.y - self.goal.y) + x_center = np.array([[(self.start.x + self.goal.x) / 2.0], + [(self.start.y + self.goal.y) / 2.0], [0]]) + a1 = np.array([[(self.goal.x - self.start.x) / c_min], + [(self.goal.y - self.start.y) / c_min], [0]]) e_theta = math.atan2(a1[1], a1[0]) # first column of identity matrix transposed id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3) - M = a1 @ id1_t - U, S, Vh = np.linalg.svd(M, True, True) - C = np.dot(np.dot(U, np.diag( - [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), - Vh) + m = a1 @ id1_t + u, s, vh = np.linalg.svd(m, True, True) + c = u @ np.diag( + [1.0, 1.0, + np.linalg.det(u) * np.linalg.det(np.transpose(vh))]) @ vh for i in range(self.max_iter): - # Sample space is defined by cBest - # cMin is the minimum distance between the start point and the goal - # xCenter is the midpoint between the start and the goal - # cBest changes when a new path is found + # Sample space is defined by c_best + # c_min is the minimum distance between the start point and + # the goal x_center is the midpoint between the start and the + # goal c_best changes when a new path is found - rnd = self.informed_sample(cBest, cMin, xCenter, C) + rnd = self.informed_sample(c_best, c_min, x_center, c) n_ind = self.get_nearest_list_index(self.node_list, rnd) - nearestNode = self.node_list[n_ind] + nearest_node = self.node_list[n_ind] # steer - theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x) - newNode = self.get_new_node(theta, n_ind, nearestNode) - d = self.line_cost(nearestNode, newNode) + theta = math.atan2(rnd[1] - nearest_node.y, + rnd[0] - nearest_node.x) + new_node = self.get_new_node(theta, n_ind, nearest_node) + d = self.line_cost(nearest_node, new_node) - noCollision = self.check_collision(nearestNode, theta, d) + no_collision = self.check_collision(nearest_node, theta, d) - if noCollision: - nearInds = self.find_near_nodes(newNode) - newNode = self.choose_parent(newNode, nearInds) + if no_collision: + near_inds = self.find_near_nodes(new_node) + new_node = self.choose_parent(new_node, near_inds) - self.node_list.append(newNode) - self.rewire(newNode, nearInds) + self.node_list.append(new_node) + self.rewire(new_node, near_inds) - if self.is_near_goal(newNode): - if self.check_segment_collision(newNode.x, newNode.y, + if self.is_near_goal(new_node): + if self.check_segment_collision(new_node.x, new_node.y, self.goal.x, self.goal.y): - solutionSet.add(newNode) - lastIndex = len(self.node_list) - 1 - tempPath = self.get_final_course(lastIndex) - tempPathLen = self.get_path_len(tempPath) - if tempPathLen < cBest: - path = tempPath - cBest = tempPathLen + solution_set.add(new_node) + last_index = len(self.node_list) - 1 + temp_path = self.get_final_course(last_index) + temp_path_len = self.get_path_len(temp_path) + if temp_path_len < c_best: + path = temp_path + c_best = temp_path_len if animation: - self.draw_graph(xCenter=xCenter, - cBest=cBest, cMin=cMin, + self.draw_graph(x_center=x_center, c_best=c_best, c_min=c_min, e_theta=e_theta, rnd=rnd) return path - def choose_parent(self, newNode, nearInds): - if len(nearInds) == 0: - return newNode + def choose_parent(self, new_node, near_inds): + if len(near_inds) == 0: + return new_node - dList = [] - for i in nearInds: - dx = newNode.x - self.node_list[i].x - dy = newNode.y - self.node_list[i].y + d_list = [] + for i in near_inds: + dx = new_node.x - self.node_list[i].x + dy = new_node.y - self.node_list[i].y d = math.hypot(dx, dy) theta = math.atan2(dy, dx) if self.check_collision(self.node_list[i], theta, d): - dList.append(self.node_list[i].cost + d) + d_list.append(self.node_list[i].cost + d) else: - dList.append(float('inf')) + d_list.append(float('inf')) - minCost = min(dList) - minInd = nearInds[dList.index(minCost)] + min_cost = min(d_list) + min_ind = near_inds[d_list.index(min_cost)] - if minCost == float('inf'): + if min_cost == float('inf'): print("min cost is inf") - return newNode + return new_node - newNode.cost = minCost - newNode.parent = minInd + new_node.cost = min_cost + new_node.parent = min_ind - return newNode + return new_node - def find_near_nodes(self, newNode): + def find_near_nodes(self, new_node): n_node = len(self.node_list) r = 50.0 * math.sqrt((math.log(n_node) / n_node)) - d_list = [(node.x - newNode.x) ** 2 + (node.y - newNode.y) ** 2 - for node in self.node_list] + d_list = [(node.x - new_node.x) ** 2 + (node.y - new_node.y) ** 2 for + node in self.node_list] near_inds = [d_list.index(i) for i in d_list if i <= r ** 2] return near_inds - def informed_sample(self, cMax, cMin, xCenter, C): - if cMax < float('inf'): - r = [cMax / 2.0, - math.sqrt(cMax ** 2 - cMin ** 2) / 2.0, - math.sqrt(cMax ** 2 - cMin ** 2) / 2.0] - L = np.diag(r) - xBall = self.sample_unit_ball() - rnd = np.dot(np.dot(C, L), xBall) + xCenter + def informed_sample(self, c_max, c_min, x_center, c): + if c_max < float('inf'): + r = [c_max / 2.0, math.sqrt(c_max ** 2 - c_min ** 2) / 2.0, + math.sqrt(c_max ** 2 - c_min ** 2) / 2.0] + rl = np.diag(r) + x_ball = self.sample_unit_ball() + rnd = np.dot(np.dot(c, rl), x_ball) + x_center rnd = [rnd[(0, 0)], rnd[(1, 0)]] else: rnd = self.sample_free_space() @@ -179,16 +178,15 @@ def sample_free_space(self): @staticmethod def get_path_len(path): - pathLen = 0 + path_len = 0 for i in range(1, len(path)): node1_x = path[i][0] node1_y = path[i][1] node2_x = path[i - 1][0] node2_y = path[i - 1][1] - pathLen += math.sqrt((node1_x - node2_x) - ** 2 + (node1_y - node2_y) ** 2) + path_len += math.hypot(node1_x - node2_x, node1_y - node2_y) - return pathLen + return path_len @staticmethod def line_cost(node1, node2): @@ -196,20 +194,20 @@ def line_cost(node1, node2): @staticmethod def get_nearest_list_index(nodes, rnd): - dList = [(node.x - rnd[0]) ** 2 - + (node.y - rnd[1]) ** 2 for node in nodes] - minIndex = dList.index(min(dList)) - return minIndex + d_list = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1]) ** 2 for node in + nodes] + min_index = d_list.index(min(d_list)) + return min_index - def get_new_node(self, theta, n_ind, nearestNode): - newNode = copy.deepcopy(nearestNode) + def get_new_node(self, theta, n_ind, nearest_node): + new_node = copy.deepcopy(nearest_node) - newNode.x += self.expand_dis * math.cos(theta) - newNode.y += self.expand_dis * math.sin(theta) + new_node.x += self.expand_dis * math.cos(theta) + new_node.y += self.expand_dis * math.sin(theta) - newNode.cost += self.expand_dis - newNode.parent = n_ind - return newNode + new_node.cost += self.expand_dis + new_node.parent = n_ind + return new_node def is_near_goal(self, node): d = self.line_cost(node, self.goal) @@ -217,21 +215,21 @@ def is_near_goal(self, node): return True return False - def rewire(self, newNode, nearInds): + def rewire(self, new_node, near_inds): n_node = len(self.node_list) - for i in nearInds: - nearNode = self.node_list[i] + for i in near_inds: + near_node = self.node_list[i] - d = math.hypot(nearNode.x - newNode.x, nearNode.y - newNode.y) + d = math.hypot(near_node.x - new_node.x, near_node.y - new_node.y) - s_cost = newNode.cost + d + s_cost = new_node.cost + d - if nearNode.cost > s_cost: - theta = math.atan2(newNode.y - nearNode.y, - newNode.x - nearNode.x) - if self.check_collision(nearNode, theta, d): - nearNode.parent = n_node - 1 - nearNode.cost = s_cost + if near_node.cost > s_cost: + theta = math.atan2(new_node.y - near_node.y, + new_node.x - near_node.x) + if self.check_collision(near_node, theta, d): + near_node.parent = n_node - 1 + near_node.cost = s_cost @staticmethod def distance_squared_point_to_segment(v, w, p): @@ -251,45 +249,44 @@ def distance_squared_point_to_segment(v, w, p): def check_segment_collision(self, x1, y1, x2, y2): for (ox, oy, size) in self.obstacle_list: dd = self.distance_squared_point_to_segment( - np.array([x1, y1]), - np.array([x2, y2]), - np.array([ox, oy])) + np.array([x1, y1]), np.array([x2, y2]), np.array([ox, oy])) if dd <= size ** 2: return False # collision return True - def check_collision(self, nearNode, theta, d): - tmpNode = copy.deepcopy(nearNode) - end_x = tmpNode.x + math.cos(theta) * d - end_y = tmpNode.y + math.sin(theta) * d - return self.check_segment_collision(tmpNode.x, tmpNode.y, end_x, end_y) + def check_collision(self, near_node, theta, d): + tmp_node = copy.deepcopy(near_node) + end_x = tmp_node.x + math.cos(theta) * d + end_y = tmp_node.y + math.sin(theta) * d + return self.check_segment_collision(tmp_node.x, tmp_node.y, + end_x, end_y) - def get_final_course(self, lastIndex): + def get_final_course(self, last_index): path = [[self.goal.x, self.goal.y]] - while self.node_list[lastIndex].parent is not None: - node = self.node_list[lastIndex] + while self.node_list[last_index].parent is not None: + node = self.node_list[last_index] path.append([node.x, node.y]) - lastIndex = node.parent + last_index = node.parent path.append([self.start.x, self.start.y]) return path - def draw_graph(self, xCenter=None, cBest=None, cMin=None, e_theta=None, + def draw_graph(self, x_center=None, c_best=None, c_min=None, e_theta=None, rnd=None): plt.clf() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect( - 'key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + 'key_release_event', lambda event: + [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd[0], rnd[1], "^k") - if cBest != float('inf'): - self.plot_ellipse(xCenter, cBest, cMin, e_theta) + if c_best != float('inf'): + self.plot_ellipse(x_center, c_best, c_min, e_theta) for node in self.node_list: if node.parent is not None: if node.x or node.y is not None: - plt.plot([node.x, self.node_list[node.parent].x], [ - node.y, self.node_list[node.parent].y], "-g") + plt.plot([node.x, self.node_list[node.parent].x], + [node.y, self.node_list[node.parent].y], "-g") for (ox, oy, size) in self.obstacle_list: plt.plot(ox, oy, "ok", ms=30 * size) @@ -301,13 +298,13 @@ def draw_graph(self, xCenter=None, cBest=None, cMin=None, e_theta=None, plt.pause(0.01) @staticmethod - def plot_ellipse(xCenter, cBest, cMin, e_theta): # pragma: no cover + def plot_ellipse(x_center, c_best, c_min, e_theta): # pragma: no cover - a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0 - b = cBest / 2.0 + a = math.sqrt(c_best ** 2 - c_min ** 2) / 2.0 + b = c_best / 2.0 angle = math.pi / 2.0 - e_theta - cx = xCenter[0] - cy = xCenter[1] + cx = x_center[0] + cy = x_center[1] t = np.arange(0, 2 * math.pi + 0.1, 0.1) x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] @@ -331,18 +328,12 @@ def main(): print("Start informed rrt star planning") # create obstacles - obstacleList = [ - (5, 5, 0.5), - (9, 6, 1), - (7, 5, 1), - (1, 5, 1), - (3, 6, 1), - (7, 9, 1) - ] + obstacle_list = [(5, 5, 0.5), (9, 6, 1), (7, 5, 1), (1, 5, 1), (3, 6, 1), + (7, 9, 1)] # Set params - rrt = InformedRRTStar(start=[0, 0], goal=[5, 10], - randArea=[-2, 15], obstacleList=obstacleList) + rrt = InformedRRTStar(start=[0, 0], goal=[5, 10], rand_area=[-2, 15], + obstacle_list=obstacle_list) path = rrt.informed_rrt_star_search(animation=show_animation) print("Done!!") From ffd8602e25d16f7801421310ca6892f08531042e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 29 Jan 2023 22:22:11 +0900 Subject: [PATCH 521/909] support python3.11 (#741) * support python3.11 * try to fix error * try to fix error * try to fix error * try to fix error * try to fix error * try to fix error * try to fix error * try to fix error * try to fix error * add docs * fix conflict * fix conflict * fix conflict * using python3.10 for appveyors * add windows CI * roll back appveyor settings * fix windows CI --- .circleci/config.yml | 2 +- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .github/workflows/Windows_CI.yml | 36 ++++++++++++++++++++++++++++++++ README.md | 2 +- docs/getting_started_main.rst | 4 ++-- requirements/environment.yml | 2 +- 7 files changed, 43 insertions(+), 7 deletions(-) create mode 100644 .github/workflows/Windows_CI.yml diff --git a/.circleci/config.yml b/.circleci/config.yml index 075a613be0..d8f1e89dc4 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,7 +6,7 @@ orbs: jobs: build_doc: docker: - - image: cimg/python:3.10 + - image: cimg/python:3.11 steps: - checkout - run: diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 83c55b17f1..a7b5aba5fe 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627 + python-version: [ '3.11' ] name: Python ${{ matrix.python-version }} CI diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index a3459f2560..6f0849a43d 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: macos-latest strategy: matrix: - python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627 + python-version: [ '3.11' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v2 diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml new file mode 100644 index 0000000000..ed267045ca --- /dev/null +++ b/.github/workflows/Windows_CI.yml @@ -0,0 +1,36 @@ +# This is a basic workflow to help you get started with Actions + +name: Windows_CI + +# Controls when the action will run. Triggers the workflow on push or pull request +# events but only for the master branch +on: + push: + branches: + - master + pull_request: + + +jobs: + build: + runs-on: windows-latest + strategy: + matrix: + python-version: [ '3.11' ] + name: Python ${{ matrix.python-version }} CI + steps: + - uses: actions/checkout@v2 + - run: git fetch --prune --unshallow + + - name: Setup python + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python --version + python -m pip install --upgrade pip + pip install -r requirements/requirements.txt + - name: do all unit tests + run: bash runtests.sh \ No newline at end of file diff --git a/README.md b/README.md index b46195f35e..4ad19353e3 100644 --- a/README.md +++ b/README.md @@ -96,7 +96,7 @@ See this paper for more details: For running each sample code: -- [Python 3.10.x](https://www.python.org/) +- [Python 3.11.x](https://www.python.org/) - [NumPy](https://numpy.org/) diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index c2be93261b..0e90d672b7 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -26,7 +26,7 @@ See this paper for more details: Requirements ------------- -- `Python 3.10.x`_ +- `Python 3.11.x`_ - `NumPy`_ - `SciPy`_ - `Matplotlib`_ @@ -40,7 +40,7 @@ For development: - sphinx (for document generation) - pycodestyle (for code style check) -.. _`Python 3.10.x`: https://www.python.org/ +.. _`Python 3.11.x`: https://www.python.org/ .. _`NumPy`: https://numpy.org/ .. _`SciPy`: https://scipy.org/ .. _`Matplotlib`: https://matplotlib.org/ diff --git a/requirements/environment.yml b/requirements/environment.yml index 202c2693d9..13dfa29f66 100644 --- a/requirements/environment.yml +++ b/requirements/environment.yml @@ -2,7 +2,7 @@ name: python_robotics channels: - conda-forge dependencies: - - python=3.10 + - python=3.11 - pip - scipy - numpy From 15a91900608199e9bccdf68b04786b30f6772ad5 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 30 Jan 2023 21:33:49 +0900 Subject: [PATCH 522/909] using ruff instead of flake8 (#787) * using ruff instead of flake8 * using ruff instead of flake8 * using ruff instead of flake8 --- docs/conf.py | 1 - docs/getting_started_main.rst | 15 ++++--- requirements/requirements.txt | 2 +- ruff.toml | 18 ++++++++ ...st_diff_codestyle.py => test_codestyle.py} | 45 ++++++++++++++----- 5 files changed, 62 insertions(+), 19 deletions(-) create mode 100644 ruff.toml rename tests/{test_diff_codestyle.py => test_codestyle.py} (78%) diff --git a/docs/conf.py b/docs/conf.py index 24b5518b6f..e7c64d07b2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index 0e90d672b7..497b85a23a 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -34,17 +34,22 @@ Requirements For development: -- pytest (for unit tests) -- pytest-xdist (for parallel unit tests) -- mypy (for type check) -- sphinx (for document generation) -- pycodestyle (for code style check) +- `pytest`_ (for unit tests) +- `pytest-xdist`_ (for parallel unit tests) +- `mypy`_ (for type check) +- `sphinx`_ (for document generation) +- `ruff`_ (for code style check) .. _`Python 3.11.x`: https://www.python.org/ .. _`NumPy`: https://numpy.org/ .. _`SciPy`: https://scipy.org/ .. _`Matplotlib`: https://matplotlib.org/ .. _`cvxpy`: https://www.cvxpy.org/ +.. _`pytest`: https://docs.pytest.org/en/latest/ +.. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist +.. _`mypy`: https://mypy-lang.org/ +.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html +.. _`ruff`: https://github.com/charliermarsh/ruff How to use diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 944c1ce217..a3a337a198 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.1.0 # For unit test mypy == 0.991 # For unit test -flake8 == 5.0.4 # For unit test +ruff == 0.0.237 # For unit test diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000000..fd02032f48 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,18 @@ +line-length = 88 + +select = ["F", "E", "W", "UP"] +ignore = ["E501", "E741"] +exclude = [ +] + +# Assume Python 3.11 +target-version = "py311" + +[per-file-ignores] + +[mccabe] +# Unlike Flake8, default to a complexity level of 10. +max-complexity = 10 + +[pydocstyle] +convention = "numpy" \ No newline at end of file diff --git a/tests/test_diff_codestyle.py b/tests/test_codestyle.py similarity index 78% rename from tests/test_diff_codestyle.py rename to tests/test_codestyle.py index 89c228e285..2ad3816ea1 100644 --- a/tests/test_diff_codestyle.py +++ b/tests/test_codestyle.py @@ -1,8 +1,8 @@ """ -Diff based code style checker with flake8 +Diff code style checker with ruff This code come from: -https://github.com/scipy/scipy/blob/main/tools/lint_diff.py +https://github.com/scipy/scipy/blob/main/tools/lint.py Scipy's licence: https://github.com/scipy/scipy/blob/main/LICENSE.txt Copyright (c) 2001-2002 Enthought, Inc. 2003-2022, SciPy Developers. @@ -37,9 +37,30 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import conftest +import os import subprocess +CONFIG = os.path.join( + os.path.abspath(os.path.dirname(os.path.dirname(__file__))), + 'ruff.toml', +) + +ROOT_DIR = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + + +def run_ruff(files, fix): + if not files: + return 0, "" + args = ['--fix'] if fix else [] + res = subprocess.run( + ['ruff', f'--config={CONFIG}'] + args + files, + stdout=subprocess.PIPE, + encoding='utf-8' + ) + return res.returncode, res.stdout + + def rev_list(branch, num_commits): """List commits in reverse chronological order. Only the first `num_commits` are shown. @@ -89,23 +110,23 @@ def find_diff(sha): return res.stdout -def run_flake8(diff): - """Run flake8 on the given diff.""" +def diff_files(sha): + """Find the diff since the given SHA.""" res = subprocess.run( - ['flake8', '--diff', '--ignore', - 'E402' # top level import for sys.path.append - ], - input=diff, + ['git', 'diff', '--name-only', '--diff-filter=ACMR', '-z', sha, '--', + '*.py', '*.pyx', '*.pxd', '*.pxi'], stdout=subprocess.PIPE, - encoding='utf-8', + encoding='utf-8' ) - return res.returncode, res.stdout + res.check_returncode() + return [os.path.join(ROOT_DIR, f) for f in res.stdout.split('\0') if f] def test(): branch_commit = find_branch_point("origin/master") - diff = find_diff(branch_commit) - rc, errors = run_flake8(diff) + files = diff_files(branch_commit) + print(files) + rc, errors = run_ruff(files, fix=False) if errors: print(errors) else: From 7c1a92a8e5d2c9e0a961728e2cb008c3a4747e58 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Feb 2023 07:21:09 +0900 Subject: [PATCH 523/909] Bump ruff from 0.0.237 to 0.0.241 in /requirements (#788) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.237 to 0.0.241. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.237...v0.0.241) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a3a337a198..1e8a407a20 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.1.0 # For unit test mypy == 0.991 # For unit test -ruff == 0.0.237 # For unit test +ruff == 0.0.241 # For unit test From f9b2fb62afb5f974cbd2ee90434d6ab8c4f6140f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Feb 2023 07:24:15 +0900 Subject: [PATCH 524/909] Bump numpy from 1.24.1 to 1.24.2 in /requirements (#790) Bumps [numpy](https://github.com/numpy/numpy) from 1.24.1 to 1.24.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.24.1...v1.24.2) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1e8a407a20..0bff2c7b5a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.24.1 +numpy == 1.24.2 scipy == 1.10.0 matplotlib == 3.6.3 cvxpy == 1.3.0 From c627f881a88c47ed6b90c90a9353e38c76d31d7c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Feb 2023 21:49:10 +0900 Subject: [PATCH 525/909] Bump mypy from 0.991 to 1.0.0 in /requirements (#789) Bumps [mypy](https://github.com/python/mypy) from 0.991 to 1.0.0. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v0.991...v1.0.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 0bff2c7b5a..01bcab20f6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.6.3 cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.1.0 # For unit test -mypy == 0.991 # For unit test +mypy == 1.0.0 # For unit test ruff == 0.0.241 # For unit test From 3c1162561ae4ae95b4f946fd75f84d198b7ff8d2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 11 Feb 2023 10:48:51 +0900 Subject: [PATCH 526/909] Bump ipython from 7.31.1 to 8.10.0 in /docs (#791) Bumps [ipython](https://github.com/ipython/ipython) from 7.31.1 to 8.10.0. - [Release notes](https://github.com/ipython/ipython/releases) - [Commits](https://github.com/ipython/ipython/compare/7.31.1...8.10.0) --- updated-dependencies: - dependency-name: ipython dependency-type: direct:production ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- docs/doc_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index b8702ee750..c01b596539 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -1,4 +1,4 @@ sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 -IPython == 7.31.1 # For sphinx documentation +IPython == 8.10.0 # For sphinx documentation sphinxcontrib-napoleon == 0.7 # For auto doc From 06bb1d82121a7c4c9d906ced5f60adc14c12810e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Feb 2023 20:07:02 +0900 Subject: [PATCH 527/909] Bump ruff from 0.0.241 to 0.0.246 in /requirements (#792) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.241 to 0.0.246. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.241...v0.0.246) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 01bcab20f6..2f1aa41f29 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.1.0 # For unit test mypy == 1.0.0 # For unit test -ruff == 0.0.241 # For unit test +ruff == 0.0.246 # For unit test From ebc1d3d2f3bd3c273da94f74da383390cdb35ce9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Feb 2023 20:07:31 +0900 Subject: [PATCH 528/909] Bump pytest-xdist from 3.1.0 to 3.2.0 in /requirements (#793) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.1.0 to 3.2.0. - [Release notes](https://github.com/pytest-dev/pytest-xdist/releases) - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.1.0...v3.2.0) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2f1aa41f29..ed3e084c58 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.10.0 matplotlib == 3.6.3 cvxpy == 1.3.0 pytest == 7.2.1 # For unit test -pytest-xdist == 3.1.0 # For unit test +pytest-xdist == 3.2.0 # For unit test mypy == 1.0.0 # For unit test ruff == 0.0.246 # For unit test From 5641a491b95c4a5f90315b71cd01792319af0032 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Feb 2023 07:25:57 +0900 Subject: [PATCH 529/909] Bump ruff from 0.0.246 to 0.0.249 in /requirements (#794) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.246 to 0.0.249. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.246...v0.0.249) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ed3e084c58..b3dcf0458a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.2.0 # For unit test mypy == 1.0.0 # For unit test -ruff == 0.0.246 # For unit test +ruff == 0.0.249 # For unit test From fa254376f6017a6f62701bf56758431bc574cf89 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Feb 2023 07:26:14 +0900 Subject: [PATCH 530/909] Bump scipy from 1.10.0 to 1.10.1 in /requirements (#797) Bumps [scipy](https://github.com/scipy/scipy) from 1.10.0 to 1.10.1. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.10.0...v1.10.1) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b3dcf0458a..8e537c44bc 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.24.2 -scipy == 1.10.0 +scipy == 1.10.1 matplotlib == 3.6.3 cvxpy == 1.3.0 pytest == 7.2.1 # For unit test From f79bc4e139a70d42f4317846c1d5b1254e0761a5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Feb 2023 08:45:48 +0900 Subject: [PATCH 531/909] Bump mypy from 1.0.0 to 1.0.1 in /requirements (#795) Bumps [mypy](https://github.com/python/mypy) from 1.0.0 to 1.0.1. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v1.0.0...v1.0.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 8e537c44bc..05b7c9aa13 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.6.3 cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.2.0 # For unit test -mypy == 1.0.0 # For unit test +mypy == 1.0.1 # For unit test ruff == 0.0.249 # For unit test From 9269e190c27f7e146190b4a3c082d04ccf32bc94 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Feb 2023 12:45:30 +0900 Subject: [PATCH 532/909] Bump matplotlib from 3.6.3 to 3.7.0 in /requirements (#796) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.3 to 3.7.0. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.3...v3.7.0) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 05b7c9aa13..afda81a29a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.24.2 scipy == 1.10.1 -matplotlib == 3.6.3 +matplotlib == 3.7.0 cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.2.0 # For unit test From 3f5b973d6cb508faf707f18cfcb76b4a4c324215 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Mar 2023 19:06:13 +0900 Subject: [PATCH 533/909] Bump ruff from 0.0.249 to 0.0.254 in /requirements (#798) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.249 to 0.0.254. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.249...v0.0.254) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index afda81a29a..5eb5d69364 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.2.0 # For unit test mypy == 1.0.1 # For unit test -ruff == 0.0.249 # For unit test +ruff == 0.0.254 # For unit test From 6e2f3df1d88731730028a78c834daf41c54669c8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Mar 2023 20:49:11 +0900 Subject: [PATCH 534/909] Bump matplotlib from 3.7.0 to 3.7.1 in /requirements (#800) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.7.0 to 3.7.1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.7.0...v3.7.1) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5eb5d69364..141c14198b 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.24.2 scipy == 1.10.1 -matplotlib == 3.7.0 +matplotlib == 3.7.1 cvxpy == 1.3.0 pytest == 7.2.1 # For unit test pytest-xdist == 3.2.0 # For unit test From 1f96f4eb55c5860d00bbefd83a28b59e0b07e128 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 8 Mar 2023 07:57:25 +0900 Subject: [PATCH 535/909] Bump pytest from 7.2.1 to 7.2.2 in /requirements (#799) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.2.1 to 7.2.2. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.2.1...7.2.2) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 141c14198b..35798d7e86 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.24.2 scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.0 -pytest == 7.2.1 # For unit test +pytest == 7.2.2 # For unit test pytest-xdist == 3.2.0 # For unit test mypy == 1.0.1 # For unit test ruff == 0.0.254 # For unit test From cb08c39a9388c90290b797014f0663c0ed53d2f7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 11 Mar 2023 17:54:18 +0900 Subject: [PATCH 536/909] Add Normal vector estimation (#781) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add normal vector calculation routine. * add normal vector calculation routine. * add normal vector estimation * fix unittests in not matplotlib frontend * fix lint ci * add ransac based normal distribution estimation * add normal_vector_estimation_main.rst * normal_vector_estimation_main.rst を更新 * update normal_vector_estimation_main.rst * update normal_vector_estimation_main.rst * normal_vector_estimation_main.rst * normal_vector_estimation_main.rst を更新 * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst --- .../rrt_star_seven_joint_arm_control.py | 6 +- .../normal_vector_estimation.py | 177 ++++++++++++++++++ .../dynamic_window_approach.py | 3 +- PathPlanning/RRTStar/rrt_star.py | 4 +- .../VisibilityRoadMap/visibility_road_map.py | 5 +- docs/modules/mapping/mapping_main.rst | 2 +- .../normal_vector_calc.png | Bin 0 -> 96411 bytes .../normal_vector_estimation_main.rst | 74 ++++++++ .../ransac_normal_vector_estimation.png | Bin 0 -> 282297 bytes ruff.toml | 2 +- tests/test_normal_vector_estimation.py | 19 ++ utils/plot.py | 79 ++++++++ 12 files changed, 360 insertions(+), 11 deletions(-) create mode 100644 Mapping/normal_vector_estimation/normal_vector_estimation.py create mode 100644 docs/modules/mapping/normal_vector_estimation/normal_vector_calc.png create mode 100644 docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst create mode 100644 docs/modules/mapping/normal_vector_estimation/ransac_normal_vector_estimation.png create mode 100644 tests/test_normal_vector_estimation.py diff --git a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py index fca55184da..0f449bbe51 100755 --- a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py +++ b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py @@ -5,7 +5,6 @@ import math import random import numpy as np -from mpl_toolkits import mplot3d import matplotlib.pyplot as plt import sys import pathlib @@ -79,8 +78,9 @@ def __init__(self, start, goal, robot, obstacle_list, rand_area, self.obstacle_list = obstacle_list self.connect_circle_dist = connect_circle_dist self.goal_node = self.Node(goal) - self.ax = plt.axes(projection='3d') self.node_list = [] + if show_animation: + self.ax = plt.axes(projection='3d') def planning(self, animation=False, search_until_max_iter=False): """ @@ -176,7 +176,7 @@ def search_best_goal_node(self): def find_near_nodes(self, new_node): nnode = len(self.node_list) + 1 - r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode)) + r = self.connect_circle_dist * math.sqrt(math.log(nnode) / nnode) # if expand_dist exists, search vertices in # a range no more than expand_dist if hasattr(self, 'expand_dis'): diff --git a/Mapping/normal_vector_estimation/normal_vector_estimation.py b/Mapping/normal_vector_estimation/normal_vector_estimation.py new file mode 100644 index 0000000000..996ba3ffee --- /dev/null +++ b/Mapping/normal_vector_estimation/normal_vector_estimation.py @@ -0,0 +1,177 @@ +import numpy as np +from matplotlib import pyplot as plt + +from utils.plot import plot_3d_vector_arrow, plot_triangle, set_equal_3d_axis + +show_animation = True + + +def calc_normal_vector(p1, p2, p3): + """Calculate normal vector of triangle + + Parameters + ---------- + p1 : np.array + 3D point + p2 : np.array + 3D point + p3 : np.array + 3D point + + Returns + ------- + normal_vector : np.array + normal vector (3,) + + """ + # calculate two vectors of triangle + v1 = p2 - p1 + v2 = p3 - p1 + + # calculate normal vector + normal_vector = np.cross(v1, v2) + + # normalize vector + normal_vector = normal_vector / np.linalg.norm(normal_vector) + + return normal_vector + + +def sample_3d_points_from_a_plane(num_samples, normal): + points_2d = np.random.normal(size=(num_samples, 2)) # 2D points on a plane + d = 0 + for i in range(len(points_2d)): + point_3d = np.append(points_2d[i], 0) + d += normal @ point_3d + d /= len(points_2d) + + points_3d = np.zeros((len(points_2d), 3)) + for i in range(len(points_2d)): + point_2d = np.append(points_2d[i], 0) + projection_length = (d - normal @ point_2d) / np.linalg.norm(normal) + points_3d[i] = point_2d + projection_length * normal + + return points_3d + + +def distance_to_plane(point, normal, origin): + dot_product = np.dot(normal, point) - np.dot(normal, origin) + if np.isclose(dot_product, 0): + return 0.0 + else: + distance = abs(dot_product) / np.linalg.norm(normal) + return distance + + +def ransac_normal_vector_estimation(points_3d, inlier_radio_th=0.7, + inlier_dist=0.1, p=0.99): + """ + RANSAC based normal vector estimation + + Parameters + ---------- + points_3d : np.array + 3D points (N, 3) + inlier_radio_th : float + Inlier ratio threshold. If inlier ratio is larger than this value, + the iteration is stopped. Default is 0.7. + inlier_dist : float + Inlier distance threshold. If distance between points and estimated + plane is smaller than this value, the point is inlier. Default is 0.1. + p : float + Probability that at least one of the sets of random samples does not + include an outlier. If this probability is near 1, the iteration + number is large. Default is 0.99. + + Returns + ------- + center_vector : np.array + Center of estimated plane. (3,) + normal_vector : np.array + Normal vector of estimated plane. (3,) + + """ + center = np.mean(points_3d, axis=0) + + max_iter = int(np.floor(np.log(1.0-p)/np.log(1.0-(1.0-inlier_radio_th)**3))) + + for ite in range(max_iter): + # Random sampling + sampled_ids = np.random.choice(points_3d.shape[0], size=3, + replace=False) + sampled_points = points_3d[sampled_ids, :] + p1 = sampled_points[0, :] + p2 = sampled_points[1, :] + p3 = sampled_points[2, :] + normal_vector = calc_normal_vector(p1, p2, p3) + + # calc inlier ratio + n_inliner = 0 + for i in range(points_3d.shape[0]): + p = points_3d[i, :] + if distance_to_plane(p, normal_vector, center) <= inlier_dist: + n_inliner += 1 + inlier_ratio = n_inliner / points_3d.shape[0] + print(f"Iter:{ite}, {inlier_ratio=}") + if inlier_ratio > inlier_radio_th: + return center, normal_vector + + return center, None + + +def main1(): + p1 = np.array([0.0, 0.0, 1.0]) + p2 = np.array([1.0, 1.0, 0.0]) + p3 = np.array([0.0, 1.0, 0.0]) + + center = np.mean([p1, p2, p3], axis=0) + normal_vector = calc_normal_vector(p1, p2, p3) + print(f"{center=}") + print(f"{normal_vector=}") + + if show_animation: + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + set_equal_3d_axis(ax, [0.0, 2.5], [0.0, 2.5], [0.0, 3.0]) + plot_triangle(p1, p2, p3, ax) + ax.plot(center[0], center[1], center[2], "ro") + plot_3d_vector_arrow(ax, center, center + normal_vector) + plt.show() + + +def main2(rng=None): + true_normal = np.array([0, 1, 1]) + true_normal = true_normal / np.linalg.norm(true_normal) + num_samples = 100 + noise_scale = 0.1 + + points_3d = sample_3d_points_from_a_plane(num_samples, true_normal) + # add random noise + points_3d += np.random.normal(size=points_3d.shape, scale=noise_scale) + + print(f"{points_3d.shape=}") + + center, estimated_normal = ransac_normal_vector_estimation( + points_3d, inlier_dist=noise_scale) + + if estimated_normal is None: + print("Failed to estimate normal vector") + return + + print(f"{true_normal=}") + print(f"{estimated_normal=}") + + if show_animation: + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.plot(points_3d[:, 0], points_3d[:, 1], points_3d[:, 2], ".r") + plot_3d_vector_arrow(ax, center, center + true_normal) + plot_3d_vector_arrow(ax, center, center + estimated_normal) + set_equal_3d_axis(ax, [-3.0, 3.0], [-3.0, 3.0], [-3.0, 3.0]) + plt.title("RANSAC based Normal vector estimation") + plt.show() + + +if __name__ == '__main__': + # main1() + main2() diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py index 74ff064e86..8664ec1745 100644 --- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py +++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py @@ -300,8 +300,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle): if show_animation: plt.plot(trajectory[:, 0], trajectory[:, 1], "-r") plt.pause(0.0001) - - plt.show() + plt.show() if __name__ == '__main__': diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index a7aad49d60..f875d3a76d 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -186,7 +186,7 @@ def find_near_nodes(self, new_node): radius r """ nnode = len(self.node_list) + 1 - r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode)) + r = self.connect_circle_dist * math.sqrt(math.log(nnode) / nnode) # if expand_dist exists, search vertices in a range no more than # expand_dist if hasattr(self, 'expand_dis'): @@ -280,7 +280,7 @@ def main(): rrt_star.draw_graph() plt.plot([x for (x, y) in path], [y for (x, y) in path], 'r--') plt.grid(True) - plt.show() + plt.show() if __name__ == '__main__': diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py index e9f8733c83..5f7ffadd16 100644 --- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py +++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py @@ -62,8 +62,9 @@ def generate_visibility_nodes(self, start_x, start_y, goal_x, goal_y, for (vx, vy) in zip(cvx_list, cvy_list): nodes.append(DijkstraSearch.Node(vx, vy)) - for node in nodes: - plt.plot(node.x, node.y, "xr") + if self.do_plot: + for node in nodes: + plt.plot(node.x, node.y, "xr") return nodes diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst index 14a7cce70d..1c02c75cb2 100644 --- a/docs/modules/mapping/mapping_main.rst +++ b/docs/modules/mapping/mapping_main.rst @@ -13,4 +13,4 @@ Mapping k_means_object_clustering/k_means_object_clustering circle_fitting/circle_fitting rectangle_fitting/rectangle_fitting - + normal_vector_estimation/normal_vector_estimation diff --git a/docs/modules/mapping/normal_vector_estimation/normal_vector_calc.png b/docs/modules/mapping/normal_vector_estimation/normal_vector_calc.png new file mode 100644 index 0000000000000000000000000000000000000000..63e2cdade70313466314c1f2ce37e6968910d830 GIT binary 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math:: v1 = p2 - p1 + +.. math:: v2 = p3 - p1 + +This is an example of normal vector calculation: + +.. figure:: normal_vector_calc.png + +API +===== + +.. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.calc_normal_vector + +Normal vector estimation with RANdam SAmpling Consensus(RANSAC) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Consider the problem of estimating the normal vector of a plane based on a +set of N 3D points where a plane can be observed. + +There is a way that uses all point cloud data to estimate a plane and +a normal vector using the `least-squares method `_ + +However, this method is vulnerable to noise of the point cloud. + +In this document, we will use a method that uses +`RANdam SAmpling Consensus(RANSAC) `_ +to estimate a plane and a normal vector. + +RANSAC is a robust estimation methods for data set with outliers. + + + +This RANSAC based normal vector estimation method is as follows: + +#. Select 3 points randomly from the point cloud. + +#. Calculate a normal vector of a plane which is consists of the sampled 3 points. + +#. Calculate the distance between the calculated plane and the all point cloud. + +#. If the distance is less than a threshold, the point is considered to be an inlier. + +#. Repeat the above steps until the inlier ratio is greater than a threshold. + + + +This is an example of RANSAC based normal vector estimation: + +.. figure:: ransac_normal_vector_estimation.png + +API +===== + +.. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.ransac_normal_vector_estimation + diff --git a/docs/modules/mapping/normal_vector_estimation/ransac_normal_vector_estimation.png b/docs/modules/mapping/normal_vector_estimation/ransac_normal_vector_estimation.png new file mode 100644 index 0000000000000000000000000000000000000000..e9b661e79c1d8283e61c9cbdc991e6cd4ef2dec9 GIT binary patch literal 282297 zcmeFYgN?tc6I1N+|Rsjj+JbxO{u`qeq7Zb$2Aso-EzVj>|S;i##;dW(dF8ife1=r0gC zEB&+VNJyB!926CG)D#uzbv)hd9Gq>DkW{17z-aGu_Xs~e2D(XLuwW1*5e5(y5Xq^d zbAOyH{_%-fj-BB}XcUc+NkSM*7&ODjrhjAI(_Qp-m^v zH=~(=?P2i1Fzn)S2Yxe*`Y~@fRdv$B7HP7_Xi~>q+s;dqi%1s9LJswn#dCaGS=~-t zTwIhNnsaSK_mW7Ib)ww8P7n8wk9zM=IyGgGbod4_LN!4WgKRoM9bnaJB)RK6&;yH6 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grid_map.calc_xy_index_from_grid_index(grid_ind) + assert x_ind == x_ind_2 + assert y_ind == y_ind_2 + + if __name__ == '__main__': conftest.run_this_test(__file__) diff --git a/utils/plot.py b/utils/plot.py index d4bbe29ffd..eb5aa7ad04 100644 --- a/utils/plot.py +++ b/utils/plot.py @@ -9,6 +9,69 @@ from mpl_toolkits.mplot3d.proj3d import proj_transform from mpl_toolkits.mplot3d import Axes3D +from utils.angle import rot_mat_2d + + +def plot_covariance_ellipse(x, y, cov, chi2=3.0, color="-r", ax=None): + """ + This function plots an ellipse that represents a covariance matrix. The ellipse is centered at (x, y) and its shape, size and rotation are determined by the covariance matrix. + + Parameters: + x : (float) The x-coordinate of the center of the ellipse. + y : (float) The y-coordinate of the center of the ellipse. + cov : (numpy.ndarray) A 2x2 covariance matrix that determines the shape, size, and rotation of the ellipse. + chi2 : (float, optional) A scalar value that scales the ellipse size. This value is typically set based on chi-squared distribution quantiles to achieve certain confidence levels (e.g., 3.0 corresponds to ~95% confidence for a 2D Gaussian). Defaults to 3.0. + color : (str, optional) The color and line style of the ellipse plot, following matplotlib conventions. Defaults to "-r" (a red solid line). + ax : (matplotlib.axes.Axes, optional) The Axes object to draw the ellipse on. If None (default), a new figure and axes are created. + + Returns: + None. This function plots the covariance ellipse on the specified axes. + """ + eig_val, eig_vec = np.linalg.eig(cov) + + if eig_val[0] >= eig_val[1]: + big_ind = 0 + small_ind = 1 + else: + big_ind = 1 + small_ind = 0 + a = math.sqrt(chi2 * eig_val[big_ind]) + b = math.sqrt(chi2 * eig_val[small_ind]) + angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind]) + plot_ellipse(x, y, a, b, angle, color=color, ax=ax) + + +def plot_ellipse(x, y, a, b, angle, color="-r", ax=None, **kwargs): + """ + This function plots an ellipse based on the given parameters. + + Parameters + ---------- + x : (float) The x-coordinate of the center of the ellipse. + y : (float) The y-coordinate of the center of the ellipse. + a : (float) The length of the semi-major axis of the ellipse. + b : (float) The length of the semi-minor axis of the ellipse. + angle : (float) The rotation angle of the ellipse, in radians. + color : (str, optional) The color and line style of the ellipse plot, following matplotlib conventions. Defaults to "-r" (a red solid line). + ax : (matplotlib.axes.Axes, optional) The Axes object to draw the ellipse on. If None (default), a new figure and axes are created. + **kwargs: Additional keyword arguments to pass to plt.plot or ax.plot. + + Returns + --------- + None. This function plots the ellipse based on the specified parameters. + """ + + t = np.arange(0, 2 * math.pi + 0.1, 0.1) + px = [a * math.cos(it) for it in t] + py = [b * math.sin(it) for it in t] + fx = rot_mat_2d(angle) @ (np.array([px, py])) + px = np.array(fx[0, :] + x).flatten() + py = np.array(fx[1, :] + y).flatten() + if ax is None: + plt.plot(px, py, color, **kwargs) + else: + ax.plot(px, py, color, **kwargs) + def plot_arrow(x, y, yaw, arrow_length=1.0, origin_point_plot_style="xr", @@ -132,7 +195,6 @@ def plot_3d_vector_arrow(ax, p1, p2): ) - def plot_triangle(p1, p2, p3, ax): ax.add_collection3d(art3d.Poly3DCollection([[p1, p2, p3]], color='b')) @@ -163,3 +225,10 @@ def set_equal_3d_axis(ax, x_lims, y_lims, z_lims): ax.set_xlim(mid_x - max_range, mid_x + max_range) ax.set_ylim(mid_y - max_range, mid_y + max_range) ax.set_zlim(mid_z - max_range, mid_z + max_range) + + +if __name__ == '__main__': + plot_ellipse(0, 0, 1, 2, np.deg2rad(15)) + plt.axis('equal') + plt.show() + From 41fec85cb974d650d2ec7965346a1f64f3015518 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Jul 2023 08:02:59 +0900 Subject: [PATCH 582/909] Bump numpy from 1.25.0 to 1.25.1 in /requirements (#874) Bumps [numpy](https://github.com/numpy/numpy) from 1.25.0 to 1.25.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.25.0...v1.25.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2fab6f69c8..c5643beee8 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.25.0 +numpy == 1.25.1 scipy == 1.11.1 matplotlib == 3.7.1 cvxpy == 1.3.2 From 4527785222d3bb885c4dfd97df54dfdd66e10595 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Jul 2023 20:10:50 +0900 Subject: [PATCH 583/909] Bump matplotlib from 3.7.1 to 3.7.2 in /requirements (#876) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.7.1 to 3.7.2. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.7.1...v3.7.2) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index c5643beee8..27bc6b32d3 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.25.1 scipy == 1.11.1 -matplotlib == 3.7.1 +matplotlib == 3.7.2 cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test From 32e5e6fd2c891b1f56061f56e73b5d3f1386f1ba Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Jul 2023 20:54:22 +0900 Subject: [PATCH 584/909] Bump ruff from 0.0.276 to 0.0.277 in /requirements (#875) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.276 to 0.0.277. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.276...v0.0.277) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 27bc6b32d3..44742ed34b 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.276 # For unit test +ruff == 0.0.277 # For unit test From 8c9eb0187e83d0abff9e3d3d5d3af4add6e2c59e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Jul 2023 09:29:15 +0900 Subject: [PATCH 585/909] Bump ruff from 0.0.277 to 0.0.278 in /requirements (#879) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.277 to 0.0.278. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.277...v0.0.278) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 44742ed34b..2f6a441d3c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.277 # For unit test +ruff == 0.0.278 # For unit test From ba5a1dda753671e5e54ac264c113a6b8f1142640 Mon Sep 17 00:00:00 2001 From: Emmanuel Ferdman Date: Wed, 19 Jul 2023 16:04:35 +0300 Subject: [PATCH 586/909] docs: update the code style test script (#880) Signed-off-by: Emmanuel Ferdman --- docs/how_to_contribute_main.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst index 8a2f73e9cd..48895d6fda 100644 --- a/docs/how_to_contribute_main.rst +++ b/docs/how_to_contribute_main.rst @@ -50,7 +50,7 @@ At the least, try to run the example code without animation in the unit test. If you want to run the test suites locally, you can use the `runtests.sh` script by just executing it. -The `test_diff_codestyle.py`_ check code style for your PR's codes. +The `test_codestyle.py`_ check code style for your PR's codes. .. _`how to write doc`: @@ -155,7 +155,7 @@ Sponsors .. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html .. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md -.. _`test_diff_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_diff_codestyle.py +.. _`test_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_codestyle.py .. _`JetBrains`: https://www.jetbrains.com/ .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma From d6d761bd8a6289f43f6f763dd876018541439765 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Jul 2023 11:59:47 +0900 Subject: [PATCH 587/909] Bump ruff from 0.0.278 to 0.0.280 in /requirements (#881) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.278 to 0.0.280. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.278...v0.0.280) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2f6a441d3c..a1999d4854 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.278 # For unit test +ruff == 0.0.280 # For unit test From 9be188feca26a78fed1e638c8ca32ac8dca7d8b6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 1 Aug 2023 19:16:32 +0900 Subject: [PATCH 588/909] Bump numpy from 1.25.1 to 1.25.2 in /requirements (#882) Bumps [numpy](https://github.com/numpy/numpy) from 1.25.1 to 1.25.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.25.1...v1.25.2) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a1999d4854..26e0eb8db3 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.25.1 +numpy == 1.25.2 scipy == 1.11.1 matplotlib == 3.7.2 cvxpy == 1.3.2 From bd0254997d82a3e40bb230db1f137c5a96c8e68e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 1 Aug 2023 19:21:48 +0900 Subject: [PATCH 589/909] Bump ruff from 0.0.280 to 0.0.281 in /requirements (#883) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.280 to 0.0.281. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.280...v0.0.281) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 26e0eb8db3..33bb44ee8f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.280 # For unit test +ruff == 0.0.281 # For unit test From e7d0a219c6a4c3277a61b3e441f260d036a41479 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Aug 2023 22:09:14 +0900 Subject: [PATCH 590/909] Bump sphinx-notes/pages from 2 to 3 (#884) Bumps [sphinx-notes/pages](https://github.com/sphinx-notes/pages) from 2 to 3. - [Release notes](https://github.com/sphinx-notes/pages/releases) - [Commits](https://github.com/sphinx-notes/pages/compare/v2...v3) --- updated-dependencies: - dependency-name: sphinx-notes/pages dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/gh-pages.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index d5af3b87c6..9cb661ae52 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -19,7 +19,7 @@ jobs: python -m pip install --upgrade pip python -m pip install -r requirements/requirements.txt - name: Build and Commit - uses: sphinx-notes/pages@v2 + uses: sphinx-notes/pages@v3 with: requirements_path: ./docs/doc_requirements.txt - name: Push changes From 5104e2c692ad8c8ffab6eb04bfa96cd9e75dc1d3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Aug 2023 22:09:42 +0900 Subject: [PATCH 591/909] Bump ruff from 0.0.281 to 0.0.282 in /requirements (#885) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.281 to 0.0.282. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.281...v0.0.282) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 33bb44ee8f..281a4c7662 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.281 # For unit test +ruff == 0.0.282 # For unit test From 801d43dd25c5bf3f553960eecfbaff7741550031 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 11:43:28 +0900 Subject: [PATCH 592/909] Bump ruff from 0.0.282 to 0.0.284 in /requirements (#887) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.282 to 0.0.284. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.282...v0.0.284) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 281a4c7662..d24bb676c6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.282 # For unit test +ruff == 0.0.284 # For unit test From de141eaf14cd3e71ef224eaa81ab4f76336a37b2 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 15 Aug 2023 12:40:52 +0900 Subject: [PATCH 593/909] Update gh-pages.yml (#889) --- .github/workflows/gh-pages.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 9cb661ae52..072c60c32e 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -6,6 +6,8 @@ on: jobs: build: runs-on: ubuntu-latest + permissions: + id-token: write steps: - name: Setup python uses: actions/setup-python@v4 From bfc91b1bc33372c697081221aefca65e7c7342b5 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 15 Aug 2023 13:04:17 +0900 Subject: [PATCH 594/909] Update gh-pages.yml --- .github/workflows/gh-pages.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 072c60c32e..f2630df162 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -8,6 +8,7 @@ jobs: runs-on: ubuntu-latest permissions: id-token: write + pages: write steps: - name: Setup python uses: actions/setup-python@v4 From 35f4f61e44ad9c3f8fc6584e3d0d7ab7889bd866 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Aug 2023 13:16:21 +0900 Subject: [PATCH 595/909] Bump mypy from 1.4.1 to 1.5.0 in /requirements (#888) Bumps [mypy](https://github.com/python/mypy) from 1.4.1 to 1.5.0. - [Commits](https://github.com/python/mypy/compare/v1.4.1...v1.5.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d24bb676c6..7ae3c54134 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.7.2 cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test -mypy == 1.4.1 # For unit test +mypy == 1.5.0 # For unit test ruff == 0.0.284 # For unit test From ff9e48852d7da400768099f54dce90f3cbab104a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Aug 2023 07:38:09 +0900 Subject: [PATCH 596/909] Bump ruff from 0.0.284 to 0.0.285 in /requirements (#893) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.284 to 0.0.285. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.284...v0.0.285) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7ae3c54134..fed24446c5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.0 # For unit test -ruff == 0.0.284 # For unit test +ruff == 0.0.285 # For unit test From 637269e0af6128acf5469c057fe6b0564e4d4768 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Aug 2023 20:42:01 +0900 Subject: [PATCH 597/909] Bump scipy from 1.11.1 to 1.11.2 in /requirements (#894) Bumps [scipy](https://github.com/scipy/scipy) from 1.11.1 to 1.11.2. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.11.1...v1.11.2) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index fed24446c5..24f5dd8341 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.25.2 -scipy == 1.11.1 +scipy == 1.11.2 matplotlib == 3.7.2 cvxpy == 1.3.2 pytest == 7.4.0 # For unit test From 3fce78a41e56b3bdb183e581bede2b19ea96a5df Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Aug 2023 20:43:32 +0900 Subject: [PATCH 598/909] Bump mypy from 1.5.0 to 1.5.1 in /requirements (#895) Bumps [mypy](https://github.com/python/mypy) from 1.5.0 to 1.5.1. - [Commits](https://github.com/python/mypy/compare/v1.5.0...v1.5.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 24f5dd8341..a0eb41aa73 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.7.2 cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test -mypy == 1.5.0 # For unit test +mypy == 1.5.1 # For unit test ruff == 0.0.285 # For unit test From 19c0fb379c070329c0e60e347a8b41179833140d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 9 Sep 2023 07:01:58 +0900 Subject: [PATCH 599/909] Bump actions/checkout from 3 to 4 (#900) Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 4. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .github/workflows/Windows_CI.yml | 2 +- .github/workflows/codeql.yml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index a152972753..b9a102c714 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -17,7 +17,7 @@ jobs: name: Python ${{ matrix.python-version }} CI steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - run: git fetch --prune --unshallow - name: Setup python diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index c8bfbbcb7e..2acabd8b44 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -19,7 +19,7 @@ jobs: python-version: [ '3.11' ] name: Python ${{ matrix.python-version }} CI steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - run: git fetch --prune --unshallow - name: Update bash diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index 13fdcea8a1..3df2e6265b 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -19,7 +19,7 @@ jobs: python-version: [ '3.11' ] name: Python ${{ matrix.python-version }} CI steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - run: git fetch --prune --unshallow - name: Setup python diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index d33545c90f..d3e888f09e 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -16,7 +16,7 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: # We must fetch at least the immediate parents so that if this is # a pull request then we can checkout the head. From 4b3544e74ddfd20107de280295e799e0b0385433 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Sep 2023 15:55:49 +0900 Subject: [PATCH 600/909] Bump pytest from 7.4.0 to 7.4.1 in /requirements (#901) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.0 to 7.4.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.4.0...7.4.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a0eb41aa73..b0d3e4d4f0 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.25.2 scipy == 1.11.2 matplotlib == 3.7.2 cvxpy == 1.3.2 -pytest == 7.4.0 # For unit test +pytest == 7.4.1 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test ruff == 0.0.285 # For unit test From c9145d348c68be78acb2652ec83587ce90d4a51f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Sep 2023 20:38:43 +0900 Subject: [PATCH 601/909] Bump ruff from 0.0.285 to 0.0.287 in /requirements (#902) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.285 to 0.0.287. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.285...v0.0.287) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b0d3e4d4f0..e4792a31bf 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.1 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test -ruff == 0.0.285 # For unit test +ruff == 0.0.287 # For unit test From 8a427a92abc7fe241dd67f69d17c80c54cd272c2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 12 Sep 2023 07:35:26 +0900 Subject: [PATCH 602/909] Bump ruff from 0.0.287 to 0.0.288 in /requirements (#905) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.287 to 0.0.288. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.287...v0.0.288) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e4792a31bf..9eeb1f024f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.1 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test -ruff == 0.0.287 # For unit test +ruff == 0.0.288 # For unit test From 8956c67c21055b9a2c44c1e0d3d20b194e4dfd95 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 13 Sep 2023 17:33:46 +0900 Subject: [PATCH 603/909] Bump pytest from 7.4.1 to 7.4.2 in /requirements (#904) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.1 to 7.4.2. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.4.1...7.4.2) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9eeb1f024f..0c616fc6d3 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.25.2 scipy == 1.11.2 matplotlib == 3.7.2 cvxpy == 1.3.2 -pytest == 7.4.1 # For unit test +pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test ruff == 0.0.288 # For unit test From 644d70d0fa3d83ae4434725ff0f82b4f7d4ec46f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Sep 2023 06:51:53 +0900 Subject: [PATCH 604/909] Bump ruff from 0.0.288 to 0.0.290 in /requirements (#908) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.288 to 0.0.290. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.288...v0.0.290) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 0c616fc6d3..6392fd2c33 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test -ruff == 0.0.288 # For unit test +ruff == 0.0.290 # For unit test From ba09b4dd90fc49d8873771782c7dc82f0a213534 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Sep 2023 06:59:38 +0900 Subject: [PATCH 605/909] Bump matplotlib from 3.7.2 to 3.8.0 in /requirements (#909) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.7.2 to 3.8.0. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.7.2...v3.8.0) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6392fd2c33..d1c0592e14 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.25.2 scipy == 1.11.2 -matplotlib == 3.7.2 +matplotlib == 3.8.0 cvxpy == 1.3.2 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test From a7513699bf7f6f3d6112e48464deddf7e8c22f68 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Sep 2023 12:36:29 +0900 Subject: [PATCH 606/909] Bump numpy from 1.25.2 to 1.26.0 in /requirements (#910) Bumps [numpy](https://github.com/numpy/numpy) from 1.25.2 to 1.26.0. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.25.2...v1.26.0) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d1c0592e14..836f8b0f54 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.25.2 +numpy == 1.26.0 scipy == 1.11.2 matplotlib == 3.8.0 cvxpy == 1.3.2 From 57ee946537a13af397bafbe8cf54507988006873 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 27 Sep 2023 20:23:08 +0900 Subject: [PATCH 607/909] Bump ruff from 0.0.290 to 0.0.291 in /requirements (#911) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.290 to 0.0.291. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.290...v0.0.291) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 836f8b0f54..d983e22916 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test -ruff == 0.0.290 # For unit test +ruff == 0.0.291 # For unit test From 6a765964409bb3f449d94bd55eb0dd8998730c71 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 3 Oct 2023 21:36:37 +0900 Subject: [PATCH 608/909] Bump scipy from 1.11.2 to 1.11.3 in /requirements (#914) Bumps [scipy](https://github.com/scipy/scipy) from 1.11.2 to 1.11.3. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.11.2...v1.11.3) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d983e22916..b67f904ea5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.26.0 -scipy == 1.11.2 +scipy == 1.11.3 matplotlib == 3.8.0 cvxpy == 1.3.2 pytest == 7.4.2 # For unit test From 2c4d74507777993d44cbb33e238689d776221243 Mon Sep 17 00:00:00 2001 From: Yuki Onishi Date: Sun, 15 Oct 2023 10:25:22 +0900 Subject: [PATCH 609/909] Revert a parameter in grid_based_sweep_coverage_path_planner to correct (#917) the planning results --- .../grid_based_sweep_coverage_path_planner.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index ee192e9200..10ba98cd35 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -50,7 +50,7 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map): # moving backward next_c_x_index = -self.moving_direction + c_x_index next_c_y_index = c_y_index - if self.check_occupied(next_c_x_index, next_c_y_index, grid_map): + if self.check_occupied(next_c_x_index, next_c_y_index, grid_map, FloatGrid(1.0)): # moved backward, but the grid is occupied by obstacle return None, None else: @@ -61,8 +61,8 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map): return next_c_x_index, next_c_y_index @staticmethod - def check_occupied(c_x_index, c_y_index, grid_map): - return grid_map.check_occupied_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5)) + def check_occupied(c_x_index, c_y_index, grid_map, occupied_val=FloatGrid(0.5)): + return grid_map.check_occupied_from_xy_index(c_x_index, c_y_index, occupied_val) def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map): From 5eed0ec1a6a8751e448a476c950f7342d864510d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 15 Oct 2023 15:34:15 +0900 Subject: [PATCH 610/909] Bump ruff from 0.0.291 to 0.0.292 in /requirements (#913) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.291 to 0.0.292. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.291...v0.0.292) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b67f904ea5..cd38b9cff7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test -ruff == 0.0.291 # For unit test +ruff == 0.0.292 # For unit test From 4adb77dcf71d3dced8e152f861106de1d192adf6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 18 Oct 2023 21:25:55 +0900 Subject: [PATCH 611/909] Bump ruff from 0.0.292 to 0.1.0 in /requirements (#920) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.292 to 0.1.0. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.292...v0.1.0) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index cd38b9cff7..acc76577c8 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test -ruff == 0.0.292 # For unit test +ruff == 0.1.0 # For unit test From 38a8be94bab0baa900a82fafa774b196bd4f1396 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 18 Oct 2023 21:26:26 +0900 Subject: [PATCH 612/909] Bump numpy from 1.26.0 to 1.26.1 in /requirements (#922) Bumps [numpy](https://github.com/numpy/numpy) from 1.26.0 to 1.26.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.26.0...v1.26.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index acc76577c8..a5875a67ec 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.26.0 +numpy == 1.26.1 scipy == 1.11.3 matplotlib == 3.8.0 cvxpy == 1.3.2 From 8f1306ac01055743a456b08cf2e0032f1942f96c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 18 Oct 2023 22:01:34 +0900 Subject: [PATCH 613/909] Bump cvxpy from 1.3.2 to 1.4.1 in /requirements (#923) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.3.2 to 1.4.1. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.3.2...v1.4.1) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a5875a67ec..46ad6feb23 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.26.1 scipy == 1.11.3 matplotlib == 3.8.0 -cvxpy == 1.3.2 +cvxpy == 1.4.1 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test From d0ff8cc165ce97c473e090e78003a8e5703a7b6d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 25 Oct 2023 20:15:46 +0900 Subject: [PATCH 614/909] Bump ruff from 0.1.0 to 0.1.1 in /requirements (#926) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.0 to 0.1.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.0...v0.1.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 46ad6feb23..0bda50edc0 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.5.1 # For unit test -ruff == 0.1.0 # For unit test +ruff == 0.1.1 # For unit test From bde8bda7536449522f3f2bfaa200a450dff41c92 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 25 Oct 2023 23:01:04 +0900 Subject: [PATCH 615/909] Bump mypy from 1.5.1 to 1.6.1 in /requirements (#924) Bumps [mypy](https://github.com/python/mypy) from 1.5.1 to 1.6.1. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.5.1...v1.6.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 0bda50edc0..1a89183ba6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.8.0 cvxpy == 1.4.1 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test -mypy == 1.5.1 # For unit test +mypy == 1.6.1 # For unit test ruff == 0.1.1 # For unit test From 3a8d93b2a54928d258362b34b31fd8adf04a7d05 Mon Sep 17 00:00:00 2001 From: Jeff Irion Date: Wed, 25 Oct 2023 07:02:14 -0700 Subject: [PATCH 616/909] Fix SE(2) inverse (#925) * Fix SE(2) inverse * Add missing ] --- SLAM/GraphBasedSLAM/graphslam/pose/se2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py index e89317a06f..2a32e765f7 100644 --- a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py +++ b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py @@ -136,8 +136,8 @@ def inverse(self): """ return PoseSE2([-self[0] * np.cos(self[2]) - self[1] * np.sin(self[2]), - self[0] * np.sin(self[2]) - self[1] * np.cos( - [self[2]])], -self[2]) + self[0] * np.sin(self[2]) - self[1] * np.cos(self[2])], + -self[2]) # ======================================================================= # # # From f374393b551b781f9c9564bd8b0e273342c8e3a0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 31 Oct 2023 20:55:50 +0900 Subject: [PATCH 617/909] Bump ruff from 0.1.1 to 0.1.3 in /requirements (#928) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.1 to 0.1.3. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.1...v0.1.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1a89183ba6..26a481cbe8 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.6.1 # For unit test -ruff == 0.1.1 # For unit test +ruff == 0.1.3 # For unit test From fc3ec3cd66f64fc889c3382c4ae41aca26adaf4e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 1 Nov 2023 08:38:39 +0900 Subject: [PATCH 618/909] Bump pytest from 7.4.2 to 7.4.3 in /requirements (#929) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.2 to 7.4.3. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.4.2...7.4.3) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 26a481cbe8..4335e42aca 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.1 scipy == 1.11.3 matplotlib == 3.8.0 cvxpy == 1.4.1 -pytest == 7.4.2 # For unit test +pytest == 7.4.3 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.6.1 # For unit test ruff == 0.1.3 # For unit test From 8be35b6e70aeb466efcfc76f480a9d734f92f34d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Nov 2023 07:03:23 +0900 Subject: [PATCH 619/909] Bump matplotlib from 3.8.0 to 3.8.1 in /requirements (#931) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.0 to 3.8.1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.0...v3.8.1) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4335e42aca..3515fbdf9f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.26.1 scipy == 1.11.3 -matplotlib == 3.8.0 +matplotlib == 3.8.1 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.3.1 # For unit test From dcf8eded0647e1f6d8695cbf98fae690e29fa6df Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Nov 2023 08:15:50 +0900 Subject: [PATCH 620/909] Bump ruff from 0.1.3 to 0.1.4 in /requirements (#932) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.3 to 0.1.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.3...v0.1.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3515fbdf9f..e65db6c92a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.6.1 # For unit test -ruff == 0.1.3 # For unit test +ruff == 0.1.4 # For unit test From 9eac2c6f2acc63925085242d9bf895b0084fad3b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Nov 2023 21:40:32 +0900 Subject: [PATCH 621/909] Bump pytest-xdist from 3.3.1 to 3.4.0 in /requirements (#933) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.3.1 to 3.4.0. - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.3.1...v3.4.0) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e65db6c92a..6f7253b4a7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.11.3 matplotlib == 3.8.1 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test -pytest-xdist == 3.3.1 # For unit test +pytest-xdist == 3.4.0 # For unit test mypy == 1.6.1 # For unit test ruff == 0.1.4 # For unit test From 3fa8f3e56cf5c742ec19548167bde266ee5e7aa8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Nov 2023 13:18:18 +0900 Subject: [PATCH 622/909] Bump numpy from 1.26.1 to 1.26.2 in /requirements (#934) Bumps [numpy](https://github.com/numpy/numpy) from 1.26.1 to 1.26.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.26.1...v1.26.2) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6f7253b4a7..f8439d2eb7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.26.1 +numpy == 1.26.2 scipy == 1.11.3 matplotlib == 3.8.1 cvxpy == 1.4.1 From 58df4be95f7ad4fa67b70cac657fcc2c07e85756 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Nov 2023 14:57:46 +0900 Subject: [PATCH 623/909] Bump mypy from 1.6.1 to 1.7.0 in /requirements (#936) Bumps [mypy](https://github.com/python/mypy) from 1.6.1 to 1.7.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.6.1...v1.7.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f8439d2eb7..f6e237366f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.8.1 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.4.0 # For unit test -mypy == 1.6.1 # For unit test +mypy == 1.7.0 # For unit test ruff == 0.1.4 # For unit test From f7dadb8157cd8ecebb2ef3eeb2e8dc1ae5bfb664 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Nov 2023 17:08:33 +0900 Subject: [PATCH 624/909] Bump ruff from 0.1.4 to 0.1.5 in /requirements (#935) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.4 to 0.1.5. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.4...v0.1.5) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f6e237366f..a17a6bc7cb 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.4.0 # For unit test mypy == 1.7.0 # For unit test -ruff == 0.1.4 # For unit test +ruff == 0.1.5 # For unit test From f5f0253e14e97856f4f6d4016cf4864fce8d46bb Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Nov 2023 12:57:31 +0900 Subject: [PATCH 625/909] Bump scipy from 1.11.3 to 1.11.4 in /requirements (#938) Bumps [scipy](https://github.com/scipy/scipy) from 1.11.3 to 1.11.4. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.11.3...v1.11.4) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a17a6bc7cb..3fcdd9ad19 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.26.2 -scipy == 1.11.3 +scipy == 1.11.4 matplotlib == 3.8.1 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test From df932b0eb25349fd43f28c1f28a92616b9e709b8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Nov 2023 18:59:57 +0900 Subject: [PATCH 626/909] Bump matplotlib from 3.8.1 to 3.8.2 in /requirements (#940) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.1 to 3.8.2. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.1...v3.8.2) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3fcdd9ad19..788e900d13 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.26.2 scipy == 1.11.4 -matplotlib == 3.8.1 +matplotlib == 3.8.2 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.4.0 # For unit test From 9093d709b5ff74c085f50ab3a1d57928ee6a5b13 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Nov 2023 19:52:11 +0900 Subject: [PATCH 627/909] Bump ruff from 0.1.5 to 0.1.6 in /requirements (#939) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.5 to 0.1.6. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.5...v0.1.6) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 788e900d13..b3b8509b68 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.4.0 # For unit test mypy == 1.7.0 # For unit test -ruff == 0.1.5 # For unit test +ruff == 0.1.6 # For unit test From 0dc6ab42bab4d3e2fb06fc25d86b1abe41d4da2a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Nov 2023 19:59:15 +0900 Subject: [PATCH 628/909] Bump pytest-xdist from 3.4.0 to 3.5.0 in /requirements (#942) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.4.0 to 3.5.0. - [Release notes](https://github.com/pytest-dev/pytest-xdist/releases) - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.4.0...v3.5.0) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b3b8509b68..afc244c1a1 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.11.4 matplotlib == 3.8.2 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test -pytest-xdist == 3.4.0 # For unit test +pytest-xdist == 3.5.0 # For unit test mypy == 1.7.0 # For unit test ruff == 0.1.6 # For unit test From e9ae79f9204df88e768de39443781a208d66ecd5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 3 Dec 2023 21:01:12 +0900 Subject: [PATCH 629/909] Bump mypy from 1.7.0 to 1.7.1 in /requirements (#943) Bumps [mypy](https://github.com/python/mypy) from 1.7.0 to 1.7.1. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.7.0...v1.7.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index afc244c1a1..f073524c51 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.8.2 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.5.0 # For unit test -mypy == 1.7.0 # For unit test +mypy == 1.7.1 # For unit test ruff == 0.1.6 # For unit test From 5676da661f09d08fb0ef188f31f71f17be0ab44a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 3 Dec 2023 21:23:50 +0900 Subject: [PATCH 630/909] Update gh-pages.yml --- .github/workflows/gh-pages.yml | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index f2630df162..42ec54f4e1 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -25,8 +25,7 @@ jobs: uses: sphinx-notes/pages@v3 with: requirements_path: ./docs/doc_requirements.txt - - name: Push changes - uses: ad-m/github-push-action@master - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - branch: gh-pages + - name: Deploy GitHub Pages site + id: deployment + uses: actions/deploy-pages@main + From 7585316933cee2febfc0a2cd68a13336020adbbc Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 3 Dec 2023 21:35:30 +0900 Subject: [PATCH 631/909] Update gh-pages.yml --- .github/workflows/gh-pages.yml | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 42ec54f4e1..17fa1d76ca 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -6,6 +6,9 @@ on: jobs: build: runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} permissions: id-token: write pages: write @@ -25,7 +28,9 @@ jobs: uses: sphinx-notes/pages@v3 with: requirements_path: ./docs/doc_requirements.txt - - name: Deploy GitHub Pages site - id: deployment - uses: actions/deploy-pages@main + - name: Push changes + uses: ad-m/github-push-action@master + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + branch: gh-pages From 6dca76ac4b566958080dac7f87f44b15e44a84aa Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 3 Dec 2023 21:41:48 +0900 Subject: [PATCH 632/909] Update gh-pages.yml --- .github/workflows/gh-pages.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 17fa1d76ca..c50a9f1865 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -1,4 +1,4 @@ -name: Pages +name: GitHub Pages update on: push: branches: From 467f27d178f05474b619f841a93b027707b421ac Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 3 Dec 2023 21:46:56 +0900 Subject: [PATCH 633/909] Update gh-pages.yml --- .github/workflows/gh-pages.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index c50a9f1865..ccc0f5a835 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -1,4 +1,4 @@ -name: GitHub Pages update +name: GitHub Pages site update on: push: branches: From ef287d7a857c52a3f34e7b86765c3467858ad2b6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 3 Dec 2023 21:53:12 +0900 Subject: [PATCH 634/909] Update gh-pages.yml --- .github/workflows/gh-pages.yml | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index ccc0f5a835..d2c7847807 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -24,13 +24,7 @@ jobs: python --version python -m pip install --upgrade pip python -m pip install -r requirements/requirements.txt - - name: Build and Commit + - name: Build and Deploy uses: sphinx-notes/pages@v3 with: - requirements_path: ./docs/doc_requirements.txt - - name: Push changes - uses: ad-m/github-push-action@master - with: - github_token: ${{ secrets.GITHUB_TOKEN }} - branch: gh-pages - + requirements_path: ./docs/doc_requirements.txt From 331f0877f6ade42901c0762fee41abcce83c7e29 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 3 Dec 2023 21:56:04 +0900 Subject: [PATCH 635/909] Update copyright for doc update test --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index df9b8fce22..46f69cf17b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = 'PythonRobotics' -copyright = '2018-2021, Atsushi Sakai' +copyright = '2018-2023, Atsushi Sakai' author = 'Atsushi Sakai' # The short X.Y version From a141cf41807c508e58691c3c42919387e5ff9ded Mon Sep 17 00:00:00 2001 From: Fabiha Tasneem <55595104+fabihatasneem@users.noreply.github.com> Date: Sun, 3 Dec 2023 19:12:00 +0600 Subject: [PATCH 636/909] Fixed Inappropriate Logical Expression (#945) --- .../arm_obstacle_navigation/arm_obstacle_navigation_2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py index 40e6746bfc..591cd401eb 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py @@ -34,7 +34,7 @@ def main(): goal = (58, 56) grid = get_occupancy_grid(arm, obstacles) route = astar_torus(grid, start, goal) - if len(route) >= 0: + if route: animate(grid, arm, route) From ff66f3babe27d1ae3775f594ffd05264ca173d6a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 12 Dec 2023 22:34:49 +0900 Subject: [PATCH 637/909] Bump actions/setup-python from 4 to 5 (#951) Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .github/workflows/Windows_CI.yml | 2 +- .github/workflows/gh-pages.yml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index b9a102c714..b83954f17e 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -21,7 +21,7 @@ jobs: - run: git fetch --prune --unshallow - name: Setup python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 2acabd8b44..19c6612b2f 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -26,7 +26,7 @@ jobs: run: brew install bash - name: Setup python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index 3df2e6265b..906edcfcdb 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -23,7 +23,7 @@ jobs: - run: git fetch --prune --unshallow - name: Setup python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index d2c7847807..e08c6106c0 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -14,7 +14,7 @@ jobs: pages: write steps: - name: Setup python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 - name: Checkout uses: actions/checkout@master with: From d25fb355f3c7a1d1e2ee895d009d0d639a37c18b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 12 Dec 2023 22:35:02 +0900 Subject: [PATCH 638/909] Bump ruff from 0.1.6 to 0.1.7 in /requirements (#952) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.6 to 0.1.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.6...v0.1.7) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f073524c51..2c2a6d47ff 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.7.1 # For unit test -ruff == 0.1.6 # For unit test +ruff == 0.1.7 # For unit test From 313ce196215f2625c1f6b20e49722b51631bdb37 Mon Sep 17 00:00:00 2001 From: Xavier Dias <93143666+Xavi007@users.noreply.github.com> Date: Mon, 18 Dec 2023 05:42:47 +0530 Subject: [PATCH 639/909] Xavi007: #903 There's an error in the transformation_matrix of Quadrotor renamed "yaw" to "roll" (#906) --- AerialNavigation/drone_3d_trajectory_following/Quadrotor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py index 413a8625a5..c379e5eda0 100644 --- a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py +++ b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py @@ -56,7 +56,7 @@ def transformation_matrix(self): [[cos(yaw) * cos(pitch), -sin(yaw) * cos(roll) + cos(yaw) * sin(pitch) * sin(roll), sin(yaw) * sin(roll) + cos(yaw) * sin(pitch) * cos(roll), x], [sin(yaw) * cos(pitch), cos(yaw) * cos(roll) + sin(yaw) * sin(pitch) * sin(roll), -cos(yaw) * sin(roll) + sin(yaw) * sin(pitch) * cos(roll), y], - [-sin(pitch), cos(pitch) * sin(roll), cos(pitch) * cos(yaw), z] + [-sin(pitch), cos(pitch) * sin(roll), cos(pitch) * cos(roll), z] ]) def plot(self): # pragma: no cover From 3efbc94f9cb5532b7a046d4fe79d3fe62dc18bd4 Mon Sep 17 00:00:00 2001 From: flyingmars <55879494+2892510130@users.noreply.github.com> Date: Mon, 18 Dec 2023 21:53:03 +0800 Subject: [PATCH 640/909] fix d_star (#898) --- PathPlanning/DStar/dstar.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py index f8954d776c..cc1d88ed66 100644 --- a/PathPlanning/DStar/dstar.py +++ b/PathPlanning/DStar/dstar.py @@ -9,6 +9,7 @@ """ import math + from sys import maxsize import matplotlib.pyplot as plt @@ -103,7 +104,7 @@ def process_state(self): if y.h <= k_old and x.h > y.h + x.cost(y): x.parent = y x.h = y.h + x.cost(y) - elif k_old == x.h: + if k_old == x.h: for y in self.map.get_neighbors(x): if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y) \ or y.parent != x and y.h > x.h + x.cost(y): @@ -116,7 +117,7 @@ def process_state(self): self.insert(y, x.h + x.cost(y)) else: if y.parent != x and y.h > x.h + x.cost(y): - self.insert(y, x.h) + self.insert(x, x.h) else: if y.parent != x and x.h > y.h + x.cost(y) \ and y.t == "close" and y.h > k_old: @@ -173,6 +174,8 @@ def run(self, start, end): s.set_state("e") tmp = start + AddNewObstacle(self.map) # add new obstacle after the first search finished + while tmp != end: tmp.set_state("*") rx.append(tmp.x) @@ -195,6 +198,15 @@ def modify(self, state): if k_min >= state.h: break +def AddNewObstacle(map:Map): + ox, oy = [], [] + for i in range(5, 21): + ox.append(i) + oy.append(40) + map.set_obstacle([(i, j) for i, j in zip(ox, oy)]) + if show_animation: + plt.pause(0.001) + plt.plot(ox, oy, ".g") def main(): m = Map(100, 100) @@ -217,7 +229,6 @@ def main(): for i in range(0, 40): ox.append(40) oy.append(60 - i) - print([(i, j) for i, j in zip(ox, oy)]) m.set_obstacle([(i, j) for i, j in zip(ox, oy)]) start = [10, 10] @@ -234,7 +245,7 @@ def main(): rx, ry = dstar.run(start, end) if show_animation: - plt.plot(rx, ry, "-r") + # plt.plot(rx, ry, "-r") plt.show() From 1801cd0dfff86de899285d02f4a1593d2df970e9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Dec 2023 07:04:43 +0900 Subject: [PATCH 641/909] Bump github/codeql-action from 2 to 3 (#955) Bumps [github/codeql-action](https://github.com/github/codeql-action) from 2 to 3. - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/github/codeql-action/compare/v2...v3) --- updated-dependencies: - dependency-name: github/codeql-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/codeql.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index d3e888f09e..878a4a4435 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -24,7 +24,7 @@ jobs: # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL - uses: github/codeql-action/init@v2 + uses: github/codeql-action/init@v3 with: config-file: ./.github/codeql/codeql-config.yml # Override language selection by uncommenting this and choosing your languages @@ -34,7 +34,7 @@ jobs: # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). # If this step fails, then you should remove it and run the build manually (see below) - name: Autobuild - uses: github/codeql-action/autobuild@v2 + uses: github/codeql-action/autobuild@v3 # ℹ️ Command-line programs to run using the OS shell. # 📚 https://git.io/JvXDl @@ -48,4 +48,4 @@ jobs: # make release - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v2 + uses: github/codeql-action/analyze@v3 From 7cca7aa6ac4c6d66cbb46011cc642b2543e5756e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Dec 2023 07:05:11 +0900 Subject: [PATCH 642/909] Bump ruff from 0.1.7 to 0.1.8 in /requirements (#956) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.7 to 0.1.8. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.7...v0.1.8) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2c2a6d47ff..68d8b2dcff 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.7.1 # For unit test -ruff == 0.1.7 # For unit test +ruff == 0.1.8 # For unit test From b2f866a5cd7bbf3f70c9c247e13f19d4a8858c92 Mon Sep 17 00:00:00 2001 From: Nivid Patel <66813410+nivid26@users.noreply.github.com> Date: Tue, 19 Dec 2023 08:03:55 -0500 Subject: [PATCH 643/909] Update car.py (#957) Changed comment at line 62 and 64 --- PathPlanning/HybridAStar/car.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py index 0367ab2775..d7631133c7 100644 --- a/PathPlanning/HybridAStar/car.py +++ b/PathPlanning/HybridAStar/car.py @@ -59,9 +59,9 @@ def rectangle_check(x, y, yaw, ox, oy): rx, ry = converted_xy[0], converted_xy[1] if not (rx > LF or rx < -LB or ry > W / 2.0 or ry < -W / 2.0): - return False # no collision + return False # collision - return True # collision + return True # no collision def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): From e3f7979c3c08de87d702e1d2006ffb55440514a5 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 22 Dec 2023 12:13:59 +0900 Subject: [PATCH 644/909] Update dstar.py (#959) --- PathPlanning/DStar/dstar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py index cc1d88ed66..b62b939f54 100644 --- a/PathPlanning/DStar/dstar.py +++ b/PathPlanning/DStar/dstar.py @@ -245,7 +245,7 @@ def main(): rx, ry = dstar.run(start, end) if show_animation: - # plt.plot(rx, ry, "-r") + plt.plot(rx, ry, "-r") plt.show() From 87b3aacb8b41e1924319eaae85ef88c39e3062b1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 26 Dec 2023 23:36:29 +0900 Subject: [PATCH 645/909] Bump mypy from 1.7.1 to 1.8.0 in /requirements (#960) Bumps [mypy](https://github.com/python/mypy) from 1.7.1 to 1.8.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.7.1...v1.8.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 68d8b2dcff..ae1aa2d749 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.8.2 cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.5.0 # For unit test -mypy == 1.7.1 # For unit test +mypy == 1.8.0 # For unit test ruff == 0.1.8 # For unit test From 748b9be4e76fa2f0d55b507a30bb578b74d6c921 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 27 Dec 2023 08:33:04 +0900 Subject: [PATCH 646/909] Bump ruff from 0.1.8 to 0.1.9 in /requirements (#961) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.8 to 0.1.9. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.8...v0.1.9) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ae1aa2d749..bb78ed4d2f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.3 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.1.8 # For unit test +ruff == 0.1.9 # For unit test From cc3fd0c55e8df8637ffa1753c6a3d973db9ed2a1 Mon Sep 17 00:00:00 2001 From: Videh Patel <66770479+videh25@users.noreply.github.com> Date: Tue, 2 Jan 2024 19:09:48 +0530 Subject: [PATCH 647/909] Using util.angle_mod in all codes. #684 (#946) * switched to using utils.angle_mod() * switched to using utils.angle_mod() * renamed mod2pi to pi_2_pi * Removed linting errors * switched to using utils.angle_mod() * switched to using utils.angle_mod() * renamed mod2pi to pi_2_pi * Removed linting errors * annotation changes and round precision * Reverted to mod2pi --------- Co-authored-by: Videh Patel --- .../n_joint_arm_to_point_control.py | 5 ++-- .../two_joint_arm_to_point_control.py | 3 ++- Control/move_to_pose/move_to_pose.py | 19 +++++++-------- .../ensemble_kalman_filter.py | 3 ++- .../ClosedLoopRRTStar/unicycle_model.py | 3 ++- .../motion_model.py | 3 ++- .../trajectory_generator.py | 2 +- .../reeds_shepp_path_planning.py | 9 ++++---- .../lqr_speed_steer_control.py | 4 ++-- .../lqr_steer_control/lqr_steer_control.py | 3 ++- ...odel_predictive_speed_and_steer_control.py | 9 ++------ .../rear_wheel_feedback.py | 23 ++++++++----------- .../stanley_controller/stanley_controller.py | 13 ++++------- SLAM/EKFSLAM/ekf_slam.py | 3 ++- SLAM/FastSLAM1/fast_slam1.py | 3 ++- SLAM/FastSLAM2/fast_slam2.py | 3 ++- SLAM/GraphBasedSLAM/graph_based_slam.py | 3 ++- 17 files changed, 51 insertions(+), 60 deletions(-) diff --git a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py index 498ef41c1f..a237523336 100644 --- a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py +++ b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py @@ -11,6 +11,7 @@ import numpy as np from ArmNavigation.n_joint_arm_to_point_control.NLinkArm import NLinkArm +from utils.angle import angle_mod # Simulation parameters Kp = 2 @@ -159,9 +160,9 @@ def ang_diff(theta1, theta2): """ Returns the difference between two angles in the range -pi to +pi """ - return (theta1 - theta2 + np.pi) % (2 * np.pi) - np.pi + return angle_mod(theta1 - theta2) if __name__ == '__main__': # main() - animation() \ No newline at end of file + animation() diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index 66d0ebeb23..c2227f18e3 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -15,6 +15,7 @@ import matplotlib.pyplot as plt import numpy as np import math +from utils.angle import angle_mod # Simulation parameters @@ -110,7 +111,7 @@ def plot_arm(theta1, theta2, target_x, target_y): # pragma: no cover def ang_diff(theta1, theta2): # Returns the difference between two angles in the range -pi to +pi - return (theta1 - theta2 + np.pi) % (2 * np.pi) - np.pi + return angle_mod(theta1 - theta2) def click(event): # pragma: no cover diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py index 73604ccd7f..cc29666297 100644 --- a/Control/move_to_pose/move_to_pose.py +++ b/Control/move_to_pose/move_to_pose.py @@ -13,7 +13,7 @@ import matplotlib.pyplot as plt import numpy as np from random import random - +from utils.angle import angle_mod class PathFinderController: """ @@ -71,9 +71,8 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal): # from 0 rad to 2*pi rad with slight turn rho = np.hypot(x_diff, y_diff) - alpha = (np.arctan2(y_diff, x_diff) - - theta + np.pi) % (2 * np.pi) - np.pi - beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi + alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta) + beta = angle_mod(theta_goal - theta - alpha) v = self.Kp_rho * rho w = self.Kp_alpha * alpha - controller.Kp_beta * beta @@ -173,16 +172,14 @@ def transformation_matrix(x, y, theta): def main(): for i in range(5): - x_start = 20 * random() - y_start = 20 * random() - theta_start = 2 * np.pi * random() - np.pi + x_start = 20.0 * random() + y_start = 20.0 * random() + theta_start: float = 2 * np.pi * random() - np.pi x_goal = 20 * random() y_goal = 20 * random() theta_goal = 2 * np.pi * random() - np.pi - print("Initial x: %.2f m\nInitial y: %.2f m\nInitial theta: %.2f rad\n" % - (x_start, y_start, theta_start)) - print("Goal x: %.2f m\nGoal y: %.2f m\nGoal theta: %.2f rad\n" % - (x_goal, y_goal, theta_goal)) + print(f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n") + print(f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n") move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal) diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index fc8280e7bf..2bab3b49c1 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -16,6 +16,7 @@ import math import matplotlib.pyplot as plt import numpy as np +from utils.angle import angle_mod from utils.angle import rot_mat_2d @@ -179,7 +180,7 @@ def plot_covariance_ellipse(xEst, PEst): # pragma: no cover def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) def main(): diff --git a/PathPlanning/ClosedLoopRRTStar/unicycle_model.py b/PathPlanning/ClosedLoopRRTStar/unicycle_model.py index 5a0fd17a4e..c05f76c84e 100644 --- a/PathPlanning/ClosedLoopRRTStar/unicycle_model.py +++ b/PathPlanning/ClosedLoopRRTStar/unicycle_model.py @@ -8,6 +8,7 @@ import math import numpy as np +from utils.angle import angle_mod dt = 0.05 # [s] L = 0.9 # [m] @@ -39,7 +40,7 @@ def update(state, a, delta): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) if __name__ == '__main__': # pragma: no cover diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py index 531bf91ef5..5ef6d2e23f 100644 --- a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py +++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py @@ -1,6 +1,7 @@ import math import numpy as np from scipy.interpolate import interp1d +from utils.angle import angle_mod # motion parameter L = 1.0 # wheel base @@ -18,7 +19,7 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) def update(state, v, delta, dt, L): diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py index 97c0ad8b80..6084fc1a07 100644 --- a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py +++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py @@ -18,7 +18,7 @@ # optimization parameter max_iter = 100 -h = np.array([0.5, 0.02, 0.02]).T # parameter sampling distance +h: np.ndarray = np.array([0.5, 0.02, 0.02]).T # parameter sampling distance cost_th = 0.1 show_animation = True diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index c55d2629e5..4d8fb7d9b8 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -9,6 +9,7 @@ import matplotlib.pyplot as plt import numpy as np +from utils.angle import angle_mod show_animation = True @@ -40,6 +41,9 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): plt.plot(x, y) +def pi_2_pi(x): + return angle_mod(x) + def mod2pi(x): # Be consistent with fmod in cplusplus here. v = np.mod(x, np.copysign(2.0 * math.pi, x)) @@ -50,7 +54,6 @@ def mod2pi(x): v -= 2.0 * math.pi return v - def straight_left_straight(x, y, phi): phi = mod2pi(phi) # only take phi in (0.01*math.pi, 0.99*math.pi) for the sake of speed. @@ -296,10 +299,6 @@ def interpolate(dist, length, mode, max_curvature, origin_x, origin_y, return x, y, yaw, 1 if length > 0.0 else -1 -def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi - - def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size): q0 = [sx, sy, syaw] q1 = [gx, gy, gyaw] diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py index 7e57abb80e..a70e30d378 100644 --- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py +++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py @@ -11,6 +11,7 @@ import numpy as np import scipy.linalg as la import pathlib +from utils.angle import angle_mod sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) from PathPlanning.CubicSpline import cubic_spline_planner @@ -52,8 +53,7 @@ def update(state, a, delta): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi - + return angle_mod(angle) def solve_dare(A, B, Q, R): """ diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py index 95a1b92ce5..4078ea56db 100644 --- a/PathTracking/lqr_steer_control/lqr_steer_control.py +++ b/PathTracking/lqr_steer_control/lqr_steer_control.py @@ -11,6 +11,7 @@ import numpy as np import sys import pathlib +from utils.angle import angle_mod sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) from PathPlanning.CubicSpline import cubic_spline_planner @@ -61,7 +62,7 @@ def PIDControl(target, current): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) def solve_DARE(A, B, Q, R): diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py index 3015313198..c55018cf26 100644 --- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py @@ -11,6 +11,7 @@ import numpy as np import sys import pathlib +from utils.angle import angle_mod sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) from PathPlanning.CubicSpline import cubic_spline_planner @@ -69,13 +70,7 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0): def pi_2_pi(angle): - while(angle > math.pi): - angle = angle - 2.0 * math.pi - - while(angle < -math.pi): - angle = angle + 2.0 * math.pi - - return angle + return angle_mod(angle) def get_linear_model_matrix(v, phi, delta): diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index ff72454f34..1702bd47dd 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -8,6 +8,7 @@ import matplotlib.pyplot as plt import math import numpy as np +from utils.angle import angle_mod from scipy import interpolate from scipy import optimize @@ -51,21 +52,21 @@ def __init__(self, x, y): self.ddY = self.Y.derivative(2) self.length = s[-1] - + def calc_yaw(self, s): dx, dy = self.dX(s), self.dY(s) return np.arctan2(dy, dx) - + def calc_curvature(self, s): dx, dy = self.dX(s), self.dY(s) ddx, ddy = self.ddX(s), self.ddY(s) return (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2)) - + def __find_nearest_point(self, s0, x, y): def calc_distance(_s, *args): _x, _y= self.X(_s), self.Y(_s) return (_x - args[0])**2 + (_y - args[1])**2 - + def calc_distance_jacobian(_s, *args): _x, _y = self.X(_s), self.Y(_s) _dx, _dy = self.dX(_s), self.dY(_s) @@ -76,7 +77,7 @@ def calc_distance_jacobian(_s, *args): def calc_track_error(self, x, y, s0): ret = self.__find_nearest_point(s0, x, y) - + s = ret[0][0] e = ret[1] @@ -96,13 +97,7 @@ def pid_control(target, current): return a def pi_2_pi(angle): - while(angle > math.pi): - angle = angle - 2.0 * math.pi - - while(angle < -math.pi): - angle = angle + 2.0 * math.pi - - return angle + return angle_mod(angle) def rear_wheel_feedback_control(state, e, k, yaw_ref): v = state.v @@ -170,7 +165,7 @@ def simulate(path_ref, goal): plt.plot(path_ref.X(s0), path_ref.Y(s0), "xg", label="target") plt.axis("equal") plt.grid(True) - plt.title("speed[km/h]:{:.2f}, target s-param:{:.2f}".format(round(state.v * 3.6, 2), s0)) + plt.title(f"speed[km/h]:{round(state.v * 3.6, 2):.2f}, target s-param:{s0:.2f}") plt.pause(0.0001) return t, x, y, yaw, v, goal_flag @@ -184,7 +179,7 @@ def calc_target_speed(state, yaw_ref): if switch: state.direction *= -1 return 0.0 - + if state.direction != 1: return -target_speed diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index 9c475cbaf2..08aa049395 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -13,6 +13,7 @@ import matplotlib.pyplot as plt import sys import pathlib +from utils.angle import angle_mod sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) from PathPlanning.CubicSpline import cubic_spline_planner @@ -26,7 +27,7 @@ show_animation = True -class State(object): +class State: """ Class representing the state of a vehicle. @@ -38,7 +39,7 @@ class State(object): def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0): """Instantiate the object.""" - super(State, self).__init__() + super().__init__() self.x = x self.y = y self.yaw = yaw @@ -106,13 +107,7 @@ def normalize_angle(angle): :param angle: (float) :return: (float) Angle in radian in [-pi, pi] """ - while angle > np.pi: - angle -= 2.0 * np.pi - - while angle < -np.pi: - angle += 2.0 * np.pi - - return angle + return angle_mod(angle) def calc_target_index(state, cx, cy): diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index bb648ce9d9..a624e8765b 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -8,6 +8,7 @@ import matplotlib.pyplot as plt import numpy as np +from utils.angle import angle_mod # EKF state covariance Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)]) ** 2 @@ -192,7 +193,7 @@ def jacob_h(q, delta, x, i): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) def main(): diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index 7b89f52df4..17f6d5e572 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -10,6 +10,7 @@ import matplotlib.pyplot as plt import numpy as np +from utils.angle import angle_mod # Fast SLAM covariance Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2 @@ -321,7 +322,7 @@ def motion_model(x, u): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) def main(): diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index aa77aa956a..2b57e3bcc3 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -10,6 +10,7 @@ import matplotlib.pyplot as plt import numpy as np +from utils.angle import angle_mod # Fast SLAM covariance Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2 @@ -346,7 +347,7 @@ def motion_model(x, u): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) def main(): diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py index 4d66ef6732..edd20a959c 100644 --- a/SLAM/GraphBasedSLAM/graph_based_slam.py +++ b/SLAM/GraphBasedSLAM/graph_based_slam.py @@ -21,6 +21,7 @@ import matplotlib.pyplot as plt import numpy as np from scipy.spatial.transform import Rotation as Rot +from utils.angle import angle_mod # Simulation parameter Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2 @@ -249,7 +250,7 @@ def motion_model(x, u): def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi + return angle_mod(angle) def main(): From 49ae54ab70f1c30ca848fef215bdbc172a8e692a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jan 2024 22:40:08 +0900 Subject: [PATCH 648/909] Bump pytest from 7.4.3 to 7.4.4 in /requirements (#963) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.3 to 7.4.4. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.4.3...7.4.4) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index bb78ed4d2f..8b3c4c1739 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.2 scipy == 1.11.4 matplotlib == 3.8.2 cvxpy == 1.4.1 -pytest == 7.4.3 # For unit test +pytest == 7.4.4 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test ruff == 0.1.9 # For unit test From d7060f60289cc0241f3411fea85764fb20f9cac0 Mon Sep 17 00:00:00 2001 From: Videh Patel <66770479+videh25@users.noreply.github.com> Date: Mon, 8 Jan 2024 18:06:49 +0530 Subject: [PATCH 649/909] Reopen: Added missing path types (#949) * added missing RS path types * modified assert condition in test3 * removed linting errors * added sign check to avoid non RS paths * Mathematical description of RS curves * Undid debugging changes * Added large step_size consideration * renamed test3 to test_too_big_step_size --------- Co-authored-by: Videh Patel --- .../reeds_shepp_path_planning.py | 320 +++++++++------ .../path_planning/reeds_shepp_path/LR_L.png | Bin 0 -> 280028 bytes .../path_planning/reeds_shepp_path/LR_LR.png | Bin 0 -> 381584 bytes .../path_planning/reeds_shepp_path/LSL.png | Bin 0 -> 239825 bytes .../reeds_shepp_path/LSL90xR.png | Bin 0 -> 301712 bytes .../path_planning/reeds_shepp_path/LSR.png | Bin 0 -> 257435 bytes .../reeds_shepp_path/LSR90_L.png | Bin 0 -> 339492 bytes .../reeds_shepp_path/L_R90SL.png | Bin 0 -> 316723 bytes .../reeds_shepp_path/L_R90SL90_R.png | Bin 0 -> 335979 bytes .../reeds_shepp_path/L_R90SR.png | Bin 0 -> 291754 bytes .../path_planning/reeds_shepp_path/L_RL.png | Bin 0 -> 277432 bytes .../path_planning/reeds_shepp_path/L_RL_R.png | Bin 0 -> 323010 bytes .../path_planning/reeds_shepp_path/L_R_L.png | Bin 0 -> 271666 bytes .../reeds_shepp_path_main.rst | 375 ++++++++++++++++++ tests/test_rrt_star_reeds_shepp.py | 6 +- 15 files changed, 586 insertions(+), 115 deletions(-) create mode 100644 docs/modules/path_planning/reeds_shepp_path/LR_L.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/LR_LR.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSL.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSL90xR.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSR.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSR90_L.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R90SL.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R90SL90_R.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R90SR.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_RL.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_RL_R.png create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R_L.png diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index 4d8fb7d9b8..8e134ff38b 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -3,6 +3,7 @@ Reeds Shepp path planner sample code author Atsushi Sakai(@Atsushi_twi) +co-author Videh Patel(@videh25) : Added the missing RS paths """ import math @@ -54,21 +55,6 @@ def mod2pi(x): v -= 2.0 * math.pi return v -def straight_left_straight(x, y, phi): - phi = mod2pi(phi) - # only take phi in (0.01*math.pi, 0.99*math.pi) for the sake of speed. - # phi in (0, 0.01*math.pi) will make test2() in test_rrt_star_reeds_shepp.py - # extremely time-consuming, since the value of xd, t will be very large. - if math.pi * 0.01 < phi < math.pi * 0.99 and y != 0: - xd = - y / math.tan(phi) + x - t = xd - math.tan(phi / 2.0) - u = phi - v = np.sign(y) * math.hypot(x - xd, y) - math.tan(phi / 2.0) - return True, t, u, v - - return False, 0.0, 0.0, 0.0 - - def set_path(paths, lengths, ctypes, step_size): path = Path() path.ctypes = ctypes @@ -90,18 +76,6 @@ def set_path(paths, lengths, ctypes, step_size): return paths -def straight_curve_straight(x, y, phi, paths, step_size): - flag, t, u, v = straight_left_straight(x, y, phi) - if flag: - paths = set_path(paths, [t, u, v], ["S", "L", "S"], step_size) - - flag, t, u, v = straight_left_straight(x, -y, -phi) - if flag: - paths = set_path(paths, [t, u, v], ["S", "R", "S"], step_size) - - return paths - - def polar(x, y): r = math.hypot(x, y) theta = math.atan2(y, x) @@ -110,117 +84,200 @@ def polar(x, y): def left_straight_left(x, y, phi): u, t = polar(x - math.sin(phi), y - 1.0 + math.cos(phi)) - if t >= 0.0: + if 0.0 <= t <= math.pi: v = mod2pi(phi - t) - if v >= 0.0: - return True, t, u, v + if 0.0 <= v <= math.pi: + return True, [t, u, v], ['L', 'S', 'L'] + + return False, [], [] + + +def left_straight_right(x, y, phi): + u1, t1 = polar(x + math.sin(phi), y - 1.0 - math.cos(phi)) + u1 = u1 ** 2 + if u1 >= 4.0: + u = math.sqrt(u1 - 4.0) + theta = math.atan2(2.0, u) + t = mod2pi(t1 + theta) + v = mod2pi(t - phi) + + if (t >= 0.0) and (v >= 0.0): + return True, [t, u, v], ['L', 'S', 'R'] - return False, 0.0, 0.0, 0.0 + return False, [], [] -def left_right_left(x, y, phi): - u1, t1 = polar(x - math.sin(phi), y - 1.0 + math.cos(phi)) +def left_x_right_x_left(x, y, phi): + zeta = x - math.sin(phi) + eeta = y - 1 + math.cos(phi) + u1, theta = polar(zeta, eeta) if u1 <= 4.0: - u = -2.0 * math.asin(0.25 * u1) - t = mod2pi(t1 + 0.5 * u + math.pi) - v = mod2pi(phi - t + u) + A = math.acos(0.25 * u1) + t = mod2pi(A + theta + math.pi/2) + u = mod2pi(math.pi - 2 * A) + v = mod2pi(phi - t - u) + return True, [t, -u, v], ['L', 'R', 'L'] - if t >= 0.0 >= u: - return True, t, u, v + return False, [], [] - return False, 0.0, 0.0, 0.0 +def left_x_right_left(x, y, phi): + zeta = x - math.sin(phi) + eeta = y - 1 + math.cos(phi) + u1, theta = polar(zeta, eeta) -def curve_curve_curve(x, y, phi, paths, step_size): - flag, t, u, v = left_right_left(x, y, phi) - if flag: - paths = set_path(paths, [t, u, v], ["L", "R", "L"], step_size) + if u1 <= 4.0: + A = math.acos(0.25 * u1) + t = mod2pi(A + theta + math.pi/2) + u = mod2pi(math.pi - 2*A) + v = mod2pi(-phi + t + u) + return True, [t, -u, -v], ['L', 'R', 'L'] - flag, t, u, v = left_right_left(-x, y, -phi) - if flag: - paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"], step_size) + return False, [], [] - flag, t, u, v = left_right_left(x, -y, -phi) - if flag: - paths = set_path(paths, [t, u, v], ["R", "L", "R"], step_size) - flag, t, u, v = left_right_left(-x, -y, phi) - if flag: - paths = set_path(paths, [-t, -u, -v], ["R", "L", "R"], step_size) +def left_right_x_left(x, y, phi): + zeta = x - math.sin(phi) + eeta = y - 1 + math.cos(phi) + u1, theta = polar(zeta, eeta) - # backwards - xb = x * math.cos(phi) + y * math.sin(phi) - yb = x * math.sin(phi) - y * math.cos(phi) + if u1 <= 4.0: + u = math.acos(1 - u1**2 * 0.125) + A = math.asin(2 * math.sin(u) / u1) + t = mod2pi(-A + theta + math.pi/2) + v = mod2pi(t - u - phi) + return True, [t, u, -v], ['L', 'R', 'L'] + + return False, [], [] + + +def left_right_x_left_right(x, y, phi): + zeta = x + math.sin(phi) + eeta = y - 1 - math.cos(phi) + u1, theta = polar(zeta, eeta) + + # Solutions refering to (2 < u1 <= 4) are considered sub-optimal in paper + # Solutions do not exist for u1 > 4 + if u1 <= 2: + A = math.acos((u1 + 2) * 0.25) + t = mod2pi(theta + A + math.pi/2) + u = mod2pi(A) + v = mod2pi(phi - t + 2*u) + if ((t >= 0) and (u >= 0) and (v >= 0)): + return True, [t, u, -u, -v], ['L', 'R', 'L', 'R'] + + return False, [], [] + + +def left_x_right_left_x_right(x, y, phi): + zeta = x + math.sin(phi) + eeta = y - 1 - math.cos(phi) + u1, theta = polar(zeta, eeta) + u2 = (20 - u1**2) / 16 + + if (0 <= u2 <= 1): + u = math.acos(u2) + A = math.asin(2 * math.sin(u) / u1) + t = mod2pi(theta + A + math.pi/2) + v = mod2pi(t - phi) + if (t >= 0) and (v >= 0): + return True, [t, -u, -u, v], ['L', 'R', 'L', 'R'] - flag, t, u, v = left_right_left(xb, yb, phi) - if flag: - paths = set_path(paths, [v, u, t], ["L", "R", "L"], step_size) + return False, [], [] - flag, t, u, v = left_right_left(-xb, yb, -phi) - if flag: - paths = set_path(paths, [-v, -u, -t], ["L", "R", "L"], step_size) - flag, t, u, v = left_right_left(xb, -yb, -phi) - if flag: - paths = set_path(paths, [v, u, t], ["R", "L", "R"], step_size) +def left_x_right90_straight_left(x, y, phi): + zeta = x - math.sin(phi) + eeta = y - 1 + math.cos(phi) + u1, theta = polar(zeta, eeta) - flag, t, u, v = left_right_left(-xb, -yb, phi) - if flag: - paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"], step_size) + if u1 >= 2.0: + u = math.sqrt(u1**2 - 4) - 2 + A = math.atan2(2, math.sqrt(u1**2 - 4)) + t = mod2pi(theta + A + math.pi/2) + v = mod2pi(t - phi + math.pi/2) + if (t >= 0) and (v >= 0): + return True, [t, -math.pi/2, -u, -v], ['L', 'R', 'S', 'L'] - return paths + return False, [], [] -def curve_straight_curve(x, y, phi, paths, step_size): - flag, t, u, v = left_straight_left(x, y, phi) - if flag: - paths = set_path(paths, [t, u, v], ["L", "S", "L"], step_size) +def left_straight_right90_x_left(x, y, phi): + zeta = x - math.sin(phi) + eeta = y - 1 + math.cos(phi) + u1, theta = polar(zeta, eeta) - flag, t, u, v = left_straight_left(-x, y, -phi) - if flag: - paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"], step_size) + if u1 >= 2.0: + u = math.sqrt(u1**2 - 4) - 2 + A = math.atan2(math.sqrt(u1**2 - 4), 2) + t = mod2pi(theta - A + math.pi/2) + v = mod2pi(t - phi - math.pi/2) + if (t >= 0) and (v >= 0): + return True, [t, u, math.pi/2, -v], ['L', 'S', 'R', 'L'] - flag, t, u, v = left_straight_left(x, -y, -phi) - if flag: - paths = set_path(paths, [t, u, v], ["R", "S", "R"], step_size) + return False, [], [] - flag, t, u, v = left_straight_left(-x, -y, phi) - if flag: - paths = set_path(paths, [-t, -u, -v], ["R", "S", "R"], step_size) - flag, t, u, v = left_straight_right(x, y, phi) - if flag: - paths = set_path(paths, [t, u, v], ["L", "S", "R"], step_size) +def left_x_right90_straight_right(x, y, phi): + zeta = x + math.sin(phi) + eeta = y - 1 - math.cos(phi) + u1, theta = polar(zeta, eeta) - flag, t, u, v = left_straight_right(-x, y, -phi) - if flag: - paths = set_path(paths, [-t, -u, -v], ["L", "S", "R"], step_size) + if u1 >= 2.0: + t = mod2pi(theta + math.pi/2) + u = u1 - 2 + v = mod2pi(phi - t - math.pi/2) + if (t >= 0) and (v >= 0): + return True, [t, -math.pi/2, -u, -v], ['L', 'R', 'S', 'R'] - flag, t, u, v = left_straight_right(x, -y, -phi) - if flag: - paths = set_path(paths, [t, u, v], ["R", "S", "L"], step_size) + return False, [], [] - flag, t, u, v = left_straight_right(-x, -y, phi) - if flag: - paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"], step_size) - return paths +def left_straight_left90_x_right(x, y, phi): + zeta = x + math.sin(phi) + eeta = y - 1 - math.cos(phi) + u1, theta = polar(zeta, eeta) + if u1 >= 2.0: + t = mod2pi(theta) + u = u1 - 2 + v = mod2pi(phi - t - math.pi/2) + if (t >= 0) and (v >= 0): + return True, [t, u, math.pi/2, -v], ['L', 'S', 'L', 'R'] + + return False, [], [] + + +def left_x_right90_straight_left90_x_right(x, y, phi): + zeta = x + math.sin(phi) + eeta = y - 1 - math.cos(phi) + u1, theta = polar(zeta, eeta) -def left_straight_right(x, y, phi): - u1, t1 = polar(x + math.sin(phi), y - 1.0 - math.cos(phi)) - u1 = u1 ** 2 if u1 >= 4.0: - u = math.sqrt(u1 - 4.0) - theta = math.atan2(2.0, u) - t = mod2pi(t1 + theta) + u = math.sqrt(u1**2 - 4) - 4 + A = math.atan2(2, math.sqrt(u1**2 - 4)) + t = mod2pi(theta + A + math.pi/2) v = mod2pi(t - phi) + if (t >= 0) and (v >= 0): + return True, [t, -math.pi/2, -u, -math.pi/2, v], ['L', 'R', 'S', 'L', 'R'] + + return False, [], [] + - if t >= 0.0 and v >= 0.0: - return True, t, u, v +def timeflip(travel_distances): + return [-x for x in travel_distances] - return False, 0.0, 0.0, 0.0 + +def reflect(steering_directions): + def switch_dir(dirn): + if dirn == 'L': + return 'R' + elif dirn == 'R': + return 'L' + else: + return 'S' + return[switch_dir(dirn) for dirn in steering_directions] def generate_path(q0, q1, max_curvature, step_size): @@ -231,11 +288,52 @@ def generate_path(q0, q1, max_curvature, step_size): s = math.sin(q0[2]) x = (c * dx + s * dy) * max_curvature y = (-s * dx + c * dy) * max_curvature + step_size *= max_curvature paths = [] - paths = straight_curve_straight(x, y, dth, paths, step_size) - paths = curve_straight_curve(x, y, dth, paths, step_size) - paths = curve_curve_curve(x, y, dth, paths, step_size) + path_functions = [left_straight_left, left_straight_right, # CSC + left_x_right_x_left, left_x_right_left, left_right_x_left, # CCC + left_right_x_left_right, left_x_right_left_x_right, # CCCC + left_x_right90_straight_left, left_x_right90_straight_right, # CCSC + left_straight_right90_x_left, left_straight_left90_x_right, # CSCC + left_x_right90_straight_left90_x_right] # CCSCC + + for path_func in path_functions: + flag, travel_distances, steering_dirns = path_func(x, y, dth) + if flag: + for distance in travel_distances: + if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size): + print("Step size too large for Reeds-Shepp paths.") + return [] + paths = set_path(paths, travel_distances, steering_dirns, step_size) + + flag, travel_distances, steering_dirns = path_func(-x, y, -dth) + if flag: + for distance in travel_distances: + if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size): + print("Step size too large for Reeds-Shepp paths.") + return [] + travel_distances = timeflip(travel_distances) + paths = set_path(paths, travel_distances, steering_dirns, step_size) + + flag, travel_distances, steering_dirns = path_func(x, -y, -dth) + if flag: + for distance in travel_distances: + if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size): + print("Step size too large for Reeds-Shepp paths.") + return [] + steering_dirns = reflect(steering_dirns) + paths = set_path(paths, travel_distances, steering_dirns, step_size) + + flag, travel_distances, steering_dirns = path_func(-x, -y, dth) + if flag: + for distance in travel_distances: + if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size): + print("Step size too large for Reeds-Shepp paths.") + return [] + travel_distances = timeflip(travel_distances) + steering_dirns = reflect(steering_dirns) + paths = set_path(paths, travel_distances, steering_dirns, step_size) return paths @@ -252,7 +350,7 @@ def calc_interpolate_dists_list(lengths, step_size): def generate_local_course(lengths, modes, max_curvature, step_size): - interpolate_dists_list = calc_interpolate_dists_list(lengths, step_size) + interpolate_dists_list = calc_interpolate_dists_list(lengths, step_size * max_curvature) origin_x, origin_y, origin_yaw = 0.0, 0.0, 0.0 @@ -307,7 +405,7 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size): for path in paths: xs, ys, yaws, directions = generate_local_course(path.lengths, path.ctypes, maxc, - step_size * maxc) + step_size) # convert global coordinate path.x = [math.cos(-q0[2]) * ix + math.sin(-q0[2]) * iy + q0[0] for @@ -354,6 +452,9 @@ def main(): curvature, step_size) + if not xs: + assert False, "No path" + if show_animation: # pragma: no cover plt.cla() plt.plot(xs, ys, label="final course " + str(modes)) @@ -368,9 +469,6 @@ def main(): plt.axis("equal") plt.show() - if not xs: - assert False, "No path" - if __name__ == '__main__': main() diff --git a/docs/modules/path_planning/reeds_shepp_path/LR_L.png b/docs/modules/path_planning/reeds_shepp_path/LR_L.png new file mode 100644 index 0000000000000000000000000000000000000000..1e64da57f2a8ca1bcdded878f9a08e4198f7febf GIT binary patch literal 280028 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z8G1`9-otbc=VLbxY&HD%Pxu~0rF(eFnvPf`Cj6k|>S->XpB|_G7@i&yZz6AdhIHTu zk!j*5ohIVdI{u{6B5%vIi0YZ6~Y`wQEq9FAQpPbx!1QM1F8|nV* zqFn;B1%4aqzoS5Oi6{BrMu7=LVm=hzc*6u_F76$1uWFlDrJQR*C zo7$Fm_CKfv7*8J#5#GQW)HaB~B0xW53oIA?iN#(}va2@{D#Pl_iqj*_t1GCCF*_9n%vegRTpJ7uoMVAH3D@ zfBAF^IS60cr)d!X2IfcT5Q$3pqXj-4(VMW|x5-@-wq@6|6K|buWcOf9UZ}!j#IFS) zI;@>bPd_Ee+WmbyV8v0@DtA~ew;@As09J$Nm(K}%h*uVmh6M~rrq4u?bWd_9{1_(m eaZ~BdH!@-Iy>!yjJ9D?ezti$()` + +Hence, we have: + +- :math:`u, t = polar(x - sin(φ), y + cos(φ) - 1)` +- :math:`v = φ - t` + + +2. **Left-Straight-Right** + +.. image:: LSR.png + +With followng notations: + +- :math:`∠MBD = t1` +- :math:`∠BDF = θ` +- :math:`BC = u1` + +We can deduce the following facts using geometry. + +- D is mid-point of BC and FG. +- :math:`t - v = φ` +- :math:`C(x + sin(φ), y - cos(φ))` +- :math:`A(0, 1)` +- :math:`u1, t1 = polar(vector)` +- :math:`\frac{u1^2}{4} = 1 + \frac{u^2}{4}` +- :math:`BF = 1` [Radius Of Curvature] +- :math:`FD = \frac{u}{2}` +- :math:`θ = arctan(\frac{BF}{FD})` +- :math:`t1 + θ = t` + +Hence, we have: + +- :math:`u1, t1 = polar(x + sin(φ), y - cos(φ) - 1)` +- :math:`u = \sqrt{u1^2 - 4}` +- :math:`θ = arctan(\frac{2}{u})` +- :math:`t = t1 + θ` +- :math:`v = t - φ` + +3. **LeftxRightxLeft** + +.. image:: L_R_L.png + +With followng notations: + +- :math:`∠CBD = ∠CDB = A` [BCD is an isoceles triangle] +- :math:`∠DBK = θ` +- :math:`BD = u1` + +We can deduce the following facts using geometry. + +- :math:`t + u + v = φ` +- :math:`D(x - sin(φ), y + cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`A = arccos(\frac{BD/2}{CD})` +- :math:`u = (π - 2*A)` +- :math:`∠ABK = \frac{π}{2}` +- :math:`∠KBD = θ` +- :math:`t = ∠ABK + ∠KBD + ∠DBC` + +Hence, we have: + +- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)` +- :math:`A = arccos(\frac{u1/2}{2})` +- :math:`t = \frac{π}{2} + θ + A` +- :math:`u = (π - 2*A)` +- :math:`v = (φ - t - u)` + +4. **LeftxRight-Left** + +.. image:: L_RL.png + +With followng notations: + +- :math:`∠CBD = ∠CDB = A` [BCD is an isoceles triangle] +- :math:`∠DBK = θ` +- :math:`BD = u1` + +We can deduce the following facts using geometry. + +- :math:`t + u - v = φ` +- :math:`D(x - sin(φ), y + cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`A = arccos(\frac{BD/2}{CD})` +- :math:`u = (π - 2*A)` +- :math:`∠ABK = \frac{π}{2}` +- :math:`∠KBD = θ` +- :math:`t = ∠ABK + ∠KBD + ∠DBC` + +Hence, we have: + +- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)` +- :math:`A = arccos(\frac{u1/2}{2})` +- :math:`t = \frac{π}{2} + θ + A` +- :math:`u = (π - 2*A)` +- :math:`v = (-φ + t + u)` + +5. **Left-RightxLeft** + +.. image:: LR_L.png + +With followng notations: + +- :math:`∠CBD = ∠CDB = A` [BCD is an isoceles triangle] +- :math:`∠DBK = θ` +- :math:`BD = u1` + +We can deduce the following facts using geometry. + +- :math:`t - u - v = φ` +- :math:`D(x - sin(φ), y + cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`BC = CD = 2` [2 * radius of curvature] +- :math:`cos(2π - u) = \frac{BC^2 + CD^2 - BD^2}{2 * BC * CD}` [Cosine Rule] +- :math:`\frac{sin(A)}{BC} = \frac{sin(u)}{u1}` [Sine Rule] +- :math:`∠ABK = \frac{π}{2}` +- :math:`∠KBD = θ` +- :math:`t = ∠ABK + ∠KBD - ∠DBC` + +Hence, we have: + +- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)` +- :math:`u = arccos(1 - \frac{u1^2}{8})` +- :math:`A = arcsin(\frac{sin(u)}{u1}*2)` +- :math:`t = \frac{π}{2} + θ - A` +- :math:`v = (t - u - φ)` + +6. **Left-RightxLeft-Right** + +.. image:: LR_LR.png + +With followng notations: + +- :math:`∠CLG = ∠BCL = ∠CBG = ∠LGB = A = u` [BGCL is an isoceles trapezium] +- :math:`∠KBG = θ` +- :math:`BG = u1` + +We can deduce the following facts using geometry. + +- :math:`t - 2u + v = φ` +- :math:`G(x + sin(φ), y - cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`BC = CL = LG = 2` [2 * radius of curvature] +- :math:`CG^2 = CL^2 + LG^2 - 2*CL*LG*cos(A)` [Cosine rule in LGC] +- :math:`CG^2 = CL^2 + LG^2 - 2*CL*LG*cos(A)` [Cosine rule in LGC] +- From the previous two equations: :math:`A = arccos(\frac{u1 + 2}{4})` +- :math:`∠ABK = \frac{π}{2}` +- :math:`t = ∠ABK + ∠KBG + ∠GBC` + +Hence, we have: + +- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)` +- :math:`u = arccos(\frac{u1 + 2}{4})` +- :math:`t = \frac{π}{2} + θ + u` +- :math:`v = (φ - t + 2u)` + +7. **LeftxRight-LeftxRight** + +.. image:: L_RL_R.png + +With followng notations: + +- :math:`∠GBC = A` [BGCL is an isoceles trapezium] +- :math:`∠KBG = θ` +- :math:`BG = u1` + +We can deduce the following facts using geometry. + +- :math:`t - v = φ` +- :math:`G(x + sin(φ), y - cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`BC = CL = LG = 2` [2 * radius of curvature] +- :math:`CD = 1` [radius of curvature] +- D is midpoint of BG +- :math:`BD = \frac{u1}{2}` +- :math:`cos(u) = \frac{BC^2 + CD^2 - BD^2}{2*BC*CD}` [Cosine rule in BCD] +- :math:`sin(A) = CD*\frac{sin(u)}{BD}` [Sine rule in BCD] +- :math:`∠ABK = \frac{π}{2}` +- :math:`t = ∠ABK + ∠KBG + ∠GBC` + +Hence, we have: + +- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)` +- :math:`u = arccos(\frac{20 - u1^2}{16})` +- :math:`A = arcsin(2*\frac{sin(u)}{u1})` +- :math:`t = \frac{π}{2} + θ + A` +- :math:`v = (t - φ)` + + +8. **LeftxRight90-Straight-Left** + +.. image:: L_R90SL.png + +With followng notations: + +- :math:`∠FBM = A` [BGCL is an isoceles trapezium] +- :math:`∠KBF = θ` +- :math:`BF = u1` + +We can deduce the following facts using geometry. + +- :math:`t + \frac{π}{2} - v = φ` +- :math:`F(x - sin(φ), y + cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`BM = CB = 2` [2 * radius of curvature] +- :math:`MD = CD = 1` [CGDM is a rectangle] +- :math:`MC = GD = u` [CGDM is a rectangle] +- :math:`MF = MD + DF = 2` +- :math:`BM = \sqrt{BF^2 - MF^2}` [Pythagoras theorem on BFM] +- :math:`tan(A) = \frac{MF}{BM}` +- :math:`u = MC = BM - CB` +- :math:`t = ∠ABK + ∠KBF + ∠FBC` + +Hence, we have: + +- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)` +- :math:`u = arccos(\sqrt{u1^2 - 4} - 2)` +- :math:`A = arctan(\frac{2}{\sqrt{u1^2 - 4}})` +- :math:`t = \frac{π}{2} + θ + A` +- :math:`v = (t - φ + \frac{π}{2})` + + +9. **Left-Straight-Right90xLeft** + +.. image:: LSR90_L.png + +With followng notations: + +- :math:`∠MBH = A` [BGCL is an isoceles trapezium] +- :math:`∠KBH = θ` +- :math:`BH = u1` + +We can deduce the following facts using geometry. + +- :math:`t - \frac{π}{2} - v = φ` +- :math:`H(x - sin(φ), y + cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`GH = 2` [2 * radius of curvature] +- :math:`CM = DG = 1` [CGDM is a rectangle] +- :math:`CD = MG = u` [CGDM is a rectangle] +- :math:`BM = BC + CM = 2` +- :math:`MH = \sqrt{BH^2 - BM^2}` [Pythagoras theorem on BHM] +- :math:`tan(A) = \frac{HM}{BM}` +- :math:`u = MC = BM - CB` +- :math:`t = ∠ABK + ∠KBH - ∠HBC` + +Hence, we have: + +- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)` +- :math:`u = arccos(\sqrt{u1^2 - 4} - 2)` +- :math:`A = arctan(\frac{2}{\sqrt{u1^2 - 4}})` +- :math:`t = \frac{π}{2} + θ - A` +- :math:`v = (t - φ - \frac{π}{2})` + + +10. **LeftxRight90-Straight-Right** + +.. image:: L_R90SR.png + +With followng notations: + +- :math:`∠KBG = θ` +- :math:`BG = u1` + +We can deduce the following facts using geometry. + +- :math:`t - \frac{π}{2} - v = φ` +- :math:`G(x + sin(φ), y - cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`BD = 2` [2 * radius of curvature] +- :math:`DG = EF = u` [DGFE is a rectangle] +- :math:`DG = BG - BD = 2` +- :math:`∠ABK = \frac{π}{2}` +- :math:`t = ∠ABK + ∠KBG` + +Hence, we have: + +- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)` +- :math:`u = u1 - 2` +- :math:`t = \frac{π}{2} + θ` +- :math:`v = (t - φ - \frac{π}{2})` + + +11. **Left-Straight-Left90xRight** + +.. image:: LSL90xR.png + +With followng notations: + +- :math:`∠KBH = θ` +- :math:`BH = u1` + +We can deduce the following facts using geometry. + +- :math:`t + \frac{π}{2} + v = φ` +- :math:`H(x + sin(φ), y - cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`GH = 2` [2 * radius of curvature] +- :math:`DC = BG = u` [DGBC is a rectangle] +- :math:`BG = BH - GH` +- :math:`∠ABC= ∠KBH` + +Hence, we have: + +- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)` +- :math:`u = u1 - 2` +- :math:`t = θ` +- :math:`v = (φ - t - \frac{π}{2})` + + +12. **LeftxRight90-Straight-Left90xRight** + +.. image:: L_R90SL90_R.png + +With followng notations: + +- :math:`∠KBH = θ` +- :math:`∠HBM = A` +- :math:`BH = u1` + +We can deduce the following facts using geometry. + +- :math:`t - v = φ` +- :math:`H(x + sin(φ), y - cos(φ))` +- :math:`B(0, 1)` +- :math:`u1, θ = polar(vector)` +- :math:`GF = ED = 1` [radius of curvature] +- :math:`BD = GH = 2` [2 * radius of curvature] +- :math:`FN = GH = 2` [ENMD is a rectangle] +- :math:`NH = GF = 1` [FNHG is a rectangle] +- :math:`MN = ED = 1` [ENMD is a rectangle] +- :math:`DO = EF = u` [DOFE is a rectangle] +- :math:`MH = MN + NH = 2` +- :math:`BM = \sqrt{BH^2 - MH^2}` [Pythagoras theorem on BHM] +- :math:`DO = BM - BD - OM` +- :math:`tan(A) = \frac{MH}{BM}` +- :math:`∠ABC = ∠ABK + ∠KBH + ∠HBM` + +Hence, we have: + +- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)` +- :math:`u = /sqrt{u1^2 - 4} - 4` +- :math:`A = arctan(\frac{2}{u1^2 - 4})` +- :math:`t = \frac{π}{2} + θ + A` +- :math:`v = (t - φ)` + + Ref: - `15.3.2 Reeds-Shepp diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py index 20d4916f96..11157aa57a 100644 --- a/tests/test_rrt_star_reeds_shepp.py +++ b/tests/test_rrt_star_reeds_shepp.py @@ -32,16 +32,14 @@ def test2(): # + 0.00000000000001 for acceptable errors arising from the planning process assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001 -def test3(): +def test_too_big_step_size(): step_size = 20 rrt_star_reeds_shepp = m.RRTStarReedsShepp(start, goal, obstacleList, [-2.0, 15.0], max_iter=100, step_size=step_size) rrt_star_reeds_shepp.set_random_seed(seed=8) path = rrt_star_reeds_shepp.planning(animation=False) - for i in range(len(path)-1): - # + 0.00000000000001 for acceptable errors arising from the planning process - assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001 + assert path is None if __name__ == '__main__': From 9efd4c07a78b52a03c37c221055db1ddf4401667 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Jan 2024 07:29:57 +0900 Subject: [PATCH 650/909] Bump numpy from 1.26.2 to 1.26.3 in /requirements (#964) Bumps [numpy](https://github.com/numpy/numpy) from 1.26.2 to 1.26.3. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.26.2...v1.26.3) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 8b3c4c1739..417ab84425 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.26.2 +numpy == 1.26.3 scipy == 1.11.4 matplotlib == 3.8.2 cvxpy == 1.4.1 From 1de7ac951041787d2a02053931ef9a618d1bf037 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Jan 2024 21:23:11 +0900 Subject: [PATCH 651/909] Bump ruff from 0.1.9 to 0.1.11 in /requirements (#965) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.9 to 0.1.11. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.9...v0.1.11) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 417ab84425..172a88423e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.4 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.1.9 # For unit test +ruff == 0.1.11 # For unit test From f8515d109e38d4f9cfbc665fe57f4a13b715e86d Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 18 Jan 2024 23:14:51 +0900 Subject: [PATCH 652/909] Update doc_requirements.txt versions (#968) * Update doc_requirements.txt * Update doc_requirements.txt * Update doc_requirements.txt --- docs/doc_requirements.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index a8cd2bde40..b29f4ba289 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -1,6 +1,6 @@ -sphinx == 4.3.2 # For sphinx documentation -sphinx_rtd_theme == 1.0.0 -IPython == 8.10.0 # For sphinx documentation +sphinx == 7.2.6 # For sphinx documentation +sphinx_rtd_theme == 2.0.0 +IPython == 8.20.0 # For sphinx documentation sphinxcontrib-napoleon == 0.7 # For auto doc sphinx-copybutton -sphinx-rtd-dark-mode \ No newline at end of file +sphinx-rtd-dark-mode From 4fcab6aa578c60d4bc594e47671a140e450c7cda Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 18 Jan 2024 23:15:25 +0900 Subject: [PATCH 653/909] Bump ruff from 0.1.11 to 0.1.13 in /requirements (#967) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.11 to 0.1.13. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.11...v0.1.13) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 172a88423e..127b89682d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.4 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.1.11 # For unit test +ruff == 0.1.13 # For unit test From cfe1e3035746454adfc02f66c6190fe58bb7101e Mon Sep 17 00:00:00 2001 From: Ryohei Sasaki Date: Mon, 22 Jan 2024 22:02:22 +0900 Subject: [PATCH 654/909] Addition of velocity scale factor estimation to EKF (#937) * Addition of velocity scale factor estimation to EKF * Format * Add a scale factor motion model in the Jacobian function description * Fix Jacobian function description * New script: 'ekf_with_velocity_correction.py' * Add doc * Fix doc * Correct the parts where the first letter of the sentence is lowercase * Fix doc title * Fix script title * Do grouping * Fix wrong motion function in ekf doc * Update docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst --------- Co-authored-by: Atsushi Sakai --- .../ekf_with_velocity_correction.py | 198 ++++++++++++++++++ .../ekf_with_velocity_correction_1_0.png | Bin 0 -> 40148 bytes ...tended_kalman_filter_localization_main.rst | 105 ++++++++++ 3 files changed, 303 insertions(+) create mode 100644 Localization/extended_kalman_filter/ekf_with_velocity_correction.py create mode 100644 docs/modules/localization/extended_kalman_filter_localization_files/ekf_with_velocity_correction_1_0.png diff --git a/Localization/extended_kalman_filter/ekf_with_velocity_correction.py b/Localization/extended_kalman_filter/ekf_with_velocity_correction.py new file mode 100644 index 0000000000..5dd97830fc --- /dev/null +++ b/Localization/extended_kalman_filter/ekf_with_velocity_correction.py @@ -0,0 +1,198 @@ +""" + +Extended kalman filter (EKF) localization with velocity correction sample + +author: Atsushi Sakai (@Atsushi_twi) +modified by: Ryohei Sasaki (@rsasaki0109) + +""" +import sys +import pathlib + +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) + +import math +import matplotlib.pyplot as plt +import numpy as np + +from utils.plot import plot_covariance_ellipse + +# Covariance for EKF simulation +Q = np.diag([ + 0.1, # variance of location on x-axis + 0.1, # variance of location on y-axis + np.deg2rad(1.0), # variance of yaw angle + 0.4, # variance of velocity + 0.1 # variance of scale factor +]) ** 2 # predict state covariance +R = np.diag([0.1, 0.1]) ** 2 # Observation x,y position covariance + +# Simulation parameter +INPUT_NOISE = np.diag([0.1, np.deg2rad(5.0)]) ** 2 +GPS_NOISE = np.diag([0.05, 0.05]) ** 2 + +DT = 0.1 # time tick [s] +SIM_TIME = 50.0 # simulation time [s] + +show_animation = True + + +def calc_input(): + v = 1.0 # [m/s] + yawrate = 0.1 # [rad/s] + u = np.array([[v], [yawrate]]) + return u + + +def observation(xTrue, xd, u): + xTrue = motion_model(xTrue, u) + + # add noise to gps x-y + z = observation_model(xTrue) + GPS_NOISE @ np.random.randn(2, 1) + + # add noise to input + ud = u + INPUT_NOISE @ np.random.randn(2, 1) + + xd = motion_model(xd, ud) + + return xTrue, z, xd, ud + + +def motion_model(x, u): + F = np.array([[1.0, 0, 0, 0, 0], + [0, 1.0, 0, 0, 0], + [0, 0, 1.0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 1.0]]) + + B = np.array([[DT * math.cos(x[2, 0]) * x[4, 0], 0], + [DT * math.sin(x[2, 0]) * x[4, 0], 0], + [0.0, DT], + [1.0, 0.0], + [0.0, 0.0]]) + + x = F @ x + B @ u + + return x + + +def observation_model(x): + H = np.array([ + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0] + ]) + z = H @ x + + return z + + +def jacob_f(x, u): + """ + Jacobian of Motion Model + + motion model + x_{t+1} = x_t+v*s*dt*cos(yaw) + y_{t+1} = y_t+v*s*dt*sin(yaw) + yaw_{t+1} = yaw_t+omega*dt + v_{t+1} = v{t} + s_{t+1} = s{t} + so + dx/dyaw = -v*s*dt*sin(yaw) + dx/dv = dt*s*cos(yaw) + dx/ds = dt*v*cos(yaw) + dy/dyaw = v*s*dt*cos(yaw) + dy/dv = dt*s*sin(yaw) + dy/ds = dt*v*sin(yaw) + """ + yaw = x[2, 0] + v = u[0, 0] + s = x[4, 0] + jF = np.array([ + [1.0, 0.0, -DT * v * s * math.sin(yaw), DT * s * math.cos(yaw), DT * v * math.cos(yaw)], + [0.0, 1.0, DT * v * s * math.cos(yaw), DT * s * math.sin(yaw), DT * v * math.sin(yaw)], + [0.0, 0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 1.0]]) + return jF + + +def jacob_h(): + jH = np.array([[1, 0, 0, 0, 0], + [0, 1, 0, 0, 0]]) + return jH + + +def ekf_estimation(xEst, PEst, z, u): + # Predict + xPred = motion_model(xEst, u) + jF = jacob_f(xEst, u) + PPred = jF @ PEst @ jF.T + Q + + # Update + jH = jacob_h() + zPred = observation_model(xPred) + y = z - zPred + S = jH @ PPred @ jH.T + R + K = PPred @ jH.T @ np.linalg.inv(S) + xEst = xPred + K @ y + PEst = (np.eye(len(xEst)) - K @ jH) @ PPred + return xEst, PEst + + +def main(): + print(__file__ + " start!!") + + time = 0.0 + + # State Vector [x y yaw v s]' + xEst = np.zeros((5, 1)) + xEst[4, 0] = 1.0 # Initial scale factor + xTrue = np.zeros((5, 1)) + true_scale_factor = 0.9 # True scale factor + xTrue[4, 0] = true_scale_factor + PEst = np.eye(5) + + xDR = np.zeros((5, 1)) # Dead reckoning + + # history + hxEst = xEst + hxTrue = xTrue + hxDR = xTrue + hz = np.zeros((2, 1)) + + while SIM_TIME >= time: + time += DT + u = calc_input() + + xTrue, z, xDR, ud = observation(xTrue, xDR, u) + + xEst, PEst = ekf_estimation(xEst, PEst, z, ud) + + # store data history + hxEst = np.hstack((hxEst, xEst)) + hxDR = np.hstack((hxDR, xDR)) + hxTrue = np.hstack((hxTrue, xTrue)) + hz = np.hstack((hz, z)) + estimated_scale_factor = hxEst[4, -1] + if show_animation: + plt.cla() + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect('key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + plt.plot(hz[0, :], hz[1, :], ".g") + plt.plot(hxTrue[0, :].flatten(), + hxTrue[1, :].flatten(), "-b") + plt.plot(hxDR[0, :].flatten(), + hxDR[1, :].flatten(), "-k") + plt.plot(hxEst[0, :].flatten(), + hxEst[1, :].flatten(), "-r") + plt.text(0.45, 0.85, f"True Velocity Scale Factor: {true_scale_factor:.2f}", ha='left', va='top', transform=plt.gca().transAxes) + plt.text(0.45, 0.95, f"Estimated Velocity Scale Factor: {estimated_scale_factor:.2f}", ha='left', va='top', transform=plt.gca().transAxes) + plot_covariance_ellipse(xEst[0, 0], xEst[1, 0], PEst) + plt.axis("equal") + plt.grid(True) + plt.pause(0.001) + + +if __name__ == '__main__': + main() diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/ekf_with_velocity_correction_1_0.png b/docs/modules/localization/extended_kalman_filter_localization_files/ekf_with_velocity_correction_1_0.png new file mode 100644 index 0000000000000000000000000000000000000000..595b651bd2354f67fd57de47e35b9848433d7c93 GIT binary patch literal 40148 zcmeFZWmr{R^gW8GbSWqeBGL%bf`lR=-5@O;(%qmaDk%ceC0!!jA>BwzcXu2byz{)j z|L;D}{eC~(5BGyF?{heN@3q&OYtAvp7>h58@{+h%WLPLDD7aFu#FSA`Q2kI)Zp>kz z!6!TeQ%mrJ-%0$nlZvgWldFM)35uM7lbw~Vla+_~$FF`l2-TIR&pZesBz2%)=vyDh)ESe7lq8J8zN9BEf@IN#k zI&|-%{P*EsQcRSO$d9&yZ}}mAL*OET`uxAIKcl^o^52&e!%-BGziH}wg~IsXjm#dQ z5Fx+(i|hZt&Hu;ehQd1W-DQszujBQlyP%NJz`9d;Unp`}sopCGZjKc$E-w1(*(N(1 z+(j|{eRjCIe|T8p%!^#*aLj>$fz$Kz2?26mnj3HKyLot^PT(#rFPkh*E$Mc4cIN9h zB*`ZV7(5}dN~ElLU}8hAyi=vnC3tf011Dds?CIH=W<&dS_$@!pHx|c;gM)7OZ1^m)FSW1ArcSEQexc)}hY^1AU$C3M(`gyO5KIvK ztPt4V-09!(_tjeh*HxqLO_nB&Cy$*bge<)6HvB!0*PT7kM##l#4z>r=!}Y4ad-rzn zMx-cVgwrDT%HNOAZr0ZL|4bQr;Z?rPMTCX&NtMyUp@roqLDE>=-sh7>uHU%*hb71%5IuF}?LH^oHxoa_%KRih zhAMcw)o41~qb)v{cxwN8?Bl{dnvcBs=hPjiK`3A z;^VZP6?8IXq~4b69m;THZ40?QMy7-~ch03x$|5OhT))VSe}9v{_VZYkz3y(8GrHLV zB}LWFnpmy>yG=C71xiXOyI}*DSK?VR;^=FU%aX1aRd~^=TzXQbSab|3jC7cVkz!aG zGwEN?(DYWw-R|J~p<@bu_lRp(ey9DM^^I(Np?MugWFv2y~Y!+p2~c3v8#B~gy$Dsyp=xw4@U??|-=hcYIz zx8s_8c0eq}u3E1=SyWar4LUphutr260^D=&)p@!3wl@aXFo#5s0q8~&W>Y5L$mK2 zkJip9hkEZ2RZyVj`mZ zSv9bxXwtV`?I-=`d|RAA?$!Y7ZGoGceqsqvH|F~9RD7dg+3G1$?xZ-Wllb7v&4(@C z++%Kz%3ZhkzVo@HB2^K7ln8pFKW}Ao1Eq;z*Dw1`iHS0+_Nr4ylt^|RVQa$D*6Ybw z3T#u!&n#pzIs>mCGMjl7i2n0X5-KL*3I3Sv{yUtkh%eio3vJlsh>@zH%)k9H$FA_J zw(#d!4o9y9*N(0f1)hNw-vdMt>_fcSdHeBa&T}EF{@(uwFVJ$ z8Ma^;8X1)&9Ct;roGk>gKXut6F)fKLHt8PBkRi@gNKRTVE$Lop4RT-4h~60&@xAua z^G1xYW(XNH-BK^qlY9O8_4h${qAy>*1XJ*TOZGYc63=HlSnut5ebVr6elvbwDC*m{ zrVp5eudAJyh}m>foYn_xPZxrMsYR&j^x`vi;NHQcTmgv9F+y^3nLPD^(~Z2sY@IV< zU&e)0WNzqi+`&0t*^Za&WYKe)(EUO!Ka$bfz!9ksaszMg;T$e2s?9(Cr|ee*W0*q-cw^p|7R^rlU^e-z_@!=$kP^@X9ia4zi-WO4iBmAM3g)RGS(0|C&ES zTiJ~dBQ4=ctb9l>8We+35Y0QgLlZhRVZKsEhMR5w`%BzEdPOd#zy+NQ?Lj6Flrh>q zJ37?kXX

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z&Kf&!tn-l+1za0cvxY%H@@!IeKlg+n|FPGJBSPBNQ$P(4$^{v7Eiavy^^J;EudygC zin>^zs>o>BX3VfalK%z_W`-^0+F{8fY!X2Qk1G1>gbK0@2}b5W%j<^uZZE#yecE&t z52AHpSybKN-~qS?X3prVwjcFgyt%p_bbI*YL2x_BcPKlM*OeU4mo&`seTH(V=e_qrv{xOTy5H!~DWpuwG9<*r~nvN>ZP3Uxi zsQA!owWN?o4QawaoDLpyfEDFfk{alVe>J$3{F10KS0I%VL{#xRWJ|*UHbf3AyQh- zqY{J1yV=~CTa1|t|JBp>D!~{|^?`?AE)jVl&ta$sJ?~<4b+}(Zz(xm$Vr1!%0}zaW z@JrOz)>cGUj=*{HVGTz}M?E+KI5TCy{u1{y1VeZEHrY`~yeR9vUM+jNyO#qA|2u&8 z$p5dvoD=Z;8lVFJTPuK$$;#65-g{KfxP-@uR#=YMd^BSU-eEE!^z1F|KN6(Hn;}8 zvQVK=SXo;iOiWCyK<8iQ!M~1ajt41MLPTet)c+>7LkuEA8uIa?bUGbe66*@bTWtD} zk|RWdSI0Py2G1f37LSbdq0G+1h4HlHf`__ +Kalman Filter with Speed Scale Factor Correction +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + +.. image:: ekf_with_velocity_correction_1_0.png + :width: 600px + +This is a Extended kalman filter (EKF) localization with velocity correction. + +This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear. + +Code: `PythonRobotics/extended_kalman_ekf_with_velocity_correctionfilter.py +AtsushiSakai/PythonRobotics `__ + Filter design ~~~~~~~~~~~~~ +Position Estimation Kalman Filter +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + In this simulation, the robot has a state vector includes 4 states at time :math:`t`. @@ -61,9 +82,28 @@ In the code, “observation” function generates the input and observation vector with noise `code `__ +Kalman Filter with Speed Scale Factor Correction +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In this simulation, the robot has a state vector includes 5 states at +time :math:`t`. + +.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t] + +x, y are a 2D x-y position, :math:`\phi` is orientation, v is +velocity, and s is a scale factor of velocity. + +In the code, “xEst” means the state vector. +`code `__ + +The rest is the same as the Position Estimation Kalman Filter. + Motion Model ~~~~~~~~~~~~ +Position Estimation Kalman Filter +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + The robot model is .. math:: \dot{x} = v \cos(\phi) @@ -97,9 +137,50 @@ Its Jacobian matrix is :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v \sin(\phi) \Delta t & \cos(\phi) \Delta t\\ 0 & 1 & v \cos(\phi) \Delta t & \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} \end{equation*}` + +Kalman Filter with Speed Scale Factor Correction +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The robot model is + +.. math:: \dot{x} = v s \cos(\phi) + +.. math:: \dot{y} = v s \sin(\phi) + +.. math:: \dot{\phi} = \omega + +So, the motion model is + +.. math:: \textbf{x}_{t+1} = f(\textbf{x}_t, \textbf{u}_t) = F\textbf{x}_t+B\textbf{u}_t + +where + +:math:`\begin{equation*} F= \begin{bmatrix} 1 & 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 & 0\\ 0 & 0 & 1 & 0 & 0\\ 0 & 0 & 0 & 0 & 0\\ 0 & 0 & 0 & 0 & 1\\ \end{bmatrix} \end{equation*}` + +:math:`\begin{equation*} B= \begin{bmatrix} cos(\phi) \Delta t s & 0\\ sin(\phi) \Delta t s & 0\\ 0 & \Delta t\\ 1 & 0\\ 0 & 0\\ \end{bmatrix} \end{equation*}` + +:math:`\Delta t` is a time interval. + +This is implemented at +`code `__ + +The motion function is that + +:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \\ w' \\ v' \end{bmatrix} = f(\textbf{x}, \textbf{u}) = \begin{bmatrix} x + v s \cos(\phi)\Delta t \\ y + v s \sin(\phi)\Delta t \\ \phi + \omega \Delta t \\ v \end{bmatrix} \end{equation*}` + +Its Jacobian matrix is + +:math:`\begin{equation*} J_f = \begin{bmatrix} \frac{\partial x'}{\partial x}& \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v} & \frac{\partial x'}{\partial s}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{\partial v} & \frac{\partial y'}{\partial s}\\ \frac{\partial \phi'}{\partial x}& \frac{\partial \phi'}{\partial y} & \frac{\partial \phi'}{\partial \phi} & \frac{\partial \phi'}{\partial v} & \frac{\partial \phi'}{\partial s}\\ \frac{\partial v'}{\partial x}& \frac{\partial v'}{\partial y} & \frac{\partial v'}{\partial \phi} & \frac{\partial v'}{\partial v} & \frac{\partial v'}{\partial s} \\ \frac{\partial s'}{\partial x}& \frac{\partial s'}{\partial y} & \frac{\partial s'}{\partial \phi} & \frac{\partial s'}{\partial v} & \frac{\partial s'}{\partial s} \end{bmatrix} \end{equation*}` + +:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v s \sin(\phi) \Delta t & s \cos(\phi) \Delta t & \cos(\phi) v \Delta t\\ 0 & 1 & v s \cos(\phi) \Delta t & s \sin(\phi) \Delta t & v \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 \end{bmatrix} \end{equation*}` + + Observation Model ~~~~~~~~~~~~~~~~~ +Position Estimation Kalman Filter +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + The robot can get x-y position infomation from GPS. So GPS Observation model is @@ -120,6 +201,30 @@ Its Jacobian matrix is :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}` +Kalman Filter with Speed Scale Factor Correction +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The robot can get x-y position infomation from GPS. + +So GPS Observation model is + +.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t + +where + +:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}` + +The observation function states that + +:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}` + +Its Jacobian matrix is + +:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v} & \frac{\partial x'}{\partial s}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v} & \frac{\partial y'}{ \partial s}\\ \end{bmatrix} \end{equation*}` + +:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 & 0\\ \end{bmatrix} \end{equation*}` + + Extended Kalman Filter ~~~~~~~~~~~~~~~~~~~~~~ From 13137952eeee39438bf6f0eb030527ab804a9481 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Jan 2024 08:07:58 +0900 Subject: [PATCH 655/909] Bump scipy from 1.11.4 to 1.12.0 in /requirements (#969) Bumps [scipy](https://github.com/scipy/scipy) from 1.11.4 to 1.12.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.11.4...v1.12.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 127b89682d..5af5be1794 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.26.3 -scipy == 1.11.4 +scipy == 1.12.0 matplotlib == 3.8.2 cvxpy == 1.4.1 pytest == 7.4.4 # For unit test From ae0676bfc0b5548ab643c8db73959edc5e68a324 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Jan 2024 20:18:49 +0900 Subject: [PATCH 656/909] Bump ruff from 0.1.13 to 0.1.14 in /requirements (#971) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.13 to 0.1.14. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.13...v0.1.14) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5af5be1794..5848b32aad 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.1 pytest == 7.4.4 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.1.13 # For unit test +ruff == 0.1.14 # For unit test From 73d1189e824c38af70f724d3ba5b0322e55f142d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Jan 2024 20:40:15 +0900 Subject: [PATCH 657/909] Bump cvxpy from 1.4.1 to 1.4.2 in /requirements (#970) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.4.1 to 1.4.2. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.4.1...v1.4.2) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5848b32aad..ceee1d03c0 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.26.3 scipy == 1.12.0 matplotlib == 3.8.2 -cvxpy == 1.4.1 +cvxpy == 1.4.2 pytest == 7.4.4 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test From 0874d5023cc79cd2d9ad535f6d008c749ce4d257 Mon Sep 17 00:00:00 2001 From: Yi-Chen Zhang Date: Sat, 27 Jan 2024 23:59:02 -0500 Subject: [PATCH 658/909] Fix predict covariance (#973) * Fix predict covariance * Remove trailing whitespace and unused return parameters --- SLAM/EKFSLAM/ekf_slam.py | 9 +- docs/modules/slam/ekf_slam/ekf_slam_main.rst | 181 +++++++++---------- 2 files changed, 91 insertions(+), 99 deletions(-) diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py index a624e8765b..5c4417d7c3 100644 --- a/SLAM/EKFSLAM/ekf_slam.py +++ b/SLAM/EKFSLAM/ekf_slam.py @@ -29,10 +29,9 @@ def ekf_slam(xEst, PEst, u, z): # Predict - S = STATE_SIZE - G, Fx = jacob_motion(xEst[0:S], u) - xEst[0:S] = motion_model(xEst[0:S], u) - PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx + G, Fx = jacob_motion(xEst, u) + xEst[0:STATE_SIZE] = motion_model(xEst[0:STATE_SIZE], u) + PEst = G.T @ PEst @ G + Fx.T @ Cx @ Fx initP = np.eye(2) # Update @@ -120,7 +119,7 @@ def jacob_motion(x, u): [0.0, 0.0, DT * u[0, 0] * math.cos(x[2, 0])], [0.0, 0.0, 0.0]], dtype=float) - G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx + G = np.eye(len(x)) + Fx.T @ jF @ Fx return G, Fx, diff --git a/docs/modules/slam/ekf_slam/ekf_slam_main.rst b/docs/modules/slam/ekf_slam/ekf_slam_main.rst index 70a7d131ae..b27971225e 100644 --- a/docs/modules/slam/ekf_slam/ekf_slam_main.rst +++ b/docs/modules/slam/ekf_slam/ekf_slam_main.rst @@ -63,27 +63,27 @@ Take care to note the difference between :math:`X` (state) and :math:`x` original author: Atsushi Sakai (@Atsushi_twi) notebook author: Andrew Tu (drewtu2) """ - + import math import numpy as np %matplotlib notebook import matplotlib.pyplot as plt - - + + # EKF state covariance Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2 # Change in covariance - + # Simulation parameter Qsim = np.diag([0.2, np.deg2rad(1.0)])**2 # Sensor Noise Rsim = np.diag([1.0, np.deg2rad(10.0)])**2 # Process Noise - + DT = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] MAX_RANGE = 20.0 # maximum observation range M_DIST_TH = 2.0 # Threshold of Mahalanobis distance for data association. STATE_SIZE = 3 # State size [x,y,yaw] LM_SIZE = 2 # LM state size [x,y] - + show_animation = True Algorithm Walk through @@ -97,23 +97,22 @@ the estimated state and measurements def ekf_slam(xEst, PEst, u, z): """ - Performs an iteration of EKF SLAM from the available information. - + Performs an iteration of EKF SLAM from the available information. + :param xEst: the belief in last position :param PEst: the uncertainty in last position - :param u: the control function applied to the last position + :param u: the control function applied to the last position :param z: measurements at this step :returns: the next estimated position and associated covariance """ - S = STATE_SIZE - + # Predict - xEst, PEst, G, Fx = predict(xEst, PEst, u) + xEst, PEst = predict(xEst, PEst, u) initP = np.eye(2) - + # Update - xEst, PEst = update(xEst, PEst, u, z, initP) - + xEst, PEst = update(xEst, PEst, z, initP) + return xEst, PEst @@ -170,26 +169,25 @@ the landmarks. def predict(xEst, PEst, u): """ Performs the prediction step of EKF SLAM - + :param xEst: nx1 state vector :param PEst: nxn covariance matrix :param u: 2x1 control vector :returns: predicted state vector, predicted covariance, jacobian of control vector, transition fx """ - S = STATE_SIZE - G, Fx = jacob_motion(xEst[0:S], u) - xEst[0:S] = motion_model(xEst[0:S], u) + G, Fx = jacob_motion(xEst, u) + xEst[0:STATE_SIZE] = motion_model(xEst[0:STATE_SIZE], u) # Fx is an an identity matrix of size (STATE_SIZE) # sigma = G*sigma*G.T + Noise - PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx - return xEst, PEst, G, Fx + PEst = G.T @ PEst @ G + Fx.T @ Cx @ Fx + return xEst, PEst .. code:: ipython3 def motion_model(x, u): """ Computes the motion model based on current state and input function. - + :param x: 3x1 pose estimation :param u: 2x1 control input [v; w] :returns: the resulting state after the control function is applied @@ -197,11 +195,11 @@ the landmarks. F = np.array([[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]]) - + B = np.array([[DT * math.cos(x[2, 0]), 0], [DT * math.sin(x[2, 0]), 0], [0.0, DT]]) - + x = (F @ x) + (B @ u) return x @@ -261,22 +259,21 @@ the changing uncertainty. .. code:: ipython3 - def update(xEst, PEst, u, z, initP): + def update(xEst, PEst, z, initP): """ Performs the update step of EKF SLAM - + :param xEst: nx1 the predicted pose of the system and the pose of the landmarks :param PEst: nxn the predicted covariance - :param u: 2x1 the control function :param z: the measurements read at new position :param initP: 2x2 an identity matrix acting as the initial covariance :returns: the updated state and covariance for the system """ for iz in range(len(z[:, 0])): # for each observation minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark - + nLM = calc_n_LM(xEst) # number of landmarks we currently know about - + if minid == nLM: # Landmark is a NEW landmark print("New LM") # Extend state and covariance matrix @@ -285,14 +282,14 @@ the changing uncertainty. np.hstack((np.zeros((LM_SIZE, len(xEst))), initP)))) xEst = xAug PEst = PAug - + lm = get_LM_Pos_from_state(xEst, minid) y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid) - + K = (PEst @ H.T) @ np.linalg.inv(S) # Calculate Kalman Gain xEst = xEst + (K @ y) PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst - + xEst[2] = pi_2_pi(xEst[2]) return xEst, PEst @@ -302,7 +299,7 @@ the changing uncertainty. def calc_innovation(lm, xEst, PEst, z, LMid): """ Calculates the innovation based on expected position and landmark position - + :param lm: landmark position :param xEst: estimated position/state :param PEst: estimated covariance @@ -315,19 +312,19 @@ the changing uncertainty. zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0] zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]]) # zp is the expected measurement based on xEst and the expected landmark position - + y = (z - zp).T # y = innovation y[1] = pi_2_pi(y[1]) - + H = jacobH(q, delta, xEst, LMid + 1) S = H @ PEst @ H.T + Cx[0:2, 0:2] - + return y, S, H - + def jacobH(q, delta, x, i): """ Calculates the jacobian of the measurement function - + :param q: the range from the system pose to the landmark :param delta: the difference between a landmark position and the estimated system position :param x: the state, including the estimated system position @@ -337,17 +334,17 @@ the changing uncertainty. sq = math.sqrt(q) G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]], [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]]) - + G = G / q nLM = calc_n_LM(x) F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM)))) F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))), np.eye(2), np.zeros((2, 2 * nLM - 2 * i)))) - + F = np.vstack((F1, F2)) - + H = G @ F - + return H Observation Step @@ -370,17 +367,17 @@ reckoning and control functions are passed along here as well. :param xd: the current noisy estimate of the system :param u: the current control input :param RFID: the true position of the landmarks - - :returns: Computes the true position, observations, dead reckoning (noisy) position, + + :returns: Computes the true position, observations, dead reckoning (noisy) position, and noisy control function """ xTrue = motion_model(xTrue, u) - + # add noise to gps x-y z = np.zeros((0, 3)) - + for i in range(len(RFID[:, 0])): # Test all beacons, only add the ones we can see (within MAX_RANGE) - + dx = RFID[i, 0] - xTrue[0, 0] dy = RFID[i, 1] - xTrue[1, 0] d = math.sqrt(dx**2 + dy**2) @@ -390,12 +387,12 @@ reckoning and control functions are passed along here as well. anglen = angle + np.random.randn() * Qsim[1, 1] # add noise zi = np.array([dn, anglen, i]) z = np.vstack((z, zi)) - + # add noise to input ud = np.array([[ u[0, 0] + np.random.randn() * Rsim[0, 0], u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T - + xd = motion_model(xd, ud) return xTrue, z, xd, ud @@ -409,32 +406,30 @@ reckoning and control functions are passed along here as well. """ n = int((len(x) - STATE_SIZE) / LM_SIZE) return n - - + + def jacob_motion(x, u): """ - Calculates the jacobian of motion model. - + Calculates the jacobian of motion model. + :param x: The state, including the estimated position of the system :param u: The control function :returns: G: Jacobian Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix """ - + # [eye(3) [0 x y; 0 x y; 0 x y]] Fx = np.hstack((np.eye(STATE_SIZE), np.zeros( (STATE_SIZE, LM_SIZE * calc_n_LM(x))))) - + jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])], [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])], [0.0, 0.0, 0.0]],dtype=object) - + G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx if calc_n_LM(x) > 0: print(Fx.shape) return G, Fx, - - .. code:: ipython3 @@ -442,68 +437,68 @@ reckoning and control functions are passed along here as well. def calc_LM_Pos(x, z): """ Calculates the pose in the world coordinate frame of a landmark at the given measurement. - + :param x: [x; y; theta] :param z: [range; bearing] :returns: [x; y] for given measurement """ zp = np.zeros((2, 1)) - + zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1]) zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1]) #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1]) #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1]) - + return zp - - + + def get_LM_Pos_from_state(x, ind): """ Returns the position of a given landmark - + :param x: The state containing all landmark positions :param ind: landmark id :returns: The position of the landmark """ lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :] - + return lm - - + + def search_correspond_LM_ID(xAug, PAug, zi): """ Landmark association with Mahalanobis distance. - - If this landmark is at least M_DIST_TH units away from all known landmarks, + + If this landmark is at least M_DIST_TH units away from all known landmarks, it is a NEW landmark. - + :param xAug: The estimated state :param PAug: The estimated covariance :param zi: the read measurements of specific landmark :returns: landmark id """ - + nLM = calc_n_LM(xAug) - + mdist = [] - + for i in range(nLM): lm = get_LM_Pos_from_state(xAug, i) y, S, H = calc_innovation(lm, xAug, PAug, zi, i) mdist.append(y.T @ np.linalg.inv(S) @ y) - + mdist.append(M_DIST_TH) # new landmark - + minid = mdist.index(min(mdist)) - + return minid - + def calc_input(): v = 1.0 # [m/s] yawrate = 0.1 # [rad/s] u = np.array([[v, yawrate]]).T return u - + def pi_2_pi(angle): return (angle + math.pi) % (2 * math.pi) - math.pi @@ -511,53 +506,53 @@ reckoning and control functions are passed along here as well. def main(): print(" start!!") - + time = 0.0 - + # RFID positions [x, y] RFID = np.array([[10.0, -2.0], [15.0, 10.0], [3.0, 15.0], [-5.0, 20.0]]) - + # State Vector [x y yaw v]' xEst = np.zeros((STATE_SIZE, 1)) xTrue = np.zeros((STATE_SIZE, 1)) PEst = np.eye(STATE_SIZE) - + xDR = np.zeros((STATE_SIZE, 1)) # Dead reckoning - + # history hxEst = xEst hxTrue = xTrue hxDR = xTrue - + while SIM_TIME >= time: time += DT u = calc_input() - + xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) - + xEst, PEst = ekf_slam(xEst, PEst, ud, z) - + x_state = xEst[0:STATE_SIZE] - + # store data history hxEst = np.hstack((hxEst, x_state)) hxDR = np.hstack((hxDR, xDR)) hxTrue = np.hstack((hxTrue, xTrue)) - + if show_animation: # pragma: no cover plt.cla() - + plt.plot(RFID[:, 0], RFID[:, 1], "*k") plt.plot(xEst[0], xEst[1], ".r") - + # plot landmark for i in range(calc_n_LM(xEst)): plt.plot(xEst[STATE_SIZE + i * 2], xEst[STATE_SIZE + i * 2 + 1], "xg") - + plt.plot(hxTrue[0, :], hxTrue[1, :], "-b") plt.plot(hxDR[0, :], @@ -587,5 +582,3 @@ References: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - `PROBABILISTIC ROBOTICS `_ - - From 6326e8c21205ca9c2d8e809c2dc2eaab60940bf0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Jan 2024 20:19:54 +0900 Subject: [PATCH 659/909] Bump pytest from 7.4.4 to 8.0.0 in /requirements (#974) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.4 to 8.0.0. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.4.4...8.0.0) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ceee1d03c0..f0729fb76e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.3 scipy == 1.12.0 matplotlib == 3.8.2 cvxpy == 1.4.2 -pytest == 7.4.4 # For unit test +pytest == 8.0.0 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test ruff == 0.1.14 # For unit test From bd253e81060c6a11a944016706bd1d87ef72bded Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 6 Feb 2024 19:07:36 +0900 Subject: [PATCH 660/909] Bump ruff from 0.1.14 to 0.2.0 in /requirements (#975) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.14 to 0.2.0. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.1.14...v0.2.0) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f0729fb76e..1d2072b01e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.0.0 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.1.14 # For unit test +ruff == 0.2.0 # For unit test From e4ddf10ad651dffa7c04f344cd13e878637fb8ca Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Feb 2024 20:21:31 +0900 Subject: [PATCH 661/909] Bump larsoner/circleci-artifacts-redirector-action from 0.3.1 to 1.0.0 (#976) Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 0.3.1 to 1.0.0. - [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases) - [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/0.3.1...v1.0.0) --- updated-dependencies: - dependency-name: larsoner/circleci-artifacts-redirector-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/circleci-artifacts-redirector.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml index 149a087a5d..504a3a1004 100644 --- a/.github/workflows/circleci-artifacts-redirector.yml +++ b/.github/workflows/circleci-artifacts-redirector.yml @@ -6,7 +6,7 @@ jobs: name: Run CircleCI artifacts redirector!! steps: - name: run-circleci-artifacts-redirector - uses: larsoner/circleci-artifacts-redirector-action@0.3.1 + uses: larsoner/circleci-artifacts-redirector-action@v1.0.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/html/index.html From 0dcb68a8020a5dd8f5cad53f69dffbcca3f03273 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Feb 2024 21:49:03 +0900 Subject: [PATCH 662/909] Bump numpy from 1.26.3 to 1.26.4 in /requirements (#977) Bumps [numpy](https://github.com/numpy/numpy) from 1.26.3 to 1.26.4. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.26.3...v1.26.4) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1d2072b01e..dd5adf0409 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.26.3 +numpy == 1.26.4 scipy == 1.12.0 matplotlib == 3.8.2 cvxpy == 1.4.2 From b1bdff203c483533c32a7357a124945b74d88aa1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Feb 2024 22:57:15 +0900 Subject: [PATCH 663/909] Bump ruff from 0.2.0 to 0.2.1 in /requirements (#978) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.0 to 0.2.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.2.0...v0.2.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index dd5adf0409..6b65ae5b8c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.0.0 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.2.0 # For unit test +ruff == 0.2.1 # For unit test From e6215ace2e62c350dcc174641fb6804d3bfd4d55 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 20 Feb 2024 20:43:17 +0900 Subject: [PATCH 664/909] Bump matplotlib from 3.8.2 to 3.8.3 in /requirements (#979) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.2 to 3.8.3. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.2...v3.8.3) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6b65ae5b8c..be43a14788 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.26.4 scipy == 1.12.0 -matplotlib == 3.8.2 +matplotlib == 3.8.3 cvxpy == 1.4.2 pytest == 8.0.0 # For unit test pytest-xdist == 3.5.0 # For unit test From 47eb01f88f0083929f1948620b313019e44b96f1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 20 Feb 2024 20:44:06 +0900 Subject: [PATCH 665/909] Bump ruff from 0.2.1 to 0.2.2 in /requirements (#980) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.1 to 0.2.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.2.1...v0.2.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index be43a14788..e18459efdb 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.0.0 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.2.1 # For unit test +ruff == 0.2.2 # For unit test From bf6acf3e31934dab4a496190ca74191cee672ea4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 20 Feb 2024 21:34:06 +0900 Subject: [PATCH 666/909] Bump pytest from 8.0.0 to 8.0.1 in /requirements (#981) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.0.0 to 8.0.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.0.0...8.0.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e18459efdb..e40542015f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.4 scipy == 1.12.0 matplotlib == 3.8.3 cvxpy == 1.4.2 -pytest == 8.0.0 # For unit test +pytest == 8.0.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test ruff == 0.2.2 # For unit test From e2412a6667d12a8b640f2dc5c6e15e22b1e36753 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 24 Feb 2024 18:26:59 +0900 Subject: [PATCH 667/909] Update circleci-artifacts-redirector.yml (#983) * Update circleci-artifacts-redirector.yml * Update circleci-artifacts-redirector.yml * Update circleci-artifacts-redirector.yml --- .github/workflows/circleci-artifacts-redirector.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml index 504a3a1004..78eb479aa1 100644 --- a/.github/workflows/circleci-artifacts-redirector.yml +++ b/.github/workflows/circleci-artifacts-redirector.yml @@ -9,5 +9,6 @@ jobs: uses: larsoner/circleci-artifacts-redirector-action@v1.0.0 with: repo-token: ${{ secrets.GITHUB_TOKEN }} + api-token: ${{ secrets.CIRCLECI_TOKEN }} artifact-path: 0/html/index.html circleci-jobs: build_doc From 6e743ef31e8f99a5e78bbb11343228d433693034 Mon Sep 17 00:00:00 2001 From: William L Thomson Jr Date: Sat, 24 Feb 2024 04:35:13 -0500 Subject: [PATCH 668/909] PathPlanning/InformedRRTStar/informed_rrt_star.py: Fix hard coded graph axis (#982) - Use self.min_rand, self.max_rand variable values in place of -2, 15 --- PathPlanning/InformedRRTStar/informed_rrt_star.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index b6f9d234dd..c37326e490 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -293,7 +293,7 @@ def draw_graph(self, x_center=None, c_best=None, c_min=None, e_theta=None, plt.plot(self.start.x, self.start.y, "xr") plt.plot(self.goal.x, self.goal.y, "xr") - plt.axis([-2, 15, -2, 15]) + plt.axis([self.min_rand, self.max_rand, self.min_rand, self.max_rand]) plt.grid(True) plt.pause(0.01) From ffd28c2d635975ba9294c6fa6603a211d074ef1b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Feb 2024 20:27:30 +0900 Subject: [PATCH 669/909] Bump pytest from 8.0.1 to 8.0.2 in /requirements (#984) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.0.1 to 8.0.2. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.0.1...8.0.2) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e40542015f..b464481003 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.4 scipy == 1.12.0 matplotlib == 3.8.3 cvxpy == 1.4.2 -pytest == 8.0.1 # For unit test +pytest == 8.0.2 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test ruff == 0.2.2 # For unit test From 9a22fb34ba4a205b254b8d469d62c73188771bf8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 6 Mar 2024 08:05:05 +0900 Subject: [PATCH 670/909] Bump ruff from 0.2.2 to 0.3.0 in /requirements (#985) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.2 to 0.3.0. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.2.2...v0.3.0) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b464481003..9d8ed2ac33 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.0.2 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.2.2 # For unit test +ruff == 0.3.0 # For unit test From 707f12fb363316ab687566a0b9819c2545eb89b8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 12 Mar 2024 07:47:43 +0900 Subject: [PATCH 671/909] Bump pytest from 8.0.2 to 8.1.1 in /requirements (#986) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.0.2 to 8.1.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.0.2...8.1.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9d8ed2ac33..e3eee34e5e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.4 scipy == 1.12.0 matplotlib == 3.8.3 cvxpy == 1.4.2 -pytest == 8.0.2 # For unit test +pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test ruff == 0.3.0 # For unit test From 1477c34cc717428ad6ae9b234451e679d8d0bc11 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 12 Mar 2024 12:19:59 +0900 Subject: [PATCH 672/909] Bump ruff from 0.3.0 to 0.3.2 in /requirements (#987) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.0 to 0.3.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.3.0...v0.3.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e3eee34e5e..2cdfeaf126 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.8.0 # For unit test -ruff == 0.3.0 # For unit test +ruff == 0.3.2 # For unit test From 655fbc71d9adc324276985693c3cac773791f1f1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 12 Mar 2024 12:57:17 +0900 Subject: [PATCH 673/909] Bump mypy from 1.8.0 to 1.9.0 in /requirements (#988) Bumps [mypy](https://github.com/python/mypy) from 1.8.0 to 1.9.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.8.0...1.9.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2cdfeaf126..1021562d1c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.8.3 cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test -mypy == 1.8.0 # For unit test +mypy == 1.9.0 # For unit test ruff == 0.3.2 # For unit test From a79cc374f3eb3f39ae1a82e7738b988c43791595 Mon Sep 17 00:00:00 2001 From: guangwu Date: Wed, 13 Mar 2024 20:20:01 +0800 Subject: [PATCH 674/909] fix: typo (#989) --- Control/move_to_pose/move_to_pose_robot.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Control/move_to_pose/move_to_pose_robot.py b/Control/move_to_pose/move_to_pose_robot.py index d5c51caa12..fe9f0d06b3 100644 --- a/Control/move_to_pose/move_to_pose_robot.py +++ b/Control/move_to_pose/move_to_pose_robot.py @@ -79,9 +79,9 @@ def set_start_target_poses(self, pose_start, pose_target): Parameters ---------- pose_start : (Pose) - Start postion of the robot (see the Pose class) + Start position of the robot (see the Pose class) pose_target : (Pose) - Target postion of the robot (see the Pose class) + Target position of the robot (see the Pose class) """ self.pose_start = copy.copy(pose_start) self.pose_target = pose_target From 1656cab0d229d294ec151ec9d4cbe086b271e1cf Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 14 Mar 2024 22:01:51 +0900 Subject: [PATCH 675/909] Support python 3.12 (#954) * Update Linux_CI.yml * Update MacOS_CI.yml * Update Windows_CI.yml * Delete .lgtm.yml * Update lqr_planner.py * Update lqr_planner.py * Update config.yml * Update environment.yml * Update ruff.toml * Update README.md * Update getting_started_main.rst * Update doc_requirements.txt --- .circleci/config.yml | 2 +- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 4 ++-- .github/workflows/Windows_CI.yml | 4 ++-- .lgtm.yml | 4 ---- README.md | 2 +- docs/getting_started_main.rst | 2 +- requirements/environment.yml | 2 +- ruff.toml | 4 ++-- 9 files changed, 11 insertions(+), 15 deletions(-) delete mode 100644 .lgtm.yml diff --git a/.circleci/config.yml b/.circleci/config.yml index d8f1e89dc4..9f803ece4a 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,7 +6,7 @@ orbs: jobs: build_doc: docker: - - image: cimg/python:3.11 + - image: cimg/python:3.12 steps: - checkout - run: diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index b83954f17e..7b3dc14751 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [ '3.11' ] + python-version: [ '3.12' ] name: Python ${{ matrix.python-version }} CI diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 19c6612b2f..5ea15ac72e 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: macos-latest strategy: matrix: - python-version: [ '3.11' ] + python-version: [ '3.12' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v4 @@ -36,4 +36,4 @@ jobs: python -m pip install --upgrade pip pip install -r requirements/requirements.txt - name: do all unit tests - run: bash runtests.sh \ No newline at end of file + run: bash runtests.sh diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index 906edcfcdb..b9c8dea649 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: windows-latest strategy: matrix: - python-version: [ '3.11' ] + python-version: [ '3.12' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v4 @@ -33,4 +33,4 @@ jobs: python -m pip install --upgrade pip pip install -r requirements/requirements.txt - name: do all unit tests - run: bash runtests.sh \ No newline at end of file + run: bash runtests.sh diff --git a/.lgtm.yml b/.lgtm.yml deleted file mode 100644 index b06edf3510..0000000000 --- a/.lgtm.yml +++ /dev/null @@ -1,4 +0,0 @@ -extraction: - python: - python_setup: - version: 3 diff --git a/README.md b/README.md index bc0198f78e..7a01ae361a 100644 --- a/README.md +++ b/README.md @@ -95,7 +95,7 @@ See this paper for more details: For running each sample code: -- [Python 3.11.x](https://www.python.org/) +- [Python 3.12.x](https://www.python.org/) - [NumPy](https://numpy.org/) diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index 497b85a23a..88f218545e 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -26,7 +26,7 @@ See this paper for more details: Requirements ------------- -- `Python 3.11.x`_ +- `Python 3.12.x`_ - `NumPy`_ - `SciPy`_ - `Matplotlib`_ diff --git a/requirements/environment.yml b/requirements/environment.yml index 13dfa29f66..afbb3fb8ce 100644 --- a/requirements/environment.yml +++ b/requirements/environment.yml @@ -2,7 +2,7 @@ name: python_robotics channels: - conda-forge dependencies: - - python=3.11 + - python=3.12 - pip - scipy - numpy diff --git a/ruff.toml b/ruff.toml index 578864b33e..5823ca3db7 100644 --- a/ruff.toml +++ b/ruff.toml @@ -6,7 +6,7 @@ exclude = [ ] # Assume Python 3.11 -target-version = "py311" +target-version = "py312" [per-file-ignores] @@ -15,4 +15,4 @@ target-version = "py311" max-complexity = 10 [pydocstyle] -convention = "numpy" \ No newline at end of file +convention = "numpy" From 4bdf9036b60eb40899079b777b208870d2c20bfe Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Mar 2024 20:15:00 +0900 Subject: [PATCH 676/909] Bump ruff from 0.3.2 to 0.3.3 in /requirements (#993) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.2 to 0.3.3. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.3.2...v0.3.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1021562d1c..f6e64cc5ab 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test -ruff == 0.3.2 # For unit test +ruff == 0.3.3 # For unit test From f8b340be53ebb6ed4b6b0d2579394b58fe8074af Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 26 Mar 2024 07:30:04 +0900 Subject: [PATCH 677/909] Bump ruff from 0.3.3 to 0.3.4 in /requirements (#996) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.3 to 0.3.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.3.3...v0.3.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f6e64cc5ab..6e7c77d836 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test -ruff == 0.3.3 # For unit test +ruff == 0.3.4 # For unit test From 61d5da8576c43c59f69655cf6324fa959031623f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Apr 2024 07:31:43 +0900 Subject: [PATCH 678/909] Bump ruff from 0.3.4 to 0.3.5 in /requirements (#999) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.4 to 0.3.5. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.3.4...v0.3.5) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6e7c77d836..c1edf2951c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test -ruff == 0.3.4 # For unit test +ruff == 0.3.5 # For unit test From c6562d787f797f56561e362ea2633ad7e3b4ee3c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Apr 2024 08:00:57 +0900 Subject: [PATCH 679/909] Bump matplotlib from 3.8.3 to 3.8.4 in /requirements (#1001) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.3 to 3.8.4. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.3...v3.8.4) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index c1edf2951c..fe97f01e24 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.26.4 scipy == 1.12.0 -matplotlib == 3.8.3 +matplotlib == 3.8.4 cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test From 84237d6007254c2a644adcabe61673da11f39eb4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Apr 2024 20:25:11 +0900 Subject: [PATCH 680/909] Bump scipy from 1.12.0 to 1.13.0 in /requirements (#1002) Bumps [scipy](https://github.com/scipy/scipy) from 1.12.0 to 1.13.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.12.0...v1.13.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index fe97f01e24..7a94111985 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.26.4 -scipy == 1.12.0 +scipy == 1.13.0 matplotlib == 3.8.4 cvxpy == 1.4.2 pytest == 8.1.1 # For unit test From 808e98133d57426b1e6fbbc2bdc897a196278d7d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 Apr 2024 20:56:21 +0900 Subject: [PATCH 681/909] Bump ruff from 0.3.5 to 0.3.7 in /requirements (#1003) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.5 to 0.3.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.3.5...v0.3.7) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7a94111985..6ba7ea1c6d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test -ruff == 0.3.5 # For unit test +ruff == 0.3.7 # For unit test From d27f7dca1d2645915d03d087bc12c2ad281bb114 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Apr 2024 21:28:01 +0900 Subject: [PATCH 682/909] Bump ruff from 0.3.7 to 0.4.1 in /requirements (#1005) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.7 to 0.4.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.3.7...v0.4.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6ba7ea1c6d..7dfadddb48 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.2 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test -ruff == 0.3.7 # For unit test +ruff == 0.4.1 # For unit test From c12faa6a2ee2ac92102467487c4b168119c6cf07 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 27 Apr 2024 16:20:45 +0900 Subject: [PATCH 683/909] Bump cvxpy from 1.4.2 to 1.4.3 in /requirements (#1006) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.4.2 to 1.4.3. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.4.2...v1.4.3) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7dfadddb48..8e4d166c4e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.26.4 scipy == 1.13.0 matplotlib == 3.8.4 -cvxpy == 1.4.2 +cvxpy == 1.4.3 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test From 8f84f0f5518797c1c11f4cee204c0a5c6bffbe82 Mon Sep 17 00:00:00 2001 From: noob3-3 Date: Sat, 27 Apr 2024 20:51:19 +0800 Subject: [PATCH 684/909] fix::Control PathFinderController bug (#1000) --- Control/move_to_pose/move_to_pose.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py index cc29666297..279ba0625b 100644 --- a/Control/move_to_pose/move_to_pose.py +++ b/Control/move_to_pose/move_to_pose.py @@ -74,7 +74,7 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal): alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta) beta = angle_mod(theta_goal - theta - alpha) v = self.Kp_rho * rho - w = self.Kp_alpha * alpha - controller.Kp_beta * beta + w = self.Kp_alpha * alpha - self.Kp_beta * beta if alpha > np.pi / 2 or alpha < -np.pi / 2: v = -v From c76f74703f6b9f7b2ec3bf993eb9c9c5d2ccb4ac Mon Sep 17 00:00:00 2001 From: Yi-Chen Zhang Date: Sun, 28 Apr 2024 07:55:25 -0400 Subject: [PATCH 685/909] Fix covariance dimension (#998) --- Localization/particle_filter/particle_filter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py index 17051623ca..ba54a3d12b 100644 --- a/Localization/particle_filter/particle_filter.py +++ b/Localization/particle_filter/particle_filter.py @@ -96,10 +96,10 @@ def calc_covariance(x_est, px, pw): calculate covariance matrix see ipynb doc """ - cov = np.zeros((3, 3)) + cov = np.zeros((4, 4)) n_particle = px.shape[1] for i in range(n_particle): - dx = (px[:, i:i + 1] - x_est)[0:3] + dx = (px[:, i:i + 1] - x_est) cov += pw[0, i] * dx @ dx.T cov *= 1.0 / (1.0 - pw @ pw.T) From de2434361cd017851e10e4866bf6e6dca3b9cc95 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 28 Apr 2024 21:17:33 +0900 Subject: [PATCH 686/909] fix plot issue in random_inverse_kinematics.py (#1007) --- ArmNavigation/n_joint_arm_3d/NLinkArm3d.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py index 0cea963168..0459e234b2 100644 --- a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py +++ b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py @@ -127,7 +127,8 @@ def inverse_kinematics(self, ref_ee_pose, plot=False): if plot: self.fig = plt.figure() - self.ax = Axes3D(self.fig) + self.ax = Axes3D(self.fig, auto_add_to_figure=False) + self.fig.add_axes(self.ax) x_list = [] y_list = [] From b93f170d32a4132762ad879ffca19befa8eb218f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Apr 2024 08:56:40 +0900 Subject: [PATCH 687/909] Bump ruff from 0.4.1 to 0.4.2 in /requirements (#1008) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.1 to 0.4.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.1...v0.4.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 8e4d166c4e..ced1457da6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.3 pytest == 8.1.1 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test -ruff == 0.4.1 # For unit test +ruff == 0.4.2 # For unit test From bbf7d1ed857dc79e306f9c7fa09323cf74d0c71d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Apr 2024 12:49:55 +0900 Subject: [PATCH 688/909] Bump pytest from 8.1.1 to 8.2.0 in /requirements (#1010) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.1.1 to 8.2.0. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.1.1...8.2.0) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ced1457da6..ad5ab50f47 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.4 scipy == 1.13.0 matplotlib == 3.8.4 cvxpy == 1.4.3 -pytest == 8.1.1 # For unit test +pytest == 8.2.0 # For unit test pytest-xdist == 3.5.0 # For unit test mypy == 1.9.0 # For unit test ruff == 0.4.2 # For unit test From 1262b0bfd1823f4a0476eb5060bd8384dea34128 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Apr 2024 16:00:24 +0900 Subject: [PATCH 689/909] Bump pytest-xdist from 3.5.0 to 3.6.1 in /requirements (#1009) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.5.0 to 3.6.1. - [Release notes](https://github.com/pytest-dev/pytest-xdist/releases) - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.5.0...v3.6.1) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ad5ab50f47..b56c6e734f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.13.0 matplotlib == 3.8.4 cvxpy == 1.4.3 pytest == 8.2.0 # For unit test -pytest-xdist == 3.5.0 # For unit test +pytest-xdist == 3.6.1 # For unit test mypy == 1.9.0 # For unit test ruff == 0.4.2 # For unit test From e14134d1e705076e83ee4228fd463bbfa5fccd61 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 1 May 2024 07:54:14 +0900 Subject: [PATCH 690/909] Bump mypy from 1.9.0 to 1.10.0 in /requirements (#1011) Bumps [mypy](https://github.com/python/mypy) from 1.9.0 to 1.10.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/1.9.0...v1.10.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b56c6e734f..0575353a11 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.8.4 cvxpy == 1.4.3 pytest == 8.2.0 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.9.0 # For unit test +mypy == 1.10.0 # For unit test ruff == 0.4.2 # For unit test From 16c16b6ff56dbebf4fd0ae0f170c9acc6df080dd Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 4 May 2024 23:08:14 +0900 Subject: [PATCH 691/909] clean up fast slam codes (#1012) * clean up fast slam codes * clean up fast slam codes * clean up fast slam codes --- SLAM/FastSLAM1/fast_slam1.py | 100 ++++++++++++++++--------------- SLAM/FastSLAM2/fast_slam2.py | 110 +++++++++++++++++------------------ 2 files changed, 104 insertions(+), 106 deletions(-) diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index 17f6d5e572..98f8a66417 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -17,8 +17,8 @@ R = np.diag([1.0, np.deg2rad(20.0)]) ** 2 # Simulation parameter -Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2 -R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2 +Q_SIM = np.diag([0.3, np.deg2rad(2.0)]) ** 2 +R_SIM = np.diag([0.5, np.deg2rad(10.0)]) ** 2 OFFSET_YAW_RATE_NOISE = 0.01 DT = 0.1 # time tick [s] @@ -72,19 +72,18 @@ def normalize_weight(particles): def calc_final_state(particles): - xEst = np.zeros((STATE_SIZE, 1)) + x_est = np.zeros((STATE_SIZE, 1)) particles = normalize_weight(particles) for i in range(N_PARTICLE): - xEst[0, 0] += particles[i].w * particles[i].x - xEst[1, 0] += particles[i].w * particles[i].y - xEst[2, 0] += particles[i].w * particles[i].yaw + x_est[0, 0] += particles[i].w * particles[i].x + x_est[1, 0] += particles[i].w * particles[i].y + x_est[2, 0] += particles[i].w * particles[i].yaw - xEst[2, 0] = pi_2_pi(xEst[2, 0]) - # print(xEst) + x_est[2, 0] = pi_2_pi(x_est[2, 0]) - return xEst + return x_est def predict_particles(particles, u): @@ -235,28 +234,27 @@ def resampling(particles): pw = np.array(pw) n_eff = 1.0 / (pw @ pw.T) # Effective particle number - # print(n_eff) if n_eff < NTH: # resampling w_cum = np.cumsum(pw) base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE - inds = [] - ind = 0 + indexes = [] + index = 0 for ip in range(N_PARTICLE): - while (ind < w_cum.shape[0] - 1) \ - and (resample_id[ip] > w_cum[ind]): - ind += 1 - inds.append(ind) + while (index < w_cum.shape[0] - 1) \ + and (resample_id[ip] > w_cum[index]): + index += 1 + indexes.append(index) tmp_particles = particles[:] - for i in range(len(inds)): - particles[i].x = tmp_particles[inds[i]].x - particles[i].y = tmp_particles[inds[i]].y - particles[i].yaw = tmp_particles[inds[i]].yaw - particles[i].lm = tmp_particles[inds[i]].lm[:, :] - particles[i].lmP = tmp_particles[inds[i]].lmP[:, :] + for i in range(len(indexes)): + particles[i].x = tmp_particles[indexes[i]].x + particles[i].y = tmp_particles[indexes[i]].y + particles[i].yaw = tmp_particles[indexes[i]].yaw + particles[i].lm = tmp_particles[indexes[i]].lm[:, :] + particles[i].lmP = tmp_particles[indexes[i]].lmP[:, :] particles[i].w = 1.0 / N_PARTICLE return particles @@ -275,34 +273,34 @@ def calc_input(time): return u -def observation(xTrue, xd, u, rfid): +def observation(x_true, xd, u, rfid): # calc true state - xTrue = motion_model(xTrue, u) + x_true = motion_model(x_true, u) # add noise to range observation z = np.zeros((3, 0)) for i in range(len(rfid[:, 0])): - dx = rfid[i, 0] - xTrue[0, 0] - dy = rfid[i, 1] - xTrue[1, 0] + dx = rfid[i, 0] - x_true[0, 0] + dy = rfid[i, 1] - x_true[1, 0] d = math.hypot(dx, dy) - angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) + angle = pi_2_pi(math.atan2(dy, dx) - x_true[2, 0]) if d <= MAX_RANGE: - dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise - angle_with_noize = angle + np.random.randn() * Q_sim[ + dn = d + np.random.randn() * Q_SIM[0, 0] ** 0.5 # add noise + angle_with_noize = angle + np.random.randn() * Q_SIM[ 1, 1] ** 0.5 # add noise zi = np.array([dn, pi_2_pi(angle_with_noize), i]).reshape(3, 1) z = np.hstack((z, zi)) # add noise to input - ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5 - ud2 = u[1, 0] + np.random.randn() * R_sim[ + ud1 = u[0, 0] + np.random.randn() * R_SIM[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * R_SIM[ 1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE ud = np.array([ud1, ud2]).reshape(2, 1) xd = motion_model(xd, ud) - return xTrue, z, xd, ud + return x_true, z, xd, ud def motion_model(x, u): @@ -331,7 +329,7 @@ def main(): time = 0.0 # RFID positions [x, y] - RFID = np.array([[10.0, -2.0], + rfid = np.array([[10.0, -2.0], [15.0, 10.0], [15.0, 15.0], [10.0, 20.0], @@ -340,17 +338,17 @@ def main(): [-5.0, 5.0], [-10.0, 15.0] ]) - n_landmark = RFID.shape[0] + n_landmark = rfid.shape[0] # State Vector [x y yaw v]' - xEst = np.zeros((STATE_SIZE, 1)) # SLAM estimation - xTrue = np.zeros((STATE_SIZE, 1)) # True state - xDR = np.zeros((STATE_SIZE, 1)) # Dead reckoning + x_est = np.zeros((STATE_SIZE, 1)) # SLAM estimation + x_true = np.zeros((STATE_SIZE, 1)) # True state + x_dr = np.zeros((STATE_SIZE, 1)) # Dead reckoning # history - hxEst = xEst - hxTrue = xTrue - hxDR = xTrue + hist_x_est = x_est + hist_x_true = x_true + hist_x_dr = x_dr particles = [Particle(n_landmark) for _ in range(N_PARTICLE)] @@ -358,18 +356,18 @@ def main(): time += DT u = calc_input(time) - xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) + x_true, z, x_dr, ud = observation(x_true, x_dr, u, rfid) particles = fast_slam1(particles, ud, z) - xEst = calc_final_state(particles) + x_est = calc_final_state(particles) - x_state = xEst[0: STATE_SIZE] + x_state = x_est[0: STATE_SIZE] # store data history - hxEst = np.hstack((hxEst, x_state)) - hxDR = np.hstack((hxDR, xDR)) - hxTrue = np.hstack((hxTrue, xTrue)) + hist_x_est = np.hstack((hist_x_est, x_state)) + hist_x_dr = np.hstack((hist_x_dr, x_dr)) + hist_x_true = np.hstack((hist_x_true, x_true)) if show_animation: # pragma: no cover plt.cla() @@ -377,16 +375,16 @@ def main(): plt.gcf().canvas.mpl_connect( 'key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) - plt.plot(RFID[:, 0], RFID[:, 1], "*k") + plt.plot(rfid[:, 0], rfid[:, 1], "*k") for i in range(N_PARTICLE): plt.plot(particles[i].x, particles[i].y, ".r") plt.plot(particles[i].lm[:, 0], particles[i].lm[:, 1], "xb") - plt.plot(hxTrue[0, :], hxTrue[1, :], "-b") - plt.plot(hxDR[0, :], hxDR[1, :], "-k") - plt.plot(hxEst[0, :], hxEst[1, :], "-r") - plt.plot(xEst[0], xEst[1], "xk") + plt.plot(hist_x_true[0, :], hist_x_true[1, :], "-b") + plt.plot(hist_x_dr[0, :], hist_x_dr[1, :], "-k") + plt.plot(hist_x_est[0, :], hist_x_est[1, :], "-r") + plt.plot(x_est[0], x_est[1], "xk") plt.axis("equal") plt.grid(True) plt.pause(0.001) diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 2b57e3bcc3..d4cf0d84dd 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -17,8 +17,8 @@ R = np.diag([1.0, np.deg2rad(20.0)]) ** 2 # Simulation parameter -Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2 -R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2 +Q_SIM = np.diag([0.3, np.deg2rad(2.0)]) ** 2 +R_SIM = np.diag([0.5, np.deg2rad(10.0)]) ** 2 OFFSET_YAW_RATE_NOISE = 0.01 DT = 0.1 # time tick [s] @@ -35,16 +35,16 @@ class Particle: - def __init__(self, N_LM): + def __init__(self, n_landmark): self.w = 1.0 / N_PARTICLE self.x = 0.0 self.y = 0.0 self.yaw = 0.0 self.P = np.eye(3) # landmark x-y positions - self.lm = np.zeros((N_LM, LM_SIZE)) + self.lm = np.zeros((n_landmark, LM_SIZE)) # landmark position covariance - self.lmP = np.zeros((N_LM * LM_SIZE, LM_SIZE)) + self.lmP = np.zeros((n_landmark * LM_SIZE, LM_SIZE)) def fast_slam2(particles, u, z): @@ -73,18 +73,18 @@ def normalize_weight(particles): def calc_final_state(particles): - xEst = np.zeros((STATE_SIZE, 1)) + x_est = np.zeros((STATE_SIZE, 1)) particles = normalize_weight(particles) for i in range(N_PARTICLE): - xEst[0, 0] += particles[i].w * particles[i].x - xEst[1, 0] += particles[i].w * particles[i].y - xEst[2, 0] += particles[i].w * particles[i].yaw + x_est[0, 0] += particles[i].w * particles[i].x + x_est[1, 0] += particles[i].w * particles[i].y + x_est[2, 0] += particles[i].w * particles[i].yaw - xEst[2, 0] = pi_2_pi(xEst[2, 0]) + x_est[2, 0] = pi_2_pi(x_est[2, 0]) - return xEst + return x_est def predict_particles(particles, u): @@ -266,21 +266,21 @@ def resampling(particles): base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE - inds = [] - ind = 0 + indexes = [] + index = 0 for ip in range(N_PARTICLE): - while (ind < w_cum.shape[0] - 1) \ - and (resample_id[ip] > w_cum[ind]): - ind += 1 - inds.append(ind) + while (index < w_cum.shape[0] - 1) \ + and (resample_id[ip] > w_cum[index]): + index += 1 + indexes.append(index) tmp_particles = particles[:] - for i in range(len(inds)): - particles[i].x = tmp_particles[inds[i]].x - particles[i].y = tmp_particles[inds[i]].y - particles[i].yaw = tmp_particles[inds[i]].yaw - particles[i].lm = tmp_particles[inds[i]].lm[:, :] - particles[i].lmP = tmp_particles[inds[i]].lmP[:, :] + for i in range(len(indexes)): + particles[i].x = tmp_particles[indexes[i]].x + particles[i].y = tmp_particles[indexes[i]].y + particles[i].yaw = tmp_particles[indexes[i]].yaw + particles[i].lm = tmp_particles[indexes[i]].lm[:, :] + particles[i].lmP = tmp_particles[indexes[i]].lmP[:, :] particles[i].w = 1.0 / N_PARTICLE return particles @@ -299,35 +299,35 @@ def calc_input(time): return u -def observation(xTrue, xd, u, RFID): +def observation(x_true, xd, u, rfid): # calc true state - xTrue = motion_model(xTrue, u) + x_true = motion_model(x_true, u) # add noise to range observation z = np.zeros((3, 0)) - for i in range(len(RFID[:, 0])): + for i in range(len(rfid[:, 0])): - dx = RFID[i, 0] - xTrue[0, 0] - dy = RFID[i, 1] - xTrue[1, 0] + dx = rfid[i, 0] - x_true[0, 0] + dy = rfid[i, 1] - x_true[1, 0] d = math.hypot(dx, dy) - angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0]) + angle = pi_2_pi(math.atan2(dy, dx) - x_true[2, 0]) if d <= MAX_RANGE: - dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5 # add noise - angle_noise = np.random.randn() * Q_sim[1, 1] ** 0.5 + dn = d + np.random.randn() * Q_SIM[0, 0] ** 0.5 # add noise + angle_noise = np.random.randn() * Q_SIM[1, 1] ** 0.5 angle_with_noise = angle + angle_noise # add noise zi = np.array([dn, pi_2_pi(angle_with_noise), i]).reshape(3, 1) z = np.hstack((z, zi)) # add noise to input - ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5 - ud2 = u[1, 0] + np.random.randn() * R_sim[ + ud1 = u[0, 0] + np.random.randn() * R_SIM[0, 0] ** 0.5 + ud2 = u[1, 0] + np.random.randn() * R_SIM[ 1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE ud = np.array([ud1, ud2]).reshape(2, 1) xd = motion_model(xd, ud) - return xTrue, z, xd, ud + return x_true, z, xd, ud def motion_model(x, u): @@ -356,7 +356,7 @@ def main(): time = 0.0 # RFID positions [x, y] - RFID = np.array([[10.0, -2.0], + rfid = np.array([[10.0, -2.0], [15.0, 10.0], [15.0, 15.0], [10.0, 20.0], @@ -365,17 +365,17 @@ def main(): [-5.0, 5.0], [-10.0, 15.0] ]) - n_landmark = RFID.shape[0] + n_landmark = rfid.shape[0] # State Vector [x y yaw v]' - xEst = np.zeros((STATE_SIZE, 1)) # SLAM estimation - xTrue = np.zeros((STATE_SIZE, 1)) # True state - xDR = np.zeros((STATE_SIZE, 1)) # Dead reckoning + x_est = np.zeros((STATE_SIZE, 1)) # SLAM estimation + x_true = np.zeros((STATE_SIZE, 1)) # True state + x_dr = np.zeros((STATE_SIZE, 1)) # Dead reckoning # history - hxEst = xEst - hxTrue = xTrue - hxDR = xTrue + hist_x_est = x_est + hist_x_true = x_true + hist_x_dr = x_dr particles = [Particle(n_landmark) for _ in range(N_PARTICLE)] @@ -383,18 +383,18 @@ def main(): time += DT u = calc_input(time) - xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID) + x_true, z, x_dr, ud = observation(x_true, x_dr, u, rfid) particles = fast_slam2(particles, ud, z) - xEst = calc_final_state(particles) + x_est = calc_final_state(particles) - x_state = xEst[0: STATE_SIZE] + x_state = x_est[0: STATE_SIZE] # store data history - hxEst = np.hstack((hxEst, x_state)) - hxDR = np.hstack((hxDR, xDR)) - hxTrue = np.hstack((hxTrue, xTrue)) + hist_x_est = np.hstack((hist_x_est, x_state)) + hist_x_dr = np.hstack((hist_x_dr, x_dr)) + hist_x_true = np.hstack((hist_x_true, x_true)) if show_animation: # pragma: no cover plt.cla() @@ -402,21 +402,21 @@ def main(): plt.gcf().canvas.mpl_connect( 'key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) - plt.plot(RFID[:, 0], RFID[:, 1], "*k") + plt.plot(rfid[:, 0], rfid[:, 1], "*k") for iz in range(len(z[:, 0])): landmark_id = int(z[2, iz]) - plt.plot([xEst[0][0], RFID[landmark_id, 0]], [ - xEst[1][0], RFID[landmark_id, 1]], "-k") + plt.plot([x_est[0][0], rfid[landmark_id, 0]], [ + x_est[1][0], rfid[landmark_id, 1]], "-k") for i in range(N_PARTICLE): plt.plot(particles[i].x, particles[i].y, ".r") plt.plot(particles[i].lm[:, 0], particles[i].lm[:, 1], "xb") - plt.plot(hxTrue[0, :], hxTrue[1, :], "-b") - plt.plot(hxDR[0, :], hxDR[1, :], "-k") - plt.plot(hxEst[0, :], hxEst[1, :], "-r") - plt.plot(xEst[0], xEst[1], "xk") + plt.plot(hist_x_true[0, :], hist_x_true[1, :], "-b") + plt.plot(hist_x_dr[0, :], hist_x_dr[1, :], "-k") + plt.plot(hist_x_est[0, :], hist_x_est[1, :], "-r") + plt.plot(x_est[0], x_est[1], "xk") plt.axis("equal") plt.grid(True) plt.pause(0.001) From 20818bfe3c7185e065435ea4d204510db6dd37f5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 May 2024 21:37:44 +0900 Subject: [PATCH 692/909] Bump ruff from 0.4.2 to 0.4.3 in /requirements (#1014) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.2 to 0.4.3. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.2...v0.4.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 0575353a11..4db551eb8c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.3 pytest == 8.2.0 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test -ruff == 0.4.2 # For unit test +ruff == 0.4.3 # For unit test From dfcc08e36723e7a371048147d2879f7b373e1902 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 May 2024 08:44:53 +0900 Subject: [PATCH 693/909] Bump ruff from 0.4.3 to 0.4.4 in /requirements (#1017) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.3 to 0.4.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.3...v0.4.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4db551eb8c..909b39faa7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.4.3 pytest == 8.2.0 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test -ruff == 0.4.3 # For unit test +ruff == 0.4.4 # For unit test From ed5004b0f84081b93a2aceebe96defe013d99e06 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?YSF=E3=83=84?= <50619491+yousefbilal@users.noreply.github.com> Date: Sat, 18 May 2024 13:56:04 +0400 Subject: [PATCH 694/909] fixed hard-coded plot limits (#1018) --- PathPlanning/RRT/rrt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index 55092e6e00..e6dd9b648b 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -199,7 +199,7 @@ def draw_graph(self, rnd=None): plt.plot(self.start.x, self.start.y, "xr") plt.plot(self.end.x, self.end.y, "xr") plt.axis("equal") - plt.axis([-2, 15, -2, 15]) + plt.axis([self.min_rand, self.max_rand, self.min_rand, self.max_rand]) plt.grid(True) plt.pause(0.01) From 36b630040224c79bfa05974b8107b7a384e8271a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 19 May 2024 20:54:57 +0900 Subject: [PATCH 695/909] Bump cvxpy from 1.4.3 to 1.5.1 in /requirements (#1016) * Bump cvxpy from 1.4.3 to 1.5.1 in /requirements Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.4.3 to 1.5.1. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.4.3...v1.5.1) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] * fix cvxpy update issue --------- Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Atsushi Sakai --- .../model_predictive_speed_and_steer_control.py | 14 ++++++++++++-- requirements/requirements.txt | 2 +- runtests.sh | 2 +- 3 files changed, 14 insertions(+), 4 deletions(-) diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py index c55018cf26..eb2d7b6a73 100644 --- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py @@ -6,14 +6,16 @@ """ import matplotlib.pyplot as plt +import time import cvxpy import math import numpy as np import sys import pathlib -from utils.angle import angle_mod sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +from utils.angle import angle_mod + from PathPlanning.CubicSpline import cubic_spline_planner NX = 4 # x = x, y, v, yaw @@ -281,7 +283,7 @@ def linear_mpc_control(xref, xbar, x0, dref): constraints += [cvxpy.abs(u[1, :]) <= MAX_STEER] prob = cvxpy.Problem(cvxpy.Minimize(cost), constraints) - prob.solve(solver=cvxpy.ECOS, verbose=False) + prob.solve(solver=cvxpy.CLARABEL, verbose=False) if prob.status == cvxpy.OPTIMAL or prob.status == cvxpy.OPTIMAL_INACCURATE: ox = get_nparray_from_matrix(x.value[0, :]) @@ -545,6 +547,7 @@ def get_switch_back_course(dl): def main(): print(__file__ + " start!!") + start = time.time() dl = 1.0 # course tick # cx, cy, cyaw, ck = get_straight_course(dl) @@ -560,6 +563,9 @@ def main(): t, x, y, yaw, v, d, a = do_simulation( cx, cy, cyaw, ck, sp, dl, initial_state) + elapsed_time = time.time() - start + print(f"calc time:{elapsed_time:.6f} [sec]") + if show_animation: # pragma: no cover plt.close("all") plt.subplots() @@ -582,6 +588,7 @@ def main(): def main2(): print(__file__ + " start!!") + start = time.time() dl = 1.0 # course tick cx, cy, cyaw, ck = get_straight_course3(dl) @@ -593,6 +600,9 @@ def main2(): t, x, y, yaw, v, d, a = do_simulation( cx, cy, cyaw, ck, sp, dl, initial_state) + elapsed_time = time.time() - start + print(f"calc time:{elapsed_time:.6f} [sec]") + if show_animation: # pragma: no cover plt.close("all") plt.subplots() diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 909b39faa7..76f203f423 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.26.4 scipy == 1.13.0 matplotlib == 3.8.4 -cvxpy == 1.4.3 +cvxpy == 1.5.1 pytest == 8.2.0 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test diff --git a/runtests.sh b/runtests.sh index a2a683e571..12d1b80453 100755 --- a/runtests.sh +++ b/runtests.sh @@ -5,4 +5,4 @@ echo "Run test suites! " # -Werror: warning as error # --durations=0: show ranking of test durations # -l (--showlocals); show local variables when test failed -pytest -n auto tests -l -Werror --durations=0 +pytest tests -l -Werror --durations=0 From e752e50421e66f3982cfd84188499137c55d2925 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 May 2024 21:16:11 +0900 Subject: [PATCH 696/909] --- (#1020) updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 76f203f423..65885f423b 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.4 scipy == 1.13.0 matplotlib == 3.8.4 cvxpy == 1.5.1 -pytest == 8.2.0 # For unit test +pytest == 8.2.1 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test ruff == 0.4.4 # For unit test From cfb736316aed6a6160e45e1c236beda0a5f01622 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 23 May 2024 21:48:35 +0900 Subject: [PATCH 697/909] --- (#1019) updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 65885f423b..cbf18ad938 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.26.4 scipy == 1.13.0 -matplotlib == 3.8.4 +matplotlib == 3.9.0 cvxpy == 1.5.1 pytest == 8.2.1 # For unit test pytest-xdist == 3.6.1 # For unit test From 5751829bab294019bb78917d91f280552bb549d2 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 25 May 2024 23:31:54 +0900 Subject: [PATCH 698/909] Enhance lqr steering control docs (#1015) * enhance lqr steering control docs * enhance lqr steering control docs * enhance lqr steering control docs * enhance lqr steering control docs * improve lqr steering control docs --- .../lqr_steer_control/lqr_steer_control.py | 14 +-- docs/README.md | 2 +- .../lqr_steering_control_main.rst | 102 ++++++++++++++++++ .../lqr_steering_control_model.png | Bin 0 -> 17366 bytes 4 files changed, 111 insertions(+), 7 deletions(-) create mode 100644 docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py index 4078ea56db..461d6e3722 100644 --- a/PathTracking/lqr_steer_control/lqr_steer_control.py +++ b/PathTracking/lqr_steer_control/lqr_steer_control.py @@ -55,7 +55,7 @@ def update(state, a, delta): return state -def PIDControl(target, current): +def pid_control(target, current): a = Kp * (target - current) return a @@ -70,10 +70,11 @@ def solve_DARE(A, B, Q, R): solve a discrete time_Algebraic Riccati equation (DARE) """ X = Q - maxiter = 150 + Xn = Q + max_iter = 150 eps = 0.01 - for i in range(maxiter): + for i in range(max_iter): Xn = A.T @ X @ A - A.T @ X @ B @ \ la.inv(R + B.T @ X @ B) @ B.T @ X @ A + Q if (abs(Xn - X)).max() < eps: @@ -178,7 +179,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): dl, target_ind, e, e_th = lqr_steering_control( state, cx, cy, cyaw, ck, e, e_th) - ai = PIDControl(speed_profile[target_ind], state.v) + ai = pid_control(speed_profile[target_ind], state.v) state = update(state, ai, dl) if abs(state.v) <= stop_speed: @@ -202,8 +203,9 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") plt.plot(x, y, "ob", label="trajectory") plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") diff --git a/docs/README.md b/docs/README.md index 5c4145e8e3..fb7d4cc3bc 100644 --- a/docs/README.md +++ b/docs/README.md @@ -8,7 +8,7 @@ This folder contains documentation for the Python Robotics project. #### Install Sphinx and Theme ``` -pip install sphinx sphinx-autobuild sphinx-rtd-theme +pip install sphinx sphinx-autobuild sphinx-rtd-theme sphinx_rtd_dark_mode sphinx_copybutton sphinx_rtd_dark_mode ``` #### Building the Docs diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst index bf6d6b5854..b18e4728cd 100644 --- a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst +++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst @@ -8,7 +8,109 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif +Overview +~~~~~~~~ + +The LQR (Linear Quadratic Regulator) steering control model implemented in lqr_steer_control.py provides a simulation +for an autonomous vehicle to track a desired trajectory by adjusting steering angle based on feedback from the current state and the desired trajectory. +This model utilizes a combination of PID speed control and LQR steering control to achieve smooth and accurate trajectory tracking. + +Vehicle motion Model +~~~~~~~~~~~~~~~~~~~~~ + +The below figure shows the geometric model of the vehicle used in this simulation: + +.. image:: lqr_steering_control_model.png + :width: 600px + +The `e` and `theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory. +And :math:`\dot{e}` and :math:`\dot{\theta}` represent the rates of change of these errors. + +The :math:`e_t` and :math:`theta_t` are the updated values of `e` and `theta` at time `t`, respectively, and can be calculated using the following kinematic equations: + +.. math:: e_t = e_{t-1} + \dot{e}_{t-1} dt + +.. math:: \theta_t = \theta_{t-1} + \dot{\theta}_{t-1} dt + +Where `dt` is the time difference. + +The change rate of the `e` can be calculated as: + +.. math:: \dot{e}_t = V \sin(\theta_{t-1}) + +Where `V` is the vehicle speed. + +If the :math:`theta` is small, + +.. math:: \theta \approx 0 + +the :math:`\sin(\theta)` can be approximated as :math:`\theta`: + +.. math:: \sin(\theta) = \theta + +So, the change rate of the `e` can be approximated as: + +.. math:: \dot{e}_t = V \theta_{t-1} + +The change rate of the :math:`\theta` can be calculated as: + +.. math:: \dot{\theta}_t = \frac{V}{L} \tan(\delta) + +where `L` is the wheelbase of the vehicle and :math:`\delta` is the steering angle. + +If the :math:`\delta` is small, + +.. math:: \delta \approx 0 + +the :math:`\tan(\delta)` can be approximated as :math:`\delta`: + +.. math:: \tan(\delta) = \delta + +So, the change rate of the :math:`\theta` can be approximated as: + +.. math:: \dot{\theta}_t = \frac{V}{L} \delta + +The above equations can be used to update the state of the vehicle at each time step. + +To formulate the state-space representation of the vehicle dynamics as a linear model, +the state vector `x` and control input vector `u` are defined as follows: + +.. math:: x_t = [e_t, \dot{e}_t, \theta_t, \dot{\theta}_t]^T + +.. math:: u_t = \delta_t + +The state transition equation can be represented as: + +.. math:: x_{t+1} = A x_t + B u_t + +where: + +:math:`\begin{equation*} A = \begin{bmatrix} 1 & dt & 0 & 0\\ 0 & 0 & v & 0\\ 0 & 0 & 1 & dt\\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}` + +:math:`\begin{equation*} B = \begin{bmatrix} 0\\ 0\\ 0\\ \frac{v}{L} \\ \end{bmatrix} \end{equation*}` + +LQR controller +~~~~~~~~~~~~~~~ + +The Linear Quadratic Regulator (LQR) controller is used to calculate the optimal control input `u` that minimizes the quadratic cost function: + +:math:`J = \sum_{t=0}^{N} (x_t^T Q x_t + u_t^T R u_t)` + +where `Q` and `R` are the weighting matrices for the state and control input, respectively. + 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docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.jpg delete mode 100644 docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst index b18e4728cd..fcc9b5278a 100644 --- a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst +++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst @@ -20,7 +20,7 @@ Vehicle motion Model The below figure shows the geometric model of the vehicle used in this simulation: -.. image:: lqr_steering_control_model.png +.. image:: lqr_steering_control_model.jpg :width: 600px The `e` and `theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory. diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.jpg 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zy_%5VUW+E@A*QAhj)A?bO1m$NLa(%`?C#s7U--Ehe6t~(^5j$8P*RPdyYaKC;5z19 zR-QW=_k_d_nj0Osk>OvW+KiKk9p0xGJuw2F>W}oa?pR{BAPw(MU$;Igyx} zsJR8t{WJmVL=i$cHZC6Surmk)~Y?<<->jGzPhG#Jx?g3}L-XB<9zV?H-17=o$CoX#4F@`ftb_WCS}0{-WNlUSj6&JhvWWTjCveso7n=iHUX?2R>w zeZL&unW}_eRhG#A!J^}6ixo!CSj zYSzgu4_`Rj3f`4iZE@ad3q#}bL5-ufc21)QL%USOCPzz8QVkB*>^3hxnSthiB0nG=(lkSB_elJ=SnxYfnfe9Z!Cr9uQ;vETCa+dSOd|$(%(y=8Ytlq-O-* zUC#-j0`9)F%dW0d7VPcCg8Q2i)e0?9Cwfp1_##Ak0up-D1k`efyK1&8&4I^V^pFbSSiolbXMV-4 ShTuQyfRXff>lG92j{hIC6RZ#b From 3504db00a666d84de0e4738fad1db78142f34c17 Mon Sep 17 00:00:00 2001 From: RyuYamamoto Date: Sun, 26 May 2024 15:42:25 +0900 Subject: [PATCH 700/909] fix: typo (#1023) --- PathPlanning/DStarLite/d_star_lite.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py index 86e777d7b7..f099530173 100644 --- a/PathPlanning/DStarLite/d_star_lite.py +++ b/PathPlanning/DStarLite/d_star_lite.py @@ -2,7 +2,7 @@ D* Lite grid planning author: vss2sn (28676655+vss2sn@users.noreply.github.com) Link to papers: -D* Lite (Link: http://idm-lab.org/bib/abstracts/papers/aaai02b.pd) +D* Lite (Link: http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf) Improved Fast Replanning for Robot Navigation in Unknown Terrain (Link: http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf) Implemented maintaining similarity with the pseudocode for understanding. From 9fed26e503fb28fbb6169e8fc3f8b4c29975e16f Mon Sep 17 00:00:00 2001 From: Tim Huff <89954856+timmarkhuff@users.noreply.github.com> Date: Sat, 25 May 2024 23:43:05 -0700 Subject: [PATCH 701/909] Fixing ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py (#1022) * fixing script * removing unnecessary code * fixing CI issue --------- Co-authored-by: Tim Huff --- .../rrt_star_seven_joint_arm_control.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py index 0f449bbe51..3bc2a5ec1d 100755 --- a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py +++ b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py @@ -265,7 +265,7 @@ def steer(self, from_node, to_node, extend_length=float("inf")): def draw_graph(self, rnd=None): plt.cla() - self.ax.axis([-1, 1, -1, 1]) + self.ax.axis([-1, 1, -1, 1, -1, 1]) self.ax.set_zlim(0, 1) self.ax.grid(True) for (ox, oy, oz, size) in self.obstacle_list: @@ -375,9 +375,9 @@ def main(): search_until_max_iter=False) if path is None: - print("Cannot find path") + print("Cannot find path.") else: - print("found path!!") + print("Found path!") # Draw final path if show_animation: @@ -396,7 +396,7 @@ def main(): [y for y in y_points], [z for z in z_points], "o-", color="grey", ms=4, mew=0.5) - plt.pause(0.01) + plt.pause(0.1) plt.show() From 1aa9a12873c49393dfa1b17a01f72e38b71ece6d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 May 2024 20:50:36 +0900 Subject: [PATCH 702/909] Bump ruff from 0.4.4 to 0.4.5 in /requirements (#1024) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.4 to 0.4.5. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.4...v0.4.5) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index cbf18ad938..6a07880f45 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.1 pytest == 8.2.1 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test -ruff == 0.4.4 # For unit test +ruff == 0.4.5 # For unit test From 01949a6edcf79100527a24ccf7d07fad235df03e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 1 Jun 2024 17:46:27 +0900 Subject: [PATCH 703/909] Bump scipy from 1.13.0 to 1.13.1 in /requirements (#1025) Bumps [scipy](https://github.com/scipy/scipy) from 1.13.0 to 1.13.1. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.13.0...v1.13.1) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6a07880f45..337f56781d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.26.4 -scipy == 1.13.0 +scipy == 1.13.1 matplotlib == 3.9.0 cvxpy == 1.5.1 pytest == 8.2.1 # For unit test From a9be11d131e404df00d9b91c91289aec7bc895ea Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 5 Jun 2024 07:21:06 +0900 Subject: [PATCH 704/909] Bump ruff from 0.4.5 to 0.4.7 in /requirements (#1026) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.5 to 0.4.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.5...v0.4.7) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 337f56781d..a7cdea961e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.1 pytest == 8.2.1 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test -ruff == 0.4.5 # For unit test +ruff == 0.4.7 # For unit test From 9d06b1e3c3179f9da85f9646bf2e25aea0e4b068 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 13 Jun 2024 12:50:05 +0900 Subject: [PATCH 705/909] Bump ruff from 0.4.7 to 0.4.8 in /requirements (#1027) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.7 to 0.4.8. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.7...v0.4.8) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a7cdea961e..7a1b36454c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.1 pytest == 8.2.1 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test -ruff == 0.4.7 # For unit test +ruff == 0.4.8 # For unit test From f289bd22b9390ab7fafe20c88fe8dda6919a4b67 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 13 Jun 2024 19:45:30 +0900 Subject: [PATCH 706/909] Bump pytest from 8.2.1 to 8.2.2 in /requirements (#1028) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.2.1 to 8.2.2. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.2.1...8.2.2) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7a1b36454c..3263de2c56 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.26.4 scipy == 1.13.1 matplotlib == 3.9.0 cvxpy == 1.5.1 -pytest == 8.2.1 # For unit test +pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test ruff == 0.4.8 # For unit test From 83cfed15b6430b01130240da53a6c4d745ef9378 Mon Sep 17 00:00:00 2001 From: Yukihiro Saito Date: Sat, 15 Jun 2024 07:46:15 +0900 Subject: [PATCH 707/909] fix(README.md): typo in reeds-shepp path (#1031) Signed-off-by: Yukihito Saito --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7a01ae361a..ee40dfc7eb 100644 --- a/README.md +++ b/README.md @@ -407,7 +407,7 @@ Ref: ### RRT\* with reeds-shepp path -![Robotics/animation.gif at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif)) +![Robotics/animation.gif at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif) Path planning for a car robot with RRT\* and reeds shepp path planner. From 15e1ed5de1b2f3d53eedf896b0c9efb9650d25bc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Jun 2024 07:57:18 +0900 Subject: [PATCH 708/909] Bump ruff from 0.4.8 to 0.4.9 in /requirements (#1033) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.8 to 0.4.9. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.8...v0.4.9) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3263de2c56..3b51f38858 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.1 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test -ruff == 0.4.8 # For unit test +ruff == 0.4.9 # For unit test From 75f3e4d56cb68ae0d6c1b2fc61e7f265cb2005d4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Jun 2024 20:27:21 +0900 Subject: [PATCH 709/909] Bump cvxpy from 1.5.1 to 1.5.2 in /requirements (#1035) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.5.1 to 1.5.2. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.5.1...v1.5.2) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3b51f38858..7cf9cfb7c3 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.26.4 scipy == 1.13.1 matplotlib == 3.9.0 -cvxpy == 1.5.1 +cvxpy == 1.5.2 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test From d6075ca7d039b0ce98fd51518b9f48ca13763b3e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Jun 2024 21:32:49 +0900 Subject: [PATCH 710/909] Bump ruff from 0.4.9 to 0.4.10 in /requirements (#1036) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.9 to 0.4.10. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.9...v0.4.10) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7cf9cfb7c3..576bfcb86d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.2 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.0 # For unit test -ruff == 0.4.9 # For unit test +ruff == 0.4.10 # For unit test From df5d99248dae9967a9e90517900e3858b0ae8b5d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Jun 2024 22:12:57 +0900 Subject: [PATCH 711/909] Bump numpy from 1.26.4 to 2.0.0 in /requirements (#1032) Bumps [numpy](https://github.com/numpy/numpy) from 1.26.4 to 2.0.0. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.26.4...v2.0.0) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 576bfcb86d..12830626aa 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.26.4 +numpy == 2.0.0 scipy == 1.13.1 matplotlib == 3.9.0 cvxpy == 1.5.2 From 71766f0a3eba78fcf6ba3820ed74464f32f751ee Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jul 2024 07:38:06 +0900 Subject: [PATCH 712/909] Bump scipy from 1.13.1 to 1.14.0 in /requirements (#1040) Bumps [scipy](https://github.com/scipy/scipy) from 1.13.1 to 1.14.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.13.1...v1.14.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 12830626aa..7285355871 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 2.0.0 -scipy == 1.13.1 +scipy == 1.14.0 matplotlib == 3.9.0 cvxpy == 1.5.2 pytest == 8.2.2 # For unit test From cca764edf600f3a29456981691af8f414c4030ea Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jul 2024 21:19:23 +0900 Subject: [PATCH 713/909] Bump mypy from 1.10.0 to 1.10.1 in /requirements (#1039) Bumps [mypy](https://github.com/python/mypy) from 1.10.0 to 1.10.1. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.10.0...v1.10.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7285355871..1c4806ab75 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.9.0 cvxpy == 1.5.2 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.10.0 # For unit test +mypy == 1.10.1 # For unit test ruff == 0.4.10 # For unit test From 0f8ce35bf236ac7c97baa99e4189f6bc283be834 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 2 Jul 2024 21:57:57 +0900 Subject: [PATCH 714/909] Bump ruff from 0.4.10 to 0.5.0 in /requirements (#1041) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.10 to 0.5.0. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.4.10...0.5.0) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1c4806ab75..5eb478dfca 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.2 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.1 # For unit test -ruff == 0.4.10 # For unit test +ruff == 0.5.0 # For unit test From af7a75fa837f98ab2980e4f637e4273ba4a78e50 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Jul 2024 07:16:58 +0900 Subject: [PATCH 715/909] Bump matplotlib from 3.9.0 to 3.9.1 in /requirements (#1045) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.0 to 3.9.1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.0...v3.9.1) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5eb478dfca..406786edbc 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 2.0.0 scipy == 1.14.0 -matplotlib == 3.9.0 +matplotlib == 3.9.1 cvxpy == 1.5.2 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test From 4b69c7a859e7658483c16cd5b17820f1056ae21c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 9 Jul 2024 21:21:56 +0900 Subject: [PATCH 716/909] Bump ruff from 0.5.0 to 0.5.1 in /requirements (#1044) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.0 to 0.5.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.0...0.5.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 406786edbc..e334ab227a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.2 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.1 # For unit test -ruff == 0.5.0 # For unit test +ruff == 0.5.1 # For unit test From 26deca19b63382e2fffecc804fceb6694c21d5aa Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 Jul 2024 08:20:28 +0900 Subject: [PATCH 717/909] Bump ruff from 0.5.1 to 0.5.2 in /requirements (#1048) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.1 to 0.5.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.1...0.5.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e334ab227a..aa91cb258f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.2 pytest == 8.2.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.1 # For unit test -ruff == 0.5.1 # For unit test +ruff == 0.5.2 # For unit test From 1e101d1f8adbbb6cdb9334807067b872ac62004a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 16 Jul 2024 23:02:31 +0900 Subject: [PATCH 718/909] improve LQT steering and speed control document (#1047) * improve LQT steering and speed control * improve LQT steering and speed control * improve LQT steering and speed control doc * improve LQT steering and speed control doc * improve LQT steering and speed control doc * improve LQT steering and speed control doc --- .../lqr_speed_steer_control.py | 13 +- .../lqr_speed_and_steering_control_main.rst | 127 ++++++++++++++++++ .../lqr_steering_control_model.jpg | Bin 0 -> 11721 bytes .../lqr_speed_and_steering_control/speed.png | Bin 0 -> 22550 bytes .../lqr_speed_and_steering_control/x-y.png | Bin 0 -> 31256 bytes .../lqr_speed_and_steering_control/yaw.png | Bin 0 -> 23991 bytes .../lqr_steering_control_main.rst | 15 ++- tests/test_codestyle.py | 2 +- 8 files changed, 149 insertions(+), 8 deletions(-) create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_steering_control_model.jpg create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/speed.png create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/x-y.png create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/yaw.png diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py index a70e30d378..d85fa98a84 100644 --- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py +++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py @@ -55,6 +55,7 @@ def update(state, a, delta): def pi_2_pi(angle): return angle_mod(angle) + def solve_dare(A, B, Q, R): """ solve a discrete time_Algebraic Riccati equation (DARE) @@ -221,8 +222,9 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal): if target_ind % 1 == 0 and show_animation: plt.cla() # for stopping simulation with the esc key. - plt.gcf().canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.plot(cx, cy, "-r", label="course") plt.plot(x, y, "ob", label="trajectory") plt.plot(cx[target_ind], cy[target_ind], "xg", label="target") @@ -290,6 +292,13 @@ def main(): plt.xlabel("x[m]") plt.ylabel("y[m]") plt.legend() + plt.subplots(1) + + plt.plot(t, np.array(v)*3.6, label="speed") + plt.grid(True) + plt.xlabel("Time [sec]") + plt.ylabel("Speed [m/s]") + plt.legend() plt.subplots(1) plt.plot(s, [np.rad2deg(iyaw) for iyaw in cyaw], "-r", label="yaw") diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst index 68ea9c88b2..a0b145456c 100644 --- a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst +++ b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst @@ -7,6 +7,133 @@ Path tracking simulation with LQR speed and steering control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif +`[Code Link] `_ + +Overview +~~~~~~~~ + +The LQR (Linear Quadratic Regulator) speed and steering control model implemented in `lqr_speed_steer_control.py` provides a simulation +for an autonomous vehicle to track: + +1. A desired speed by adjusting acceleration based on feedback from the current state and the desired speed. + +2. A desired trajectory by adjusting steering angle based on feedback from the current state and the desired trajectory. + +by only using one LQT controller. + +Vehicle motion Model +~~~~~~~~~~~~~~~~~~~~~ + +The below figure shows the geometric model of the vehicle used in this simulation: + +.. image:: lqr_steering_control_model.jpg + :width: 600px + +The `e`, :math:`{\theta}`, and :math:`\upsilon` represent the lateral error, orientation error, and velocity error, respectively, with respect to the desired trajectory and speed. +And :math:`\dot{e}` and :math:`\dot{\theta}` represent the rates of change of these errors. + +The :math:`e_t` and :math:`\theta_t`, and :math:`\upsilon` are the updated values of `e`, :math:`\theta`, :math:`\upsilon` and at time `t`, respectively, and can be calculated using the following kinematic equations: + +.. math:: e_t = e_{t-1} + \dot{e}_{t-1} dt + +.. math:: \theta_t = \theta_{t-1} + \dot{\theta}_{t-1} dt + +.. math:: \upsilon_t = \upsilon_{t-1} + a_{t-1} dt + +Where `dt` is the time difference and :math:`a_t` is the acceleration at the time `t`. + +The change rate of the `e` can be calculated as: + +.. math:: \dot{e}_t = V \sin(\theta_{t-1}) + +Where `V` is the current vehicle speed. + +If the :math:`\theta` is small, + +.. math:: \theta \approx 0 + +the :math:`\sin(\theta)` can be approximated as :math:`\theta`: + +.. math:: \sin(\theta) = \theta + +So, the change rate of the `e` can be approximated as: + +.. math:: \dot{e}_t = V \theta_{t-1} + +The change rate of the :math:`\theta` can be calculated as: + +.. math:: \dot{\theta}_t = \frac{V}{L} \tan(\delta) + +where `L` is the wheelbase of the vehicle and :math:`\delta` is the steering angle. + +If the :math:`\delta` is small, + +.. math:: \delta \approx 0 + +the :math:`\tan(\delta)` can be approximated as :math:`\delta`: + +.. math:: \tan(\delta) = \delta + +So, the change rate of the :math:`\theta` can be approximated as: + +.. math:: \dot{\theta}_t = \frac{V}{L} \delta + +The above equations can be used to update the state of the vehicle at each time step. + +Control Model +~~~~~~~~~~~~~~ + +To formulate the state-space representation of the vehicle dynamics as a linear model, +the state vector `x` and control input vector `u` are defined as follows: + +.. math:: x_t = [e_t, \dot{e}_t, \theta_t, \dot{\theta}_t, \upsilon_t]^T + +.. math:: u_t = [\delta_t, a_t]^T + +The linear state transition equation can be represented as: + +.. math:: x_{t+1} = A x_t + B u_t + +where: + +:math:`\begin{equation*} A = \begin{bmatrix} 1 & dt & 0 & 0 & 0\\ 0 & 0 & v & 0 & 0\\ 0 & 0 & 1 & dt & 0\\ 0 & 0 & 0 & 0 & 0\\ 0 & 0 & 0 & 0 & 1\\\end{bmatrix} \end{equation*}` + +:math:`\begin{equation*} B = \begin{bmatrix} 0 & 0\\ 0 & 0\\ 0 & 0\\ \frac{v}{L} & 0\\ 0 & dt \\ \end{bmatrix} \end{equation*}` + +LQR controller +~~~~~~~~~~~~~~~ + +The Linear Quadratic Regulator (LQR) controller is used to calculate the optimal control input `u` that minimizes the quadratic cost function: + +:math:`J = \sum_{t=0}^{N} (x_t^T Q x_t + u_t^T R u_t)` + +where `Q` and `R` are the weighting matrices for the state and control input, respectively. + +for the linear model: + +:math:`x_{t+1} = A x_t + B u_t` + +The optimal control input `u` can be calculated as: + +:math:`u_t = -K x_t` + +where `K` is the feedback gain matrix obtained by solving the Riccati equation. + +Simulation results +~~~~~~~~~~~~~~~~~~~ + + +.. image:: x-y.png + :width: 600px + +.. image:: yaw.png + :width: 600px + +.. image:: speed.png + :width: 600px + + + References: ~~~~~~~~~~~ diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_steering_control_model.jpg b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_steering_control_model.jpg new file mode 100644 index 0000000000000000000000000000000000000000..83754d5bb01c9a1a4339248be09b476578291922 GIT binary patch literal 11721 zcmdUUc|26_`}c?xr9voMWl2Ve3?WpEHOnw(CdTr~7GocxkhV`^L?49g8Ov;DWHMwK 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r$D_jjjL0WV5yk$8K>b-%?-5V+bm{K!U@*sEqAsz{f?=L#=5pwteF^G4 literal 0 HcmV?d00001 diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst index fcc9b5278a..baca7a33fc 100644 --- a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst +++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst @@ -8,6 +8,8 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif +`[Code Link] `_ + Overview ~~~~~~~~ @@ -23,10 +25,10 @@ The below figure shows the geometric model of the vehicle used in this simulatio .. image:: lqr_steering_control_model.jpg :width: 600px -The `e` and `theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory. +The `e` and :math:`\theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory. And :math:`\dot{e}` and :math:`\dot{\theta}` represent the rates of change of these errors. -The :math:`e_t` and :math:`theta_t` are the updated values of `e` and `theta` at time `t`, respectively, and can be calculated using the following kinematic equations: +The :math:`e_t` and :math:`\theta_t` are the updated values of `e` and :math:`\theta` at time `t`, respectively, and can be calculated using the following kinematic equations: .. math:: e_t = e_{t-1} + \dot{e}_{t-1} dt @@ -38,9 +40,9 @@ The change rate of the `e` can be calculated as: .. math:: \dot{e}_t = V \sin(\theta_{t-1}) -Where `V` is the vehicle speed. +Where `V` is the current vehicle speed. -If the :math:`theta` is small, +If the :math:`\theta` is small, .. math:: \theta \approx 0 @@ -72,6 +74,9 @@ So, the change rate of the :math:`\theta` can be approximated as: The above equations can be used to update the state of the vehicle at each time step. +Control Model +~~~~~~~~~~~~~~ + To formulate the state-space representation of the vehicle dynamics as a linear model, the state vector `x` and control input vector `u` are defined as follows: @@ -79,7 +84,7 @@ the state vector `x` and control input vector `u` are defined as follows: .. math:: u_t = \delta_t -The state transition equation can be represented as: +The linear state transition equation can be represented as: .. math:: x_{t+1} = A x_t + B u_t diff --git a/tests/test_codestyle.py b/tests/test_codestyle.py index a7d11d270f..55e558c184 100644 --- a/tests/test_codestyle.py +++ b/tests/test_codestyle.py @@ -54,7 +54,7 @@ def run_ruff(files, fix): return 0, "" args = ['--fix'] if fix else [] res = subprocess.run( - ['ruff', f'--config={CONFIG}'] + args + files, + ['ruff', 'check', f'--config={CONFIG}'] + args + files, stdout=subprocess.PIPE, encoding='utf-8' ) From c862d496f415399f28ba131abe503353c7b6dfcb Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Jul 2024 10:15:23 +0900 Subject: [PATCH 719/909] Bump numpy from 2.0.0 to 2.0.1 in /requirements (#1051) Bumps [numpy](https://github.com/numpy/numpy) from 2.0.0 to 2.0.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.0.0...v2.0.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index aa91cb258f..a0af999bba 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.0.0 +numpy == 2.0.1 scipy == 1.14.0 matplotlib == 3.9.1 cvxpy == 1.5.2 From f3ca769076b11f03f02508ed112c85901e4b79c5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Jul 2024 20:14:40 +0900 Subject: [PATCH 720/909] Bump pytest from 8.2.2 to 8.3.1 in /requirements (#1049) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.2.2 to 8.3.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.2.2...8.3.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a0af999bba..9cbe4747a0 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 2.0.1 scipy == 1.14.0 matplotlib == 3.9.1 cvxpy == 1.5.2 -pytest == 8.2.2 # For unit test +pytest == 8.3.1 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.10.1 # For unit test ruff == 0.5.2 # For unit test From 76ba4ba1913fc7153e51fef434b3e955a58b3c2c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 Jul 2024 21:44:01 +0900 Subject: [PATCH 721/909] Bump mypy from 1.10.1 to 1.11.0 in /requirements (#1052) Bumps [mypy](https://github.com/python/mypy) from 1.10.1 to 1.11.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.10.1...v1.11) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9cbe4747a0..1055194460 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.9.1 cvxpy == 1.5.2 pytest == 8.3.1 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.10.1 # For unit test +mypy == 1.11.0 # For unit test ruff == 0.5.2 # For unit test From 65cb7f5b41b75ef0d198949e5274546b9f015cc6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 26 Jul 2024 17:53:37 +0900 Subject: [PATCH 722/909] Bump ruff from 0.5.2 to 0.5.4 in /requirements (#1050) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.2 to 0.5.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.2...0.5.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1055194460..2c2e156d62 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.2 pytest == 8.3.1 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.0 # For unit test -ruff == 0.5.2 # For unit test +ruff == 0.5.4 # For unit test From b1856d5ef3384d7ce1e8ecdd4937d1f76b28d99f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Jul 2024 22:09:58 +0900 Subject: [PATCH 723/909] Bump ruff from 0.5.4 to 0.5.5 in /requirements (#1053) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.4 to 0.5.5. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.4...0.5.5) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2c2e156d62..7a89f692fb 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.2 pytest == 8.3.1 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.0 # For unit test -ruff == 0.5.4 # For unit test +ruff == 0.5.5 # For unit test From f9ae5e5f887e527b7333b83d0332d5d25f1e0564 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 Jul 2024 22:10:12 +0900 Subject: [PATCH 724/909] Bump pytest from 8.3.1 to 8.3.2 in /requirements (#1054) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.1 to 8.3.2. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.3.1...8.3.2) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7a89f692fb..528c8f713c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 2.0.1 scipy == 1.14.0 matplotlib == 3.9.1 cvxpy == 1.5.2 -pytest == 8.3.1 # For unit test +pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.0 # For unit test ruff == 0.5.5 # For unit test From 171e3a8f60c984afd493c333fb97a8b8f5fb19df Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Aug 2024 09:06:26 +0900 Subject: [PATCH 725/909] Bump matplotlib from 3.9.1 to 3.9.1.post1 in /requirements (#1059) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.1 to 3.9.1.post1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.1...v3.9.1.post1) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 528c8f713c..d9e5930498 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 2.0.1 scipy == 1.14.0 -matplotlib == 3.9.1 +matplotlib == 3.9.1.post1 cvxpy == 1.5.2 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test From e1e3d8f5d7266229d46cd5b09d0c034d9c389eda Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Aug 2024 09:54:41 +0900 Subject: [PATCH 726/909] Bump ruff from 0.5.5 to 0.5.7 in /requirements (#1058) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.5 to 0.5.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.5...0.5.7) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d9e5930498..1bcbf1ac34 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.2 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.0 # For unit test -ruff == 0.5.5 # For unit test +ruff == 0.5.7 # For unit test From e6c6814f7bd8865940f31354a84b18ebd659db87 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 20 Aug 2024 22:24:41 +0900 Subject: [PATCH 727/909] Bump cvxpy from 1.5.2 to 1.5.3 in /requirements (#1062) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.5.2 to 1.5.3. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.5.2...v1.5.3) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1bcbf1ac34..bc09cdb54e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 2.0.1 scipy == 1.14.0 matplotlib == 3.9.1.post1 -cvxpy == 1.5.2 +cvxpy == 1.5.3 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.0 # For unit test From 12d6e694dc62d4ab4441cfafb429623e07803270 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 20 Aug 2024 22:33:02 +0900 Subject: [PATCH 728/909] Bump ruff from 0.5.7 to 0.6.1 in /requirements (#1063) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.7 to 0.6.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.5.7...0.6.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index bc09cdb54e..54a3c3eaf5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.0 # For unit test -ruff == 0.5.7 # For unit test +ruff == 0.6.1 # For unit test From 791d4e7f003e6e5e75df2b4223fce40e05f87eef Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 21 Aug 2024 21:21:17 +0900 Subject: [PATCH 729/909] Bump matplotlib from 3.9.1.post1 to 3.9.2 in /requirements (#1061) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.1.post1 to 3.9.2. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.1.post1...v3.9.2) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 54a3c3eaf5..dcba39a756 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 2.0.1 scipy == 1.14.0 -matplotlib == 3.9.1.post1 +matplotlib == 3.9.2 cvxpy == 1.5.3 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test From 6eed25537f3c683047ff154bcd09d7dea2dd898f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Aug 2024 07:07:58 +0900 Subject: [PATCH 730/909] Bump ruff from 0.6.1 to 0.6.2 in /requirements (#1064) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.1 to 0.6.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.1...0.6.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index dcba39a756..f219e1d279 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.0 # For unit test -ruff == 0.6.1 # For unit test +ruff == 0.6.2 # For unit test From 2b6c9b0888734b4caa4bf1a8f3c810def3dfa0ba Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Aug 2024 20:14:07 +0900 Subject: [PATCH 731/909] Bump scipy from 1.14.0 to 1.14.1 in /requirements (#1066) Bumps [scipy](https://github.com/scipy/scipy) from 1.14.0 to 1.14.1. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.14.0...v1.14.1) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f219e1d279..500002ff0e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 2.0.1 -scipy == 1.14.0 +scipy == 1.14.1 matplotlib == 3.9.2 cvxpy == 1.5.3 pytest == 8.3.2 # For unit test From b139717b297f90daa063e107154fc1ce96c5066e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Aug 2024 20:23:07 +0900 Subject: [PATCH 732/909] Bump mypy from 1.11.0 to 1.11.2 in /requirements (#1065) Bumps [mypy](https://github.com/python/mypy) from 1.11.0 to 1.11.2. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.11...v1.11.2) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 500002ff0e..0b8a51fcac 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.9.2 cvxpy == 1.5.3 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.11.0 # For unit test +mypy == 1.11.2 # For unit test ruff == 0.6.2 # For unit test From dab1a9b2dca0b27bc4eef30d98c516ec655ac2aa Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 3 Sep 2024 21:34:35 +0900 Subject: [PATCH 733/909] Bump ruff from 0.6.2 to 0.6.3 in /requirements (#1069) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.2 to 0.6.3. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.2...0.6.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 0b8a51fcac..8a27c1e301 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.2 # For unit test -ruff == 0.6.2 # For unit test +ruff == 0.6.3 # For unit test From 4ae9555a699b2823ea2e065e0ffe11b3622aa2b6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Sep 2024 21:39:10 +0900 Subject: [PATCH 734/909] Bump numpy from 2.0.1 to 2.1.1 in /requirements (#1071) Bumps [numpy](https://github.com/numpy/numpy) from 2.0.1 to 2.1.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.0.1...v2.1.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 8a27c1e301..87107816bb 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.0.1 +numpy == 2.1.1 scipy == 1.14.1 matplotlib == 3.9.2 cvxpy == 1.5.3 From ad600cd9023e67cd8064b678213d6586d076dd15 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Sep 2024 21:39:27 +0900 Subject: [PATCH 735/909] Bump ruff from 0.6.3 to 0.6.4 in /requirements (#1072) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.3 to 0.6.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.3...0.6.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 87107816bb..c614c4e96a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.2 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.2 # For unit test -ruff == 0.6.3 # For unit test +ruff == 0.6.4 # For unit test From bf69f444afcfffd8ebb44ec8f0988598a0b37ab6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 16 Sep 2024 22:03:54 +0900 Subject: [PATCH 736/909] add turning radius calculation doc (#1068) * add turning radius calculation doc * add turning radius calculation doc * add turning radius calculation doc * add turning radius calculation doc * add turning radius calculation doc * add turning radius calculation doc * add turning radius calculation doc --- docs/modules/appendix/appendix_main.rst | 1 + .../steering_motion_model/steering_model.png | Bin 0 -> 72443 bytes .../turning_radius_calc1.png | Bin 0 -> 14926 bytes .../turning_radius_calc2.png | Bin 0 -> 21792 bytes .../appendix/steering_motion_model_main.rst | 97 ++++++++++++++++++ 5 files changed, 98 insertions(+) create mode 100644 docs/modules/appendix/steering_motion_model/steering_model.png create mode 100644 docs/modules/appendix/steering_motion_model/turning_radius_calc1.png create mode 100644 docs/modules/appendix/steering_motion_model/turning_radius_calc2.png create mode 100644 docs/modules/appendix/steering_motion_model_main.rst diff --git a/docs/modules/appendix/appendix_main.rst b/docs/modules/appendix/appendix_main.rst index 8a29d84676..cb1ac04066 100644 --- a/docs/modules/appendix/appendix_main.rst +++ b/docs/modules/appendix/appendix_main.rst @@ -7,6 +7,7 @@ Appendix :maxdepth: 2 :caption: Contents + steering_motion_model 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+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The turning Radius represents the radius of the circle when the robot turns, as shown in the diagram below. + +.. image:: steering_motion_model/steering_model.png + +When the steering angle is tilted by :math:`\delta`, +the turning radius :math:`R` can be calculated using the following equation, +based on the geometric relationship between the wheelbase (WB), +which is the distance between the rear wheel center and the front wheel center, +and the assumption that the turning radius circle passes through the center of +the rear wheels in the diagram above. + +:math:`R = \frac{WB}{tan\delta}` + +The curvature :math:`\kappa` is the reciprocal of the turning radius: + +:math:`\kappa = \frac{tan\delta}{WB}` + +In the diagram above, the angular difference :math:`\Delta \theta` in the vehicle’s heading between two points on the turning radius :math:`R` +is the same as the angle of the vector connecting the two points from the center of the turn. + +From the formula for the length of an arc and the radius, + +:math:`\Delta \theta = \frac{s}{R}` + +Here, :math:`s` is the distance between two points on the turning radius. + +So, yaw rate :math:`\omega` can be calculated as follows. + +:math:`\omega = \frac{v}{R}` + +and + +:math:`\omega = v\kappa` + +here, :math:`v` is the velocity of the vehicle. + + +Turning radius calculation by 2 consecutive positions of the robot trajectory +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In this section, we will derive the formula for the turning radius from 2 consecutive positions of the robot trajectory. + +.. image:: steering_motion_model/turning_radius_calc1.png + +As shown in the upper diagram above, the robot moves from a point at time :math:`t` to a point at time :math:`t+1`. +Each point is represented by a 2D position :math:`(x_t, y_t)` and an orientation :math:`\theta_t`. + +The distance between the two points is :math:`d = \sqrt{(x_{t+1} - x_t)^2 + (y_{t+1} - y_t)^2}`. + +The angle between the two vectors from the turning center to the two points is :math:`\theta = \theta_{t+1} - \theta_t`. +Here, by drawing a perpendicular line from the center of the turning radius +to a straight line of length :math:`d` connecting two points, +the following equation can be derived from the resulting right triangle. + +:math:`sin\frac{\theta}{2} = \frac{d}{2R}` + +So, the turning radius :math:`R` can be calculated as follows. + +:math:`R = \frac{d}{2sin\frac{\theta}{2}}` + +The curvature :math:`\kappa` is the reciprocal of the turning radius. +So, the curvature can be calculated as follows. + +:math:`\kappa = \frac{2sin\frac{\theta}{2}}{d}` + +Target speed by maximum steering speed +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If the maximum steering speed is given as :math:`\dot{\delta}_{max}`, +the maximum curvature change rate :math:`\dot{\kappa}_{max}` can be calculated as follows: + +:math:`\dot{\kappa}_{max} = \frac{tan\dot{\delta}_{max}}{WB}` + +From the curvature calculation by 2 consecutive positions of the robot trajectory, + +the maximum curvature change rate :math:`\dot{\kappa}_{max}` can be calculated as follows: + +:math:`\dot{\kappa}_{max} = \frac{\kappa_{t+1}-\kappa_{t}}{\Delta t}` + +If we can assume that the vehicle will not exceed the maximum curvature change rate, + +the target minimum velocity :math:`v_{min}` can be calculated as follows: + +:math:`v_{min} = \frac{d_{t+1}+d_{t}}{\Delta t} = \frac{d_{t+1}+d_{t}}{(\kappa_{t+1}-\kappa_{t})}\frac{tan\dot{\delta}_{max}}{WB}` + + +References: +~~~~~~~~~~~ + +- `Vehicle Dynamics and Control `_ From e26d5cb99aed3d282508cc18457feb7a7b8ca18f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 17 Sep 2024 21:13:36 +0900 Subject: [PATCH 737/909] Bump pytest from 8.3.2 to 8.3.3 in /requirements (#1077) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.2 to 8.3.3. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.3.2...8.3.3) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index c614c4e96a..6bbe33e378 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 2.1.1 scipy == 1.14.1 matplotlib == 3.9.2 cvxpy == 1.5.3 -pytest == 8.3.2 # For unit test +pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.2 # For unit test ruff == 0.6.4 # For unit test From 3e178598d467b65154fb1b409ac9fab8339957b3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 18 Sep 2024 21:55:03 +0900 Subject: [PATCH 738/909] Bump ruff from 0.6.4 to 0.6.5 in /requirements (#1078) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.4 to 0.6.5. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.4...0.6.5) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6bbe33e378..f149183e96 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.2 # For unit test -ruff == 0.6.4 # For unit test +ruff == 0.6.5 # For unit test From 660646a361162b9c286d5182558775eda5207ebe Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 24 Sep 2024 06:19:38 -0700 Subject: [PATCH 739/909] Bump ruff from 0.6.5 to 0.6.7 in /requirements (#1080) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.5 to 0.6.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.5...0.6.7) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f149183e96..453922eb8c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.2 # For unit test -ruff == 0.6.5 # For unit test +ruff == 0.6.7 # For unit test From fafae87236eaf95785f5566e35fe9d6ba2fa3701 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 1 Oct 2024 20:51:46 +0900 Subject: [PATCH 740/909] Bump ruff from 0.6.7 to 0.6.8 in /requirements (#1081) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.7 to 0.6.8. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.7...0.6.8) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 453922eb8c..83f9bc438f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.2 # For unit test -ruff == 0.6.7 # For unit test +ruff == 0.6.8 # For unit test From 857a80febf8aef959d7df441f8948fe073e790d5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Oct 2024 21:05:58 +0900 Subject: [PATCH 741/909] Bump numpy from 2.1.1 to 2.1.2 in /requirements (#1083) Bumps [numpy](https://github.com/numpy/numpy) from 2.1.1 to 2.1.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.1.1...v2.1.2) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 83f9bc438f..e006f944ff 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.1.1 +numpy == 2.1.2 scipy == 1.14.1 matplotlib == 3.9.2 cvxpy == 1.5.3 From a791c79da200435cb89d707320e106c10ca5b6db Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Oct 2024 21:37:36 +0900 Subject: [PATCH 742/909] Bump ruff from 0.6.8 to 0.6.9 in /requirements (#1082) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.8 to 0.6.9. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.8...0.6.9) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e006f944ff..62f9bc183a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.11.2 # For unit test -ruff == 0.6.8 # For unit test +ruff == 0.6.9 # For unit test From 4d009a7916bffa53d2747cc6100180a3521e376c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Oct 2024 12:48:45 +0900 Subject: [PATCH 743/909] Bump mypy from 1.11.2 to 1.12.0 in /requirements (#1084) Bumps [mypy](https://github.com/python/mypy) from 1.11.2 to 1.12.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.11.2...v1.12.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 62f9bc183a..cbab816c8a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.9.2 cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.11.2 # For unit test +mypy == 1.12.0 # For unit test ruff == 0.6.9 # For unit test From 74e9dcc0921f8128488b449901812b1b51e54bad Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Oct 2024 22:01:11 +0900 Subject: [PATCH 744/909] Bump ruff from 0.6.9 to 0.7.0 in /requirements (#1088) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.9 to 0.7.0. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.6.9...0.7.0) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index cbab816c8a..f275d9d82e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.12.0 # For unit test -ruff == 0.6.9 # For unit test +ruff == 0.7.0 # For unit test From 899f749f33637159be3515264705ca7fe2d8609e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Oct 2024 22:24:33 +0900 Subject: [PATCH 745/909] Bump mypy from 1.12.0 to 1.12.1 in /requirements (#1089) Bumps [mypy](https://github.com/python/mypy) from 1.12.0 to 1.12.1. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.12.0...v1.12.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f275d9d82e..0b44092a15 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.9.2 cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.12.0 # For unit test +mypy == 1.12.1 # For unit test ruff == 0.7.0 # For unit test From 9ecc98d3e6af70865d902a88b5d96dffd4764b7c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 30 Oct 2024 23:27:05 +0900 Subject: [PATCH 746/909] Bump mypy from 1.12.1 to 1.13.0 in /requirements (#1093) Bumps [mypy](https://github.com/python/mypy) from 1.12.1 to 1.13.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.12.1...v1.13.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 0b44092a15..92ffcf3a74 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.9.2 cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.12.1 # For unit test +mypy == 1.13.0 # For unit test ruff == 0.7.0 # For unit test From 41fea75c0ee0cfb6a1c24307f0e0c1f1bb0af133 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 31 Oct 2024 21:32:30 +0900 Subject: [PATCH 747/909] Bump ruff from 0.7.0 to 0.7.1 in /requirements (#1092) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.0 to 0.7.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.7.0...0.7.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 92ffcf3a74..9fd97ba106 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test -ruff == 0.7.0 # For unit test +ruff == 0.7.1 # For unit test From 7c5959c5f2d0dbde7889870fc0056491577a5653 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 5 Nov 2024 21:01:38 +0900 Subject: [PATCH 748/909] Bump numpy from 2.1.2 to 2.1.3 in /requirements (#1095) Bumps [numpy](https://github.com/numpy/numpy) from 2.1.2 to 2.1.3. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.1.2...v2.1.3) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9fd97ba106..38a0aed0f8 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.1.2 +numpy == 2.1.3 scipy == 1.14.1 matplotlib == 3.9.2 cvxpy == 1.5.3 From 5d8326957bcb40d62076e2920f5d61290f658fe6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 13 Nov 2024 21:44:25 +0900 Subject: [PATCH 749/909] Bump ruff from 0.7.1 to 0.7.3 in /requirements (#1097) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.1 to 0.7.3. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.7.1...0.7.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 38a0aed0f8..21c9c64364 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test -ruff == 0.7.1 # For unit test +ruff == 0.7.3 # For unit test From 3f87bd4cfc9b8cc5a6e3c97dcfd113f020023801 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 19 Nov 2024 07:40:59 +0900 Subject: [PATCH 750/909] Bump ruff from 0.7.3 to 0.7.4 in /requirements (#1099) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.3 to 0.7.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.7.3...0.7.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 21c9c64364..d3d3627d5c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.3 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test -ruff == 0.7.3 # For unit test +ruff == 0.7.4 # For unit test From 0f91e58ca3d3e67bdbc592105140791efa6b2dbc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 4 Dec 2024 08:25:12 +0900 Subject: [PATCH 751/909] Bump pytest from 8.3.3 to 8.3.4 in /requirements (#1102) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.3 to 8.3.4. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.3.3...8.3.4) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d3d3627d5c..44875f28d6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 2.1.3 scipy == 1.14.1 matplotlib == 3.9.2 cvxpy == 1.5.3 -pytest == 8.3.3 # For unit test +pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test ruff == 0.7.4 # For unit test From 455542d7178565517ac751a025e103d5ff11ced0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 4 Dec 2024 15:36:51 +0900 Subject: [PATCH 752/909] Bump ruff from 0.7.4 to 0.8.1 in /requirements (#1103) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.4 to 0.8.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.7.4...0.8.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 44875f28d6..a224af5d40 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test -ruff == 0.7.4 # For unit test +ruff == 0.8.1 # For unit test From 115b32d2feb03bd96b52efeb2403dfb272c5e703 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 6 Dec 2024 08:31:54 +0900 Subject: [PATCH 753/909] Bump matplotlib from 3.9.2 to 3.9.3 in /requirements (#1101) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.2 to 3.9.3. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.2...v3.9.3) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a224af5d40..91566508d6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 2.1.3 scipy == 1.14.1 -matplotlib == 3.9.2 +matplotlib == 3.9.3 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test From f86eecec7fa851fcc52aeb09a7c58b4c3c0110d0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Dec 2024 06:46:52 +0900 Subject: [PATCH 754/909] Bump numpy from 2.1.3 to 2.2.0 in /requirements (#1106) Bumps [numpy](https://github.com/numpy/numpy) from 2.1.3 to 2.2.0. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.1.3...v2.2.0) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 91566508d6..a99ed51781 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.1.3 +numpy == 2.2.0 scipy == 1.14.1 matplotlib == 3.9.3 cvxpy == 1.5.3 From 4ebe7543ffac5f4eb40bceedec14f11703cc6a1f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 10 Dec 2024 13:17:18 +0900 Subject: [PATCH 755/909] Bump ruff from 0.8.1 to 0.8.2 in /requirements (#1105) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.1 to 0.8.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.8.1...0.8.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a99ed51781..dbc555e0c9 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test -ruff == 0.8.1 # For unit test +ruff == 0.8.2 # For unit test From 0d8cd9bb6cca5697a3cd3f1b279e4cd8a27d3cb7 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 17 Dec 2024 09:32:02 +0900 Subject: [PATCH 756/909] Bump matplotlib from 3.9.3 to 3.10.0 in /requirements (#1107) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.3 to 3.10.0. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.3...v3.10.0) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index dbc555e0c9..650e7a4048 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 2.2.0 scipy == 1.14.1 -matplotlib == 3.9.3 +matplotlib == 3.10.0 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test From e5eea35ee4a03082cd5acf5efea208d677df0a0a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 17 Dec 2024 09:37:51 +0900 Subject: [PATCH 757/909] Bump ruff from 0.8.2 to 0.8.3 in /requirements (#1108) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.2 to 0.8.3. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.8.2...0.8.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 650e7a4048..cd4a01f88f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test -ruff == 0.8.2 # For unit test +ruff == 0.8.3 # For unit test From 4bc70480448295205fceb28053d0ed9c5efca99d Mon Sep 17 00:00:00 2001 From: Weipu Shan Date: Fri, 20 Dec 2024 20:32:57 +0800 Subject: [PATCH 758/909] fix: arm_obstacle_navigation calc_heuristic_map node through 4 corners error (#1034) * fix: arm_obstacle_navigation calc_heuristic_map node through 4 corners error * style: remove trailing whitespace --- .../arm_obstacle_navigation/arm_obstacle_navigation.py | 8 ++++---- .../arm_obstacle_navigation_2.py | 10 +++++----- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py index 75b1f9d2c4..e377897e54 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py @@ -203,10 +203,10 @@ def calc_heuristic_map(M, goal_node): for i in range(heuristic_map.shape[0]): for j in range(heuristic_map.shape[1]): heuristic_map[i, j] = min(heuristic_map[i, j], - i + 1 + heuristic_map[M - 1, j], - M - i + heuristic_map[0, j], - j + 1 + heuristic_map[i, M - 1], - M - j + heuristic_map[i, 0] + M - i - 1 + heuristic_map[M - 1, j], + i + heuristic_map[0, j], + M - j - 1 + heuristic_map[i, M - 1], + j + heuristic_map[i, 0] ) return heuristic_map diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py index 591cd401eb..10f4615c34 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py @@ -105,7 +105,7 @@ def get_occupancy_grid(arm, obstacles): Args: arm: An instance of NLinkArm obstacles: A list of obstacles, with each obstacle defined as a list - of xy coordinates and a radius. + of xy coordinates and a radius. Returns: Occupancy grid in joint space @@ -234,10 +234,10 @@ def calc_heuristic_map(M, goal_node): for i in range(heuristic_map.shape[0]): for j in range(heuristic_map.shape[1]): heuristic_map[i, j] = min(heuristic_map[i, j], - i + 1 + heuristic_map[M - 1, j], - M - i + heuristic_map[0, j], - j + 1 + heuristic_map[i, M - 1], - M - j + heuristic_map[i, 0] + M - i - 1 + heuristic_map[M - 1, j], + i + heuristic_map[0, j], + M - j - 1 + heuristic_map[i, M - 1], + j + heuristic_map[i, 0] ) return heuristic_map From 1f4214012a612f2163b01930444d83712d99b5f9 Mon Sep 17 00:00:00 2001 From: Aryaz Eghbali <64126826+AryazE@users.noreply.github.com> Date: Fri, 20 Dec 2024 14:00:49 +0100 Subject: [PATCH 759/909] Performance improvement (#1075) --- PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py index 8968c54dcc..7e93caa8fb 100644 --- a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py +++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py @@ -64,7 +64,7 @@ def transform( is_visited = np.zeros_like(transform_matrix, dtype=bool) is_visited[src[0], src[1]] = True traversal_queue = [src] - calculated = [(src[0] - 1) * n_cols + src[1]] + calculated = set([(src[0] - 1) * n_cols + src[1]]) def is_valid_neighbor(g_i, g_j): return 0 <= g_i < n_rows and 0 <= g_j < n_cols \ @@ -86,7 +86,7 @@ def is_valid_neighbor(g_i, g_j): if not is_visited[ni][nj] \ and ((ni - 1) * n_cols + nj) not in calculated: traversal_queue.append((ni, nj)) - calculated.append((ni - 1) * n_cols + nj) + calculated.add((ni - 1) * n_cols + nj) return transform_matrix From af456c70b0ec336442b0b32c5aab1a220aefaf74 Mon Sep 17 00:00:00 2001 From: Surabhi Gupta <103124390+SurabhiGupta17@users.noreply.github.com> Date: Fri, 20 Dec 2024 18:42:31 +0530 Subject: [PATCH 760/909] Implement Catmull-Rom Spline with test and documentation (#1085) --- .../blending_functions.py | 34 ++++++ .../catmull_rom_spline_path.py | 86 +++++++++++++++ .../catmull_rom_spline/blending_functions.png | Bin 0 -> 41129 bytes .../catmull_rom_path_planning.png | Bin 0 -> 33505 bytes .../catmull_rom_spline_main.rst | 103 ++++++++++++++++++ tests/test_catmull_rom_spline.py | 16 +++ 6 files changed, 239 insertions(+) create mode 100644 PathPlanning/Catmull_RomSplinePath/blending_functions.py create mode 100644 PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py create mode 100644 docs/modules/path_planning/catmull_rom_spline/blending_functions.png create mode 100644 docs/modules/path_planning/catmull_rom_spline/catmull_rom_path_planning.png create mode 100644 docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst create mode 100644 tests/test_catmull_rom_spline.py diff --git a/PathPlanning/Catmull_RomSplinePath/blending_functions.py b/PathPlanning/Catmull_RomSplinePath/blending_functions.py new file mode 100644 index 0000000000..f3ef5dd323 --- /dev/null +++ b/PathPlanning/Catmull_RomSplinePath/blending_functions.py @@ -0,0 +1,34 @@ +import numpy as np +import matplotlib.pyplot as plt + +def blending_function_1(t): + return -t + 2*t**2 - t**3 + +def blending_function_2(t): + return 2 - 5*t**2 + 3*t**3 + +def blending_function_3(t): + return t + 4*t**2 - 3*t**3 + +def blending_function_4(t): + return -t**2 + t**3 + +def plot_blending_functions(): + t = np.linspace(0, 1, 100) + + plt.plot(t, blending_function_1(t), label='b1') + plt.plot(t, blending_function_2(t), label='b2') + plt.plot(t, blending_function_3(t), label='b3') + plt.plot(t, blending_function_4(t), label='b4') + + plt.title("Catmull-Rom Blending Functions") + plt.xlabel("t") + plt.ylabel("Value") + plt.legend() + plt.grid(True) + plt.axhline(y=0, color='k', linestyle='--') + plt.axvline(x=0, color='k', linestyle='--') + plt.show() + +if __name__ == "__main__": + plot_blending_functions() \ No newline at end of file diff --git a/PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py b/PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py new file mode 100644 index 0000000000..79916330c9 --- /dev/null +++ b/PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py @@ -0,0 +1,86 @@ +""" +Path Planner with Catmull-Rom Spline +Author: Surabhi Gupta (@this_is_surabhi) +Source: http://graphics.cs.cmu.edu/nsp/course/15-462/Fall04/assts/catmullRom.pdf +""" + +import sys +import pathlib +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) + +import numpy as np +import matplotlib.pyplot as plt + +def catmull_rom_point(t, p0, p1, p2, p3): + """ + Parameters + ---------- + t : float + Parameter value (0 <= t <= 1) (0 <= t <= 1) + p0, p1, p2, p3 : np.ndarray + Control points for the spline segment + + Returns + ------- + np.ndarray + Calculated point on the spline + """ + return 0.5 * ((2 * p1) + + (-p0 + p2) * t + + (2 * p0 - 5 * p1 + 4 * p2 - p3) * t**2 + + (-p0 + 3 * p1 - 3 * p2 + p3) * t**3) + + +def catmull_rom_spline(control_points, num_points): + """ + Parameters + ---------- + control_points : list + List of control points + num_points : int + Number of points to generate on the spline + + Returns + ------- + tuple + x and y coordinates of the spline points + """ + t_vals = np.linspace(0, 1, num_points) + spline_points = [] + + control_points = np.array(control_points) + + for i in range(len(control_points) - 1): + if i == 0: + p1, p2, p3 = control_points[i:i+3] + p0 = p1 + elif i == len(control_points) - 2: + p0, p1, p2 = control_points[i-1:i+2] + p3 = p2 + else: + p0, p1, p2, p3 = control_points[i-1:i+3] + + for t in t_vals: + point = catmull_rom_point(t, p0, p1, p2, p3) + spline_points.append(point) + + return np.array(spline_points).T + + +def main(): + print(__file__ + " start!!") + + way_points = [[-1.0, -2.0], [1.0, -1.0], [3.0, -2.0], [4.0, -1.0], [3.0, 1.0], [1.0, 2.0], [0.0, 2.0]] + n_course_point = 100 + spline_x, spline_y = catmull_rom_spline(way_points, n_course_point) + + plt.plot(spline_x,spline_y, '-r', label="Catmull-Rom Spline Path") + plt.plot(np.array(way_points).T[0], np.array(way_points).T[1], '-og', label="Way points") + plt.title("Catmull-Rom Spline Path") + plt.grid(True) + plt.legend() + plt.axis("equal") + plt.show() + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/docs/modules/path_planning/catmull_rom_spline/blending_functions.png b/docs/modules/path_planning/catmull_rom_spline/blending_functions.png new file mode 100644 index 0000000000000000000000000000000000000000..928946df46c7f14524247d675697d81e049c9a86 GIT binary patch literal 41129 zcmdqJN!ydR9~j(xAa){HU79CN=@QIf~Oq`*W%Lc)3aL`Dq>=~e&|67oFy9eCyO;LI}o zBj_TlhsPR>^Lc03${9NcX8EL~h2oP{1edi{SN z;IMbHctod!G6@&KaCoBYjD&<|g7|}+Bc5%Agp^MDR7O(6BYE?WyT1DPP5YiUX%L-) z1QqjkZN*q^g{$&RmaA6Y)l{y5TV8(tSY)}@ix>ITi(}S*s<+w0i6-wqI60AL70-Pp 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zOj$=Go~T+-P=FvAAjj_r56>b{0)K>(k|T~fDMI1V=}AzxU&=L1{3t9ucI0A+-$kku~aW=^D&*_}sI0_^k8VHQ7 z%EfsK!%>i^Zder`!R=6}93`#{XgmwV3QA4qXv2Y`h@53!oVw?WEf4$SkQ(Yn(Z#Y~6NzE0ZS@ZSp5 z+KZbMiV?ISBo!mD4!4>OL(q_3{DU_1frl3Hg@SF(n5P+wg$<@U>}kM6`8T-IJe({aV4p;vhPAHquqp0A!YApDTs*-6b*@Uo9je((haakAn_;60GB z5J4*-e~|EA;3FtiN;9W-Du*I#JdZ|hC@9P?DaRg2m!lpX2#=XpkPzL2|9v2QZfrPE t6i4~79|)siQTy)}Gf-szfBcu%P^(%jdmL7Ct0eHxwaeO<3NKoP{|{|BcqRY< literal 0 HcmV?d00001 diff --git a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst new file mode 100644 index 0000000000..4d8d3bdefe --- /dev/null +++ b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst @@ -0,0 +1,103 @@ +Catmull-Rom Spline Planning +----------------- + +.. image:: catmull_rom_path_planning.png + +This is a Catmull-Rom spline path planning routine. + +If you provide waypoints, the Catmull-Rom spline generates a smooth path that always passes through the control points, +exhibits local control, and maintains 𝐶1 continuity. + + +Catmull-Rom Spline Fundamentals +~~~~~~~~~~~~~~ + +Catmull-Rom splines are a type of cubic spline that passes through a given set of points, known as control points. + +They are defined by the following equation for calculating a point on the spline: + +:math:`P(t) = 0.5 \times \left( 2P_1 + (-P_0 + P_2)t + (2P_0 - 5P_1 + 4P_2 - P_3)t^2 + (-P_0 + 3P_1 - 3P_2 + P_3)t^3 \right)` + +Where: + +* :math:`P(t)` is the point on the spline at parameter :math:`t`. +* :math:`P_0, P_1, P_2, P_3` are the control points surrounding the parameter :math:`t`. + +Types of Catmull-Rom Splines +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +There are different types of Catmull-Rom splines based on the choice of the **tau** parameter, which influences how the curve +behaves in relation to the control points: + +1. **Uniform Catmull-Rom Spline**: + The standard implementation where the parameterization is uniform. Each segment of the spline is treated equally, + regardless of the distances between control points. + + +2. **Chordal Catmull-Rom Spline**: + This spline type takes into account the distance between control points. The parameterization is based on the actual distance + along the spline, ensuring smoother transitions. The equation can be modified to include the chord length :math:`L_i` between + points :math:`P_i` and :math:`P_{i+1}`: + + .. math:: + \tau_i = \sqrt{(x_{i+1} - x_i)^2 + (y_{i+1} - y_i)^2} + +3. **Centripetal Catmull-Rom Spline**: + This variation improves upon the chordal spline by using the square root of the distance to determine the parameterization, + which avoids oscillations and creates a more natural curve. The parameter :math:`t_i` is adjusted using the following relation: + + .. math:: + t_i = \sqrt{(x_{i+1} - x_i)^2 + (y_{i+1} - y_i)^2} + + +Blending Functions +~~~~~~~~~~~~~~~~~~ + +In Catmull-Rom spline interpolation, blending functions are used to calculate the influence of each control point on the spline at a +given parameter :math:`t`. The blending functions ensure that the spline is smooth and passes through the control points while +maintaining continuity. The four blending functions used in Catmull-Rom splines are defined as follows: + +1. **Blending Function 1**: + + .. math:: + b_1(t) = -t + 2t^2 - t^3 + +2. **Blending Function 2**: + + .. math:: + b_2(t) = 2 - 5t^2 + 3t^3 + +3. **Blending Function 3**: + + .. math:: + b_3(t) = t + 4t^2 - 3t^3 + +4. **Blending Function 4**: + + .. math:: + b_4(t) = -t^2 + t^3 + +The blending functions are combined in the spline equation to create a smooth curve that reflects the influence of each control point. + +The following figure illustrates the blending functions over the interval :math:`[0, 1]`: + +.. image:: blending_functions.png + +Catmull-Rom Spline API +~~~~~~~~~~~~~~~~~~~~~~~ + +This section provides an overview of the functions used for Catmull-Rom spline path planning. + +API +++++ + +.. autofunction:: PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path.catmull_rom_point + +.. autofunction:: PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path.catmull_rom_spline + + +References +~~~~~~~~~~ + +- `Catmull-Rom Spline - Wikipedia `__ +- `Catmull-Rom Splines `__ \ No newline at end of file diff --git a/tests/test_catmull_rom_spline.py b/tests/test_catmull_rom_spline.py new file mode 100644 index 0000000000..41a73588c3 --- /dev/null +++ b/tests/test_catmull_rom_spline.py @@ -0,0 +1,16 @@ +import conftest +from PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path import catmull_rom_spline + +def test_catmull_rom_spline(): + way_points = [[0, 0], [1, 2], [2, 0], [3, 3]] + num_points = 100 + + spline_x, spline_y = catmull_rom_spline(way_points, num_points) + + assert spline_x.size > 0, "Spline X coordinates should not be empty" + assert spline_y.size > 0, "Spline Y coordinates should not be empty" + + assert spline_x.shape == spline_y.shape, "Spline X and Y coordinates should have the same shape" + +if __name__ == '__main__': + conftest.run_this_test(__file__) From 7eeb9d215cb5652a800db507f066047bfd5d023f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 21 Dec 2024 15:04:02 +0900 Subject: [PATCH 761/909] Add catmull_rom_spline doc (#1109) --- docs/modules/path_planning/path_planning_main.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst index a3237f16f1..30802fd7a6 100644 --- a/docs/modules/path_planning/path_planning_main.rst +++ b/docs/modules/path_planning/path_planning_main.rst @@ -18,6 +18,7 @@ Path Planning rrt/rrt cubic_spline/cubic_spline bspline_path/bspline_path + catmull_rom_spline/catmull_rom_spline clothoid_path/clothoid_path eta3_spline/eta3_spline bezier_path/bezier_path From b5988dbcbc3c1d8cdbb3f9f3f9dec75cf63defb9 Mon Sep 17 00:00:00 2001 From: parmaski <89462537+parmaski@users.noreply.github.com> Date: Sat, 21 Dec 2024 19:39:16 +0900 Subject: [PATCH 762/909] Fix: Hybrid A* direction is incorrect (#1086) * fix to preseve direction history * add path to Reeds Shepp and its users e.g. hybrid A* --- PathPlanning/HybridAStar/hybrid_a_star.py | 5 +++-- PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py | 4 ++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 03db0dc167..1962efe295 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -105,12 +105,13 @@ def calc_next_node(current, steer, direction, config, ox, oy, kd_tree): x, y, yaw = current.x_list[-1], current.y_list[-1], current.yaw_list[-1] arc_l = XY_GRID_RESOLUTION * 1.5 - x_list, y_list, yaw_list = [], [], [] + x_list, y_list, yaw_list, direction_list = [], [], [], [] for _ in np.arange(0, arc_l, MOTION_RESOLUTION): x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION * direction, steer) x_list.append(x) y_list.append(y) yaw_list.append(yaw) + direction_list.append(direction == 1) if not check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree): return None @@ -134,7 +135,7 @@ def calc_next_node(current, steer, direction, config, ox, oy, kd_tree): cost = current.cost + added_cost + arc_l node = Node(x_ind, y_ind, yaw_ind, d, x_list, - y_list, yaw_list, [d], + y_list, yaw_list, direction_list, parent_index=calc_index(current, config), cost=cost, steer=steer) diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index 8e134ff38b..618d1d99ba 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -6,6 +6,10 @@ co-author Videh Patel(@videh25) : Added the missing RS paths """ +import sys +import pathlib +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) + import math import matplotlib.pyplot as plt From b137ba1817af11ed944a28f8c1c8ad1a95848726 Mon Sep 17 00:00:00 2001 From: parmaski <89462537+parmaski@users.noreply.github.com> Date: Sat, 21 Dec 2024 19:40:30 +0900 Subject: [PATCH 763/909] fix behavior when path is not found (#1104) --- PathPlanning/HybridAStar/car.py | 2 +- PathPlanning/HybridAStar/hybrid_a_star.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py index d7631133c7..959db74078 100644 --- a/PathPlanning/HybridAStar/car.py +++ b/PathPlanning/HybridAStar/car.py @@ -97,7 +97,7 @@ def pi_2_pi(angle): def move(x, y, yaw, distance, steer, L=WB): x += distance * cos(yaw) y += distance * sin(yaw) - yaw += pi_2_pi(distance * tan(steer) / L) # distance/2 + yaw = pi_2_pi(yaw + distance * tan(steer) / L) # distance/2 return x, y, yaw diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 1962efe295..0fa04189c6 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -282,7 +282,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution): while True: if not openList: print("Error: Cannot find path, No open set") - return [], [], [] + return Path([], [], [], [], 0) cost, c_id = heapq.heappop(pq) if c_id in openList: From ca54f1b6880b9731b06fc33db338bc28bd63c864 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 24 Dec 2024 07:50:00 +0900 Subject: [PATCH 764/909] Bump ruff from 0.8.3 to 0.8.4 in /requirements (#1111) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.3 to 0.8.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.8.3...0.8.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index cd4a01f88f..d28cd5f956 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.13.0 # For unit test -ruff == 0.8.3 # For unit test +ruff == 0.8.4 # For unit test From d1f1190106d624e555b0fdd0853b65ad844e3204 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 24 Dec 2024 07:59:16 +0900 Subject: [PATCH 765/909] Bump numpy from 2.2.0 to 2.2.1 in /requirements (#1113) Bumps [numpy](https://github.com/numpy/numpy) from 2.2.0 to 2.2.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.2.0...v2.2.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d28cd5f956..0992ab6cf0 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.2.0 +numpy == 2.2.1 scipy == 1.14.1 matplotlib == 3.10.0 cvxpy == 1.5.3 From aacae0d79a9595356bd7f15ad8e565220a04de61 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 24 Dec 2024 09:13:35 +0900 Subject: [PATCH 766/909] Bump mypy from 1.13.0 to 1.14.0 in /requirements (#1112) Bumps [mypy](https://github.com/python/mypy) from 1.13.0 to 1.14.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.13.0...v1.14.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 0992ab6cf0..47db4245fd 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.10.0 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.13.0 # For unit test +mypy == 1.14.0 # For unit test ruff == 0.8.4 # For unit test From 7d33f5f7140513d14bad016f48d132a08bc5c8a3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 31 Dec 2024 07:04:57 +0900 Subject: [PATCH 767/909] Bump mypy from 1.14.0 to 1.14.1 in /requirements (#1116) Bumps [mypy](https://github.com/python/mypy) from 1.14.0 to 1.14.1. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.14.0...v1.14.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 47db4245fd..bcfbbf97ac 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.10.0 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.14.0 # For unit test +mypy == 1.14.1 # For unit test ruff == 0.8.4 # For unit test From c27803f781c00384c9de29fe1fd82ace8279d788 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Jan 2025 11:49:14 +0900 Subject: [PATCH 768/909] build(deps): bump ruff from 0.8.4 to 0.8.6 in /requirements (#1117) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.4 to 0.8.6. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.8.4...0.8.6) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index bcfbbf97ac..5291c45c58 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.14.1 # For unit test -ruff == 0.8.4 # For unit test +ruff == 0.8.6 # For unit test From 01d398f4ab276bcbcac3a7ce8d62b2e2712f0a5a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 7 Jan 2025 13:01:34 +0900 Subject: [PATCH 769/909] build(deps): bump scipy from 1.14.1 to 1.15.0 in /requirements (#1118) Bumps [scipy](https://github.com/scipy/scipy) from 1.14.1 to 1.15.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.14.1...v1.15.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5291c45c58..5aeded446f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 2.2.1 -scipy == 1.14.1 +scipy == 1.15.0 matplotlib == 3.10.0 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test From 647ce9ccfa1f581bff558a16803bfd7e2c343c3e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Jan 2025 07:24:59 +0900 Subject: [PATCH 770/909] build(deps): bump scipy from 1.15.0 to 1.15.1 in /requirements (#1119) Bumps [scipy](https://github.com/scipy/scipy) from 1.15.0 to 1.15.1. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.15.0...v1.15.1) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5aeded446f..f5a7ff315c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 2.2.1 -scipy == 1.15.0 +scipy == 1.15.1 matplotlib == 3.10.0 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test From 4cf7531839a12faa9b7daf6a70aab4f596aa1b0c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Jan 2025 07:25:29 +0900 Subject: [PATCH 771/909] build(deps): bump ruff from 0.8.6 to 0.9.1 in /requirements (#1120) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.6 to 0.9.1. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.8.6...0.9.1) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f5a7ff315c..d212202cdb 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.14.1 # For unit test -ruff == 0.8.6 # For unit test +ruff == 0.9.1 # For unit test From 4c2e3e8f9b686baef5774dcfe0afd7dda6150af4 Mon Sep 17 00:00:00 2001 From: Mritunjai <101278178+Mritunjaii@users.noreply.github.com> Date: Mon, 20 Jan 2025 09:59:15 +0530 Subject: [PATCH 772/909] hashmap instead of a linear search (#1110) --- .../AStar/a_star_searching_from_two_side.py | 70 +++++++++---------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/PathPlanning/AStar/a_star_searching_from_two_side.py b/PathPlanning/AStar/a_star_searching_from_two_side.py index 822f5ea500..f43cea71b4 100644 --- a/PathPlanning/AStar/a_star_searching_from_two_side.py +++ b/PathPlanning/AStar/a_star_searching_from_two_side.py @@ -78,43 +78,43 @@ def boundary_and_obstacles(start, goal, top_vertex, bottom_vertex, obs_number): def find_neighbor(node, ob, closed): - # generate neighbors in certain condition - ob_list = ob.tolist() - neighbor: list = [] + # Convert obstacles to a set for faster lookup + ob_set = set(map(tuple, ob.tolist())) + neighbor_set = set() + + # Generate neighbors within the 3x3 grid around the node for x in range(node.coordinate[0] - 1, node.coordinate[0] + 2): for y in range(node.coordinate[1] - 1, node.coordinate[1] + 2): - if [x, y] not in ob_list: - # find all possible neighbor nodes - neighbor.append([x, y]) - # remove node violate the motion rule - # 1. remove node.coordinate itself - neighbor.remove(node.coordinate) - # 2. remove neighbor nodes who cross through two diagonal - # positioned obstacles since there is no enough space for - # robot to go through two diagonal positioned obstacles - - # top bottom left right neighbors of node - top_nei = [node.coordinate[0], node.coordinate[1] + 1] - bottom_nei = [node.coordinate[0], node.coordinate[1] - 1] - left_nei = [node.coordinate[0] - 1, node.coordinate[1]] - right_nei = [node.coordinate[0] + 1, node.coordinate[1]] - # neighbors in four vertex - lt_nei = [node.coordinate[0] - 1, node.coordinate[1] + 1] - rt_nei = [node.coordinate[0] + 1, node.coordinate[1] + 1] - lb_nei = [node.coordinate[0] - 1, node.coordinate[1] - 1] - rb_nei = [node.coordinate[0] + 1, node.coordinate[1] - 1] - - # remove the unnecessary neighbors - if top_nei and left_nei in ob_list and lt_nei in neighbor: - neighbor.remove(lt_nei) - if top_nei and right_nei in ob_list and rt_nei in neighbor: - neighbor.remove(rt_nei) - if bottom_nei and left_nei in ob_list and lb_nei in neighbor: - neighbor.remove(lb_nei) - if bottom_nei and right_nei in ob_list and rb_nei in neighbor: - neighbor.remove(rb_nei) - neighbor = [x for x in neighbor if x not in closed] - return neighbor + coord = (x, y) + if coord not in ob_set and coord != tuple(node.coordinate): + neighbor_set.add(coord) + + # Define neighbors in cardinal and diagonal directions + top_nei = (node.coordinate[0], node.coordinate[1] + 1) + bottom_nei = (node.coordinate[0], node.coordinate[1] - 1) + left_nei = (node.coordinate[0] - 1, node.coordinate[1]) + right_nei = (node.coordinate[0] + 1, node.coordinate[1]) + lt_nei = (node.coordinate[0] - 1, node.coordinate[1] + 1) + rt_nei = (node.coordinate[0] + 1, node.coordinate[1] + 1) + lb_nei = (node.coordinate[0] - 1, node.coordinate[1] - 1) + rb_nei = (node.coordinate[0] + 1, node.coordinate[1] - 1) + + # Remove neighbors that violate diagonal motion rules + if top_nei in ob_set and left_nei in ob_set: + neighbor_set.discard(lt_nei) + if top_nei in ob_set and right_nei in ob_set: + neighbor_set.discard(rt_nei) + if bottom_nei in ob_set and left_nei in ob_set: + neighbor_set.discard(lb_nei) + if bottom_nei in ob_set and right_nei in ob_set: + neighbor_set.discard(rb_nei) + + # Filter out neighbors that are in the closed set + closed_set = set(map(tuple, closed)) + neighbor_set -= closed_set + + return list(neighbor_set) + def find_node_index(coordinate, node_list): From 1cb45a5d3bdcb07b254c20ce7763d527435c355e Mon Sep 17 00:00:00 2001 From: Aryaz Eghbali <64126826+AryazE@users.noreply.github.com> Date: Mon, 20 Jan 2025 09:16:20 +0100 Subject: [PATCH 773/909] Fixed multitype list (#1076) * Fixed multitype list * Cast to float * Reverted to all floats * Moved all remaining to float --- .../BreadthFirstSearch/breadth_first_search.py | 10 +++++----- PathPlanning/Dijkstra/dijkstra.py | 10 +++++----- .../ProbabilisticRoadMap/probabilistic_road_map.py | 10 +++++----- PathPlanning/VoronoiRoadMap/voronoi_road_map.py | 10 +++++----- 4 files changed, 20 insertions(+), 20 deletions(-) diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index ff662170e7..ad994732a5 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -220,20 +220,20 @@ def main(): # set obstacle positions ox, oy = [], [] for i in range(-10, 60): - ox.append(i) + ox.append(float(i)) oy.append(-10.0) for i in range(-10, 60): ox.append(60.0) - oy.append(i) + oy.append(float(i)) for i in range(-10, 61): - ox.append(i) + ox.append(float(i)) oy.append(60.0) for i in range(-10, 61): ox.append(-10.0) - oy.append(i) + oy.append(float(i)) for i in range(-10, 40): ox.append(20.0) - oy.append(i) + oy.append(float(i)) for i in range(0, 40): ox.append(40.0) oy.append(60.0 - i) diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index 004e49f15a..8a585e4b18 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -221,20 +221,20 @@ def main(): # set obstacle positions ox, oy = [], [] for i in range(-10, 60): - ox.append(i) + ox.append(float(i)) oy.append(-10.0) for i in range(-10, 60): ox.append(60.0) - oy.append(i) + oy.append(float(i)) for i in range(-10, 61): - ox.append(i) + ox.append(float(i)) oy.append(60.0) for i in range(-10, 61): ox.append(-10.0) - oy.append(i) + oy.append(float(i)) for i in range(-10, 40): ox.append(20.0) - oy.append(i) + oy.append(float(i)) for i in range(0, 40): ox.append(40.0) oy.append(60.0 - i) diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py index 294c389023..8bacfd5d19 100644 --- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py +++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py @@ -273,20 +273,20 @@ def main(rng=None): oy = [] for i in range(60): - ox.append(i) + ox.append(float(i)) oy.append(0.0) for i in range(60): ox.append(60.0) - oy.append(i) + oy.append(float(i)) for i in range(61): - ox.append(i) + ox.append(float(i)) oy.append(60.0) for i in range(61): ox.append(0.0) - oy.append(i) + oy.append(float(i)) for i in range(40): ox.append(20.0) - oy.append(i) + oy.append(float(i)) for i in range(40): ox.append(40.0) oy.append(60.0 - i) diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py index 0a1f6f5526..a27e1b6928 100644 --- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py +++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py @@ -146,20 +146,20 @@ def main(): oy = [] for i in range(60): - ox.append(i) + ox.append(float(i)) oy.append(0.0) for i in range(60): ox.append(60.0) - oy.append(i) + oy.append(float(i)) for i in range(61): - ox.append(i) + ox.append(float(i)) oy.append(60.0) for i in range(61): ox.append(0.0) - oy.append(i) + oy.append(float(i)) for i in range(40): ox.append(20.0) - oy.append(i) + oy.append(float(i)) for i in range(40): ox.append(40.0) oy.append(60.0 - i) From 9672434483459a00b13ece407079fbb53dd8a6b0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Jan 2025 08:02:30 +0900 Subject: [PATCH 774/909] build(deps): bump numpy from 2.2.1 to 2.2.2 in /requirements (#1122) Bumps [numpy](https://github.com/numpy/numpy) from 2.2.1 to 2.2.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.2.1...v2.2.2) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index d212202cdb..e1c117cb1e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.2.1 +numpy == 2.2.2 scipy == 1.15.1 matplotlib == 3.10.0 cvxpy == 1.5.3 From 2bc59fe61247982eaed5b0b7eab842658a5c07d8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Jan 2025 08:15:53 +0900 Subject: [PATCH 775/909] build(deps): bump ruff from 0.9.1 to 0.9.2 in /requirements (#1123) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.1 to 0.9.2. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.1...0.9.2) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e1c117cb1e..11b91abb1f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.14.1 # For unit test -ruff == 0.9.1 # For unit test +ruff == 0.9.2 # For unit test From 5a66105ff5dc9924fc6b8f8986afaf52f0fd870d Mon Sep 17 00:00:00 2001 From: Aglargil <34728006+Aglargil@users.noreply.github.com> Date: Wed, 22 Jan 2025 17:57:12 +0800 Subject: [PATCH 776/909] Extend frenet_optimal_trajectory to support more scenarios (#1114) * feat: Add third derivative and curvature rate calculations to CubicSpline classes * feat: Extend frenet_optimal_trajectory to support more scenarios * fix: frenet optimal trajectory type check * fix: cubic spline planner code style check * fix: frenet optimal trajectory review * feat: frenet_optimal_trajectory update doc * fix: frenet optimal trajectory review * fix: frenet optimal trajectory * fix: frenet optimal trajectory --- .../CubicSpline/cubic_spline_planner.py | 67 +++ .../cartesian_frenet_converter.py | 144 +++++ .../frenet_optimal_trajectory.py | 550 +++++++++++++----- .../frenet_frame_path_main.rst | 30 +- tests/test_frenet_optimal_trajectory.py | 40 +- 5 files changed, 674 insertions(+), 157 deletions(-) create mode 100644 PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py index 7cc7bedc79..2391f67c39 100644 --- a/PathPlanning/CubicSpline/cubic_spline_planner.py +++ b/PathPlanning/CubicSpline/cubic_spline_planner.py @@ -76,6 +76,11 @@ def calc_position(self, x): if `x` is outside the data point's `x` range, return None. + Parameters + ---------- + x : float + x position to calculate y. + Returns ------- y : float @@ -99,6 +104,11 @@ def calc_first_derivative(self, x): if x is outside the input x, return None + Parameters + ---------- + x : float + x position to calculate first derivative. + Returns ------- dy : float @@ -121,6 +131,11 @@ def calc_second_derivative(self, x): if x is outside the input x, return None + Parameters + ---------- + x : float + x position to calculate second derivative. + Returns ------- ddy : float @@ -137,6 +152,31 @@ def calc_second_derivative(self, x): ddy = 2.0 * self.c[i] + 6.0 * self.d[i] * dx return ddy + def calc_third_derivative(self, x): + """ + Calc third derivative at given x. + + if x is outside the input x, return None + + Parameters + ---------- + x : float + x position to calculate third derivative. + + Returns + ------- + dddy : float + third derivative for given x. + """ + if x < self.x[0]: + return None + elif x > self.x[-1]: + return None + + i = self.__search_index(x) + dddy = 6.0 * self.d[i] + return dddy + def __search_index(self, x): """ search data segment index @@ -287,6 +327,33 @@ def calc_curvature(self, s): k = (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2)) return k + def calc_curvature_rate(self, s): + """ + calc curvature rate + + Parameters + ---------- + s : float + distance from the start point. if `s` is outside the data point's + range, return None. + + Returns + ------- + k : float + curvature rate for given s. + """ + dx = self.sx.calc_first_derivative(s) + dy = self.sy.calc_first_derivative(s) + ddx = self.sx.calc_second_derivative(s) + ddy = self.sy.calc_second_derivative(s) + dddx = self.sx.calc_third_derivative(s) + dddy = self.sy.calc_third_derivative(s) + a = dx * ddy - dy * ddx + b = dx * dddy - dy * dddx + c = dx * ddx + dy * ddy + d = dx * dx + dy * dy + return (b * d - 3.0 * a * c) / (d * d * d) + def calc_yaw(self, s): """ calc yaw diff --git a/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py new file mode 100644 index 0000000000..4cc8650c89 --- /dev/null +++ b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py @@ -0,0 +1,144 @@ +""" + +A converter between Cartesian and Frenet coordinate systems + +author: Wang Zheng (@Aglargil) + +Ref: + +- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame] +(https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf) + +""" + +import math + + +class CartesianFrenetConverter: + """ + A class for converting states between Cartesian and Frenet coordinate systems + """ + + @ staticmethod + def cartesian_to_frenet(rs, rx, ry, rtheta, rkappa, rdkappa, x, y, v, a, theta, kappa): + """ + Convert state from Cartesian coordinate to Frenet coordinate + + Parameters + ---------- + rs: reference line s-coordinate + rx, ry: reference point coordinates + rtheta: reference point heading + rkappa: reference point curvature + rdkappa: reference point curvature rate + x, y: current position + v: velocity + a: acceleration + theta: heading angle + kappa: curvature + + Returns + ------- + s_condition: [s(t), s'(t), s''(t)] + d_condition: [d(s), d'(s), d''(s)] + """ + dx = x - rx + dy = y - ry + + cos_theta_r = math.cos(rtheta) + sin_theta_r = math.sin(rtheta) + + cross_rd_nd = cos_theta_r * dy - sin_theta_r * dx + d = math.copysign(math.hypot(dx, dy), cross_rd_nd) + + delta_theta = theta - rtheta + tan_delta_theta = math.tan(delta_theta) + cos_delta_theta = math.cos(delta_theta) + + one_minus_kappa_r_d = 1 - rkappa * d + d_dot = one_minus_kappa_r_d * tan_delta_theta + + kappa_r_d_prime = rdkappa * d + rkappa * d_dot + + d_ddot = (-kappa_r_d_prime * tan_delta_theta + + one_minus_kappa_r_d / (cos_delta_theta * cos_delta_theta) * + (kappa * one_minus_kappa_r_d / cos_delta_theta - rkappa)) + + s = rs + s_dot = v * cos_delta_theta / one_minus_kappa_r_d + + delta_theta_prime = one_minus_kappa_r_d / cos_delta_theta * kappa - rkappa + s_ddot = (a * cos_delta_theta - + s_dot * s_dot * + (d_dot * delta_theta_prime - kappa_r_d_prime)) / one_minus_kappa_r_d + + return [s, s_dot, s_ddot], [d, d_dot, d_ddot] + + @ staticmethod + def frenet_to_cartesian(rs, rx, ry, rtheta, rkappa, rdkappa, s_condition, d_condition): + """ + Convert state from Frenet coordinate to Cartesian coordinate + + Parameters + ---------- + rs: reference line s-coordinate + rx, ry: reference point coordinates + rtheta: reference point heading + rkappa: reference point curvature + rdkappa: reference point curvature rate + s_condition: [s(t), s'(t), s''(t)] + d_condition: [d(s), d'(s), d''(s)] + + Returns + ------- + x, y: position + theta: heading angle + kappa: curvature + v: velocity + a: acceleration + """ + if abs(rs - s_condition[0]) >= 1.0e-6: + raise ValueError( + "The reference point s and s_condition[0] don't match") + + cos_theta_r = math.cos(rtheta) + sin_theta_r = math.sin(rtheta) + + x = rx - sin_theta_r * d_condition[0] + y = ry + cos_theta_r * d_condition[0] + + one_minus_kappa_r_d = 1 - rkappa * d_condition[0] + + tan_delta_theta = d_condition[1] / one_minus_kappa_r_d + delta_theta = math.atan2(d_condition[1], one_minus_kappa_r_d) + cos_delta_theta = math.cos(delta_theta) + + theta = CartesianFrenetConverter.normalize_angle(delta_theta + rtheta) + + kappa_r_d_prime = rdkappa * d_condition[0] + rkappa * d_condition[1] + + kappa = (((d_condition[2] + kappa_r_d_prime * tan_delta_theta) * + cos_delta_theta * cos_delta_theta) / one_minus_kappa_r_d + rkappa) * \ + cos_delta_theta / one_minus_kappa_r_d + + d_dot = d_condition[1] * s_condition[1] + v = math.sqrt(one_minus_kappa_r_d * one_minus_kappa_r_d * + s_condition[1] * s_condition[1] + d_dot * d_dot) + + delta_theta_prime = one_minus_kappa_r_d / cos_delta_theta * kappa - rkappa + + a = (s_condition[2] * one_minus_kappa_r_d / cos_delta_theta + + s_condition[1] * s_condition[1] / cos_delta_theta * + (d_condition[1] * delta_theta_prime - kappa_r_d_prime)) + + return x, y, theta, kappa, v, a + + @ staticmethod + def normalize_angle(angle): + """ + Normalize angle to [-pi, pi] + """ + a = math.fmod(angle + math.pi, 2.0 * math.pi) + if a < 0.0: + a += 2.0 * math.pi + return a - math.pi diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index 331df36309..248894c1c6 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -17,43 +17,314 @@ import numpy as np import matplotlib.pyplot as plt import copy -import math import sys import pathlib + sys.path.append(str(pathlib.Path(__file__).parent.parent)) -from QuinticPolynomialsPlanner.quintic_polynomials_planner import \ - QuinticPolynomial +from QuinticPolynomialsPlanner.quintic_polynomials_planner import QuinticPolynomial from CubicSpline import cubic_spline_planner -SIM_LOOP = 500 +from enum import Enum, auto +from FrenetOptimalTrajectory.cartesian_frenet_converter import ( + CartesianFrenetConverter, +) + + +class LateralMovement(Enum): + HIGH_SPEED = auto() + LOW_SPEED = auto() + + +class LongitudinalMovement(Enum): + MERGING_AND_STOPPING = auto() + VELOCITY_KEEPING = auto() + + +# Default Parameters + +LATERAL_MOVEMENT = LateralMovement.HIGH_SPEED +LONGITUDINAL_MOVEMENT = LongitudinalMovement.VELOCITY_KEEPING -# Parameter MAX_SPEED = 50.0 / 3.6 # maximum speed [m/s] -MAX_ACCEL = 2.0 # maximum acceleration [m/ss] +MAX_ACCEL = 5.0 # maximum acceleration [m/ss] MAX_CURVATURE = 1.0 # maximum curvature [1/m] -MAX_ROAD_WIDTH = 7.0 # maximum road width [m] -D_ROAD_W = 1.0 # road width sampling length [m] DT = 0.2 # time tick [s] MAX_T = 5.0 # max prediction time [m] MIN_T = 4.0 # min prediction time [m] -TARGET_SPEED = 30.0 / 3.6 # target speed [m/s] -D_T_S = 5.0 / 3.6 # target speed sampling length [m/s] N_S_SAMPLE = 1 # sampling number of target speed -ROBOT_RADIUS = 2.0 # robot radius [m] # cost weights K_J = 0.1 K_T = 0.1 +K_S_DOT = 1.0 K_D = 1.0 +K_S = 1.0 K_LAT = 1.0 K_LON = 1.0 +SIM_LOOP = 500 show_animation = True -class QuarticPolynomial: +if LATERAL_MOVEMENT == LateralMovement.LOW_SPEED: + MAX_ROAD_WIDTH = 1.0 # maximum road width [m] + D_ROAD_W = 0.2 # road width sampling length [m] + TARGET_SPEED = 3.0 / 3.6 # maximum speed [m/s] + D_T_S = 0.5 / 3.6 # target speed sampling length [m/s] + # Waypoints + WX = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0] + WY = [0.0, 0.0, 1.0, 0.0, -1.0, -2.0] + OBSTACLES = np.array([[3.0, 1.0], [5.0, -0.0], [6.0, 0.5], [8.0, -1.5]]) + ROBOT_RADIUS = 0.5 # robot radius [m] + + # Initial state parameters + INITIAL_SPEED = 1.0 / 3.6 # current speed [m/s] + INITIAL_ACCEL = 0.0 # current acceleration [m/ss] + INITIAL_LAT_POSITION = 0.5 # current lateral position [m] + INITIAL_LAT_SPEED = 0.0 # current lateral speed [m/s] + INITIAL_LAT_ACCELERATION = 0.0 # current lateral acceleration [m/s] + INITIAL_COURSE_POSITION = 0.0 # current course position + + ANIMATION_AREA = 5.0 # Animation area length [m] + + STOP_S = 4.0 # Merge and stop distance [m] + D_S = 0.3 # Stop point sampling length [m] + N_STOP_S_SAMPLE = 3 # Stop point sampling number +else: + MAX_ROAD_WIDTH = 7.0 # maximum road width [m] + D_ROAD_W = 1.0 # road width sampling length [m] + TARGET_SPEED = 30.0 / 3.6 # target speed [m/s] + D_T_S = 5.0 / 3.6 # target speed sampling length [m/s] + # Waypoints + WX = [0.0, 10.0, 20.5, 35.0, 70.5] + WY = [0.0, -6.0, 5.0, 6.5, 0.0] + # Obstacle list + OBSTACLES = np.array( + [[20.0, 10.0], [30.0, 6.0], [30.0, 8.0], [35.0, 8.0], [50.0, 3.0]] + ) + ROBOT_RADIUS = 2.0 # robot radius [m] + + # Initial state parameters + INITIAL_SPEED = 10.0 / 3.6 # current speed [m/s] + INITIAL_ACCEL = 0.0 # current acceleration [m/ss] + INITIAL_LAT_POSITION = 2.0 # current lateral position [m] + INITIAL_LAT_SPEED = 0.0 # current lateral speed [m/s] + INITIAL_LAT_ACCELERATION = 0.0 # current lateral acceleration [m/s] + INITIAL_COURSE_POSITION = 0.0 # current course position + + ANIMATION_AREA = 20.0 # Animation area length [m] + STOP_S = 25.0 # Merge and stop distance [m] + D_S = 2 # Stop point sampling length [m] + N_STOP_S_SAMPLE = 4 # Stop point sampling number + + +class LateralMovementStrategy: + def calc_lateral_trajectory(self, fp, di, c_d, c_d_d, c_d_dd, Ti): + """ + Calculate the lateral trajectory + """ + raise NotImplementedError("calc_lateral_trajectory not implemented") + + def calc_cartesian_parameters(self, fp, csp): + """ + Calculate the cartesian parameters (x, y, yaw, curvature, v, a) + """ + raise NotImplementedError("calc_cartesian_parameters not implemented") + + +class HighSpeedLateralMovementStrategy(LateralMovementStrategy): + def calc_lateral_trajectory(self, fp, di, c_d, c_d_d, c_d_dd, Ti): + tp = copy.deepcopy(fp) + s0_d = fp.s_d[0] + s0_dd = fp.s_dd[0] + # d'(t) = d'(s) * s'(t) + # d''(t) = d''(s) * s'(t)^2 + d'(s) * s''(t) + lat_qp = QuinticPolynomial( + c_d, c_d_d * s0_d, c_d_dd * s0_d**2 + c_d_d * s0_dd, di, 0.0, 0.0, Ti + ) + + tp.d = [] + tp.d_d = [] + tp.d_dd = [] + tp.d_ddd = [] + + # Calculate all derivatives in a single loop to reduce iterations + for i in range(len(fp.t)): + t = fp.t[i] + s_d = fp.s_d[i] + s_dd = fp.s_dd[i] + + s_d_inv = 1.0 / (s_d + 1e-6) + 1e-6 # Avoid division by zero + s_d_inv_sq = s_d_inv * s_d_inv # Square of inverse + + d = lat_qp.calc_point(t) + d_d = lat_qp.calc_first_derivative(t) + d_dd = lat_qp.calc_second_derivative(t) + d_ddd = lat_qp.calc_third_derivative(t) + + tp.d.append(d) + # d'(s) = d'(t) / s'(t) + tp.d_d.append(d_d * s_d_inv) + # d''(s) = (d''(t) - d'(s) * s''(t)) / s'(t)^2 + tp.d_dd.append((d_dd - tp.d_d[i] * s_dd) * s_d_inv_sq) + tp.d_ddd.append(d_ddd) + + return tp + + def calc_cartesian_parameters(self, fp, csp): + # calc global positions + for i in range(len(fp.s)): + ix, iy = csp.calc_position(fp.s[i]) + if ix is None: + break + i_yaw = csp.calc_yaw(fp.s[i]) + i_kappa = csp.calc_curvature(fp.s[i]) + i_dkappa = csp.calc_curvature_rate(fp.s[i]) + s_condition = [fp.s[i], fp.s_d[i], fp.s_dd[i]] + d_condition = [ + fp.d[i], + fp.d_d[i], + fp.d_dd[i], + ] + x, y, theta, kappa, v, a = CartesianFrenetConverter.frenet_to_cartesian( + fp.s[i], ix, iy, i_yaw, i_kappa, i_dkappa, s_condition, d_condition + ) + fp.x.append(x) + fp.y.append(y) + fp.yaw.append(theta) + fp.c.append(kappa) + fp.v.append(v) + fp.a.append(a) + return fp + + +class LowSpeedLateralMovementStrategy(LateralMovementStrategy): + def calc_lateral_trajectory(self, fp, di, c_d, c_d_d, c_d_dd, Ti): + s0 = fp.s[0] + s1 = fp.s[-1] + tp = copy.deepcopy(fp) + # d = d(s), d_d = d'(s), d_dd = d''(s) + # * shift s range from [s0, s1] to [0, s1 - s0] + lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, s1 - s0) + + tp.d = [lat_qp.calc_point(s - s0) for s in fp.s] + tp.d_d = [lat_qp.calc_first_derivative(s - s0) for s in fp.s] + tp.d_dd = [lat_qp.calc_second_derivative(s - s0) for s in fp.s] + tp.d_ddd = [lat_qp.calc_third_derivative(s - s0) for s in fp.s] + return tp + + def calc_cartesian_parameters(self, fp, csp): + # calc global positions + for i in range(len(fp.s)): + ix, iy = csp.calc_position(fp.s[i]) + if ix is None: + break + i_yaw = csp.calc_yaw(fp.s[i]) + i_kappa = csp.calc_curvature(fp.s[i]) + i_dkappa = csp.calc_curvature_rate(fp.s[i]) + s_condition = [fp.s[i], fp.s_d[i], fp.s_dd[i]] + d_condition = [fp.d[i], fp.d_d[i], fp.d_dd[i]] + x, y, theta, kappa, v, a = CartesianFrenetConverter.frenet_to_cartesian( + fp.s[i], ix, iy, i_yaw, i_kappa, i_dkappa, s_condition, d_condition + ) + fp.x.append(x) + fp.y.append(y) + fp.yaw.append(theta) + fp.c.append(kappa) + fp.v.append(v) + fp.a.append(a) + return fp + + +class LongitudinalMovementStrategy: + def calc_longitudinal_trajectory(self, c_speed, c_accel, Ti, s0): + """ + Calculate the longitudinal trajectory + """ + raise NotImplementedError("calc_longitudinal_trajectory not implemented") + + def get_d_arrange(self, s0): + """ + Get the d sample range + """ + raise NotImplementedError("get_d_arrange not implemented") + + def calc_destination_cost(self, fp): + """ + Calculate the destination cost + """ + raise NotImplementedError("calc_destination_cost not implemented") + + +class VelocityKeepingLongitudinalMovementStrategy(LongitudinalMovementStrategy): + def calc_longitudinal_trajectory(self, c_speed, c_accel, Ti, s0): + fplist = [] + for tv in np.arange( + TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S + ): + fp = FrenetPath() + lon_qp = QuarticPolynomial(s0, c_speed, c_accel, tv, 0.0, Ti) + fp.t = [t for t in np.arange(0.0, Ti, DT)] + fp.s = [lon_qp.calc_point(t) for t in fp.t] + fp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t] + fp.s_dd = [lon_qp.calc_second_derivative(t) for t in fp.t] + fp.s_ddd = [lon_qp.calc_third_derivative(t) for t in fp.t] + fplist.append(fp) + return fplist + + def get_d_arrange(self, s0): + return np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W) + + def calc_destination_cost(self, fp): + ds = (TARGET_SPEED - fp.s_d[-1]) ** 2 + return K_S_DOT * ds + + +class MergingAndStoppingLongitudinalMovementStrategy(LongitudinalMovementStrategy): + def calc_longitudinal_trajectory(self, c_speed, c_accel, Ti, s0): + if s0 >= STOP_S: + return [] + fplist = [] + for s in np.arange( + STOP_S - D_S * N_STOP_S_SAMPLE, STOP_S + D_S * N_STOP_S_SAMPLE, D_S + ): + fp = FrenetPath() + lon_qp = QuinticPolynomial(s0, c_speed, c_accel, s, 0.0, 0.0, Ti) + fp.t = [t for t in np.arange(0.0, Ti, DT)] + fp.s = [lon_qp.calc_point(t) for t in fp.t] + fp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t] + fp.s_dd = [lon_qp.calc_second_derivative(t) for t in fp.t] + fp.s_ddd = [lon_qp.calc_third_derivative(t) for t in fp.t] + fplist.append(fp) + return fplist + + def get_d_arrange(self, s0): + # Only if s0 is less than STOP_S / 3, then we sample the road width + if s0 < STOP_S / 3: + return np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W) + else: + return [0.0] + + def calc_destination_cost(self, fp): + ds = (STOP_S - fp.s[-1]) ** 2 + return K_S * ds + +LATERAL_MOVEMENT_STRATEGY: LateralMovementStrategy +LONGITUDINAL_MOVEMENT_STRATEGY: LongitudinalMovementStrategy + +if LATERAL_MOVEMENT == LateralMovement.HIGH_SPEED: + LATERAL_MOVEMENT_STRATEGY = HighSpeedLateralMovementStrategy() +else: + LATERAL_MOVEMENT_STRATEGY = LowSpeedLateralMovementStrategy() + +if LONGITUDINAL_MOVEMENT == LongitudinalMovement.VELOCITY_KEEPING: + LONGITUDINAL_MOVEMENT_STRATEGY = VelocityKeepingLongitudinalMovementStrategy() +else: + LONGITUDINAL_MOVEMENT_STRATEGY = MergingAndStoppingLongitudinalMovementStrategy() + +class QuarticPolynomial: def __init__(self, xs, vxs, axs, vxe, axe, time): # calc coefficient of quartic polynomial @@ -61,29 +332,25 @@ def __init__(self, xs, vxs, axs, vxe, axe, time): self.a1 = vxs self.a2 = axs / 2.0 - A = np.array([[3 * time ** 2, 4 * time ** 3], - [6 * time, 12 * time ** 2]]) - b = np.array([vxe - self.a1 - 2 * self.a2 * time, - axe - 2 * self.a2]) + A = np.array([[3 * time**2, 4 * time**3], [6 * time, 12 * time**2]]) + b = np.array([vxe - self.a1 - 2 * self.a2 * time, axe - 2 * self.a2]) x = np.linalg.solve(A, b) self.a3 = x[0] self.a4 = x[1] def calc_point(self, t): - xt = self.a0 + self.a1 * t + self.a2 * t ** 2 + \ - self.a3 * t ** 3 + self.a4 * t ** 4 + xt = self.a0 + self.a1 * t + self.a2 * t**2 + self.a3 * t**3 + self.a4 * t**4 return xt def calc_first_derivative(self, t): - xt = self.a1 + 2 * self.a2 * t + \ - 3 * self.a3 * t ** 2 + 4 * self.a4 * t ** 3 + xt = self.a1 + 2 * self.a2 * t + 3 * self.a3 * t**2 + 4 * self.a4 * t**3 return xt def calc_second_derivative(self, t): - xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t ** 2 + xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2 return xt @@ -94,111 +361,85 @@ def calc_third_derivative(self, t): class FrenetPath: - def __init__(self): self.t = [] self.d = [] - self.d_d = [] - self.d_dd = [] - self.d_ddd = [] + self.d_d = [] # d'(s) + self.d_dd = [] # d''(s) + self.d_ddd = [] # d'''(t) in low speed / d'''(s) in high speed self.s = [] - self.s_d = [] - self.s_dd = [] - self.s_ddd = [] - self.cd = 0.0 - self.cv = 0.0 + self.s_d = [] # s'(t) + self.s_dd = [] # s''(t) + self.s_ddd = [] # s'''(t) self.cf = 0.0 self.x = [] self.y = [] self.yaw = [] + self.v = [] + self.a = [] self.ds = [] self.c = [] - -def calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0): + def pop_front(self): + self.x.pop(0) + self.y.pop(0) + self.yaw.pop(0) + self.v.pop(0) + self.a.pop(0) + self.s.pop(0) + self.s_d.pop(0) + self.s_dd.pop(0) + self.s_ddd.pop(0) + self.d.pop(0) + self.d_d.pop(0) + self.d_dd.pop(0) + self.d_ddd.pop(0) + + +def calc_frenet_paths(c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, s0): frenet_paths = [] - # generate path to each offset goal - for di in np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W): - - # Lateral motion planning - for Ti in np.arange(MIN_T, MAX_T, DT): - fp = FrenetPath() - - # lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti) - lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti) - - fp.t = [t for t in np.arange(0.0, Ti, DT)] - fp.d = [lat_qp.calc_point(t) for t in fp.t] - fp.d_d = [lat_qp.calc_first_derivative(t) for t in fp.t] - fp.d_dd = [lat_qp.calc_second_derivative(t) for t in fp.t] - fp.d_ddd = [lat_qp.calc_third_derivative(t) for t in fp.t] - - # Longitudinal motion planning (Velocity keeping) - for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, - TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S): - tfp = copy.deepcopy(fp) - lon_qp = QuarticPolynomial(s0, c_speed, c_accel, tv, 0.0, Ti) - - tfp.s = [lon_qp.calc_point(t) for t in fp.t] - tfp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t] - tfp.s_dd = [lon_qp.calc_second_derivative(t) for t in fp.t] - tfp.s_ddd = [lon_qp.calc_third_derivative(t) for t in fp.t] - - Jp = sum(np.power(tfp.d_ddd, 2)) # square of jerk - Js = sum(np.power(tfp.s_ddd, 2)) # square of jerk - - # square of diff from target speed - ds = (TARGET_SPEED - tfp.s_d[-1]) ** 2 - - tfp.cd = K_J * Jp + K_T * Ti + K_D * tfp.d[-1] ** 2 - tfp.cv = K_J * Js + K_T * Ti + K_D * ds - tfp.cf = K_LAT * tfp.cd + K_LON * tfp.cv - - frenet_paths.append(tfp) + for Ti in np.arange(MIN_T, MAX_T, DT): + lon_paths = LONGITUDINAL_MOVEMENT_STRATEGY.calc_longitudinal_trajectory( + c_s_d, c_s_dd, Ti, s0 + ) + + for fp in lon_paths: + for di in LONGITUDINAL_MOVEMENT_STRATEGY.get_d_arrange(s0): + tp = LATERAL_MOVEMENT_STRATEGY.calc_lateral_trajectory( + fp, di, c_d, c_d_d, c_d_dd, Ti + ) + + Jp = sum(np.power(tp.d_ddd, 2)) # square of jerk + Js = sum(np.power(tp.s_ddd, 2)) # square of jerk + + lat_cost = K_J * Jp + K_T * Ti + K_D * tp.d[-1] ** 2 + lon_cost = ( + K_J * Js + + K_T * Ti + + LONGITUDINAL_MOVEMENT_STRATEGY.calc_destination_cost(tp) + ) + tp.cf = K_LAT * lat_cost + K_LON * lon_cost + frenet_paths.append(tp) return frenet_paths def calc_global_paths(fplist, csp): - for fp in fplist: - - # calc global positions - for i in range(len(fp.s)): - ix, iy = csp.calc_position(fp.s[i]) - if ix is None: - break - i_yaw = csp.calc_yaw(fp.s[i]) - di = fp.d[i] - fx = ix + di * math.cos(i_yaw + math.pi / 2.0) - fy = iy + di * math.sin(i_yaw + math.pi / 2.0) - fp.x.append(fx) - fp.y.append(fy) - - # calc yaw and ds - for i in range(len(fp.x) - 1): - dx = fp.x[i + 1] - fp.x[i] - dy = fp.y[i + 1] - fp.y[i] - fp.yaw.append(math.atan2(dy, dx)) - fp.ds.append(math.hypot(dx, dy)) - - fp.yaw.append(fp.yaw[-1]) - fp.ds.append(fp.ds[-1]) - - # calc curvature - for i in range(len(fp.yaw) - 1): - fp.c.append((fp.yaw[i + 1] - fp.yaw[i]) / fp.ds[i]) - - return fplist + return [ + LATERAL_MOVEMENT_STRATEGY.calc_cartesian_parameters(fp, csp) for fp in fplist + ] def check_collision(fp, ob): for i in range(len(ob[:, 0])): - d = [((ix - ob[i, 0]) ** 2 + (iy - ob[i, 1]) ** 2) - for (ix, iy) in zip(fp.x, fp.y)] + d = [ + ((ix - ob[i, 0]) ** 2 + (iy - ob[i, 1]) ** 2) + for (ix, iy) in zip(fp.x, fp.y) + ] - collision = any([di <= ROBOT_RADIUS ** 2 for di in d]) + collision = any([di <= ROBOT_RADIUS**2 for di in d]) if collision: return False @@ -207,38 +448,41 @@ def check_collision(fp, ob): def check_paths(fplist, ob): - ok_ind = [] + path_dict = { + "max_speed_error": [], + "max_accel_error": [], + "max_curvature_error": [], + "collision_error": [], + "ok": [], + } for i, _ in enumerate(fplist): - if any([v > MAX_SPEED for v in fplist[i].s_d]): # Max speed check - continue - elif any([abs(a) > MAX_ACCEL for a in - fplist[i].s_dd]): # Max accel check - continue - elif any([abs(c) > MAX_CURVATURE for c in - fplist[i].c]): # Max curvature check - continue + if any([v > MAX_SPEED for v in fplist[i].v]): # Max speed check + path_dict["max_speed_error"].append(fplist[i]) + elif any([abs(a) > MAX_ACCEL for a in fplist[i].a]): # Max accel check + path_dict["max_accel_error"].append(fplist[i]) + elif any([abs(c) > MAX_CURVATURE for c in fplist[i].c]): # Max curvature check + path_dict["max_curvature_error"].append(fplist[i]) elif not check_collision(fplist[i], ob): - continue + path_dict["collision_error"].append(fplist[i]) + else: + path_dict["ok"].append(fplist[i]) + return path_dict - ok_ind.append(i) - return [fplist[i] for i in ok_ind] - - -def frenet_optimal_planning(csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob): - fplist = calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0) +def frenet_optimal_planning(csp, s0, c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, ob): + fplist = calc_frenet_paths(c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, s0) fplist = calc_global_paths(fplist, csp) - fplist = check_paths(fplist, ob) + fpdict = check_paths(fplist, ob) # find minimum cost path min_cost = float("inf") best_path = None - for fp in fplist: + for fp in fpdict["ok"]: if min_cost >= fp.cf: min_cost = fp.cf best_path = fp - return best_path + return [best_path, fpdict] def generate_target_course(x, y): @@ -259,40 +503,39 @@ def generate_target_course(x, y): def main(): print(__file__ + " start!!") - # way points - wx = [0.0, 10.0, 20.5, 35.0, 70.5] - wy = [0.0, -6.0, 5.0, 6.5, 0.0] - # obstacle lists - ob = np.array([[20.0, 10.0], - [30.0, 6.0], - [30.0, 8.0], - [35.0, 8.0], - [50.0, 3.0] - ]) - - tx, ty, tyaw, tc, csp = generate_target_course(wx, wy) - - # initial state - c_speed = 10.0 / 3.6 # current speed [m/s] - c_accel = 0.0 # current acceleration [m/ss] - c_d = 2.0 # current lateral position [m] - c_d_d = 0.0 # current lateral speed [m/s] - c_d_dd = 0.0 # current lateral acceleration [m/s] - s0 = 0.0 # current course position - - area = 20.0 # animation area length [m] + tx, ty, tyaw, tc, csp = generate_target_course(WX, WY) + + # Initialize state using global parameters + c_s_d = INITIAL_SPEED + c_s_dd = INITIAL_ACCEL + c_d = INITIAL_LAT_POSITION + c_d_d = INITIAL_LAT_SPEED + c_d_dd = INITIAL_LAT_ACCELERATION + s0 = INITIAL_COURSE_POSITION + + area = ANIMATION_AREA + + last_path = None for i in range(SIM_LOOP): - path = frenet_optimal_planning( - csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob) + [path, fpdict] = frenet_optimal_planning( + csp, s0, c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, OBSTACLES + ) + + if path is None: + path = copy.deepcopy(last_path) + path.pop_front() + if len(path.x) <= 1: + print("Finish") + break + last_path = path s0 = path.s[1] c_d = path.d[1] c_d_d = path.d_d[1] c_d_dd = path.d_dd[1] - c_speed = path.s_d[1] - c_accel = path.s_dd[1] - + c_s_d = path.s_d[1] + c_s_dd = path.s_dd[1] if np.hypot(path.x[1] - tx[-1], path.y[1] - ty[-1]) <= 1.0: print("Goal") break @@ -301,15 +544,16 @@ def main(): plt.cla() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect( - 'key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + "key_release_event", + lambda event: [exit(0) if event.key == "escape" else None], + ) plt.plot(tx, ty) - plt.plot(ob[:, 0], ob[:, 1], "xk") + plt.plot(OBSTACLES[:, 0], OBSTACLES[:, 1], "xk") plt.plot(path.x[1:], path.y[1:], "-or") plt.plot(path.x[1], path.y[1], "vc") plt.xlim(path.x[1] - area, path.x[1] + area) plt.ylim(path.y[1] - area, path.y[1] + area) - plt.title("v[km/h]:" + str(c_speed * 3.6)[0:4]) + plt.title("v[km/h]:" + str(path.v[1] * 3.6)[0:4]) plt.grid(True) plt.pause(0.0001) @@ -320,5 +564,5 @@ def main(): plt.show() -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst index e9d9041e0e..38efaf2b53 100644 --- a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst +++ b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst @@ -1,14 +1,40 @@ Optimal Trajectory in a Frenet Frame ------------------------------------ -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif - This is optimal trajectory generation in a Frenet Frame. The cyan line is the target course and black crosses are obstacles. The red line is predicted path. +High Speed and Velocity Keeping Scenario +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_velocity_keeping_frenet_path.gif + +This scenario shows how the trajectory is maintained at high speeds while keeping a consistent velocity. + +High Speed and Merging and Stopping Scenario +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_merging_and_stopping_frenet_path.gif + +This scenario demonstrates the trajectory planning at high speeds with merging and stopping behaviors. + +Low Speed and Velocity Keeping Scenario +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/low_speed_and_velocity_keeping_frenet_path.gif + +This scenario demonstrates how the trajectory is managed at low speeds while maintaining a steady velocity. + +Low Speed and Merging and Stopping Scenario +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/low_speed_and_merging_and_stopping_frenet_path.gif + +This scenario illustrates the trajectory planning at low speeds with merging and stopping behaviors. + Ref: - `Optimal Trajectory Generation for Dynamic Street Scenarios in a diff --git a/tests/test_frenet_optimal_trajectory.py b/tests/test_frenet_optimal_trajectory.py index 72bb7a8341..b8761ff4a6 100644 --- a/tests/test_frenet_optimal_trajectory.py +++ b/tests/test_frenet_optimal_trajectory.py @@ -1,12 +1,48 @@ import conftest from PathPlanning.FrenetOptimalTrajectory import frenet_optimal_trajectory as m +from PathPlanning.FrenetOptimalTrajectory.frenet_optimal_trajectory import ( + LateralMovement, + LongitudinalMovement, +) -def test1(): +def default_scenario_test(): m.show_animation = False m.SIM_LOOP = 5 m.main() -if __name__ == '__main__': +def high_speed_and_merging_and_stopping_scenario_test(): + m.show_animation = False + m.LATERAL_MOVEMENT = LateralMovement.HIGH_SPEED + m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.MERGING_AND_STOPPING + m.SIM_LOOP = 5 + m.main() + + +def high_speed_and_velocity_keeping_scenario_test(): + m.show_animation = False + m.LATERAL_MOVEMENT = LateralMovement.HIGH_SPEED + m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.VELOCITY_KEEPING + m.SIM_LOOP = 5 + m.main() + + +def low_speed_and_velocity_keeping_scenario_test(): + m.show_animation = False + m.LATERAL_MOVEMENT = LateralMovement.LOW_SPEED + m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.VELOCITY_KEEPING + m.SIM_LOOP = 5 + m.main() + + +def low_speed_and_merging_and_stopping_scenario_test(): + m.show_animation = False + m.LATERAL_MOVEMENT = LateralMovement.LOW_SPEED + m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.MERGING_AND_STOPPING + m.SIM_LOOP = 5 + m.main() + + +if __name__ == "__main__": conftest.run_this_test(__file__) From 95eedba44713883d805fb92ff74aabbe1fa4f735 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 24 Jan 2025 13:24:26 +0900 Subject: [PATCH 777/909] Fix lint error (#1124) --- .../arm_obstacle_navigation.py | 4 ++-- .../arm_obstacle_navigation_2.py | 4 ++-- .../dynamic_movement_primitives.py | 13 ++++++------- .../Eta3SplineTrajectory/eta3_spline_trajectory.py | 7 +++---- PathPlanning/RRT/sobol/sobol.py | 12 ++++++------ 5 files changed, 19 insertions(+), 21 deletions(-) diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py index e377897e54..593db42b34 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py @@ -158,7 +158,7 @@ def astar_torus(grid, start_node, goal_node): while parent_map[route[0][0]][route[0][1]] != (): route.insert(0, parent_map[route[0][0]][route[0][1]]) - print("The route found covers %d grid cells." % len(route)) + print(f"The route found covers {len(route)} grid cells.") for i in range(1, len(route)): grid[route[i]] = 6 plt.cla() @@ -212,7 +212,7 @@ def calc_heuristic_map(M, goal_node): return heuristic_map -class NLinkArm(object): +class NLinkArm: """ Class for controlling and plotting a planar arm with an arbitrary number of links. """ diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py index 10f4615c34..5295b7ca3a 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py @@ -189,7 +189,7 @@ def astar_torus(grid, start_node, goal_node): while parent_map[route[0][0]][route[0][1]] != (): route.insert(0, parent_map[route[0][0]][route[0][1]]) - print("The route found covers %d grid cells." % len(route)) + print(f"The route found covers {len(route)} grid cells.") for i in range(1, len(route)): grid[route[i]] = 6 plt.cla() @@ -243,7 +243,7 @@ def calc_heuristic_map(M, goal_node): return heuristic_map -class NLinkArm(object): +class NLinkArm: """ Class for controlling and plotting a planar arm with an arbitrary number of links. """ diff --git a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py index 468d3b9f97..aa34934c0e 100644 --- a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py +++ b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py @@ -18,7 +18,7 @@ import numpy as np -class DMP(object): +class DMP: def __init__(self, training_data, data_period, K=156.25, B=25): """ @@ -215,21 +215,21 @@ def show_DMP_purpose(self): T_vals = np.linspace(T_orig, 2*T_orig, 20) for new_q0_value in q0_vals: - plot_title = "Initial Position = [%s, %s]" % \ - (round(new_q0_value[0], 2), round(new_q0_value[1], 2)) + plot_title = (f"Initial Position = [{round(new_q0_value[0], 2)}," + f" {round(new_q0_value[1], 2)}]") _, path = self.recreate_trajectory(new_q0_value, g_orig, T_orig) self.view_trajectory(path, title=plot_title, demo=True) for new_g_value in g_vals: - plot_title = "Goal Position = [%s, %s]" % \ - (round(new_g_value[0], 2), round(new_g_value[1], 2)) + plot_title = (f"Goal Position = [{round(new_g_value[0], 2)}," + f" {round(new_g_value[1], 2)}]") _, path = self.recreate_trajectory(q0_orig, new_g_value, T_orig) self.view_trajectory(path, title=plot_title, demo=True) for new_T_value in T_vals: - plot_title = "Period = %s [sec]" % round(new_T_value, 2) + plot_title = f"Period = {round(new_T_value, 2)} [sec]" _, path = self.recreate_trajectory(q0_orig, g_orig, new_T_value) self.view_trajectory(path, title=plot_title, demo=True) @@ -257,5 +257,4 @@ def example_DMP(): if __name__ == '__main__': - example_DMP() diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py index d034cb8a32..2dab7a7add 100644 --- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py +++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py @@ -26,8 +26,7 @@ class MaxVelocityNotReached(Exception): def __init__(self, actual_vel, max_vel): - self.message = 'Actual velocity {} does not equal desired max velocity {}!'.format( - actual_vel, max_vel) + self.message = f'Actual velocity {actual_vel} does not equal desired max velocity {max_vel}!' class eta3_trajectory(Eta3Path): @@ -42,7 +41,7 @@ def __init__(self, segments, max_vel, v0=0.0, a0=0.0, max_accel=2.0, max_jerk=5. # ensure that all inputs obey the assumptions of the model assert max_vel > 0 and v0 >= 0 and a0 >= 0 and max_accel > 0 and max_jerk > 0 \ and a0 <= max_accel and v0 <= max_vel - super(eta3_trajectory, self).__init__(segments=segments) + super(__class__, self).__init__(segments=segments) self.total_length = sum([s.segment_length for s in self.segments]) self.max_vel = float(max_vel) self.v0 = float(v0) @@ -166,7 +165,7 @@ def velocity_profile(self): try: assert np.isclose(self.vels[index], 0) except AssertionError as e: - print('The final velocity {} is not zero'.format(self.vels[index])) + print(f'The final velocity {self.vels[index]} is not zero') raise e self.seg_lengths[index] = s_sf diff --git a/PathPlanning/RRT/sobol/sobol.py b/PathPlanning/RRT/sobol/sobol.py index 428ba58a98..cddc9d7c72 100644 --- a/PathPlanning/RRT/sobol/sobol.py +++ b/PathPlanning/RRT/sobol/sobol.py @@ -370,8 +370,8 @@ def i4_sobol(dim_num, seed): if (dim_num < 1 or dim_max < dim_num): print('I4_SOBOL - Fatal error!') print(' The spatial dimension DIM_NUM should satisfy:') - print(' 1 <= DIM_NUM <= %d' % dim_max) - print(' But this input value is DIM_NUM = %d' % dim_num) + print(f' 1 <= DIM_NUM <= {dim_max:d}') + print(f' But this input value is DIM_NUM = {dim_num:d}') return None dim_num_save = dim_num @@ -443,7 +443,7 @@ def i4_sobol(dim_num, seed): # l_var = i4_bit_lo0(seed) - elif (seed <= seed_save): + elif seed <= seed_save: seed_save = 0 lastq = np.zeros(dim_num) @@ -471,8 +471,8 @@ def i4_sobol(dim_num, seed): if maxcol < l_var: print('I4_SOBOL - Fatal error!') print(' Too many calls!') - print(' MAXCOL = %d\n' % maxcol) - print(' L = %d\n' % l_var) + print(f' MAXCOL = {maxcol:d}\n') + print(f' L = {l_var:d}\n') return None @@ -819,7 +819,7 @@ def r8mat_write(filename, m, n, a): with open(filename, 'w') as output: for i in range(0, m): for j in range(0, n): - s = ' %g' % (a[i, j]) + s = f' {a[i, j]:g}' output.write(s) output.write('\n') From 2a489b3b82d3d0a3152cf7641fa137deb116972d Mon Sep 17 00:00:00 2001 From: parmaski <89462537+parmaski@users.noreply.github.com> Date: Fri, 24 Jan 2025 13:28:12 +0900 Subject: [PATCH 778/909] Fix: dead link URL in doc (#1087) * fix dead url links * change link to MPC course * remove dead link --- .github/FUNDING.yml | 2 +- .../arm_obstacle_navigation.py | 2 +- .../arm_obstacle_navigation_2.py | 2 +- PathPlanning/BugPlanning/bug.py | 2 +- .../dynamic_movement_primitives.py | 2 +- .../eta3_spline_trajectory.py | 2 +- .../InformedRRTStar/informed_rrt_star.py | 2 +- .../quintic_polynomials_planner.py | 2 +- PathPlanning/RRT/sobol/sobol.py | 2 +- .../state_lattice_planner.py | 4 ++-- .../wavefront_coverage_path_planner.py | 2 +- PathTracking/cgmres_nmpc/cgmres_nmpc.py | 2 +- README.md | 21 +++++++++---------- SLAM/GraphBasedSLAM/data/README.rst | 2 +- appveyor.yml | 2 +- docs/conf.py | 2 +- docs/getting_started_main.rst | 2 +- docs/how_to_contribute_main.rst | 2 +- docs/make.bat | 2 +- .../particle_filter_localization_main.rst | 2 +- .../rectangle_fitting_main.rst | 4 ++-- .../bezier_path/bezier_path_main.rst | 2 +- .../bugplanner/bugplanner_main.rst | 2 +- .../coverage_path/coverage_path_main.rst | 2 +- .../dubins_path/dubins_path_main.rst | 2 +- ...l_predictive_trajectory_generator_main.rst | 2 +- .../quintic_polynomials_planner_main.rst | 2 +- .../reeds_shepp_path_main.rst | 2 +- docs/modules/path_planning/rrt/rrt_main.rst | 8 +++---- docs/modules/path_planning/rrt/rrt_star.rst | 2 +- .../state_lattice_planner_main.rst | 4 ++-- .../cgmres_nmpc/cgmres_nmpc_main.rst | 2 +- .../lqr_speed_and_steering_control_main.rst | 2 +- ...ictive_speed_and_steering_control_main.rst | 4 ++-- users_comments.md | 16 +++++--------- 35 files changed, 55 insertions(+), 62 deletions(-) diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 24f0926299..0d943696cc 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1,4 +1,4 @@ # These are supported funding model platforms github: AtsushiSakai patreon: myenigma -custom: https://www.paypal.me/myenigmapay/ +custom: https://www.paypal.com/paypalme/myenigmapay/ diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py index 593db42b34..9047c13851 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py @@ -34,7 +34,7 @@ def detect_collision(line_seg, circle): """ Determines whether a line segment (arm link) is in contact with a circle (obstacle). - Credit to: http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html + Credit to: https://web.archive.org/web/20200130224918/http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html Args: line_seg: List of coordinates of line segment endpoints e.g. [[1, 1], [2, 2]] circle: List of circle coordinates and radius e.g. [0, 0, 0.5] is a circle centered diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py index 5295b7ca3a..f5d435082a 100644 --- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py +++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py @@ -66,7 +66,7 @@ def detect_collision(line_seg, circle): """ Determines whether a line segment (arm link) is in contact with a circle (obstacle). - Credit to: http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html + Credit to: https://web.archive.org/web/20200130224918/http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html Args: line_seg: List of coordinates of line segment endpoints e.g. [[1, 1], [2, 2]] circle: List of circle coordinates and radius e.g. [0, 0, 0.5] is a circle centered diff --git a/PathPlanning/BugPlanning/bug.py b/PathPlanning/BugPlanning/bug.py index 62fec7c109..34890cb55a 100644 --- a/PathPlanning/BugPlanning/bug.py +++ b/PathPlanning/BugPlanning/bug.py @@ -1,7 +1,7 @@ """ Bug Planning author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it) -Source: https://sites.google.com/site/ece452bugalgorithms/ +Source: https://web.archive.org/web/20201103052224/https://sites.google.com/site/ece452bugalgorithms/ """ import numpy as np diff --git a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py index aa34934c0e..9ccd18b7c2 100644 --- a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py +++ b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py @@ -9,7 +9,7 @@ More information on Dynamic Movement Primitives available at: https://arxiv.org/abs/2102.03861 -https://www.frontiersin.org/articles/10.3389/fncom.2013.00138/full +https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2013.00138/full """ diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py index 2dab7a7add..a73797dacb 100644 --- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py +++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py @@ -6,7 +6,7 @@ Atsushi Sakai (@Atsushi_twi) Refs: -- https://jwdinius.github.io/blog/2018/eta3traj +- https://jwdinius.github.io/blog/2018/eta3traj/ - [eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots] (https://ieeexplore.ieee.org/document/4339545/) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index c37326e490..0483949c99 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -6,7 +6,7 @@ Reference: Informed RRT*: Optimal Sampling-based Path planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic -https://arxiv.org/pdf/1404.2334.pdf +https://arxiv.org/pdf/1404.2334 """ import sys diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index 8ba11a23d2..fdc181afab 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -6,7 +6,7 @@ Ref: -- [Local Path planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/) +- [Local Path planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/) """ diff --git a/PathPlanning/RRT/sobol/sobol.py b/PathPlanning/RRT/sobol/sobol.py index cddc9d7c72..520d686a1d 100644 --- a/PathPlanning/RRT/sobol/sobol.py +++ b/PathPlanning/RRT/sobol/sobol.py @@ -13,7 +13,7 @@ PYTHON versions by Corrado Chisari Original code is available at - http://people.sc.fsu.edu/~jburkardt/py_src/sobol/sobol.html + https://people.sc.fsu.edu/~jburkardt/py_src/sobol/sobol.html Note: the i4 prefix means that the function takes a numeric argument or returns a number which is interpreted inside the function as a 4 diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py index f4b1461b7a..7f8e725e0a 100644 --- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py +++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py @@ -12,8 +12,8 @@ - State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments -http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.8210&rep=rep1 -&type=pdf +https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf +&doi=e2256b5b24137f89e473f01df288cb3aa72e56a0 """ import sys diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py index 7e93caa8fb..c5a139454b 100644 --- a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py +++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py @@ -4,7 +4,7 @@ author: Todd Tang paper: Planning paths of complete coverage of an unstructured environment by a mobile robot - Zelinsky et.al. -link: http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf +link: https://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf """ import os diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py index bb5699b888..a582c9da81 100644 --- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py +++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py @@ -6,7 +6,7 @@ Ref: Shunichi09/nonlinear_control: Implementing the nonlinear model predictive -control, sliding mode control https://github.com/Shunichi09/nonlinear_control +control, sliding mode control https://github.com/Shunichi09/PythonLinearNonlinearControl """ diff --git a/README.md b/README.md index ee40dfc7eb..ec65342430 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,6 @@ ![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg) ![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg) [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) -[![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics) Python codes for robotics algorithm. @@ -111,7 +110,7 @@ For development: - [pytest-xdist](https://pypi.org/project/pytest-xdist/) (for parallel unit tests) -- [mypy](http://mypy-lang.org/) (for type check) +- [mypy](https://mypy-lang.org/) (for type check) - [sphinx](https://www.sphinx-doc.org/) (for document generation) @@ -328,7 +327,7 @@ The animation shows a robot finding its path and rerouting to avoid obstacles as Refs: -- [D* Lite](http://idm-lab.org/bib/abstracts/papers/aaai02b.pd) +- [D* Lite](http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf) - [Improved Fast Replanning for Robot Navigation in Unknown Terrain](http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf) ### Potential Field algorithm @@ -357,9 +356,9 @@ This code uses the model predictive trajectory generator to solve boundary probl Ref: -- [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328) +- [Optimal rough terrain trajectory generation for wheeled mobile robots](https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328) -- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf) +- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](https://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf) ### Biased polar sampling @@ -403,7 +402,7 @@ Ref: - [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416) -- [Sampling-based Algorithms for Optimal Motion Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf) +- [Sampling-based Algorithms for Optimal Motion Planning](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bddbc99f97173430aa49a0ada53ab5bade5902fa) ### RRT\* with reeds-shepp path @@ -421,7 +420,7 @@ A double integrator motion model is used for LQR local planner. Ref: -- [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](http://lis.csail.mit.edu/pubs/perez-icra12.pdf) +- [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](https://lis.csail.mit.edu/pubs/perez-icra12.pdf) - [MahanFathi/LQR\-RRTstar: LQR\-RRT\* method is used for random motion planning of a simple pendulum in its phase plot](https://github.com/MahanFathi/LQR-RRTstar) @@ -436,7 +435,7 @@ It can calculate a 2D path, velocity, and acceleration profile based on quintic Ref: -- [Local Path Planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/) +- [Local Path Planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/) ## Reeds Shepp planning @@ -523,7 +522,7 @@ Path tracking simulation with LQR speed and steering control. Ref: -- [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](http://ieeexplore.ieee.org/document/5940562/) +- [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](https://ieeexplore.ieee.org/document/5940562/) ## Model predictive speed and steering control @@ -630,7 +629,7 @@ If you or your company would like to support this project, please consider: - [Become a backer or sponsor on Patreon](https://www.patreon.com/myenigma) -- [One-time donation via PayPal](https://www.paypal.me/myenigmapay/) +- [One-time donation via PayPal](https://www.paypal.com/paypalme/myenigmapay/) If you would like to support us in some other way, please contact with creating an issue. @@ -640,7 +639,7 @@ If you would like to support us in some other way, please contact with creating They are providing a free license of their IDEs for this OSS development. -### [1Password](https://github.com/1Password/1password-teams-open-source) +### [1Password](https://github.com/1Password/for-open-source) They are providing a free license of their 1Password team license for this OSS project. diff --git a/SLAM/GraphBasedSLAM/data/README.rst b/SLAM/GraphBasedSLAM/data/README.rst index c875cce73f..15bc5b6c03 100644 --- a/SLAM/GraphBasedSLAM/data/README.rst +++ b/SLAM/GraphBasedSLAM/data/README.rst @@ -3,4 +3,4 @@ Acknowledgments and References Thanks to Luca Larlone for allowing inclusion of the `Intel dataset `_ in this repo. -1. Carlone, L. and Censi, A., 2014. `From angular manifolds to the integer lattice: Guaranteed orientation estimation with application to pose graph optimization `_. IEEE Transactions on Robotics, 30(2), pp.475-492. +1. Carlone, L. and Censi, A., 2014. `From angular manifolds to the integer lattice: Guaranteed orientation estimation with application to pose graph optimization `_. IEEE Transactions on Robotics, 30(2), pp.475-492. diff --git a/appveyor.yml b/appveyor.yml index 4964bab3da..05ad8a2820 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -4,7 +4,7 @@ environment: global: # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the # /E:ON and /V:ON options are not enabled in the batch script intepreter - # See: http://stackoverflow.com/a/13751649/163740 + # See: https://stackoverflow.com/a/13751649/163740 CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd" matrix: diff --git a/docs/conf.py b/docs/conf.py index 46f69cf17b..4e06f3f988 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -3,7 +3,7 @@ # # This file does only contain a selection of the most common options. For a # full list see the documentation: -# http://www.sphinx-doc.org/en/master/config +# https://www.sphinx-doc.org/en/master/config # -- Path setup -------------------------------------------------------------- diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index 88f218545e..004d68227b 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -49,7 +49,7 @@ For development: .. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist .. _`mypy`: https://mypy-lang.org/ .. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html -.. _`ruff`: https://github.com/charliermarsh/ruff +.. _`ruff`: https://github.com/astral-sh/ruff How to use diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst index 48895d6fda..915b2155e4 100644 --- a/docs/how_to_contribute_main.rst +++ b/docs/how_to_contribute_main.rst @@ -159,6 +159,6 @@ Sponsors .. _`JetBrains`: https://www.jetbrains.com/ .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma -.. _`One-time donation via PayPal`: https://www.paypal.me/myenigmapay/ +.. _`One-time donation via PayPal`: https://www.paypal.com/paypalme/myenigmapay/ diff --git a/docs/make.bat b/docs/make.bat index 6aab964dcc..07dcebea41 100644 --- a/docs/make.bat +++ b/docs/make.bat @@ -22,7 +22,7 @@ if errorlevel 9009 ( echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ + echo.https://sphinx-doc.org/ exit /b 1 ) diff --git a/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst index c8652ce8a7..20a9eb58fc 100644 --- a/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst +++ b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst @@ -34,4 +34,4 @@ References: ~~~~~~~~~~~ - `_PROBABILISTIC ROBOTICS: `_ -- `Improving the particle filter in high dimensions using conjugate artificial process noise `_ +- `Improving the particle filter in high dimensions using conjugate artificial process noise `_ diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst index 50a50141b2..b6ced1dc1d 100644 --- a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst +++ b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst @@ -7,7 +7,7 @@ This is an object shape recognition using rectangle fitting. This example code is based on this paper algorithm: -- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University `_ +- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University `_ The algorithm consists of 2 steps as below. @@ -66,4 +66,4 @@ API References ~~~~~~~~~~ -- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University `_ +- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University `_ diff --git a/docs/modules/path_planning/bezier_path/bezier_path_main.rst b/docs/modules/path_planning/bezier_path/bezier_path_main.rst index fbba6a4496..d306a85352 100644 --- a/docs/modules/path_planning/bezier_path/bezier_path_main.rst +++ b/docs/modules/path_planning/bezier_path/bezier_path_main.rst @@ -17,4 +17,4 @@ Ref: - `Continuous Curvature Path Generation Based on Bezier Curves for Autonomous - Vehicles `__ + Vehicles ` diff --git a/docs/modules/path_planning/bugplanner/bugplanner_main.rst b/docs/modules/path_planning/bugplanner/bugplanner_main.rst index 72cc46709f..81c91c0313 100644 --- a/docs/modules/path_planning/bugplanner/bugplanner_main.rst +++ b/docs/modules/path_planning/bugplanner/bugplanner_main.rst @@ -5,4 +5,4 @@ This is a 2D planning with Bug algorithm. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif -- `ECE452 Bug Algorithms `_ +- `ECE452 Bug Algorithms `_ diff --git a/docs/modules/path_planning/coverage_path/coverage_path_main.rst b/docs/modules/path_planning/coverage_path/coverage_path_main.rst index 828342a294..0a688b5ed2 100644 --- a/docs/modules/path_planning/coverage_path/coverage_path_main.rst +++ b/docs/modules/path_planning/coverage_path/coverage_path_main.rst @@ -29,6 +29,6 @@ This is a 2D grid based wavefront coverage path planner simulation: .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif -- `Planning paths of complete coverage of an unstructured environment by a mobile robot `_ +- `Planning paths of complete coverage of an unstructured environment by a mobile robot `_ diff --git a/docs/modules/path_planning/dubins_path/dubins_path_main.rst b/docs/modules/path_planning/dubins_path/dubins_path_main.rst index 516638d83d..12172fb51e 100644 --- a/docs/modules/path_planning/dubins_path/dubins_path_main.rst +++ b/docs/modules/path_planning/dubins_path/dubins_path_main.rst @@ -72,5 +72,5 @@ Reference ~~~~~~~~~~~~~~~~~~~~ - `On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents `__ - `Dubins path - Wikipedia `__ -- `15.3.1 Dubins Curves `__ +- `15.3.1 Dubins Curves `__ - `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths `__ diff --git a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst index 0fc997898e..463363ddcf 100644 --- a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst +++ b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst @@ -19,4 +19,4 @@ Lookup table generation sample Ref: - `Optimal rough terrain trajectory generation for wheeled mobile - robots `__ + robots `__ diff --git a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst index feec345bae..0412b3c9b3 100644 --- a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst +++ b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst @@ -101,6 +101,6 @@ References: ~~~~~~~~~~~ - `Local Path Planning And Motion Control For Agv In - Positioning `__ + Positioning `__ diff --git a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst index 6d456417fe..ff377eb91b 100644 --- a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst +++ b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst @@ -383,7 +383,7 @@ Hence, we have: Ref: - `15.3.2 Reeds-Shepp - Curves `__ + Curves `__ - `optimal paths for a car that goes both forwards and backwards `__ diff --git a/docs/modules/path_planning/rrt/rrt_main.rst b/docs/modules/path_planning/rrt/rrt_main.rst index 5a3a30dcff..e5f351a7ba 100644 --- a/docs/modules/path_planning/rrt/rrt_main.rst +++ b/docs/modules/path_planning/rrt/rrt_main.rst @@ -57,7 +57,7 @@ Ref: - `Informed RRT\*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal - Heuristic `__ + Heuristic `__ .. _batch-informed-rrt*: @@ -90,10 +90,10 @@ PID is used for speed control. Ref: - `Motion Planning in Complex Environments using Closed-loop - Prediction `__ + Prediction `__ - `Real-time Motion Planning with Applications to Autonomous Urban - Driving `__ + Driving `__ - `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction `__ @@ -113,6 +113,6 @@ Ref: - `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension - Heuristics `__ + Heuristics `__ - `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion planning of a simple pendulum in its phase plot `__ \ No newline at end of file diff --git a/docs/modules/path_planning/rrt/rrt_star.rst b/docs/modules/path_planning/rrt/rrt_star.rst index d36eaac70e..6deb6b9172 100644 --- a/docs/modules/path_planning/rrt/rrt_star.rst +++ b/docs/modules/path_planning/rrt/rrt_star.rst @@ -16,6 +16,6 @@ Simulation Ref ^^^ -- `Sampling-based Algorithms for Optimal Motion Planning `__ +- `Sampling-based Algorithms for Optimal Motion Planning `__ - `Incremental Sampling-based Algorithms for Optimal Motion Planning `__ diff --git a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst index 3ef0c7eca2..d5e7ed17d1 100644 --- a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst +++ b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst @@ -25,9 +25,9 @@ Lane sampling Ref: - `Optimal rough terrain trajectory generation for wheeled mobile - robots `__ + robots `__ - `State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex - Environments `__ + Environments `__ diff --git a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst index a1980ba17e..23f1218a94 100644 --- a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst +++ b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst @@ -60,7 +60,7 @@ Ref - `Shunichi09/nonlinear_control: Implementing the nonlinear model predictive control, sliding mode - control `__ + control `__ - `非線形モデル予測制御におけるCGMRES法をpythonで実装する - Qiita `__ diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst index a0b145456c..ded187e972 100644 --- a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst +++ b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst @@ -137,4 +137,4 @@ Simulation results References: ~~~~~~~~~~~ -- `Towards fully autonomous driving: Systems and algorithms `__ +- `Towards fully autonomous driving: Systems and algorithms `__ diff --git a/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst index 59a7166674..de55b545ba 100644 --- a/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst +++ b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst @@ -133,5 +133,5 @@ Reference - `Vehicle Dynamics and Control \| Rajesh Rajamani \| Springer `__ -- `MPC Course Material - MPC Lab @ - UC-Berkeley `__ +- `MPC Book - MPC Lab @ + UC-Berkeley `__ diff --git a/users_comments.md b/users_comments.md index 66e8b4022e..a2f798eac4 100644 --- a/users_comments.md +++ b/users_comments.md @@ -17,13 +17,7 @@ Ref: # Educational users -This is a list of users who are using PythonRobotics for education. - -If you found other users, please make an issue to let me know. - -- [CSCI/ARTI 4530/6530: Introduction to Robotics (Fall 2018), University of Georgia ](http://cobweb.cs.uga.edu/~ramviyas/csci_x530.html) - -- [CIT Modules & Programmes \- COMP9073 \- Automation with Python](https://courses.cit.ie/index.cfm/page/module/moduleId/14416) +If you found users who are using PythonRobotics for education, please make an issue to let me know. # Stargazers location map @@ -386,14 +380,14 @@ Dear Atsushi Sakai,
    Thank you so much for creating PythonRobotics and docume 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236. doi: 10.1109/ICCP.2018.8516589 keywords: {Automobiles;Task analysis;Autonomous vehicles;Path planning;Global Positioning System;Cameras;miniature autonomous vehicle;path planning;navigation;parking assist;lane detection and tracking;traffic sign recognition}, -URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=8516425 +URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=8516425 2. Peggy (Yuchun) Wang and Caitlin Hogan, "Path Planning with Dynamic Obstacle Avoidance for a Jumping-Enabled Robot", AA228/CS238 class report, Department of Computer Science, Stanford University, URL: https://web.stanford.edu/class/aa228/reports/2018/final113.pdf -3. Welburn, E, Hakim Khalili, H, Gupta, A, Watson, S & Carrasco, J 2019, A Navigational System for Quadcopter Remote Inspection of Offshore Substations. in The Fifteenth International Conference on Autonomic and Autonomous Systems 2019. URL:https://www.research.manchester.ac.uk/portal/files/107169964/ICAS19_A_Navigational_System_for_Quadcopter_Remote_Inspection_of_Offshore_Substations.pdf +3. Welburn, E, Hakim Khalili, H, Gupta, A, Watson, S & Carrasco, J 2019, A Navigational System for Quadcopter Remote Inspection of Offshore Substations. in The Fifteenth International Conference on Autonomic and Autonomous Systems 2019. URL:https://research.manchester.ac.uk/portal/files/107169964/ICAS19_A_Navigational_System_for_Quadcopter_Remote_Inspection_of_Offshore_Substations.pdf 4. E. Horváth, C. Hajdu, C. Radu and Á. Ballagi, "Range Sensor-based Occupancy Grid Mapping with Signatures," 2019 20th International Carpathian Control Conference (ICCC), Krakow-Wieliczka, Poland, 2019, pp. 1-5. -URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=8765679 +URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=8765679 5. Josie Hughes, Masaru Shimizu, and Arnoud Visser, "A Review of Robot Rescue Simulation Platforms for Robotics Education" URL: https://2019.robocup.org/downloads/program/HughesEtAl2019.pdf @@ -408,7 +402,7 @@ URL: https://arxiv.org/abs/1910.01557 URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5.pdf 9. Brijen Thananjeyan, et al. "ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions" -URL: https://arxiv.org/pdf/2003.01410.pdf +URL: https://arxiv.org/pdf/2003.01410 # Others From 732db3d40d1dbdcfe60a3a5a02948d306c6a4c88 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 25 Jan 2025 21:06:23 +0900 Subject: [PATCH 779/909] Fix Doc generation warning (#1125) --- docs/getting_started_main.rst | 2 +- .../catmull_rom_spline/catmull_rom_spline_main.rst | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index 004d68227b..256319ab8f 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -40,7 +40,7 @@ For development: - `sphinx`_ (for document generation) - `ruff`_ (for code style check) -.. _`Python 3.11.x`: https://www.python.org/ +.. _`Python 3.12.x`: https://www.python.org/ .. _`NumPy`: https://numpy.org/ .. _`SciPy`: https://scipy.org/ .. _`Matplotlib`: https://matplotlib.org/ diff --git a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst index 4d8d3bdefe..72e558c486 100644 --- a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst +++ b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst @@ -1,5 +1,5 @@ Catmull-Rom Spline Planning ------------------ +---------------------------- .. image:: catmull_rom_path_planning.png @@ -10,7 +10,7 @@ exhibits local control, and maintains 𝐶1 continuity. Catmull-Rom Spline Fundamentals -~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Catmull-Rom splines are a type of cubic spline that passes through a given set of points, known as control points. @@ -24,7 +24,7 @@ Where: * :math:`P_0, P_1, P_2, P_3` are the control points surrounding the parameter :math:`t`. Types of Catmull-Rom Splines -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ There are different types of Catmull-Rom splines based on the choice of the **tau** parameter, which influences how the curve behaves in relation to the control points: @@ -51,7 +51,7 @@ behaves in relation to the control points: Blending Functions -~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~ In Catmull-Rom spline interpolation, blending functions are used to calculate the influence of each control point on the spline at a given parameter :math:`t`. The blending functions ensure that the spline is smooth and passes through the control points while @@ -97,7 +97,7 @@ API References -~~~~~~~~~~ +~~~~~~~~~~~~~~ - `Catmull-Rom Spline - Wikipedia `__ - `Catmull-Rom Splines `__ \ No newline at end of file From bf8f1774b753d3b0915960628386bb220de8f0af Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 25 Jan 2025 23:30:14 +0900 Subject: [PATCH 780/909] Update doc organization (#1126) --- docs/conf.py | 2 +- docs/index_main.rst | 24 +++++----- .../{control => 10_control}/control_main.rst | 0 .../inverted-pendulum.png | Bin .../inverted_pendulum_control_main.rst | 0 .../move_to_a_pose_control_main.rst | 0 .../plot/curvature_plot.png | Bin .../{utils => 11_utils}/plot/plot_main.rst | 0 .../{utils => 11_utils}/utils_main.rst | 0 .../Kalmanfilter_basics_2_5_0.png | Bin .../Kalmanfilter_basics_2_main.rst | 0 .../Kalmanfilter_basics_14_1.png | Bin .../Kalmanfilter_basics_16_0.png | Bin .../Kalmanfilter_basics_19_1.png | Bin 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docs/modules/{arm_navigation => 7_arm_navigation}/n_joint_arm_to_point_control_main.rst (100%) rename docs/modules/{arm_navigation => 7_arm_navigation}/obstacle_avoidance_arm_navigation_main.rst (100%) rename docs/modules/{arm_navigation => 7_arm_navigation}/planar_two_link_ik_main.rst (100%) rename docs/modules/{aerial_navigation => 8_aerial_navigation}/aerial_navigation_main.rst (100%) rename docs/modules/{aerial_navigation => 8_aerial_navigation}/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst (100%) rename docs/modules/{aerial_navigation => 8_aerial_navigation}/rocket_powered_landing/rocket_powered_landing_main.rst (100%) rename docs/modules/{bipedal => 9_bipedal}/bipedal_main.rst (100%) rename docs/modules/{bipedal => 9_bipedal}/bipedal_planner/bipedal_planner_main.rst (100%) delete mode 100644 docs/modules/introduction_main.rst delete mode 100644 docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif diff --git a/docs/conf.py b/docs/conf.py index 4e06f3f988..919fa9ac76 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = 'PythonRobotics' -copyright = '2018-2023, Atsushi Sakai' +copyright = '2018-now, Atsushi Sakai' author = 'Atsushi Sakai' # The short X.Y version diff --git a/docs/index_main.rst b/docs/index_main.rst index 67bd9889f2..08c318ea83 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -34,18 +34,18 @@ See this paper for more details: :caption: Contents getting_started - modules/introduction - modules/localization/localization - modules/mapping/mapping - modules/slam/slam - modules/path_planning/path_planning - modules/path_tracking/path_tracking - modules/arm_navigation/arm_navigation - modules/aerial_navigation/aerial_navigation - modules/bipedal/bipedal - modules/control/control - modules/utils/utils - modules/appendix/appendix + modules/1_introduction/introduction + modules/2_localization/localization + modules/3_mapping/mapping + modules/4_slam/slam + modules/5_path_planning/path_planning + modules/6_path_tracking/path_tracking + modules/7_arm_navigation/arm_navigation + modules/8_aerial_navigation/aerial_navigation + modules/9_bipedal/bipedal + modules/10_control/control + modules/11_utils/utils + modules/12_appendix/appendix how_to_contribute diff --git a/docs/modules/control/control_main.rst b/docs/modules/10_control/control_main.rst similarity index 100% rename from docs/modules/control/control_main.rst rename to docs/modules/10_control/control_main.rst diff --git a/docs/modules/control/inverted_pendulum_control/inverted-pendulum.png b/docs/modules/10_control/inverted_pendulum_control/inverted-pendulum.png similarity index 100% rename from docs/modules/control/inverted_pendulum_control/inverted-pendulum.png rename to docs/modules/10_control/inverted_pendulum_control/inverted-pendulum.png diff --git 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a/docs/modules/appendix/steering_motion_model_main.rst b/docs/modules/12_appendix/steering_motion_model_main.rst similarity index 100% rename from docs/modules/appendix/steering_motion_model_main.rst rename to docs/modules/12_appendix/steering_motion_model_main.rst diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst new file mode 100644 index 0000000000..bdc7dc53e9 --- /dev/null +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -0,0 +1,4 @@ +Definition of Robotics +---------------------- + +TBD diff --git a/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst b/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst new file mode 100644 index 0000000000..835441f85d --- /dev/null +++ b/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst @@ -0,0 +1,7 @@ +Software for Robotics +---------------------- + +Python for Robotics +~~~~~~~~~~~~~~~~~~~~~ + +TBD diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst new file mode 100644 index 0000000000..4c60ab8851 --- /dev/null +++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst @@ -0,0 +1,4 @@ +Technology for Robotics +------------------------- + +TBD diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst new file mode 100644 index 0000000000..4561efd349 --- /dev/null +++ b/docs/modules/1_introduction/introduction_main.rst @@ -0,0 +1,13 @@ +.. _introduction: + +Introduction +============ + +.. toctree:: + :maxdepth: 2 + :caption: Table of Contents + + 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z^%k!`{Dq3^OHn**P;zxD?MMcy4E{@Js8SJ!@ok<0~7TYcVjhBhtJW`QDqmJzgpHP&tKySaq0W)0p$dC3T%# z?!#tPIc#&m;%;wMQdJYWRr{Kr?-nP<`nlt3P-zL5pC|uo69VTtmrxfusbkTSx1BLN zEbnk_w(!c$(Kkt$CAwO$JYw=E_k-ET%%V*KefaC%0`O1c%Fio^S@9CtgUJE zCnRL_Pt~80P&2|K-mSl8b+@KzPsi|P_ORRd`3l{QucXVq~>NEUb zYZt%1qtlSm&`=g{eK+6R&k}U}_q+A|z_)jq@B73dq5lho(*JiQ2_Hj*5{t%nZ&VgG zC1FlRLe|X=-7O}P08n$XzW5F~WSJGNVbft?5&{6z%1SOKOaZk!QB3qCULhH>4&OkA z%kZ~lLw1qe3oj!Bg`a`=*h%o(pnsJmpuKUvgGTB^LAsw^2nmIP`m&Z=V;6(#q{St-LQvWYEkC?F>gC|W9QIcr z<918f)ac4;lU}c{RF?s+F<^Lng*G4}gsjZ2hW+@8zul@H2W={3@%0AI;4Y4{-Ta%I zB;|+2w}p04{4z9FJI2rGT#4&K{bhhJ<>AB(2(Ugb1B*p|cL&+)?sY}S4U;alcAu^u n1bp8i9={4d81rZV00i)b-%LrT_UQcoLPCF_P*NiU0I>QW)2;|X From f1b171d88b00d8579e8cd4b0b1ee7cc42e0c8407 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 26 Jan 2025 22:12:28 +0900 Subject: [PATCH 781/909] Update head doc (#1127) * update head doc * update head doc --- LICENSE | 2 +- docs/getting_started_main.rst | 19 +++++++++++++++++-- docs/index_main.rst | 32 ++++++++++++++++---------------- 3 files changed, 34 insertions(+), 19 deletions(-) diff --git a/LICENSE b/LICENSE index c0c52a4321..1c9b928b54 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ The MIT License (MIT) -Copyright (c) 2016 - 2022 Atsushi Sakai and other contributors: +Copyright (c) 2016 - now Atsushi Sakai and other contributors: https://github.com/AtsushiSakai/PythonRobotics/contributors Permission is hereby granted, free of charge, to any person obtaining a copy diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index 256319ab8f..db71960dcf 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -3,11 +3,15 @@ Getting Started =============== -What is this? -------------- +.. _`What is PythonRobotics?`: + +What is PythonRobotics? +------------------------ This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. +This is developped under `MIT license`_ and on `GitHub`_. + The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. In this project, the algorithms which are practical and widely used in both academia and industry are selected. @@ -16,6 +20,17 @@ Each sample code is written in Python3 and only depends on some standard modules It includes intuitive animations to understand the behavior of the simulation. +.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics +.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE + +Features: + +1. Easy to read for understanding each algorithm's basic idea. + +2. Widely used and practical algorithms are selected. + +3. Minimum dependency. + See this paper for more details: diff --git a/docs/index_main.rst b/docs/index_main.rst index 08c318ea83..8c967ac59e 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -6,32 +6,32 @@ Welcome to PythonRobotics's documentation! ========================================== -Python codes for robotics algorithm. The project is on `GitHub`_. +"PythonRobotics" is a Python code collections and html based text book +(This document) for robotics algorithm, which is developed on `GitHub`_. -This is a Python code collection of robotics algorithms. +See this section (:ref:`What is PythonRobotics?`) for more details of this project. -Features: +This project is `the one of the most popular open-source software (OSS) in +the field of robotics on GitHub`_. +This is `user comments about this project`_, and +this graph shows GitHub star history of this project: -1. Easy to read for understanding each algorithm's basic idea. +.. image:: https://api.star-history.com/svg?repos=AtsushiSakai/PythonRobotics&type=Date + :alt: Star History + :width: 80% + :align: center -2. Widely used and practical algorithms are selected. -3. Minimum dependency. - -See this paper for more details: - -- `[1808.10703] PythonRobotics: a Python code collection of robotics - algorithms`_ (`BibTeX`_) - - -.. _`[1808.10703] PythonRobotics: a Python code collection of robotics algorithms`: https://arxiv.org/abs/1808.10703 -.. _BibTeX: https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib .. _GitHub: https://github.com/AtsushiSakai/PythonRobotics +.. _`user comments about this project`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md +.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE +.. _`the one of the most popular open-source software (OSS) in the field of robotics on GitHub`: https://github.com/topics/robotics +---------------------------------- .. toctree:: :maxdepth: 2 - :caption: Contents + :caption: Table of Contents getting_started modules/1_introduction/introduction From e93ada6cb84ad6be0cb7dd70af22e4defe84b0af Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 27 Jan 2025 22:11:15 +0900 Subject: [PATCH 782/909] update README (#1128) --- README.md | 12 ++++++------ docs/getting_started_main.rst | 2 +- docs/index_main.rst | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index ec65342430..8621a40923 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ ![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg) [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) -Python codes for robotics algorithm. +Python codes and textbook for robotics algorithm. # Table of Contents @@ -73,9 +73,9 @@ Python codes for robotics algorithm. * [1Password](#1password) * [Authors](#authors) -# What is this? +# What is PythonRobotics? -This is a Python code collection of robotics algorithms. +PythonRobotics is a Python code collection and a [textbook](https://atsushisakai.github.io/PythonRobotics/index.html) of robotics algorithms. Features: @@ -90,7 +90,7 @@ See this paper for more details: - [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib)) -# Requirements +# Requirements to run the code For running each sample code: @@ -116,13 +116,13 @@ For development: - [pycodestyle](https://pypi.org/project/pycodestyle/) (for code style check) -# Documentation +# Documentation (Textbook) This README only shows some examples of this project. If you are interested in other examples or mathematical backgrounds of each algorithm, -You can check the full documentation online: [Welcome to PythonRobotics’s documentation\! — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/index.html) +You can check the full documentation (textbook) online: [Welcome to PythonRobotics’s documentation\! — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/index.html) All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs) diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index db71960dcf..86049ac2bf 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -10,7 +10,7 @@ What is PythonRobotics? This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. -This is developped under `MIT license`_ and on `GitHub`_. +This is developed under `MIT license`_ and on `GitHub`_. The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. diff --git a/docs/index_main.rst b/docs/index_main.rst index 8c967ac59e..b9f334dd9b 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -6,7 +6,7 @@ Welcome to PythonRobotics's documentation! ========================================== -"PythonRobotics" is a Python code collections and html based text book +"PythonRobotics" is a Python code collections and textbook (This document) for robotics algorithm, which is developed on `GitHub`_. See this section (:ref:`What is PythonRobotics?`) for more details of this project. From 6744d4b104d9281f0a6c3404860b996f9d53d0a8 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 27 Jan 2025 22:41:52 +0900 Subject: [PATCH 783/909] copy paper contents (#1129) * copy paper contents * copy paper contents --- README.md | 8 +++- docs/getting_started_main.rst | 42 ++++++++++++++----- .../definition_of_robotics_main.rst | 5 ++- .../technology_for_robotics_main.rst | 10 ++++- .../2_localization/localization_main.rst | 1 + docs/modules/3_mapping/mapping_main.rst | 2 + docs/modules/4_slam/slam_main.rst | 2 + .../5_path_planning/path_planning_main.rst | 2 + .../6_path_tracking/path_tracking_main.rst | 2 + 9 files changed, 60 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 8621a40923..cb1816c2b5 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ ![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg) [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) -Python codes and textbook for robotics algorithm. +Python codes and [textbook](https://atsushisakai.github.io/PythonRobotics/index.html) for robotics algorithm. # Table of Contents @@ -85,7 +85,11 @@ Features: 3. Minimum dependency. -See this paper for more details: +See this documentation + +- [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/getting_started.html#what-is-pythonrobotics) + +or this paper for more details: - [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib)) diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index 86049ac2bf..aaf304514d 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -9,27 +9,49 @@ What is PythonRobotics? ------------------------ This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. +These codes are developed under `MIT license`_ and on `GitHub`_. -This is developed under `MIT license`_ and on `GitHub`_. +This project has three main philosophies below: -The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm. +1. Easy to understand each algorithm's basic idea. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In this project, the algorithms which are practical and widely used in both academia and industry are selected. +The goal is for beginners in robotics to understand the basic ideas behind each algorithm. +If the code is not easy to read, it would be difficult to achieve our goal of +allowing beginners to understand the algorithms. -Each sample code is written in Python3 and only depends on some standard modules for readability and ease of use. +Python[12] programming language is adopted in this project. +Python has great libraries for matrix operation, mathematical and scientific operation, +and visualization, which makes code more readable because such operations +don’t need to be re-implemented. +Having the core Python packages allows the user to focus on the algorithms, +rather than the implementations. It includes intuitive animations to understand the behavior of the simulation. -.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics -.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE - -Features: - -1. Easy to read for understanding each algorithm's basic idea. +about documenttion 2. Widely used and practical algorithms are selected. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The second philosophy is that implemented algorithms have to be practical +and widely used in both academia and industry. +We believe learning these algorithms will be useful in many applications. +For example, Kalman filters and particle filter for localization, +grid mapping for mapping, +dynamic programming based approaches and sampling based approaches for path planning, +and optimal control based approach for path tracking. +These algorithms are implemented in this project. 3. Minimum dependency. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Each sample code is written in Python3 and only depends on some standard +modules for readability and ease of use. + + +.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics +.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE See this paper for more details: diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index bdc7dc53e9..2f31834017 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -1,4 +1,7 @@ Definition of Robotics ---------------------- -TBD +In recent years, autonomous navigation technologies have received huge +attention in many fields. +Such fields include, autonomous driving[22], drone flight navigation, +and other transportation systems. diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst index 4c60ab8851..4dd1d1842f 100644 --- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst +++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst @@ -1,4 +1,12 @@ Technology for Robotics ------------------------- -TBD +An autonomous navigation system is a system that can move to a goal over long +periods of time without any external control by an operator. +The system requires a wide range of technologies: +- It needs to know where it is (localization) +- Where it is safe (mapping) +- Where and how to move (path planning) +- How to control its motion (path following). + +The autonomous system would not work correctly if any of these technologies is missing. diff --git a/docs/modules/2_localization/localization_main.rst b/docs/modules/2_localization/localization_main.rst index db6651f6b8..22cbd094da 100644 --- a/docs/modules/2_localization/localization_main.rst +++ b/docs/modules/2_localization/localization_main.rst @@ -2,6 +2,7 @@ Localization ============ +Localization is the ability of a robot to know its position and orientation with sensors such as Global Navigation Satellite System:GNSS etc. In localization, Bayesian filters such as Kalman filters, histogram filter, and particle filter are widely used[31]. Fig.2 shows localization simulations using histogram filter and particle filter. .. toctree:: :maxdepth: 2 diff --git a/docs/modules/3_mapping/mapping_main.rst b/docs/modules/3_mapping/mapping_main.rst index a98acaaf50..28e18984d3 100644 --- a/docs/modules/3_mapping/mapping_main.rst +++ b/docs/modules/3_mapping/mapping_main.rst @@ -2,6 +2,8 @@ Mapping ======= +Mapping is the ability of a robot to understand its surroundings with external sensors such as LIDAR and camera. Robots have to recognize the position and shape of obstacles to avoid them. In mapping, grid mapping and machine learning algorithms are widely used[31][18]. Fig.3 shows mapping simulation results using grid mapping with 2D ray casting and 2D object clustering with k-means algorithm. + .. toctree:: :maxdepth: 2 :caption: Contents diff --git a/docs/modules/4_slam/slam_main.rst b/docs/modules/4_slam/slam_main.rst index 86befa6e35..dec04f253a 100644 --- a/docs/modules/4_slam/slam_main.rst +++ b/docs/modules/4_slam/slam_main.rst @@ -4,6 +4,8 @@ SLAM ==== Simultaneous Localization and Mapping(SLAM) examples +Simultaneous Localization and Mapping (SLAM) is an ability to estimate the pose of a robot and the map of the environment at the same time. The SLAM problem is hard to +solve, because a map is needed for localization and localization is needed for mapping. In this way, SLAM is often said to be similar to a ‘chicken-and-egg’ problem. Popular SLAM solution methods include the extended Kalman filter, particle filter, and Fast SLAM algorithm[31]. Fig.4 shows SLAM simulation results using extended Kalman filter and results using FastSLAM2.0[31]. .. toctree:: :maxdepth: 2 diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst index 30802fd7a6..4960330b3e 100644 --- a/docs/modules/5_path_planning/path_planning_main.rst +++ b/docs/modules/5_path_planning/path_planning_main.rst @@ -3,6 +3,8 @@ Path Planning ============= +Path planning is the ability of a robot to search feasible and efficient path to the goal. The path has to satisfy some constraints based on the robot’s motion model and obstacle positions, and optimize some objective functions such as time to goal and distance to obstacle. In path planning, dynamic programming based approaches and sampling based approaches are widely used[22]. Fig.5 shows simulation results of potential field path planning and LQRRRT* path planning[27]. + .. toctree:: :maxdepth: 2 :caption: Contents diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst index 504ba08795..d7a895b562 100644 --- a/docs/modules/6_path_tracking/path_tracking_main.rst +++ b/docs/modules/6_path_tracking/path_tracking_main.rst @@ -3,6 +3,8 @@ Path Tracking ============= +Path tracking is the ability of a robot to follow the reference path generated by a path planner while simultaneously stabilizing the robot. The path tracking controller may need to account for modeling error and other forms of uncertainty. In path tracking, feedback control techniques and optimization based control techniques are widely used[22]. Fig.6 shows simulations using rear wheel feedback steering control and PID speed control, and iterative linear model predictive path tracking control[27]. + .. toctree:: :maxdepth: 2 :caption: Contents From 53f1ba35d5c1b981eca55a91b72c53fb1000263a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Jan 2025 09:19:20 +0900 Subject: [PATCH 784/909] build(deps): bump ruff from 0.9.2 to 0.9.3 in /requirements (#1130) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.2 to 0.9.3. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.2...0.9.3) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 11b91abb1f..e8bed7b6d7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.14.1 # For unit test -ruff == 0.9.2 # For unit test +ruff == 0.9.3 # For unit test From fc3562733814e43134da8a1d7d73e370d44b9f4e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 28 Jan 2025 22:25:53 +0900 Subject: [PATCH 785/909] update getting started (#1131) --- docs/getting_started_main.rst | 122 ++------------------------ docs/how_to_read_textbook_main.rst | 4 + docs/how_to_use_main.rst | 30 +++++++ docs/what_is_python_robotics_main.rst | 100 +++++++++++++++++++++ 4 files changed, 140 insertions(+), 116 deletions(-) create mode 100644 docs/how_to_read_textbook_main.rst create mode 100644 docs/how_to_use_main.rst create mode 100644 docs/what_is_python_robotics_main.rst diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index aaf304514d..07abeec451 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -3,120 +3,10 @@ Getting Started =============== -.. _`What is PythonRobotics?`: - -What is PythonRobotics? ------------------------- - -This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. -These codes are developed under `MIT license`_ and on `GitHub`_. - -This project has three main philosophies below: - -1. Easy to understand each algorithm's basic idea. -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The goal is for beginners in robotics to understand the basic ideas behind each algorithm. -If the code is not easy to read, it would be difficult to achieve our goal of -allowing beginners to understand the algorithms. - -Python[12] programming language is adopted in this project. -Python has great libraries for matrix operation, mathematical and scientific operation, -and visualization, which makes code more readable because such operations -don’t need to be re-implemented. -Having the core Python packages allows the user to focus on the algorithms, -rather than the implementations. - -It includes intuitive animations to understand the behavior of the simulation. - -about documenttion - -2. Widely used and practical algorithms are selected. -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The second philosophy is that implemented algorithms have to be practical -and widely used in both academia and industry. -We believe learning these algorithms will be useful in many applications. -For example, Kalman filters and particle filter for localization, -grid mapping for mapping, -dynamic programming based approaches and sampling based approaches for path planning, -and optimal control based approach for path tracking. -These algorithms are implemented in this project. - -3. Minimum dependency. -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Each sample code is written in Python3 and only depends on some standard -modules for readability and ease of use. - - -.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics -.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE - - -See this paper for more details: - -- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703 - -.. _`Requirements`: - -Requirements -------------- - -- `Python 3.12.x`_ -- `NumPy`_ -- `SciPy`_ -- `Matplotlib`_ -- `cvxpy`_ - -For development: - -- `pytest`_ (for unit tests) -- `pytest-xdist`_ (for parallel unit tests) -- `mypy`_ (for type check) -- `sphinx`_ (for document generation) -- `ruff`_ (for code style check) - -.. _`Python 3.12.x`: https://www.python.org/ -.. _`NumPy`: https://numpy.org/ -.. _`SciPy`: https://scipy.org/ -.. _`Matplotlib`: https://matplotlib.org/ -.. _`cvxpy`: https://www.cvxpy.org/ -.. _`pytest`: https://docs.pytest.org/en/latest/ -.. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist -.. _`mypy`: https://mypy-lang.org/ -.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html -.. _`ruff`: https://github.com/astral-sh/ruff - - -How to use ----------- - -1. Clone this repo and go into dir. - -.. code-block:: - - >$ git clone https://github.com/AtsushiSakai/PythonRobotics.git - - >$ cd PythonRobotics - - -2. Install the required libraries. - -using conda : - -.. code-block:: - - >$ conda env create -f requirements/environment.yml - -using pip : - -.. code-block:: - - >$ pip install -r requirements/requirements.txt - - -3. Execute python script in each directory. - -4. Add star to this repo if you like it 😃. +.. toctree:: + :maxdepth: 2 + :caption: Contents + what_is_python_robotics + how_to_use + how_to_read_textbook diff --git a/docs/how_to_read_textbook_main.rst b/docs/how_to_read_textbook_main.rst new file mode 100644 index 0000000000..3b92970e26 --- /dev/null +++ b/docs/how_to_read_textbook_main.rst @@ -0,0 +1,4 @@ +How To read this textbook +-------------------------- + +TBD \ No newline at end of file diff --git a/docs/how_to_use_main.rst b/docs/how_to_use_main.rst new file mode 100644 index 0000000000..d21b07c1a3 --- /dev/null +++ b/docs/how_to_use_main.rst @@ -0,0 +1,30 @@ +How to use +---------- + +1. Clone this repo and go into dir. + +.. code-block:: + + >$ git clone https://github.com/AtsushiSakai/PythonRobotics.git + + >$ cd PythonRobotics + + +2. Install the required libraries. + +using conda : + +.. code-block:: + + >$ conda env create -f requirements/environment.yml + +using pip : + +.. code-block:: + + >$ pip install -r requirements/requirements.txt + + +3. Execute python script in each directory. + +4. Add star to this repo if you like it 😃. diff --git a/docs/what_is_python_robotics_main.rst b/docs/what_is_python_robotics_main.rst new file mode 100644 index 0000000000..ce6e059dba --- /dev/null +++ b/docs/what_is_python_robotics_main.rst @@ -0,0 +1,100 @@ +.. _`What is PythonRobotics?`: + +What is PythonRobotics? +------------------------ + +This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. +These codes are developed under `MIT license`_ and on `GitHub`_. + +This project has three main philosophies below: + +1. Easy to understand each algorithm's basic idea. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The goal is for beginners in robotics to understand the basic ideas behind each algorithm. + +`Python`_ programming language is adopted in this project to achieve the goal. +Python is a high-level programming language that is easy to read and write. +If the code is not easy to read, it would be difficult to achieve our goal of +allowing beginners to understand the algorithms. +Python has great libraries for matrix operation, mathematical and scientific operation, +and visualization, which makes code more readable because such operations +don’t need to be re-implemented. +Having the core Python packages allows the user to focus on the algorithms, +rather than the implementations. + +PythonRobotics provides not only the code but also intuitive animations that +visually demonstrate the process and behavior of each algorithm over time. +This is an example of an animation gif file that shows the process of the +path planning in a highway scenario for autonomous vehicle. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_velocity_keeping_frenet_path.gif + +This animation is a gif file and is created using the `Matplotlib`_ library. +All animaion gif files are stored in the `PythonRoboticsGifs`_ repository and +all animation movies are uploaded to this `YouTube channel`_. + +PythonRobotics also provides a textbook that explains the basic ideas of each algorithm. + +.. _`Python`: https://www.python.org/ +.. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs +.. _`YouTube channel`: https://youtube.com/playlist?list=PL12URV8HFpCozuz0SDxke6b2ae5UZvIwa&si=AH2fNPPYufPtK20S + + +2. Widely used and practical algorithms are selected. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The second philosophy is that implemented algorithms have to be practical +and widely used in both academia and industry. +We believe learning these algorithms will be useful in many applications. +For example, Kalman filters and particle filter for localization, +grid mapping for mapping, +dynamic programming based approaches and sampling based approaches for path planning, +and optimal control based approach for path tracking. +These algorithms are implemented in this project. + +3. Minimum dependency. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Each sample code is written in Python3 and only depends on some standard +modules for readability and ease of use. + + +.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics +.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE + + +See this paper for more details: + +- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703 + +.. _`Requirements`: + +Requirements +============ + +- `Python 3.12.x`_ +- `NumPy`_ +- `SciPy`_ +- `Matplotlib`_ +- `cvxpy`_ + +For development: + +- `pytest`_ (for unit tests) +- `pytest-xdist`_ (for parallel unit tests) +- `mypy`_ (for type check) +- `sphinx`_ (for document generation) +- `ruff`_ (for code style check) + +.. _`Python 3.12.x`: https://www.python.org/ +.. _`NumPy`: https://numpy.org/ +.. _`SciPy`: https://scipy.org/ +.. _`Matplotlib`: https://matplotlib.org/ +.. _`cvxpy`: https://www.cvxpy.org/ +.. _`pytest`: https://docs.pytest.org/en/latest/ +.. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist +.. _`mypy`: https://mypy-lang.org/ +.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html +.. _`ruff`: https://github.com/astral-sh/ruff + From 44bad786829e713c44259bc18cc7ac24b6c1e24c Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 29 Jan 2025 21:54:18 +0900 Subject: [PATCH 786/909] update getting started (#1132) --- docs/how_to_read_textbook_main.rst | 4 ---- docs/index_main.rst | 3 +-- .../0_getting_started_main.rst} | 7 +++--- .../1_what_is_python_robotics_main.rst} | 23 +++++++++++++------ .../0_getting_started/2_how_to_use_main.rst} | 0 .../3_how_to_contribute_main.rst} | 0 .../4_how_to_read_textbook_main.rst | 6 +++++ 7 files changed, 27 insertions(+), 16 deletions(-) delete mode 100644 docs/how_to_read_textbook_main.rst rename docs/{getting_started_main.rst => modules/0_getting_started/0_getting_started_main.rst} (53%) rename docs/{what_is_python_robotics_main.rst => modules/0_getting_started/1_what_is_python_robotics_main.rst} (79%) rename docs/{how_to_use_main.rst => modules/0_getting_started/2_how_to_use_main.rst} (100%) rename docs/{how_to_contribute_main.rst => modules/0_getting_started/3_how_to_contribute_main.rst} (100%) create mode 100644 docs/modules/0_getting_started/4_how_to_read_textbook_main.rst diff --git a/docs/how_to_read_textbook_main.rst b/docs/how_to_read_textbook_main.rst deleted file mode 100644 index 3b92970e26..0000000000 --- a/docs/how_to_read_textbook_main.rst +++ /dev/null @@ -1,4 +0,0 @@ -How To read this textbook --------------------------- - -TBD \ No newline at end of file diff --git a/docs/index_main.rst b/docs/index_main.rst index b9f334dd9b..c1e8d22d32 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -33,7 +33,7 @@ this graph shows GitHub star history of this project: :maxdepth: 2 :caption: Table of Contents - getting_started + modules/0_getting_started/0_getting_started modules/1_introduction/introduction modules/2_localization/localization modules/3_mapping/mapping @@ -46,7 +46,6 @@ this graph shows GitHub star history of this project: modules/10_control/control modules/11_utils/utils modules/12_appendix/appendix - how_to_contribute Indices and tables diff --git a/docs/getting_started_main.rst b/docs/modules/0_getting_started/0_getting_started_main.rst similarity index 53% rename from docs/getting_started_main.rst rename to docs/modules/0_getting_started/0_getting_started_main.rst index 07abeec451..cfec5b3c73 100644 --- a/docs/getting_started_main.rst +++ b/docs/modules/0_getting_started/0_getting_started_main.rst @@ -7,6 +7,7 @@ Getting Started :maxdepth: 2 :caption: Contents - what_is_python_robotics - how_to_use - how_to_read_textbook + 1_what_is_python_robotics + 2_how_to_use + 3_how_to_contribute + 4_how_to_read_textbook diff --git a/docs/what_is_python_robotics_main.rst b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst similarity index 79% rename from docs/what_is_python_robotics_main.rst rename to docs/modules/0_getting_started/1_what_is_python_robotics_main.rst index ce6e059dba..32b1ad4e5d 100644 --- a/docs/what_is_python_robotics_main.rst +++ b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst @@ -8,7 +8,7 @@ These codes are developed under `MIT license`_ and on `GitHub`_. This project has three main philosophies below: -1. Easy to understand each algorithm's basic idea. +Philosophy 1. Easy to understand each algorithm's basic idea. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The goal is for beginners in robotics to understand the basic ideas behind each algorithm. @@ -31,17 +31,26 @@ path planning in a highway scenario for autonomous vehicle. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_velocity_keeping_frenet_path.gif This animation is a gif file and is created using the `Matplotlib`_ library. -All animaion gif files are stored in the `PythonRoboticsGifs`_ repository and -all animation movies are uploaded to this `YouTube channel`_. +All animation gif files are stored in the `PythonRoboticsGifs`_ repository and +all animation movies are also uploaded to this `YouTube channel`_. PythonRobotics also provides a textbook that explains the basic ideas of each algorithm. +The PythonRobotics textbook allows you to learn fundamental algorithms used in +robotics with minimal mathematical formulas and textual explanations, +based on PythonRobotics’ sample codes and animations. +The contents of this document, like the code, are managed in the PythonRobotics +`GitHub`_ repository and converted into HTML-based online documents using `Sphinx`_, +which is a Python-based documentation builder. +Please refer to this section ":ref:`How to read this textbook`" for information on +how to read this document for learning. + .. _`Python`: https://www.python.org/ .. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs .. _`YouTube channel`: https://youtube.com/playlist?list=PL12URV8HFpCozuz0SDxke6b2ae5UZvIwa&si=AH2fNPPYufPtK20S -2. Widely used and practical algorithms are selected. +Philosophy 2. Widely used and practical algorithms are selected. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The second philosophy is that implemented algorithms have to be practical @@ -53,7 +62,7 @@ dynamic programming based approaches and sampling based approaches for path plan and optimal control based approach for path tracking. These algorithms are implemented in this project. -3. Minimum dependency. +Philosophy 3. Minimum dependency. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Each sample code is written in Python3 and only depends on some standard @@ -84,7 +93,7 @@ For development: - `pytest`_ (for unit tests) - `pytest-xdist`_ (for parallel unit tests) - `mypy`_ (for type check) -- `sphinx`_ (for document generation) +- `Sphinx`_ (for document generation) - `ruff`_ (for code style check) .. _`Python 3.12.x`: https://www.python.org/ @@ -95,6 +104,6 @@ For development: .. _`pytest`: https://docs.pytest.org/en/latest/ .. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist .. _`mypy`: https://mypy-lang.org/ -.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html +.. _`Sphinx`: https://www.sphinx-doc.org/en/master/index.html .. _`ruff`: https://github.com/astral-sh/ruff diff --git a/docs/how_to_use_main.rst b/docs/modules/0_getting_started/2_how_to_use_main.rst similarity index 100% rename from docs/how_to_use_main.rst rename to docs/modules/0_getting_started/2_how_to_use_main.rst diff --git a/docs/how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst similarity index 100% rename from docs/how_to_contribute_main.rst rename to docs/modules/0_getting_started/3_how_to_contribute_main.rst diff --git a/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst new file mode 100644 index 0000000000..fb8db348c6 --- /dev/null +++ b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst @@ -0,0 +1,6 @@ +.. _`How to read this textbook`: + +How to read this textbook +-------------------------- + +TBD \ No newline at end of file From 2b9cc06000a4ae57dce885240d04e38e30cff8e3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 30 Jan 2025 22:25:45 +0900 Subject: [PATCH 787/909] update getting started (#1133) --- .../0_getting_started_main.rst | 2 +- .../1_what_is_python_robotics_main.rst | 49 ++++++++++++------- ...rst => 2_how_to_run_sample_codes_main.rst} | 6 ++- 3 files changed, 37 insertions(+), 20 deletions(-) rename docs/modules/0_getting_started/{2_how_to_use_main.rst => 2_how_to_run_sample_codes_main.rst} (84%) diff --git a/docs/modules/0_getting_started/0_getting_started_main.rst b/docs/modules/0_getting_started/0_getting_started_main.rst index cfec5b3c73..cb2cba4784 100644 --- a/docs/modules/0_getting_started/0_getting_started_main.rst +++ b/docs/modules/0_getting_started/0_getting_started_main.rst @@ -8,6 +8,6 @@ Getting Started :caption: Contents 1_what_is_python_robotics - 2_how_to_use + 2_how_to_run_sample_codes 3_how_to_contribute 4_how_to_read_textbook diff --git a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst index 32b1ad4e5d..8c932b7263 100644 --- a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst +++ b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst @@ -6,6 +6,9 @@ What is PythonRobotics? This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms. These codes are developed under `MIT license`_ and on `GitHub`_. +.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics +.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE + This project has three main philosophies below: Philosophy 1. Easy to understand each algorithm's basic idea. @@ -60,35 +63,32 @@ For example, Kalman filters and particle filter for localization, grid mapping for mapping, dynamic programming based approaches and sampling based approaches for path planning, and optimal control based approach for path tracking. -These algorithms are implemented in this project. +These algorithms are implemented and explained in the textbook in this project. Philosophy 3. Minimum dependency. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Each sample code is written in Python3 and only depends on some standard -modules for readability and ease of use. - - -.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics -.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE - - -See this paper for more details: +Each sample code of PythonRobotics is written in Python3 and only depends on +some standard modules for readability and ease to setup and use. -- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703 .. _`Requirements`: Requirements ============ -- `Python 3.12.x`_ -- `NumPy`_ -- `SciPy`_ -- `Matplotlib`_ -- `cvxpy`_ +PythonRobotics depends only on the following five libraries, +including Python itself, to run each sample code. -For development: +- `Python 3.12.x`_ (for Python runtime) +- `NumPy`_ (for matrix operation) +- `SciPy`_ (for scientific operation) +- `cvxpy`_ (for convex optimization) +- `Matplotlib`_ (for visualization) + +If you only need to run the code, the five libraries mentioned above are sufficient. +However, for code development or creating documentation for the textbook, +the following additional libraries are required. - `pytest`_ (for unit tests) - `pytest-xdist`_ (for parallel unit tests) @@ -107,3 +107,18 @@ For development: .. _`Sphinx`: https://www.sphinx-doc.org/en/master/index.html .. _`ruff`: https://github.com/astral-sh/ruff +For instructions on installing the above libraries, please refer to +this section ":ref:`How to run sample codes`". + +Arxiv paper +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We have published a paper on this project on Arxiv in 2018. + +See this paper for more details about this Project: + +- `PythonRobotics: a Python code collection of robotics algorithms`_ (`BibTeX`_) + +.. _`PythonRobotics: a Python code collection of robotics algorithms`: https://arxiv.org/abs/1808.10703 +.. _`BibTeX`: https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib + diff --git a/docs/modules/0_getting_started/2_how_to_use_main.rst b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst similarity index 84% rename from docs/modules/0_getting_started/2_how_to_use_main.rst rename to docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst index d21b07c1a3..eff54d3426 100644 --- a/docs/modules/0_getting_started/2_how_to_use_main.rst +++ b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst @@ -1,5 +1,7 @@ -How to use ----------- +.. _`How to run sample codes`: + +How to run sample codes +------------------------- 1. Clone this repo and go into dir. From a5fc2d039db9cdf6182d9fc97073b0d6e7388830 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 31 Jan 2025 21:38:06 +0900 Subject: [PATCH 788/909] update getting started (#1134) Co-authored-by: Atsushi Sakai --- .../2_how_to_run_sample_codes_main.rst | 94 ++++++++++++++++++- 1 file changed, 90 insertions(+), 4 deletions(-) diff --git a/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst index eff54d3426..b92fc9bde0 100644 --- a/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst +++ b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst @@ -3,7 +3,21 @@ How to run sample codes ------------------------- -1. Clone this repo and go into dir. +In this chapter, we will explain the setup process for running each sample code +in PythonRobotics and describe the contents of each directory. + +Steps to setup and run sample codes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +1. Install `Python 3.12.x`_ + +Note that older versions of Python3 might work, but we recommend using +the specified version, because the sample codes are only tested with it. + +2. If you prefer to use conda for package management, install +Anaconda or Miniconda to use `conda`_ command. + +3. Clone this repo and go into dir. .. code-block:: @@ -12,7 +26,10 @@ How to run sample codes >$ cd PythonRobotics -2. Install the required libraries. +4. Install the required libraries. + +We have prepared requirements management files for `conda`_ and `pip`_ under +the requirements directory. Using these files makes it easy to install the necessary libraries. using conda : @@ -27,6 +44,75 @@ using pip : >$ pip install -r requirements/requirements.txt -3. Execute python script in each directory. +5. Execute python script in each directory. + +For example, to run the sample code of `Extented Kalman Filter` in the +`localization` directory, execute the following command: + +.. code-block:: + + >$ cd localization/extended_kalman_filter + + >$ python extended_kalman_filter.py + +Then, you can see this animation of the EKF algorithm based localization: + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif + +Please refer to the `Directory structure`_ section for more details on the contents of each directory. + +6. Add star to this repo if you like it 😃. + +.. _`Python 3.12.x`: https://www.python.org/ +.. _`conda`: https://docs.conda.io/projects/conda/en/stable/user-guide/install/index.html +.. _`pip`: https://pip.pypa.io/en/stable/ +.. _`the requirements directory`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/requirements + +.. _`Directory structure`: + +Directory structure +~~~~~~~~~~~~~~~~~~~~ + +The top-level directory structure of PythonRobotics is as follows: + +Sample codes directories: + +- `AerialNavigation`_ : the sample codes of aerial navigation algorithms for drones and rocket landing. +- `ArmNavigation`_ : the sample codes of arm navigation algorithms for robotic arms. +- `Localization`_ : the sample codes of localization algorithms. +- `Bipedal`_ : the sample codes of bipedal walking algorithms for legged robots. +- `Control`_ : the sample codes of control algorithms for robotic systems. +- `Mapping`_ : the sample codes of mapping or obstacle shape recognition algorithms. +- `PathPlanning`_ : the sample codes of path planning algorithms. +- `PathTracking`_ : the sample codes of path tracking algorithms for car like robots. +- `SLAM`_ : the sample codes of SLAM algorithms. + +Other directories: + +- `docs`_ : This directory contains the documentation of PythonRobotics. +- `requirements`_ : This directory contains the requirements management files. +- `tests`_ : This directory contains the unit test files. +- `utils`_ : This directory contains utility functions used in some sample codes in common. +- `.github`_ : This directory contains the GitHub Actions configuration files. +- `.circleci`_ : This directory contains the CircleCI configuration files. + +The structure of this document is the same as that of the sample code +directories mentioned above. +For more details, please refer to the :ref:`How to read this textbook` section. + -4. Add star to this repo if you like it 😃. +.. _`AerialNavigation`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/AerialNavigation +.. _`ArmNavigation`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/ArmNavigation +.. _`Localization`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Localization +.. _`Bipedal`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Bipedal +.. _`Control`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Control +.. _`Mapping`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Mapping +.. _`PathPlanning`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/PathPlanning +.. _`PathTracking`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/PathTracking +.. _`SLAM`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/SLAM +.. _`docs`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs +.. _`requirements`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/requirements +.. _`tests`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/tests +.. _`utils`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/utils +.. _`.github`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/.github +.. _`.circleci`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/.circleci From f225c1845775cf8e1d8c62d04164743c2e9e00fc Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 1 Feb 2025 22:47:52 +0900 Subject: [PATCH 789/909] update getting started (#1135) Co-authored-by: Atsushi Sakai --- .../3_how_to_contribute_main.rst | 45 +++++++++++++++---- 1 file changed, 37 insertions(+), 8 deletions(-) diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst index 915b2155e4..296e4e8658 100644 --- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst +++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst @@ -2,9 +2,18 @@ How To Contribute ================= This document describes how to contribute this project. +There are several ways to contribute to this project as below: + +#. `Adding a new algorithm example`_ +#. `Reporting and fixing a defect`_ +#. `Adding missed documentations for existing examples`_ +#. `Supporting this project`_ + + +.. _`Adding a new algorithm example`: Adding a new algorithm example -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This is a step by step manual to add a new algorithm example. @@ -67,9 +76,16 @@ Please check other documents for details. You can build the doc locally based on `doc README`_. -Note that the `reStructuredText`_ based doc should only focus on the mathematics and the algorithm of the example. +For creating a gif animation, you can use this tool: `matplotrecorder`_. + +The created gif file should be stored in the `PythonRoboticsGifs`_ repository, +so please create a PR to add it and refer to it in the doc. + +Note that the `reStructuredText`_ based doc should only focus on the +mathematics and the algorithm of the example. -Documentations related codes should be in the python script as the header comments of the script or docstrings of each function. +Documentations related codes should be in the python script as the header +comments of the script or docstrings of each function. .. _`submit a pull request`: @@ -92,6 +108,9 @@ After that, I will start the review. Note that this is my hobby project; I appreciate your patience during the review process. +　 + +.. _`Reporting and fixing a defect`: Reporting and fixing a defect ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -115,19 +134,24 @@ in the test code to show the issue was solved. This doc `submit a pull request`_ can be helpful to submit a pull request. +.. _`Adding missed documentations for existing examples`: + Adding missed documentations for existing examples ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Adding the missed documentations for existing examples is also great contribution. If you check the `Python Robotics Docs`_, you can notice that some of the examples -only have a simulation gif or short overview descriptions, +only have a simulation gif or short overview descriptions or just TBD., but no detailed algorithm or mathematical description. +These documents needs to be improved. This doc `how to write doc`_ can be helpful to write documents. +.. _`Supporting this project`: + Supporting this project -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^^^^^ Supporting this project financially is also a great contribution!!. @@ -141,10 +165,12 @@ If you or your company would like to support this project, please consider: If you would like to support us in some other way, please contact with creating an issue. -Sponsors ---------- +Current Major Sponsors +----------------------- + +#. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development. +#. `1Password`_ : They are providing a free license of their 1Password team license for this OSS project. -1. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development. .. _`Python Robotics Docs`: https://atsushisakai.github.io/PythonRobotics @@ -160,5 +186,8 @@ Sponsors .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma .. _`One-time donation via PayPal`: https://www.paypal.com/paypalme/myenigmapay/ +.. _`1Password`: https://github.com/1Password/for-open-source +.. _`matplotrecorder`: https://github.com/AtsushiSakai/matplotrecorder +.. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs From e6f5dfe537d97cf79d55ad2803911a135aeaa1fd Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 2 Feb 2025 21:34:26 +0900 Subject: [PATCH 790/909] update getting started (#1136) Co-authored-by: Atsushi Sakai --- .../0_getting_started/3_how_to_contribute_main.rst | 2 +- .../0_getting_started/4_how_to_read_textbook_main.rst | 10 +++++++++- docs/modules/1_introduction/introduction_main.rst | 2 +- 3 files changed, 11 insertions(+), 3 deletions(-) diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst index 296e4e8658..6e5c1be8ee 100644 --- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst +++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst @@ -1,4 +1,4 @@ -How To Contribute +How to contribute ================= This document describes how to contribute this project. diff --git a/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst index fb8db348c6..1625c838af 100644 --- a/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst +++ b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst @@ -3,4 +3,12 @@ How to read this textbook -------------------------- -TBD \ No newline at end of file +This document is structured to help you learn the fundamental concepts +behind each sample code in PythonRobotics. + +If you already have some knowledge of robotics technologies, you can start +by reading any document that interests you. + +However, if you have no prior knowledge of robotics technologies, it is +recommended that you first read the :ref:`Introduction` section and then proceed +to the documents related to the technical fields that interest you. \ No newline at end of file diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst index 4561efd349..ec1f237545 100644 --- a/docs/modules/1_introduction/introduction_main.rst +++ b/docs/modules/1_introduction/introduction_main.rst @@ -1,4 +1,4 @@ -.. _introduction: +.. _Introduction: Introduction ============ From 70269fe9601b6189914a739c4c0d42b598c3425b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 3 Feb 2025 22:19:10 +0900 Subject: [PATCH 791/909] update getting started (#1138) --- .../python_for_robotics_main.rst} | 5 +---- docs/modules/1_introduction/introduction_main.rst | 7 ++++++- 2 files changed, 7 insertions(+), 5 deletions(-) rename docs/modules/1_introduction/{2_software_for_robotics/software_for_robotics_main.rst => 2_python_for_robotics/python_for_robotics_main.rst} (51%) diff --git a/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst similarity index 51% rename from docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst rename to docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst index 835441f85d..23f31da779 100644 --- a/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst +++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst @@ -1,7 +1,4 @@ -Software for Robotics ----------------------- - Python for Robotics -~~~~~~~~~~~~~~~~~~~~~ +---------------------- TBD diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst index ec1f237545..a7ce55f9bf 100644 --- a/docs/modules/1_introduction/introduction_main.rst +++ b/docs/modules/1_introduction/introduction_main.rst @@ -3,11 +3,16 @@ Introduction ============ +PythonRobotics is composed of two words: "Python" and "Robotics". +Therefore, I will first explain these two topics, Robotics and Python. +After that, I will provide an overview of the robotics technologies +covered in PythonRobotics. + .. toctree:: :maxdepth: 2 :caption: Table of Contents 1_definition_of_robotics/definition_of_robotics - 2_software_for_robotics/software_for_robotics + 2_python_for_robotics/python_for_robotics 3_technology_for_robotics/technology_for_robotics From 5b06435be9e24a73c8ee9d0f0acb1e409a118141 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Feb 2025 07:37:17 +0900 Subject: [PATCH 792/909] build(deps): bump ruff from 0.9.3 to 0.9.4 in /requirements (#1139) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.3 to 0.9.4. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.3...0.9.4) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e8bed7b6d7..9d4e7deb4d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.14.1 # For unit test -ruff == 0.9.3 # For unit test +ruff == 0.9.4 # For unit test From 7b7bd784093e4d46108b667c54261d054894e875 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 5 Feb 2025 13:41:28 +0900 Subject: [PATCH 793/909] update introduction (#1141) --- .../python_for_robotics_main.rst | 46 +++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst index 23f31da779..1ad5316f53 100644 --- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst +++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst @@ -1,4 +1,50 @@ Python for Robotics ---------------------- +Python for general-purpose programming +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +`Python `_ is an general-purpose programming language developed by +`Guido van Rossum `_ in the late 1980s. + +It features as follows: + +#. High-level +#. Interpreted +#. Dynamic type system (also type annotation is supported) +#. Emphasizes code readability +#. Rapid prototyping +#. Batteries included +#. Interoperability for C and Fortran + +Due to these features, Python is the most popular programming language +for educational purposes for programming beginners. + +Python for Scientific Computing +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Python itself was not designed for scientific computing. +However, scientists quickly recognized its strengths. +For example, + +#. High-level and interpreted features enable scientists to focus on their problems without dealing with low-level programming tasks like memory management. +#. Code readability, rapid prototyping, and batteries included features enables scientists who are not professional programmers, to solve their problems easily. +#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful scientific computing libraries. + +To address the more needs of scientific computing, many libraries have been developed. + +- `NumPy `_ is the fundamental package for scientific computing with Python. +- `SciPy `_ is a library that builds on NumPy and provides a large number of functions that operate on NumPy arrays and are useful for different types of scientific and engineering applications. +- `Matplotlib `_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy. +- `Pandas `_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy. +- `SymPy `_ is a Python library for symbolic mathematics. + +And more domain-specific libraries have been developed: +- `Scikit-learn `_ is a free software machine learning library for the Python programming language. +- `Scikit-image `_ is a collection of algorithms for image processing. + +Python for Robotics +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + TBD + From 322fead45a62417c5d0d7dbb07ef17067122da7b Mon Sep 17 00:00:00 2001 From: Aglargil <34728006+Aglargil@users.noreply.github.com> Date: Wed, 5 Feb 2025 20:56:13 +0800 Subject: [PATCH 794/909] feat: add DistanceMap (#1142) * feat: add DistanceMap * feat: add DistanceMap test * feat: add DistanceMap doc * feat: DistanceMap doc update * feat: DistanceMap update --- Mapping/DistanceMap/distance_map.py | 151 ++++++++++++++++++ .../3_mapping/distance_map/distance_map.png | Bin 0 -> 32698 bytes .../distance_map/distance_map_main.rst | 27 ++++ docs/modules/3_mapping/mapping_main.rst | 1 + tests/test_distance_map.py | 118 ++++++++++++++ 5 files changed, 297 insertions(+) create mode 100644 Mapping/DistanceMap/distance_map.py create mode 100644 docs/modules/3_mapping/distance_map/distance_map.png create mode 100644 docs/modules/3_mapping/distance_map/distance_map_main.rst create mode 100644 tests/test_distance_map.py diff --git a/Mapping/DistanceMap/distance_map.py b/Mapping/DistanceMap/distance_map.py new file mode 100644 index 0000000000..54c98c6a75 --- /dev/null +++ b/Mapping/DistanceMap/distance_map.py @@ -0,0 +1,151 @@ +""" +Distance Map + +author: Wang Zheng (@Aglargil) + +Ref: + +- [Distance Map] +(https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf) +""" + +import numpy as np +import matplotlib.pyplot as plt + +INF = 1e20 +ENABLE_PLOT = True + + +def compute_sdf(obstacles): + """ + Compute the signed distance field (SDF) from a boolean field. + + Parameters + ---------- + obstacles : array_like + A 2D boolean array where '1' represents obstacles and '0' represents free space. + + Returns + ------- + array_like + A 2D array representing the signed distance field, where positive values indicate distance + to the nearest obstacle, and negative values indicate distance to the nearest free space. + """ + a = compute_udf(obstacles) + b = compute_udf(obstacles == 0) + return a - b + + +def compute_udf(obstacles): + """ + Compute the unsigned distance field (UDF) from a boolean field. + + Parameters + ---------- + obstacles : array_like + A 2D boolean array where '1' represents obstacles and '0' represents free space. + + Returns + ------- + array_like + A 2D array of distances from the nearest obstacle, with the same dimensions as `bool_field`. + """ + edt = obstacles.copy() + if not np.all(np.isin(edt, [0, 1])): + raise ValueError("Input array should only contain 0 and 1") + edt = np.where(edt == 0, INF, edt) + edt = np.where(edt == 1, 0, edt) + for row in range(len(edt)): + dt(edt[row]) + edt = edt.T + for row in range(len(edt)): + dt(edt[row]) + edt = edt.T + return np.sqrt(edt) + + +def dt(d): + """ + Compute 1D distance transform under the squared Euclidean distance + + Parameters + ---------- + d : array_like + Input array containing the distances. + + Returns: + -------- + d : array_like + The transformed array with computed distances. + """ + v = np.zeros(len(d) + 1) + z = np.zeros(len(d) + 1) + k = 0 + v[0] = 0 + z[0] = -INF + z[1] = INF + for q in range(1, len(d)): + s = ((d[q] + q * q) - (d[int(v[k])] + v[k] * v[k])) / (2 * q - 2 * v[k]) + while s <= z[k]: + k = k - 1 + s = ((d[q] + q * q) - (d[int(v[k])] + v[k] * v[k])) / (2 * q - 2 * v[k]) + k = k + 1 + v[k] = q + z[k] = s + z[k + 1] = INF + k = 0 + for q in range(len(d)): + while z[k + 1] < q: + k = k + 1 + dx = q - v[k] + d[q] = dx * dx + d[int(v[k])] + + +def main(): + obstacles = np.array( + [ + [1, 0, 0, 0, 0], + [0, 1, 1, 1, 0], + [0, 1, 1, 1, 0], + [0, 0, 1, 1, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 1, 0, 0], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + ] + ) + + # Compute the signed distance field + sdf = compute_sdf(obstacles) + udf = compute_udf(obstacles) + + if ENABLE_PLOT: + _, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5)) + + obstacles_plot = ax1.imshow(obstacles, cmap="binary") + ax1.set_title("Obstacles") + ax1.set_xlabel("x") + ax1.set_ylabel("y") + plt.colorbar(obstacles_plot, ax=ax1) + + udf_plot = ax2.imshow(udf, cmap="viridis") + ax2.set_title("Unsigned Distance Field") + ax2.set_xlabel("x") + ax2.set_ylabel("y") + plt.colorbar(udf_plot, ax=ax2) + + sdf_plot = ax3.imshow(sdf, cmap="RdBu") + ax3.set_title("Signed Distance Field") + ax3.set_xlabel("x") + ax3.set_ylabel("y") + plt.colorbar(sdf_plot, ax=ax3) + + plt.tight_layout() + plt.show() + + +if __name__ == "__main__": + main() diff --git a/docs/modules/3_mapping/distance_map/distance_map.png b/docs/modules/3_mapping/distance_map/distance_map.png new file mode 100644 index 0000000000000000000000000000000000000000..2d89252a70358ad1b60025d7bb3d9a217a2cefcc GIT binary patch literal 32698 zcmbrm1yq&m_C35Yjx81jAO;wKf*_zYqJoHoq>?Hk-3=0U5sD&82#R!x(rJRC(ug1_ z0s_*pX}pF~IQnuGgV3br1&E)};?wTvS? zdXb_A+vZ0VUp1Cr^EJfF3q``7P}NxWSD>*qy> 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zAXB***F&|1dmJVm!Sz;&1o%Xn`Qsc4f6JfDTbw>MgHs(mQcvozu8qizg(~*kBr92qhGAE8@_|aCrVO91R3CUW7S-UoA2~DgACu9zcjel4?m^;uBbG z7cn}jMOX>S{6`&joogGIFFsP6L;0lsHvjhjHsd>?5R(tD!fK9|24?!9p#~qsCDSzP z*Tv!RZ49D^#5if2Y*yF?cJ(b}$%+vqi3qS@a8a}{+5T}f&1^w;2%sPAZoNl#w}T}8 zRqVzr057o3k#|t0Mkl6@rK7tum7XAz@ysTk!I;6;h?F)DtsYL5OxdyCZ*$+|ztsud<&qni*UaCVaxrFfj*=_rPI9 z`Z(biQ1(;al>!}T23`R?hvFeDsjAT@5&uHs5`6Rj#a#COVR$F$VRoO-@0C9`M-a?m hO~OY>xBs)BLaGTf+b>>o`Ia8lKzHk=)Q!8p{~vM(KKB3s literal 0 HcmV?d00001 diff --git a/docs/modules/3_mapping/distance_map/distance_map_main.rst b/docs/modules/3_mapping/distance_map/distance_map_main.rst new file mode 100644 index 0000000000..45273cd3bb --- /dev/null +++ b/docs/modules/3_mapping/distance_map/distance_map_main.rst @@ -0,0 +1,27 @@ +Distance Map +------------ + +This is an implementation of the Distance Map algorithm for path planning. + +The Distance Map algorithm computes the unsigned distance field (UDF) and signed distance field (SDF) from a boolean field representing obstacles. + +The UDF gives the distance from each point to the nearest obstacle. The SDF gives positive distances for points outside obstacles and negative distances for points inside obstacles. + +Example +~~~~~~~ + +The algorithm is demonstrated on a simple 2D grid with obstacles: + +.. image:: distance_map.png + +API +~~~ + +.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_sdf + +.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_udf + +References +~~~~~~~~~~ + +- `Distance Transforms of Sampled Functions `_ paper by Pedro F. Felzenszwalb and Daniel P. Huttenlocher. \ No newline at end of file diff --git a/docs/modules/3_mapping/mapping_main.rst b/docs/modules/3_mapping/mapping_main.rst index 28e18984d3..825b08d3ec 100644 --- a/docs/modules/3_mapping/mapping_main.rst +++ b/docs/modules/3_mapping/mapping_main.rst @@ -17,3 +17,4 @@ Mapping is the ability of a robot to understand its surroundings with external s circle_fitting/circle_fitting rectangle_fitting/rectangle_fitting normal_vector_estimation/normal_vector_estimation + distance_map/distance_map diff --git a/tests/test_distance_map.py b/tests/test_distance_map.py new file mode 100644 index 0000000000..df6e394e2c --- /dev/null +++ b/tests/test_distance_map.py @@ -0,0 +1,118 @@ +import conftest # noqa +import numpy as np +from Mapping.DistanceMap import distance_map as m + + +def test_compute_sdf(): + """Test the computation of Signed Distance Field (SDF)""" + # Create a simple obstacle map for testing + obstacles = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) + + sdf = m.compute_sdf(obstacles) + + # Verify basic properties of SDF + assert sdf.shape == obstacles.shape, "SDF should have the same shape as input map" + assert np.all(np.isfinite(sdf)), "SDF should not contain infinite values" + + # Verify SDF value is negative at obstacle position + assert sdf[1, 1] < 0, "SDF value should be negative at obstacle position" + + # Verify SDF value is positive in free space + assert sdf[0, 0] > 0, "SDF value should be positive in free space" + + +def test_compute_udf(): + """Test the computation of Unsigned Distance Field (UDF)""" + # Create obstacle map for testing + obstacles = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) + + udf = m.compute_udf(obstacles) + + # Verify basic properties of UDF + assert udf.shape == obstacles.shape, "UDF should have the same shape as input map" + assert np.all(np.isfinite(udf)), "UDF should not contain infinite values" + assert np.all(udf >= 0), "All UDF values should be non-negative" + + # Verify UDF value is 0 at obstacle position + assert np.abs(udf[1, 1]) < 1e-10, "UDF value should be 0 at obstacle position" + + # Verify UDF value is 1 for adjacent cells + assert np.abs(udf[0, 1] - 1.0) < 1e-10, ( + "UDF value should be 1 for cells adjacent to obstacle" + ) + assert np.abs(udf[1, 0] - 1.0) < 1e-10, ( + "UDF value should be 1 for cells adjacent to obstacle" + ) + assert np.abs(udf[1, 2] - 1.0) < 1e-10, ( + "UDF value should be 1 for cells adjacent to obstacle" + ) + assert np.abs(udf[2, 1] - 1.0) < 1e-10, ( + "UDF value should be 1 for cells adjacent to obstacle" + ) + + +def test_dt(): + """Test the computation of 1D distance transform""" + # Create test data + d = np.array([m.INF, 0, m.INF]) + m.dt(d) + + # Verify distance transform results + assert np.all(np.isfinite(d)), ( + "Distance transform result should not contain infinite values" + ) + assert d[1] == 0, "Distance at obstacle position should be 0" + assert d[0] == 1, "Distance at adjacent position should be 1" + assert d[2] == 1, "Distance at adjacent position should be 1" + + +def test_compute_sdf_empty(): + """Test SDF computation with empty map""" + # Test with empty map (no obstacles) + empty_map = np.zeros((5, 5)) + sdf = m.compute_sdf(empty_map) + + assert np.all(sdf > 0), "All SDF values should be positive for empty map" + assert sdf.shape == empty_map.shape, "Output shape should match input shape" + + +def test_compute_sdf_full(): + """Test SDF computation with fully occupied map""" + # Test with fully occupied map + full_map = np.ones((5, 5)) + sdf = m.compute_sdf(full_map) + + assert np.all(sdf < 0), "All SDF values should be negative for fully occupied map" + assert sdf.shape == full_map.shape, "Output shape should match input shape" + + +def test_compute_udf_invalid_input(): + """Test UDF computation with invalid input values""" + # Test with invalid values (not 0 or 1) + invalid_map = np.array([[0, 2, 0], [0, -1, 0], [0, 0.5, 0]]) + + try: + m.compute_udf(invalid_map) + assert False, "Should raise ValueError for invalid input values" + except ValueError: + pass + + +def test_compute_udf_empty(): + """Test UDF computation with empty map""" + # Test with empty map + empty_map = np.zeros((5, 5)) + udf = m.compute_udf(empty_map) + + assert np.all(udf > 0), "All UDF values should be positive for empty map" + assert np.all(np.isfinite(udf)), "UDF should not contain infinite values" + + +def test_main(): + """Test the execution of main function""" + m.ENABLE_PLOT = False + m.main() + + +if __name__ == "__main__": + conftest.run_this_test(__file__) From 2234abf63d07e5496e37c1b57a1b927c303272a8 Mon Sep 17 00:00:00 2001 From: Aglargil <34728006+Aglargil@users.noreply.github.com> Date: Thu, 6 Feb 2025 12:05:20 +0800 Subject: [PATCH 795/909] fix: DistanceMap doc autofunction (#1143) --- docs/modules/3_mapping/distance_map/distance_map_main.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/modules/3_mapping/distance_map/distance_map_main.rst b/docs/modules/3_mapping/distance_map/distance_map_main.rst index 45273cd3bb..0ef9e3022f 100644 --- a/docs/modules/3_mapping/distance_map/distance_map_main.rst +++ b/docs/modules/3_mapping/distance_map/distance_map_main.rst @@ -17,9 +17,9 @@ The algorithm is demonstrated on a simple 2D grid with obstacles: API ~~~ -.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_sdf +.. autofunction:: Mapping.DistanceMap.distance_map.compute_sdf -.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_udf +.. autofunction:: Mapping.DistanceMap.distance_map.compute_udf References ~~~~~~~~~~ From 0676dfd67e099198c34c485e176b077ad6fa7374 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 6 Feb 2025 15:16:17 +0900 Subject: [PATCH 796/909] update introduction (#1144) --- .../python_for_robotics_main.rst | 57 +++++++++++++++++-- 1 file changed, 51 insertions(+), 6 deletions(-) diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst index 1ad5316f53..2f89f0c7b5 100644 --- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst +++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst @@ -1,11 +1,13 @@ Python for Robotics ---------------------- +This section explains the Python itself and features for Robotics. + Python for general-purpose programming ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ `Python `_ is an general-purpose programming language developed by -`Guido van Rossum `_ in the late 1980s. +`Guido van Rossum `_ from the late 1980s. It features as follows: @@ -17,7 +19,7 @@ It features as follows: #. Batteries included #. Interoperability for C and Fortran -Due to these features, Python is the most popular programming language +Due to these features, Python is one of the most popular programming language for educational purposes for programming beginners. Python for Scientific Computing @@ -29,9 +31,9 @@ For example, #. High-level and interpreted features enable scientists to focus on their problems without dealing with low-level programming tasks like memory management. #. Code readability, rapid prototyping, and batteries included features enables scientists who are not professional programmers, to solve their problems easily. -#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful scientific computing libraries. +#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful and optimized scientific computing libraries. -To address the more needs of scientific computing, many libraries have been developed. +To address the more needs of scientific computing, many fundamental scientific computation libraries have been developed based on the upper features. - `NumPy `_ is the fundamental package for scientific computing with Python. - `SciPy `_ is a library that builds on NumPy and provides a large number of functions that operate on NumPy arrays and are useful for different types of scientific and engineering applications. @@ -39,12 +41,55 @@ To address the more needs of scientific computing, many libraries have been deve - `Pandas `_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy. - `SymPy `_ is a Python library for symbolic mathematics. -And more domain-specific libraries have been developed: +Also, more domain-specific libraries have been developed based on these fundamental libraries: + - `Scikit-learn `_ is a free software machine learning library for the Python programming language. - `Scikit-image `_ is a collection of algorithms for image processing. +- `Networkx `_ is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. +- `SunPy `_ is a community-developed free and open-source software package for solar physics. +- `Astropy `_ is a community-developed free and open-source software package for astronomy. + +Currently, Python is one of the most popular programming languages for scientific computing. Python for Robotics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -TBD +Scientific computation routine are very important for robotics. +For example, matrix operation, optimization, and visualization are fundamental for robotics. + +Python has become an increasingly popular language in robotics due to its versatility, readability, and extensive libraries. Here's a breakdown of why Python is a great choice for robotics development: + +Advantages of Python for Robotics: + +Simplicity and Readability: Python's syntax is clear and concise, making it easier to learn and write code. This is crucial in robotics where complex algorithms and control logic are involved. +Extensive Libraries: Python boasts a rich collection of libraries specifically designed for robotics: +ROS (Robot Operating System): ROS, a widely used framework for robotics development, has strong Python support (rospy). This allows developers to easily create nodes, manage communication between different parts of a robot system, and utilize various ROS tools. +OpenCV: This powerful library provides tools for computer vision tasks like image processing, object detection, and motion tracking, essential for robots that perceive and interact with their environment. +NumPy and SciPy: These libraries offer efficient numerical computation and scientific tools, enabling developers to implement complex mathematical models and control algorithms. +Scikit-learn: This library provides machine learning algorithms, which are increasingly important in robotics for tasks like perception, planning, and control. +Cross-Platform Compatibility: Python code can run on various operating systems (Windows, macOS, Linux), providing flexibility in choosing hardware platforms for robotics projects. +Large Community and Support: Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development. +Use Cases of Python in Robotics: + +Robot Control: Python can be used to write control algorithms for robot manipulators, mobile robots, and other robotic systems. +Perception: Python, combined with libraries like OpenCV, enables robots to process sensor data (camera images, lidar data) to understand their surroundings. +Path Planning: Python algorithms can be used to plan collision-free paths for robots to navigate in complex environments. +Machine Learning: Python libraries like Scikit-learn empower robots to learn from data and improve their performance in tasks like object recognition and manipulation. +Simulation: Python can be used to create simulated environments for testing and developing robot algorithms before deploying them on real hardware. +Examples of Python in Robotics: + +Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning. +Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation. +Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments. +Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility. +Getting Started with Python in Robotics: + +Learn Python Basics: Familiarize yourself with Python syntax, data structures, and programming concepts. +Explore Robotics Libraries: Dive into libraries like ROS, OpenCV, and others relevant to your robotics interests. +Practice with Projects: Start with small projects to gain hands-on experience, gradually increasing complexity. +Join the Community: Engage with the robotics community through online forums, workshops, and conferences to learn from others and share your knowledge. +In conclusion, Python's simplicity, extensive libraries, and strong community support make it an ideal language for robotics development. Whether you're a beginner or an experienced programmer, Python offers the tools and resources you need to build innovative and capable robots. + +Python is used for this `PythonRobotics` project because of the above features +to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`. From 9936f344635e146b9a2ee402ab1672b4e7216d6e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 7 Feb 2025 13:32:11 +0900 Subject: [PATCH 797/909] update introduction (#1145) --- .../definition_of_robotics_main.rst | 7 +++ .../python_for_robotics_main.rst | 63 +++++++++---------- 2 files changed, 37 insertions(+), 33 deletions(-) diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index 2f31834017..fd151e3f20 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -5,3 +5,10 @@ In recent years, autonomous navigation technologies have received huge attention in many fields. Such fields include, autonomous driving[22], drone flight navigation, and other transportation systems. + +Examples of Python in Robotics: + +Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning. +Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation. +Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments. +Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility. \ No newline at end of file diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst index 2f89f0c7b5..90edd5dc0c 100644 --- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst +++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst @@ -40,55 +40,52 @@ To address the more needs of scientific computing, many fundamental scientific c - `Matplotlib `_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy. - `Pandas `_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy. - `SymPy `_ is a Python library for symbolic mathematics. +- `CVXPy `_ is a Python-embedded modeling language for convex optimization problems. Also, more domain-specific libraries have been developed based on these fundamental libraries: - `Scikit-learn `_ is a free software machine learning library for the Python programming language. - `Scikit-image `_ is a collection of algorithms for image processing. -- `Networkx `_ is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. +- `Networkx `_ is a package for the creation, manipulation for complex networks. - `SunPy `_ is a community-developed free and open-source software package for solar physics. - `Astropy `_ is a community-developed free and open-source software package for astronomy. Currently, Python is one of the most popular programming languages for scientific computing. Python for Robotics -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^ -Scientific computation routine are very important for robotics. -For example, matrix operation, optimization, and visualization are fundamental for robotics. +Python has become an increasingly popular language in robotics. -Python has become an increasingly popular language in robotics due to its versatility, readability, and extensive libraries. Here's a breakdown of why Python is a great choice for robotics development: +These are advantages of Python for Robotics: -Advantages of Python for Robotics: +Simplicity and Readability +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Python's syntax is clear and concise, making it easier to learn and write code. +This is crucial in robotics where complex algorithms and control logic are involved. -Simplicity and Readability: Python's syntax is clear and concise, making it easier to learn and write code. This is crucial in robotics where complex algorithms and control logic are involved. -Extensive Libraries: Python boasts a rich collection of libraries specifically designed for robotics: + +Extensive libraries for scientific computation. +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Scientific computation routine are fundamental for robotics. +For example: + +- Matrix operation is needed for rigid body transformation, state estimation, and model based control. +- Optimization is needed for optimization based SLAM, optimal path planning, and optimal control. +- Visualization is needed for robot teleoperation, debugging, and simulation. + +ROS supports Python +~~~~~~~~~~~~~~~~~~~~~~~~~~~ ROS (Robot Operating System): ROS, a widely used framework for robotics development, has strong Python support (rospy). This allows developers to easily create nodes, manage communication between different parts of a robot system, and utilize various ROS tools. -OpenCV: This powerful library provides tools for computer vision tasks like image processing, object detection, and motion tracking, essential for robots that perceive and interact with their environment. -NumPy and SciPy: These libraries offer efficient numerical computation and scientific tools, enabling developers to implement complex mathematical models and control algorithms. -Scikit-learn: This library provides machine learning algorithms, which are increasingly important in robotics for tasks like perception, planning, and control. -Cross-Platform Compatibility: Python code can run on various operating systems (Windows, macOS, Linux), providing flexibility in choosing hardware platforms for robotics projects. -Large Community and Support: Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development. -Use Cases of Python in Robotics: - -Robot Control: Python can be used to write control algorithms for robot manipulators, mobile robots, and other robotic systems. -Perception: Python, combined with libraries like OpenCV, enables robots to process sensor data (camera images, lidar data) to understand their surroundings. -Path Planning: Python algorithms can be used to plan collision-free paths for robots to navigate in complex environments. -Machine Learning: Python libraries like Scikit-learn empower robots to learn from data and improve their performance in tasks like object recognition and manipulation. -Simulation: Python can be used to create simulated environments for testing and developing robot algorithms before deploying them on real hardware. -Examples of Python in Robotics: - -Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning. -Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation. -Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments. -Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility. -Getting Started with Python in Robotics: - -Learn Python Basics: Familiarize yourself with Python syntax, data structures, and programming concepts. -Explore Robotics Libraries: Dive into libraries like ROS, OpenCV, and others relevant to your robotics interests. -Practice with Projects: Start with small projects to gain hands-on experience, gradually increasing complexity. -Join the Community: Engage with the robotics community through online forums, workshops, and conferences to learn from others and share your knowledge. -In conclusion, Python's simplicity, extensive libraries, and strong community support make it an ideal language for robotics development. Whether you're a beginner or an experienced programmer, Python offers the tools and resources you need to build innovative and capable robots. + +Cross-Platform Compatibility +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Python code can run on various operating systems (Windows, macOS, Linux), providing flexibility in choosing hardware platforms for robotics projects. + +Large Community and Support +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development. + Python is used for this `PythonRobotics` project because of the above features to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`. From 15e106839285033e1fa68cbed6f14e219f3af88f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 7 Feb 2025 13:42:56 +0900 Subject: [PATCH 798/909] Update CONTRIBUTING.md --- CONTRIBUTING.md | 24 +++--------------------- 1 file changed, 3 insertions(+), 21 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 91f6dfa822..3bcc499e6a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,23 +1,5 @@ -# Contributing to Python +# Contributing -:+1::tada: First of off, thanks very much for taking the time to contribute! :tada::+1: +:+1::tada: First of all, thank you very much for taking the time to contribute! :tada::+1: -The following is a set of guidelines for contributing to PythonRobotics. - -These are mostly guidelines, not rules. - -Use your best judgment, and feel free to propose changes to this document in a pull request. - -# Before contributing - -## Taking a look at the paper. - -Please check this paper to understand the philosophy of this project. - -- [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib)) - -## Check your Python version. - -We only accept a PR for Python 3.8.x or higher. - -We will not accept a PR for Python 2.x. +Please check this document for contribution: [How to contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/3_how_to_contribute.html) From a8f3388bbe80e41655acedc44c2cbb86d011fb48 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 9 Feb 2025 21:00:02 +0900 Subject: [PATCH 799/909] update introduction (#1146) --- .../3_how_to_contribute_main.rst | 37 ++++++++++++++----- .../python_for_robotics_main.rst | 29 ++++++++++++--- 2 files changed, 51 insertions(+), 15 deletions(-) diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst index 6e5c1be8ee..874564cbb8 100644 --- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst +++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst @@ -9,10 +9,30 @@ There are several ways to contribute to this project as below: #. `Adding missed documentations for existing examples`_ #. `Supporting this project`_ +Before contributing +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Please check following items before contributing: + +Understanding this project +--------------------------- + +Please check this :ref:`What is PythonRobotics?` section and this paper +`PythonRobotics: a Python code collection of robotics algorithms`_ +to understand the philosophies of this project. + +.. _`PythonRobotics: a Python code collection of robotics algorithms`: https://arxiv.org/abs/1808.10703 + +Check your Python version. +--------------------------- + +We only accept a PR for Python 3.12.x or higher. + +We will not accept a PR for Python 2.x. .. _`Adding a new algorithm example`: -Adding a new algorithm example +1. Adding a new algorithm example ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This is a step by step manual to add a new algorithm example. @@ -112,8 +132,8 @@ Note that this is my hobby project; I appreciate your patience during the review .. _`Reporting and fixing a defect`: -Reporting and fixing a defect -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +2. Reporting and fixing a defect +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Reporting and fixing a defect is also great contribution. @@ -136,8 +156,8 @@ This doc `submit a pull request`_ can be helpful to submit a pull request. .. _`Adding missed documentations for existing examples`: -Adding missed documentations for existing examples -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3. Adding missed documentations for existing examples +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Adding the missed documentations for existing examples is also great contribution. @@ -150,8 +170,8 @@ This doc `how to write doc`_ can be helpful to write documents. .. _`Supporting this project`: -Supporting this project -^^^^^^^^^^^^^^^^^^^^^^^^ +4. Supporting this project +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Supporting this project financially is also a great contribution!!. @@ -165,8 +185,7 @@ If you or your company would like to support this project, please consider: If you would like to support us in some other way, please contact with creating an issue. -Current Major Sponsors ------------------------ +Current Major Sponsors: #. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development. #. `1Password`_ : They are providing a free license of their 1Password team license for this OSS project. diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst index 90edd5dc0c..65b1705150 100644 --- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst +++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst @@ -1,6 +1,8 @@ Python for Robotics ---------------------- +Python is used for this `PythonRobotics` project because of the above features +to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`. This section explains the Python itself and features for Robotics. Python for general-purpose programming @@ -76,7 +78,27 @@ For example: ROS supports Python ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -ROS (Robot Operating System): ROS, a widely used framework for robotics development, has strong Python support (rospy). This allows developers to easily create nodes, manage communication between different parts of a robot system, and utilize various ROS tools. +`ROS`_ (Robot Operating System) is an open-source and widely used framework for robotics development. +It is designed to help developping complicated robotic applications. +ROS provides essential tools, libraries, and drivers to simplify robot programming and integration. + +Key Features of ROS: + +- Modular Architecture – Uses a node-based system where different components (nodes) communicate via messages. +- Hardware Abstraction – Supports various robots, sensors, and actuators, making development more flexible. +- Powerful Communication System – Uses topics, services, and actions for efficient data exchange between components. +- Rich Ecosystem – Offers many pre-built packages for navigation, perception, and manipulation. +- Multi-language Support – Primarily uses Python and C++, but also supports other languages. +- Simulation & Visualization – Tools like Gazebo (for simulation) and RViz (for visualization) aid in development and testing. +- Scalability & Community Support – Widely used in academia and industry, with a large open-source community. + +ROS has strong Python support (`rospy`_ for ROS1 and `rclpy`_ for ROS2). +This allows developers to easily create nodes, manage communication between +different parts of a robot system, and utilize various ROS tools. + +.. _`ROS`: https://www.ros.org/ +.. _`rospy`: http://wiki.ros.org/rospy +.. _`rclpy`: https://docs.ros.org/en/jazzy/Tutorials/Beginner-Client-Libraries/Writing-A-Simple-Py-Publisher-And-Subscriber.html Cross-Platform Compatibility ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -85,8 +107,3 @@ Python code can run on various operating systems (Windows, macOS, Linux), provid Large Community and Support ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development. - - -Python is used for this `PythonRobotics` project because of the above features -to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`. - From e304f07a997bf40977a004ac2a66f476aa2aa861 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 10 Feb 2025 11:21:19 +0900 Subject: [PATCH 800/909] update introduction (#1147) --- .../python_for_robotics_main.rst | 32 +++++++++++++++++-- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst index 65b1705150..b677d2c59b 100644 --- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst +++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst @@ -1,9 +1,10 @@ Python for Robotics ---------------------- -Python is used for this `PythonRobotics` project because of the above features -to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`. -This section explains the Python itself and features for Robotics. +A programing language, Python is used for this `PythonRobotics` project +to achieve the purposes of this project described in the :ref:`What is PythonRobotics?`. + +This section explains the Python itself and features for science computing Robotics. Python for general-purpose programming ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -107,3 +108,28 @@ Python code can run on various operating systems (Windows, macOS, Linux), provid Large Community and Support ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development. + +Situations which Python is NOT suitable for Robotics +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We explained the advantages of Python for robotics. +However, Python is not always the best choice for robotics development. + +These are situations where Python is NOT suitable for robotics: + +High-speed real-time control +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Python is an interpreted language, which means it is slower than compiled languages like C++. +This can be a disadvantage when real-time control is required, +such as in high-speed motion control or safety-critical systems. + +So, for these applications, we recommend to understand the each algorithm you +needed using this project and implement it in other suitable languages like C++. + +Resource-constrained systems +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Python is a high-level language that requires more memory and processing power +compared to low-level languages. +So, it is difficult to run Python on resource-constrained systems like +microcontrollers or embedded devices. +In such cases, C or C++ is more suitable for these applications. From 610f35ff58e6535efa619f9ed73a113c6ca2c7f7 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Feb 2025 10:42:10 +0900 Subject: [PATCH 801/909] build(deps): bump mypy from 1.14.1 to 1.15.0 in /requirements (#1148) Bumps [mypy](https://github.com/python/mypy) from 1.14.1 to 1.15.0. - [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md) - [Commits](https://github.com/python/mypy/compare/v1.14.1...v1.15.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9d4e7deb4d..178046ac0d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.10.0 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test -mypy == 1.14.1 # For unit test +mypy == 1.15.0 # For unit test ruff == 0.9.4 # For unit test From ba307673013376204ceb5a6def16da0e3a86a15d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Feb 2025 11:35:28 +0900 Subject: [PATCH 802/909] build(deps): bump ruff from 0.9.4 to 0.9.6 in /requirements (#1149) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.4 to 0.9.6. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.4...0.9.6) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 178046ac0d..f5f674d7d2 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.9.4 # For unit test +ruff == 0.9.6 # For unit test From b298609b2832c8029baa98b2d3906abce0263ec4 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 11 Feb 2025 21:15:34 +0900 Subject: [PATCH 803/909] update introduction doc (#1151) --- .../definition_of_robotics_main.rst | 81 ++++++++++++++++--- .../python_for_robotics_main.rst | 2 +- .../technology_for_robotics_main.rst | 9 +++ 3 files changed, 82 insertions(+), 10 deletions(-) diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index fd151e3f20..f6fba646b4 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -1,14 +1,77 @@ Definition of Robotics ---------------------- -In recent years, autonomous navigation technologies have received huge -attention in many fields. -Such fields include, autonomous driving[22], drone flight navigation, -and other transportation systems. +What is Robotics? +^^^^^^^^^^^^^^^^^^ -Examples of Python in Robotics: +Robot is a machine that can perform tasks automatically or semi-autonomously. +Robotics is the study of robots. +The field of robotics has wide areas of technologies such as mechanical engineering, +electrical engineering, computer science, and artificial intelligence (AI), +to create machines that can perform tasks autonomously or semi-autonomously. + +The History of Robots +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +This timeline highlights key milestones in the history of robotics: + +Ancient and Early Concepts (Before 1500s) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The idea of **automated machines** has existed for thousands of years. Ancient civilizations imagined mechanical beings: + +- **Ancient Greece (4th Century BC)** – Greek engineer **Hero of Alexandria** designed early **automata** (self-operating machines) powered by water or air. +- **Chinese and Arabic Automata (9th–13th Century)** – Inventors like **Al-Jazari** created intricate mechanical devices, including water clocks and humanoid robots. + +The Birth of Modern Robotics (1500s–1800s) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **Leonardo da Vinci’s Robot (1495)** – Designed a humanoid knight with mechanical movement. +- **Jacques de Vaucanson’s Automata (1738)** – Created robotic figures like a mechanical duck that could "eat" and "digest." +- **Industrial Revolution (18th–19th Century)** – Machines began replacing human labor in factories, setting the foundation for automation. + +The Rise of Industrial Robots (1900s–1950s) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **The Term “Robot” (1921)** – Czech writer **Karel Čapek** introduced the word *“robot”* in his play *R.U.R. (Rossum’s Universal Robots)*. +- **Early Cybernetics (1940s–1950s)** – Scientists like **Norbert Wiener** developed theories of self-regulating machines, influencing modern robotics. + +The Birth of Modern Robotics (1950s–1980s) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **First Industrial Robot (1961)** – *Unimate*, created by **George Devol and Joseph Engelberger**, was the first programmable robot used in a factory. +- **Rise of AI & Autonomous Robots (1970s–1980s)** – Researchers developed mobile robots like **Shakey** (Stanford, 1966) and AI-based control systems. + +Advanced Robotics and AI Integration (1990s–Present) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **Autonomous Vehicles & Drones** – Self-driving cars and UAVs (unmanned aerial vehicles) became more advanced. +- **Medical Robotics** – Robots like **da Vinci Surgical System** revolutionized healthcare. +- **Personal Robots** – Devices like **Roomba** (vacuum robot) and **Sophia** (AI humanoid) became popular. +- **Collaborative Robots (Cobots)** – Robots started working alongside humans in industries. + +Key Components of Robotics +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Robotics consists of several essential components: + +#. Sensors – Gather information from the environment (e.g., cameras, LiDAR, gyro, accelerometer, wheel encoders). +#. Actuators – Enable movement and interaction with the world (e.g., motors, hydraulic systems). +#. Computers – Process sensor data and make decisions (e.g., micro-controllers, CPUs, GPUs). +#. Power Supply – Provides energy to run the robot (e.g., batteries, solar power). +#. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, machine learning models, localization, mapping, path planning, control). + +This project, PythonRobotics, focuses on the software and algorithms part of robotics. + +Applications of Robots +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Robots come in various forms depending on their purpose: + +#. 🤖 Industrial Robots – Used in manufacturing (e.g., robotic arms in manufacturing factories). +#. 🏠 Service Robots – Assist in daily life (e.g., vacuum robots, delivery robots). +#. 🚗 Autonomous Vehicles – Self-driving cars and drones. +#. 👨‍⚕️ Medical Robots – Assist in surgeries and healthcare. +#. 🚀 Space & Exploration Robots – Used for planetary exploration (e.g., NASA’s Mars rovers). +#. 🐶 Humanoid & Social Robots – Designed to interact with humans (e.g., ASIMO, Sophia). -Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning. -Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation. -Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments. -Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility. \ No newline at end of file diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst index b677d2c59b..c47c122853 100644 --- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst +++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst @@ -4,7 +4,7 @@ Python for Robotics A programing language, Python is used for this `PythonRobotics` project to achieve the purposes of this project described in the :ref:`What is PythonRobotics?`. -This section explains the Python itself and features for science computing Robotics. +This section explains the Python itself and features for science computing and robotics. Python for general-purpose programming ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst index 4dd1d1842f..93dc9e3466 100644 --- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst +++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst @@ -1,6 +1,15 @@ Technology for Robotics ------------------------- + +Autonomous Navigation +^^^^^^^^^^^^^^^^^^^^^^^^ + +In recent years, autonomous navigation technologies have received huge +attention in many fields. +Such fields include, autonomous driving[22], drone flight navigation, +and other transportation systems. + An autonomous navigation system is a system that can move to a goal over long periods of time without any external control by an operator. The system requires a wide range of technologies: From be608f067cbd96f34955b81d3e5be9e22a46f588 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 12 Feb 2025 21:51:29 +0900 Subject: [PATCH 804/909] update introduction doc (#1152) --- .../definition_of_robotics_main.rst | 38 ++++++++++++++----- .../technology_for_robotics_main.rst | 3 ++ 2 files changed, 31 insertions(+), 10 deletions(-) diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index f6fba646b4..63525057fe 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -1,14 +1,24 @@ Definition of Robotics ---------------------- +This section explains the definition, history, key components, and applications of robotics. + What is Robotics? ^^^^^^^^^^^^^^^^^^ Robot is a machine that can perform tasks automatically or semi-autonomously. Robotics is the study of robots. -The field of robotics has wide areas of technologies such as mechanical engineering, -electrical engineering, computer science, and artificial intelligence (AI), -to create machines that can perform tasks autonomously or semi-autonomously. + +The word “robot” comes from the Czech word “robota,” which means “forced labor” or “drudgery.” +It was first used in the 1920 science fiction play `R.U.R.`_ (Rossum’s Universal Robots) +by the Czech writer `Karel Čapek`_. +In the play, robots were artificial workers created to serve humans, but they eventually rebelled. + +Over time, “robot” came to refer to machines or automated systems that can perform tasks, +often with some level of intelligence or autonomy. + +.. _`R.U.R.`: https://thereader.mitpress.mit.edu/origin-word-robot-rur/ +.. _`Karel Čapek`: https://en.wikipedia.org/wiki/Karel_%C4%8Capek The History of Robots ^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -18,22 +28,30 @@ This timeline highlights key milestones in the history of robotics: Ancient and Early Concepts (Before 1500s) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The idea of **automated machines** has existed for thousands of years. Ancient civilizations imagined mechanical beings: +The idea of **automated machines** has existed for thousands of years. +Ancient civilizations imagined mechanical beings: + +- **Ancient Greece (4th Century BC)** – Greek engineer `Hero of Alexandria`_ designed early **automata** (self-operating machines) powered by water or air. +- **Chinese and Arabic Automata (9th–13th Century)** – Inventors like `Ismail Al-Jazari`_ created intricate mechanical devices, including water clocks and automated moving peacocks driven by hydropower. -- **Ancient Greece (4th Century BC)** – Greek engineer **Hero of Alexandria** designed early **automata** (self-operating machines) powered by water or air. -- **Chinese and Arabic Automata (9th–13th Century)** – Inventors like **Al-Jazari** created intricate mechanical devices, including water clocks and humanoid robots. +.. _`Hero of Alexandria`: https://en.wikipedia.org/wiki/Hero_of_Alexandria +.. _`Ismail Al-Jazari`: https://en.wikipedia.org/wiki/Ismail_al-Jazari The Birth of Modern Robotics (1500s–1800s) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -- **Leonardo da Vinci’s Robot (1495)** – Designed a humanoid knight with mechanical movement. -- **Jacques de Vaucanson’s Automata (1738)** – Created robotic figures like a mechanical duck that could "eat" and "digest." -- **Industrial Revolution (18th–19th Century)** – Machines began replacing human labor in factories, setting the foundation for automation. +- `Leonardo da Vinci’s Robot`_ (1495) – Designed a humanoid knight with mechanical movement. +- `Jacques de Vaucanson’s Digesting Duck`_ (1738) – Created robotic figures like a mechanical duck that could "eat" and "digest." +- `Industrial Revolution`_ (18th–19th Century) – Machines began replacing human labor in factories, setting the foundation for automation. + +.. _`Leonardo da Vinci’s Robot`: https://en.wikipedia.org/wiki/Leonardo%27s_robot +.. _`Jacques de Vaucanson’s Digesting Duck`: https://en.wikipedia.org/wiki/Jacques_de_Vaucanson +.. _`Industrial Revolution`: https://en.wikipedia.org/wiki/Industrial_Revolution The Rise of Industrial Robots (1900s–1950s) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -- **The Term “Robot” (1921)** – Czech writer **Karel Čapek** introduced the word *“robot”* in his play *R.U.R. (Rossum’s Universal Robots)*. +- **The Term “Robot” (1921)** – Czech writer `Karel Čapek`_ introduced the word *“robot”* in his play `R.U.R.`_ (Rossum’s Universal Robots). - **Early Cybernetics (1940s–1950s)** – Scientists like **Norbert Wiener** developed theories of self-regulating machines, influencing modern robotics. The Birth of Modern Robotics (1950s–1980s) diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst index 93dc9e3466..e460059e20 100644 --- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst +++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst @@ -1,6 +1,9 @@ Technology for Robotics ------------------------- +The field of robotics needs wide areas of technologies such as mechanical engineering, +electrical engineering, computer science, and artificial intelligence (AI). + Autonomous Navigation ^^^^^^^^^^^^^^^^^^^^^^^^ From 1ecc154fbaf9bc2342671fe93a305e8f0e3f510f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 13 Feb 2025 17:19:03 +0900 Subject: [PATCH 805/909] update contribution link in README.md to fix invalid link (#1154) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cb1816c2b5..818d7b0d4e 100644 --- a/README.md +++ b/README.md @@ -617,7 +617,7 @@ This is a list of user's comment and references:[users\_comments](https://github Any contribution is welcome!! -Please check this document:[How To Contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html) +Please check this document:[How To Contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/3_how_to_contribute.html) # Citing From 156483000524488d64687a853ac030e8d57c18a6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 13 Feb 2025 17:56:17 +0900 Subject: [PATCH 806/909] update robotics definition document to enhance references and clarity (#1155) --- .../definition_of_robotics_main.rst | 29 ++++++++++++++----- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index 63525057fe..265814e068 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -52,21 +52,36 @@ The Rise of Industrial Robots (1900s–1950s) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - **The Term “Robot” (1921)** – Czech writer `Karel Čapek`_ introduced the word *“robot”* in his play `R.U.R.`_ (Rossum’s Universal Robots). -- **Early Cybernetics (1940s–1950s)** – Scientists like **Norbert Wiener** developed theories of self-regulating machines, influencing modern robotics. +- **Early Cybernetics (1940s–1950s)** – Scientists like `Norbert Wiener`_ developed theories of self-regulating machines, influencing modern robotics (Cybernetics). + +.. _`Norbert Wiener`: https://en.wikipedia.org/wiki/Norbert_Wiener The Birth of Modern Robotics (1950s–1980s) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -- **First Industrial Robot (1961)** – *Unimate*, created by **George Devol and Joseph Engelberger**, was the first programmable robot used in a factory. -- **Rise of AI & Autonomous Robots (1970s–1980s)** – Researchers developed mobile robots like **Shakey** (Stanford, 1966) and AI-based control systems. +- **First Industrial Robot (1961)** – `Unimate`_, created by `George Devol`_ and `Joseph Engelberger`_, was the first programmable robot used in a factory. +- **Rise of AI & Autonomous Robots (1970s–1980s)** – Researchers developed mobile robots like `Shakey`_ (Stanford, 1966) and AI-based control systems. + +.. _`Unimate`: https://en.wikipedia.org/wiki/Unimate +.. _`George Devol`: https://en.wikipedia.org/wiki/George_Devol +.. _`Joseph Engelberger`: https://en.wikipedia.org/wiki/Joseph_Engelberger +.. _`Shakey`: https://en.wikipedia.org/wiki/Shakey_the_robot Advanced Robotics and AI Integration (1990s–Present) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -- **Autonomous Vehicles & Drones** – Self-driving cars and UAVs (unmanned aerial vehicles) became more advanced. -- **Medical Robotics** – Robots like **da Vinci Surgical System** revolutionized healthcare. -- **Personal Robots** – Devices like **Roomba** (vacuum robot) and **Sophia** (AI humanoid) became popular. -- **Collaborative Robots (Cobots)** – Robots started working alongside humans in industries. +- **Autonomous Vehicles** – Self-driving cars for robo taxi like `Waymo`_ and autonomous haulage system in mining like `AHS`_ became more advanced and bisiness-ready. +- **Medical Robotics** – Robots like `da Vinci Surgical System`_ revolutionized healthcare. +- **Personal Robots** – Devices like `Roomba`_ (vacuum robot) and `Sophia`_ (AI humanoid) became popular. +- **Service Robots** - Assistive robots like serving robots in restaurants and hotels like `Bellabot`_. +- **Collaborative Robots (Drones)** – Collaborative robots like UAV (Unmanned Aerial Vehicle) in drone shows and delivery services. + +.. _`Waymo`: https://waymo.com/ +.. _`AHS`: https://www.futurebridge.com/industry/perspectives-industrial-manufacturing/autonomous-haulage-systems-the-future-of-mining-operations/ +.. _`da Vinci Surgical System`: https://en.wikipedia.org/wiki/Da_Vinci_Surgical_System +.. _`Roomba`: https://en.wikipedia.org/wiki/Roomba +.. _`Sophia`: https://en.wikipedia.org/wiki/Sophia_(robot) +.. _`Bellabot`: https://www.pudurobotics.com/en Key Components of Robotics ^^^^^^^^^^^^^^^^^^^^^^^^^^^ From 77ad3344b5e9df26ca370725d3921dd354c755b1 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 14 Feb 2025 21:20:35 +0900 Subject: [PATCH 807/909] update robotics definition document to improve clarity and add references (#1157) --- .../definition_of_robotics_main.rst | 28 +++++++------------ 1 file changed, 10 insertions(+), 18 deletions(-) diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index 265814e068..f54d4d41fa 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -70,12 +70,17 @@ The Birth of Modern Robotics (1950s–1980s) Advanced Robotics and AI Integration (1990s–Present) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +- **Industrial Robots** – Advanced robots like `Baxter`_ and `Amazon Robotics`_ revolutionized manufacturing and logistics. - **Autonomous Vehicles** – Self-driving cars for robo taxi like `Waymo`_ and autonomous haulage system in mining like `AHS`_ became more advanced and bisiness-ready. +- **Exploration Robots** – Used for planetary exploration (e.g., NASA’s `Mars rovers`_). - **Medical Robotics** – Robots like `da Vinci Surgical System`_ revolutionized healthcare. - **Personal Robots** – Devices like `Roomba`_ (vacuum robot) and `Sophia`_ (AI humanoid) became popular. - **Service Robots** - Assistive robots like serving robots in restaurants and hotels like `Bellabot`_. - **Collaborative Robots (Drones)** – Collaborative robots like UAV (Unmanned Aerial Vehicle) in drone shows and delivery services. +.. _`Baxter`: https://en.wikipedia.org/wiki/Baxter_(robot) +.. _`Amazon Robotics`: https://en.wikipedia.org/wiki/Amazon_Robotics +.. _`Mars rovers`: https://en.wikipedia.org/wiki/Mars_rover .. _`Waymo`: https://waymo.com/ .. _`AHS`: https://www.futurebridge.com/industry/perspectives-industrial-manufacturing/autonomous-haulage-systems-the-future-of-mining-operations/ .. _`da Vinci Surgical System`: https://en.wikipedia.org/wiki/Da_Vinci_Surgical_System @@ -88,23 +93,10 @@ Key Components of Robotics Robotics consists of several essential components: -#. Sensors – Gather information from the environment (e.g., cameras, LiDAR, gyro, accelerometer, wheel encoders). -#. Actuators – Enable movement and interaction with the world (e.g., motors, hydraulic systems). -#. Computers – Process sensor data and make decisions (e.g., micro-controllers, CPUs, GPUs). -#. Power Supply – Provides energy to run the robot (e.g., batteries, solar power). -#. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, machine learning models, localization, mapping, path planning, control). +#. Sensors – Gather information from the environment (e.g., Cameras, LiDAR, GNSS, Gyro, Accelerometer, Wheel encoders). +#. Actuators – Enable movement and interaction with the world (e.g., Motors, Hydraulic systems). +#. Computers – Process sensor data and make decisions (e.g., Micro-controllers, CPUs, GPUs). +#. Power Supply – Provides energy to run the robot (e.g., Batteries, Solar power). +#. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control). This project, PythonRobotics, focuses on the software and algorithms part of robotics. - -Applications of Robots -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Robots come in various forms depending on their purpose: - -#. 🤖 Industrial Robots – Used in manufacturing (e.g., robotic arms in manufacturing factories). -#. 🏠 Service Robots – Assist in daily life (e.g., vacuum robots, delivery robots). -#. 🚗 Autonomous Vehicles – Self-driving cars and drones. -#. 👨‍⚕️ Medical Robots – Assist in surgeries and healthcare. -#. 🚀 Space & Exploration Robots – Used for planetary exploration (e.g., NASA’s Mars rovers). -#. 🐶 Humanoid & Social Robots – Designed to interact with humans (e.g., ASIMO, Sophia). - From 35c08824d00bd9fb452d1ee951018b177e65fd00 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 15 Feb 2025 16:08:23 +0900 Subject: [PATCH 808/909] add external sensors documentation to appendix (#1159) --- docs/modules/12_appendix/appendix_main.rst | 1 + .../12_appendix/external_sensors_main.rst | 56 +++++++++++++++++++ 2 files changed, 57 insertions(+) create mode 100644 docs/modules/12_appendix/external_sensors_main.rst diff --git a/docs/modules/12_appendix/appendix_main.rst b/docs/modules/12_appendix/appendix_main.rst index cb1ac04066..89a7fa9303 100644 --- a/docs/modules/12_appendix/appendix_main.rst +++ b/docs/modules/12_appendix/appendix_main.rst @@ -10,4 +10,5 @@ Appendix steering_motion_model Kalmanfilter_basics Kalmanfilter_basics_2 + external_sensors diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst new file mode 100644 index 0000000000..a1dba3b214 --- /dev/null +++ b/docs/modules/12_appendix/external_sensors_main.rst @@ -0,0 +1,56 @@ +External Sensors for Robots +============================ + +Introduction +------------ + +In recent years, the application of robotic technology has advanced, +particularly in areas such as autonomous vehicles and disaster response robots. +A crucial element in these technologies is external recognition—the robot's ability to understand its surrounding environment, identify safe zones, and detect moving objects using onboard sensors. Achieving effective external recognition involves various techniques, but equally important is the selection of appropriate sensors. Robots, like the sensors they employ, come in many forms, but external recognition sensors can be broadly categorized into three types. Developing an advanced external recognition system requires a thorough understanding of each sensor's principles and characteristics to determine their optimal application. This article summarizes the principles and features of these sensors for personal study purposes. + +Laser Sensors +------------- + +Laser sensors measure distances by utilizing light, commonly referred to as Light Detection and Ranging (LIDAR). They operate by emitting light towards an object and calculating the distance based on the time it takes for the reflected light to return, using the speed of light as a constant. + +Radar Sensors +------------- + +TBD + + +Monocular Cameras +----------------- + +Monocular cameras utilize a single camera to recognize the external environment. Compared to other sensors, they can detect color and brightness information, making them primarily useful for object recognition. However, they face challenges in independently measuring distances to surrounding objects and may struggle in low-light or dark conditions. + +Requirements for Cameras and Image Processing in Robotics +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +While camera sensors are widely used in applications like surveillance, deploying them in robotics necessitates meeting specific requirements: + +1. High dynamic range to adapt to various lighting conditions +2. Wide measurement range +3. Capability for three-dimensional measurement through techniques like motion stereo +4. Real-time processing with high frame rates +5. Cost-effectiveness + +Stereo Cameras +-------------- + +Stereo cameras employ multiple cameras to measure distances to surrounding objects. By knowing the positions and orientations of each camera and analyzing the disparity in the images (parallax), the distance to a specific point (the object represented by a particular pixel) can be calculated. + +Characteristics of Stereo Cameras +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Advantages of stereo cameras include the ability to obtain high-precision and high-density distance information at close range, depending on factors like camera resolution and the distance between cameras (baseline). This makes them suitable for indoor robots that require precise shape recognition of nearby objects. Additionally, stereo cameras are relatively cost-effective compared to other sensors, leading to their use in consumer products like Subaru's EyeSight system. However, stereo cameras are less effective for long-distance measurements due to a decrease in accuracy proportional to the square of the distance. They are also susceptible to environmental factors such as lighting conditions. + +Ultrasonic Sensors +------------------ + +Ultrasonic sensors are commonly used in indoor robots and some automotive autonomous driving systems. Their features include affordability compared to laser or radar sensors, the ability to detect very close objects, and the capability to sense materials like glass, which may be challenging for lasers or cameras. However, they have limitations such as shorter maximum measurement distances and lower resolution and accuracy. + +References +---------- + +TBD From e82a12319b5336beaff01ab9fd2ce7818bb08dfb Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 16 Feb 2025 21:41:22 +0900 Subject: [PATCH 809/909] add internal sensors documentation to appendix and create new internal sensors overview (#1161) --- docs/modules/12_appendix/appendix_main.rst | 1 + .../12_appendix/external_sensors_main.rst | 4 +++ .../12_appendix/internal_sensors_main.rst | 32 +++++++++++++++++++ 3 files changed, 37 insertions(+) create mode 100644 docs/modules/12_appendix/internal_sensors_main.rst diff --git a/docs/modules/12_appendix/appendix_main.rst b/docs/modules/12_appendix/appendix_main.rst index 89a7fa9303..a55389e1e6 100644 --- a/docs/modules/12_appendix/appendix_main.rst +++ b/docs/modules/12_appendix/appendix_main.rst @@ -10,5 +10,6 @@ Appendix steering_motion_model Kalmanfilter_basics Kalmanfilter_basics_2 + internal_sensors external_sensors diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst index a1dba3b214..d36b852d42 100644 --- a/docs/modules/12_appendix/external_sensors_main.rst +++ b/docs/modules/12_appendix/external_sensors_main.rst @@ -1,6 +1,10 @@ External Sensors for Robots ============================ +This project, `PythonRobotics`, focuses on algorithms, so hardware is not included. +However, having basic knowledge of hardware in robotics is also important for understanding algorithms. +Therefore, we will provide an overview. + Introduction ------------ diff --git a/docs/modules/12_appendix/internal_sensors_main.rst b/docs/modules/12_appendix/internal_sensors_main.rst new file mode 100644 index 0000000000..13b8de4203 --- /dev/null +++ b/docs/modules/12_appendix/internal_sensors_main.rst @@ -0,0 +1,32 @@ +Internal Sensors for Robots +============================ + +This project, `PythonRobotics`, focuses on algorithms, so hardware is not included. +However, having basic knowledge of hardware in robotics is also important for understanding algorithms. +Therefore, we will provide an overview. + +Introduction +------------ + +Global Navigation Satellite System (GNSS) +------------------------------------------- + +Gyroscope +---------- + +Accelerometer +-------------- + +Magnetometer +-------------- + +Inertial Measurement Unit (IMU) +-------------------------------- + +Pressure Sensor +----------------- + +Temperature Sensor +-------------------- + + From c92aaf36d8665a3b9ecd1a661f151cfd6ede4c66 Mon Sep 17 00:00:00 2001 From: Aglargil <34728006+Aglargil@users.noreply.github.com> Date: Mon, 17 Feb 2025 18:47:04 +0800 Subject: [PATCH 810/909] feat: add ElasticBands (#1156) * feat: add ElasticBands * feat: Elastic Bands update * feat: ElasticBands update * feat: ElasticBands add test * feat: ElasticBands reduce occupation * fix: ElasticBands test * feat: ElasticBands remove tangential component * feat: Elastic Bands update * feat: Elastic Bands doc * feat: Elastic Bands update * feat: ElasticBands update --- Mapping/DistanceMap/distance_map.py | 51 +++ PathPlanning/ElasticBands/elastic_bands.py | 300 ++++++++++++++++++ PathPlanning/ElasticBands/obstacles.npy | Bin 0 -> 384 bytes PathPlanning/ElasticBands/path.npy | Bin 0 -> 224 bytes .../elastic_bands/elastic_bands_main.rst | 73 +++++ .../5_path_planning/path_planning_main.rst | 1 + tests/test_elastic_bands.py | 23 ++ 7 files changed, 448 insertions(+) create mode 100644 PathPlanning/ElasticBands/elastic_bands.py create mode 100644 PathPlanning/ElasticBands/obstacles.npy create mode 100644 PathPlanning/ElasticBands/path.npy create mode 100644 docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst create mode 100644 tests/test_elastic_bands.py diff --git a/Mapping/DistanceMap/distance_map.py b/Mapping/DistanceMap/distance_map.py index 54c98c6a75..0dcc7380c5 100644 --- a/Mapping/DistanceMap/distance_map.py +++ b/Mapping/DistanceMap/distance_map.py @@ -11,11 +11,62 @@ import numpy as np import matplotlib.pyplot as plt +import scipy INF = 1e20 ENABLE_PLOT = True +def compute_sdf_scipy(obstacles): + """ + Compute the signed distance field (SDF) from a boolean field using scipy. + This function has the same functionality as compute_sdf. + However, by using scipy.ndimage.distance_transform_edt, it can compute much faster. + + Example: 500×500 map + • compute_sdf: 3 sec + • compute_sdf_scipy: 0.05 sec + + Parameters + ---------- + obstacles : array_like + A 2D boolean array where '1' represents obstacles and '0' represents free space. + + Returns + ------- + array_like + A 2D array representing the signed distance field, where positive values indicate distance + to the nearest obstacle, and negative values indicate distance to the nearest free space. + """ + # distance_transform_edt use '0' as obstacles, so we need to convert the obstacles to '0' + a = scipy.ndimage.distance_transform_edt(obstacles == 0) + b = scipy.ndimage.distance_transform_edt(obstacles == 1) + return a - b + + +def compute_udf_scipy(obstacles): + """ + Compute the unsigned distance field (UDF) from a boolean field using scipy. + This function has the same functionality as compute_udf. + However, by using scipy.ndimage.distance_transform_edt, it can compute much faster. + + Example: 500×500 map + • compute_udf: 1.5 sec + • compute_udf_scipy: 0.02 sec + + Parameters + ---------- + obstacles : array_like + A 2D boolean array where '1' represents obstacles and '0' represents free space. + + Returns + ------- + array_like + A 2D array of distances from the nearest obstacle, with the same dimensions as `bool_field`. + """ + return scipy.ndimage.distance_transform_edt(obstacles == 0) + + def compute_sdf(obstacles): """ Compute the signed distance field (SDF) from a boolean field. diff --git a/PathPlanning/ElasticBands/elastic_bands.py b/PathPlanning/ElasticBands/elastic_bands.py new file mode 100644 index 0000000000..785f822d14 --- /dev/null +++ b/PathPlanning/ElasticBands/elastic_bands.py @@ -0,0 +1,300 @@ +""" +Elastic Bands + +author: Wang Zheng (@Aglargil) + +Ref: + +- [Elastic Bands: Connecting Path Planning and Control] +(http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf) +""" + +import numpy as np +import sys +import pathlib +import matplotlib.pyplot as plt +from matplotlib.patches import Circle + +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) + +from Mapping.DistanceMap.distance_map import compute_sdf_scipy + +# Elastic Bands Params +MAX_BUBBLE_RADIUS = 100 +MIN_BUBBLE_RADIUS = 10 +RHO0 = 20.0 # Maximum distance for applying repulsive force +KC = 0.05 # Contraction force gain +KR = -0.1 # Repulsive force gain +LAMBDA = 0.7 # Overlap constraint factor +STEP_SIZE = 3.0 # Step size for calculating gradient + +# Visualization Params +ENABLE_PLOT = True +# ENABLE_INTERACTIVE is True allows user to add obstacles by left clicking +# and add path points by right clicking and start planning by middle clicking +ENABLE_INTERACTIVE = False +# ENABLE_SAVE_DATA is True allows saving the path and obstacles which added +# by user in interactive mode to file +ENABLE_SAVE_DATA = False +MAX_ITER = 50 + + +class Bubble: + def __init__(self, position, radius): + self.pos = np.array(position) # Bubble center coordinates [x, y] + self.radius = radius # Safety distance radius ρ(b) + if self.radius > MAX_BUBBLE_RADIUS: + self.radius = MAX_BUBBLE_RADIUS + if self.radius < MIN_BUBBLE_RADIUS: + self.radius = MIN_BUBBLE_RADIUS + + +class ElasticBands: + def __init__( + self, + initial_path, + obstacles, + rho0=RHO0, + kc=KC, + kr=KR, + lambda_=LAMBDA, + step_size=STEP_SIZE, + ): + self.distance_map = compute_sdf_scipy(obstacles) + self.bubbles = [ + Bubble(p, self.compute_rho(p)) for p in initial_path + ] # Initialize bubble chain + self.kc = kc # Contraction force gain + self.kr = kr # Repulsive force gain + self.rho0 = rho0 # Maximum distance for applying repulsive force + self.lambda_ = lambda_ # Overlap constraint factor + self.step_size = step_size # Step size for calculating gradient + self._maintain_overlap() + + def compute_rho(self, position): + """Compute the distance field value at the position""" + return self.distance_map[int(position[0]), int(position[1])] + + def contraction_force(self, i): + """Calculate internal contraction force for the i-th bubble""" + if i == 0 or i == len(self.bubbles) - 1: + return np.zeros(2) + + prev = self.bubbles[i - 1].pos + next_ = self.bubbles[i + 1].pos + current = self.bubbles[i].pos + + # f_c = kc * ( (prev-current)/|prev-current| + (next-current)/|next-current| ) + dir_prev = (prev - current) / (np.linalg.norm(prev - current) + 1e-6) + dir_next = (next_ - current) / (np.linalg.norm(next_ - current) + 1e-6) + return self.kc * (dir_prev + dir_next) + + def repulsive_force(self, i): + """Calculate external repulsive force for the i-th bubble""" + h = self.step_size # Step size + b = self.bubbles[i].pos + rho = self.bubbles[i].radius + + if rho >= self.rho0: + return np.zeros(2) + + # Finite difference approximation of the gradient ∂ρ/∂b + dx = np.array([h, 0]) + dy = np.array([0, h]) + grad_x = (self.compute_rho(b - dx) - self.compute_rho(b + dx)) / (2 * h) + grad_y = (self.compute_rho(b - dy) - self.compute_rho(b + dy)) / (2 * h) + grad = np.array([grad_x, grad_y]) + + return self.kr * (self.rho0 - rho) * grad + + def update_bubbles(self): + """Update bubble positions""" + new_bubbles = [] + for i in range(len(self.bubbles)): + if i == 0 or i == len(self.bubbles) - 1: + new_bubbles.append(self.bubbles[i]) # Fixed start and end points + continue + + f_total = self.contraction_force(i) + self.repulsive_force(i) + v = self.bubbles[i - 1].pos - self.bubbles[i + 1].pos + + # Remove tangential component + f_star = f_total - f_total * v * v / (np.linalg.norm(v) ** 2 + 1e-6) + + alpha = self.bubbles[i].radius # Adaptive step size + new_pos = self.bubbles[i].pos + alpha * f_star + new_pos = np.clip(new_pos, 0, 499) + new_radius = self.compute_rho(new_pos) + + # Update bubble and maintain overlap constraint + new_bubble = Bubble(new_pos, new_radius) + new_bubbles.append(new_bubble) + + self.bubbles = new_bubbles + self._maintain_overlap() + + def _maintain_overlap(self): + """Maintain bubble chain continuity (simplified insertion/deletion mechanism)""" + # Insert bubbles + i = 0 + while i < len(self.bubbles) - 1: + bi, bj = self.bubbles[i], self.bubbles[i + 1] + dist = np.linalg.norm(bi.pos - bj.pos) + if dist > self.lambda_ * (bi.radius + bj.radius): + new_pos = (bi.pos + bj.pos) / 2 + rho = self.compute_rho( + new_pos + ) # Calculate new radius using environment model + self.bubbles.insert(i + 1, Bubble(new_pos, rho)) + i += 2 # Skip the processed region + else: + i += 1 + + # Delete redundant bubbles + i = 1 + while i < len(self.bubbles) - 1: + prev = self.bubbles[i - 1] + next_ = self.bubbles[i + 1] + dist = np.linalg.norm(prev.pos - next_.pos) + if dist <= self.lambda_ * (prev.radius + next_.radius): + del self.bubbles[i] # Delete if redundant + else: + i += 1 + + +class ElasticBandsVisualizer: + def __init__(self): + self.obstacles = np.zeros((500, 500)) + self.obstacles_points = [] + self.path_points = [] + self.elastic_band = None + self.running = True + + if ENABLE_PLOT: + self.fig, self.ax = plt.subplots(figsize=(8, 8)) + self.fig.canvas.mpl_connect("close_event", self.on_close) + self.ax.set_xlim(0, 500) + self.ax.set_ylim(0, 500) + + if ENABLE_INTERACTIVE: + self.path_points = [] # Add a list to store path points + # Connect mouse events + self.fig.canvas.mpl_connect("button_press_event", self.on_click) + else: + self.path_points = np.load(pathlib.Path(__file__).parent / "path.npy") + self.obstacles_points = np.load( + pathlib.Path(__file__).parent / "obstacles.npy" + ) + for x, y in self.obstacles_points: + self.add_obstacle(x, y) + self.plan_path() + + self.plot_background() + + def on_close(self, event): + """Handle window close event""" + self.running = False + plt.close("all") # Close all figure windows + + def plot_background(self): + """Plot the background grid""" + if not ENABLE_PLOT or not self.running: + return + + self.ax.cla() + self.ax.set_xlim(0, 500) + self.ax.set_ylim(0, 500) + self.ax.grid(True) + + if ENABLE_INTERACTIVE: + self.ax.set_title( + "Elastic Bands Path Planning\n" + "Left click: Add obstacles\n" + "Right click: Add path points\n" + "Middle click: Start planning", + pad=20, + ) + else: + self.ax.set_title("Elastic Bands Path Planning", pad=20) + + if self.path_points: + self.ax.plot( + [p[0] for p in self.path_points], + [p[1] for p in self.path_points], + "yo", + markersize=8, + ) + + self.ax.imshow(self.obstacles.T, origin="lower", cmap="binary", alpha=0.8) + self.ax.plot([], [], color="black", label="obstacles") + if self.elastic_band is not None: + path = [b.pos.tolist() for b in self.elastic_band.bubbles] + path = np.array(path) + self.ax.plot(path[:, 0], path[:, 1], "b-", linewidth=2, label="path") + + for bubble in self.elastic_band.bubbles: + circle = Circle( + bubble.pos, bubble.radius, fill=False, color="g", alpha=0.3 + ) + self.ax.add_patch(circle) + self.ax.plot(bubble.pos[0], bubble.pos[1], "bo", markersize=10) + self.ax.plot([], [], color="green", label="bubbles") + + self.ax.legend(loc="upper right") + plt.draw() + plt.pause(0.01) + + def add_obstacle(self, x, y): + """Add an obstacle at the given coordinates""" + size = 30 # Side length of the square + half_size = size // 2 + x_start = max(0, x - half_size) + x_end = min(self.obstacles.shape[0], x + half_size) + y_start = max(0, y - half_size) + y_end = min(self.obstacles.shape[1], y + half_size) + self.obstacles[x_start:x_end, y_start:y_end] = 1 + + def on_click(self, event): + """Handle mouse click events""" + if event.inaxes != self.ax: + return + + x, y = int(event.xdata), int(event.ydata) + + if event.button == 1: # Left click to add obstacles + self.add_obstacle(x, y) + self.obstacles_points.append([x, y]) + + elif event.button == 3: # Right click to add path points + self.path_points.append([x, y]) + + elif event.button == 2: # Middle click to end path input and start planning + if len(self.path_points) >= 2: + if ENABLE_SAVE_DATA: + np.save( + pathlib.Path(__file__).parent / "path.npy", self.path_points + ) + np.save( + pathlib.Path(__file__).parent / "obstacles.npy", + self.obstacles_points, + ) + self.plan_path() + + self.plot_background() + + def plan_path(self): + """Plan the path""" + + initial_path = self.path_points + # Create an elastic band object and optimize + self.elastic_band = ElasticBands(initial_path, self.obstacles) + for _ in range(MAX_ITER): + self.elastic_band.update_bubbles() + self.path_points = [b.pos for b in self.elastic_band.bubbles] + self.plot_background() + + +if __name__ == "__main__": + _ = ElasticBandsVisualizer() + if ENABLE_PLOT: + plt.show(block=True) diff --git a/PathPlanning/ElasticBands/obstacles.npy b/PathPlanning/ElasticBands/obstacles.npy new file mode 100644 index 0000000000000000000000000000000000000000..af4376afcf0e987bbb62c4a80c7afac3d221961d GIT binary patch literal 384 zcmbR27wQ`j$;eQ~P_3SlTAW;@Zl$1ZlWC!@qoAIaUsO_*m=~X4l#&V(cT3DEP6dh= zXCxM+0{I$-W;zN+nmP)#3giN=d=|8_M@a;}<~r z%~1IiD1RoD9|z^LL+NCwxEhq72<3}I`IS(%lCm`7X literal 0 HcmV?d00001 diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst new file mode 100644 index 0000000000..139996f291 --- /dev/null +++ b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst @@ -0,0 +1,73 @@ +Elastic Bands +------------- + +This is a path planning with Elastic Bands. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ElasticBands/animation.gif + + +Core Concept +~~~~~~~~~~~~ +- **Elastic Band**: A dynamically deformable collision-free path initialized by a global planner. +- **Objective**: + + * Shorten and smooth the path. + * Maximize obstacle clearance. + * Maintain global path connectivity. + +Bubble Representation +~~~~~~~~~~~~~~~~~~~ +- **Definition**: A local free-space region around configuration :math:`b`: + + .. math:: + B(b) = \{ q: \|q - b\| < \rho(b) \}, + + where :math:`\rho(b)` is the radius of the bubble. + + +Force-Based Deformation +~~~~~~~~~~~~~~~~~~~~~~~ +The elastic band deforms under artificial forces: + +Internal Contraction Force +++++++++++++++++++++++++++ +- **Purpose**: Reduces path slack and length. +- **Formula**: For node :math:`b_i`: + + .. math:: + f_c(b_i) = k_c \left( \frac{b_{i-1} - b_i}{\|b_{i-1} - b_i\|} + \frac{b_{i+1} - b_i}{\|b_{i+1} - b_i\|} \right) + + where :math:`k_c` is the contraction gain. + +External Repulsion Force ++++++++++++++++++++++++++ +- **Purpose**: Pushes the path away from obstacles. +- **Formula**: For node :math:`b_i`: + + .. math:: + f_r(b_i) = \begin{cases} + k_r (\rho_0 - \rho(b_i)) \nabla \rho(b_i) & \text{if } \rho(b_i) < \rho_0, \\ + 0 & \text{otherwise}. + \end{cases} + + where :math:`k_r` is the repulsion gain, :math:`\rho_0` is the maximum distance for applying repulsion force, and :math:`\nabla \rho(b_i)` is approximated via finite differences: + + .. math:: + \frac{\partial \rho}{\partial x} \approx \frac{\rho(b_i + h) - \rho(b_i - h)}{2h}. + +Dynamic Path Maintenance +~~~~~~~~~~~~~~~~~~~~~~~ +1. **Node Update**: + + .. math:: + b_i^{\text{new}} = b_i^{\text{old}} + \alpha (f_c + f_r), + + where :math:`\alpha` is a step-size parameter, which often proportional to :math:`\rho(b_i^{\text{old}})` + +2. **Overlap Enforcement**: +- Insert new nodes if adjacent nodes are too far apart +- Remove redundant nodes if adjacent nodes are too close + +Ref: + +- `Elastic Bands: Connecting Path Planning and Control `__ diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst index 4960330b3e..65fbfdbc3d 100644 --- a/docs/modules/5_path_planning/path_planning_main.rst +++ b/docs/modules/5_path_planning/path_planning_main.rst @@ -31,3 +31,4 @@ Path planning is the ability of a robot to search feasible and efficient path to hybridastar/hybridastar frenet_frame_path/frenet_frame_path coverage_path/coverage_path + elastic_bands/elastic_bands \ No newline at end of file diff --git a/tests/test_elastic_bands.py b/tests/test_elastic_bands.py new file mode 100644 index 0000000000..ad4e13af1a --- /dev/null +++ b/tests/test_elastic_bands.py @@ -0,0 +1,23 @@ +import conftest +import numpy as np +from PathPlanning.ElasticBands.elastic_bands import ElasticBands + + +def test_1(): + path = np.load("PathPlanning/ElasticBands/path.npy") + obstacles_points = np.load("PathPlanning/ElasticBands/obstacles.npy") + obstacles = np.zeros((500, 500)) + for x, y in obstacles_points: + size = 30 # Side length of the square + half_size = size // 2 + x_start = max(0, x - half_size) + x_end = min(obstacles.shape[0], x + half_size) + y_start = max(0, y - half_size) + y_end = min(obstacles.shape[1], y + half_size) + obstacles[x_start:x_end, y_start:y_end] = 1 + elastic_bands = ElasticBands(path, obstacles) + elastic_bands.update_bubbles() + + +if __name__ == "__main__": + conftest.run_this_test(__file__) From cbe61f8ca62785a072652741d8b33832caee1942 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Feb 2025 10:45:49 +0900 Subject: [PATCH 811/909] build(deps): bump scipy from 1.15.1 to 1.15.2 in /requirements (#1163) Bumps [scipy](https://github.com/scipy/scipy) from 1.15.1 to 1.15.2. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.15.1...v1.15.2) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f5f674d7d2..ea4550edb8 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 2.2.2 -scipy == 1.15.1 +scipy == 1.15.2 matplotlib == 3.10.0 cvxpy == 1.5.3 pytest == 8.3.4 # For unit test From 395fca59cc1f9c44e9ea7f7c08ce41958e399481 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 18 Feb 2025 20:06:34 +0900 Subject: [PATCH 812/909] fix: update robotics documentation for clarity and correct terminology (#1165) --- docs/modules/12_appendix/external_sensors_main.rst | 2 ++ docs/modules/12_appendix/internal_sensors_main.rst | 2 ++ .../1_definition_of_robotics/definition_of_robotics_main.rst | 5 +++++ .../technologies_for_robotics_main.rst} | 4 +++- docs/modules/1_introduction/introduction_main.rst | 2 +- 5 files changed, 13 insertions(+), 2 deletions(-) rename docs/modules/1_introduction/{3_technology_for_robotics/technology_for_robotics_main.rst => 3_technologies_for_robotics/technologies_for_robotics_main.rst} (88%) diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst index d36b852d42..3597418150 100644 --- a/docs/modules/12_appendix/external_sensors_main.rst +++ b/docs/modules/12_appendix/external_sensors_main.rst @@ -1,3 +1,5 @@ +.. _`External Sensors for Robots`: + External Sensors for Robots ============================ diff --git a/docs/modules/12_appendix/internal_sensors_main.rst b/docs/modules/12_appendix/internal_sensors_main.rst index 13b8de4203..18f209098e 100644 --- a/docs/modules/12_appendix/internal_sensors_main.rst +++ b/docs/modules/12_appendix/internal_sensors_main.rst @@ -1,3 +1,5 @@ +.. _`Internal Sensors for Robots`: + Internal Sensors for Robots ============================ diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index f54d4d41fa..1e7a833b19 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -17,6 +17,10 @@ In the play, robots were artificial workers created to serve humans, but they ev Over time, “robot” came to refer to machines or automated systems that can perform tasks, often with some level of intelligence or autonomy. +Currently, 2 millions robots are working in the world, and the number is increasing every year. +In South Korea, where the adoption of robots is particularly rapid, +50 robots are operating per 1,000 people. + .. _`R.U.R.`: https://thereader.mitpress.mit.edu/origin-word-robot-rur/ .. _`Karel Čapek`: https://en.wikipedia.org/wiki/Karel_%C4%8Capek @@ -100,3 +104,4 @@ Robotics consists of several essential components: #. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control). This project, PythonRobotics, focuses on the software and algorithms part of robotics. +If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robotics`_ or :ref:`External Sensors for Robotics`_. diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst similarity index 88% rename from docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst rename to docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst index e460059e20..64c6945c32 100644 --- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst +++ b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst @@ -1,8 +1,10 @@ -Technology for Robotics +Technologies for Robotics ------------------------- The field of robotics needs wide areas of technologies such as mechanical engineering, electrical engineering, computer science, and artificial intelligence (AI). +This project, `PythonRobotics`, only focus on computer science and artificial intelligence. + Autonomous Navigation diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst index a7ce55f9bf..1871dfc3b1 100644 --- a/docs/modules/1_introduction/introduction_main.rst +++ b/docs/modules/1_introduction/introduction_main.rst @@ -14,5 +14,5 @@ covered in PythonRobotics. 1_definition_of_robotics/definition_of_robotics 2_python_for_robotics/python_for_robotics - 3_technology_for_robotics/technology_for_robotics + 3_technologies_for_robotics/technologies_for_robotics From d53711998aeb1d0b3aab7a62862ef5d358f2aff9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 18 Feb 2025 21:49:28 +0900 Subject: [PATCH 813/909] fix: update robotics documentation for clarity and correct terminology (#1166) --- README.md | 4 ++++ .../0_getting_started/1_what_is_python_robotics_main.rst | 6 ++++++ .../definition_of_robotics_main.rst | 2 +- .../5_path_planning/elastic_bands/elastic_bands_main.rst | 7 ++++--- 4 files changed, 15 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 818d7b0d4e..9e605435ce 100644 --- a/README.md +++ b/README.md @@ -89,6 +89,10 @@ See this documentation - [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/getting_started.html#what-is-pythonrobotics) +or this Youtube video: + +- [PythonRobotics project audio overview](https://www.youtube.com/watch?v=uMeRnNoJAfU) + or this paper for more details: - [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib)) diff --git a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst index 8c932b7263..2a7bd574f0 100644 --- a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst +++ b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst @@ -110,6 +110,12 @@ the following additional libraries are required. For instructions on installing the above libraries, please refer to this section ":ref:`How to run sample codes`". +Audio overview of this project +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +For an audio overview of this project, please refer to this `YouTube video`_. + +.. _`YouTube video`: https://www.youtube.com/watch?v=uMeRnNoJAfU + Arxiv paper ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index 1e7a833b19..5d78bf858b 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -103,5 +103,5 @@ Robotics consists of several essential components: #. Power Supply – Provides energy to run the robot (e.g., Batteries, Solar power). #. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control). -This project, PythonRobotics, focuses on the software and algorithms part of robotics. +This project, `PythonRobotics`, focuses on the software and algorithms part of robotics. If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robotics`_ or :ref:`External Sensors for Robotics`_. diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst index 139996f291..8a3e517105 100644 --- a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst +++ b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst @@ -16,7 +16,7 @@ Core Concept * Maintain global path connectivity. Bubble Representation -~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~ - **Definition**: A local free-space region around configuration :math:`b`: .. math:: @@ -56,7 +56,7 @@ External Repulsion Force \frac{\partial \rho}{\partial x} \approx \frac{\rho(b_i + h) - \rho(b_i - h)}{2h}. Dynamic Path Maintenance -~~~~~~~~~~~~~~~~~~~~~~~ +~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. **Node Update**: .. math:: @@ -68,6 +68,7 @@ Dynamic Path Maintenance - Insert new nodes if adjacent nodes are too far apart - Remove redundant nodes if adjacent nodes are too close -Ref: +References +~~~~~~~~~~~~~~~~~~~~~~~ - `Elastic Bands: Connecting Path Planning and Control `__ From 8064488a1d63a1579d8c9d786a376c8b583c02a8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Feb 2025 21:49:40 +0900 Subject: [PATCH 814/909] build(deps): bump numpy from 2.2.2 to 2.2.3 in /requirements (#1164) Bumps [numpy](https://github.com/numpy/numpy) from 2.2.2 to 2.2.3. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.2.2...v2.2.3) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ea4550edb8..8176364c29 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.2.2 +numpy == 2.2.3 scipy == 1.15.2 matplotlib == 3.10.0 cvxpy == 1.5.3 From 2b7080991e5289c9524a505244fcd2a4995da06e Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 19 Feb 2025 13:36:07 +0900 Subject: [PATCH 815/909] Add GitHub copilot pro sponser (#1167) * fix: correct terminology in documentation and update Sphinx options * fix: correct terminology in documentation and update Sphinx options * fix: correct terminology in documentation and update Sphinx options * fix: correct terminology in documentation and update Sphinx options * fix: correct terminology in documentation and update Sphinx options --- docs/Makefile | 3 ++- docs/modules/0_getting_started/3_how_to_contribute_main.rst | 2 ++ .../1_definition_of_robotics/definition_of_robotics_main.rst | 2 +- docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst | 2 +- 4 files changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/Makefile b/docs/Makefile index 9296811e02..ae495eb36d 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -2,7 +2,8 @@ # # You can set these variables from the command line. -SPHINXOPTS = +# SPHINXOPTS with -W means turn warnings into errors to fail the build when warnings are present. +SPHINXOPTS = -W SPHINXBUILD = sphinx-build SPHINXPROJ = PythonRobotics SOURCEDIR = . diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst index 874564cbb8..1e61760649 100644 --- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst +++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst @@ -187,6 +187,7 @@ If you would like to support us in some other way, please contact with creating Current Major Sponsors: +#. `GitHub`_ : They are providing a GitHub Copilot Pro license for this OSS development. #. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development. #. `1Password`_ : They are providing a free license of their 1Password team license for this OSS project. @@ -202,6 +203,7 @@ Current Major Sponsors: .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md .. _`test_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_codestyle.py .. _`JetBrains`: https://www.jetbrains.com/ +.. _`GitHub`: https://www.github.com/ .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma .. _`One-time donation via PayPal`: https://www.paypal.com/paypalme/myenigmapay/ diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst index 5d78bf858b..ca595301a6 100644 --- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst +++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst @@ -104,4 +104,4 @@ Robotics consists of several essential components: #. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control). This project, `PythonRobotics`, focuses on the software and algorithms part of robotics. -If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robotics`_ or :ref:`External Sensors for Robotics`_. +If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robots` or :ref:`External Sensors for Robots`. diff --git a/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst b/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst index 491320512b..15963aff79 100644 --- a/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst +++ b/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst @@ -165,7 +165,7 @@ different data sources into a single optimization problem. 6 215.8405 -0.000000 -.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/Graph_SLAM_optimization.gif .. code:: ipython3 From c7fb228d24856d6c7a0cb28c715176bfb8b17281 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 20 Feb 2025 12:11:04 +0900 Subject: [PATCH 816/909] fix: update section references to use consistent formatting (#1169) --- .../technologies_for_robotics_main.rst | 48 +++++++++++++++---- .../2_localization/localization_main.rst | 2 +- docs/modules/3_mapping/mapping_main.rst | 2 +- docs/modules/4_slam/slam_main.rst | 2 +- .../5_path_planning/path_planning_main.rst | 2 +- .../6_path_tracking/path_tracking_main.rst | 2 +- .../7_arm_navigation/arm_navigation_main.rst | 2 +- .../aerial_navigation_main.rst | 2 +- docs/modules/9_bipedal/bipedal_main.rst | 2 +- 9 files changed, 48 insertions(+), 16 deletions(-) diff --git a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst index 64c6945c32..c77997a138 100644 --- a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst +++ b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst @@ -5,22 +5,54 @@ The field of robotics needs wide areas of technologies such as mechanical engine electrical engineering, computer science, and artificial intelligence (AI). This project, `PythonRobotics`, only focus on computer science and artificial intelligence. +The technologies for robotics are categorized as following 3 categories: +#. `Autonomous Navigation`_ +#. `Manipulation`_ +#. `Robot type specific technologies`_ + +.. _`Autonomous Navigation`: Autonomous Navigation ^^^^^^^^^^^^^^^^^^^^^^^^ - -In recent years, autonomous navigation technologies have received huge -attention in many fields. -Such fields include, autonomous driving[22], drone flight navigation, -and other transportation systems. - -An autonomous navigation system is a system that can move to a goal over long +Autonomous navigation is a capability that can move to a goal over long periods of time without any external control by an operator. -The system requires a wide range of technologies: + +To achieve autonomous navigation, the robot needs to have the following technologies: - It needs to know where it is (localization) - Where it is safe (mapping) +- Where is is safe and where the robot is in the map (Simultaneous Localization and Mapping (SLAM)) - Where and how to move (path planning) - How to control its motion (path following). The autonomous system would not work correctly if any of these technologies is missing. + +In recent years, autonomous navigation technologies have received huge +attention in many fields. +For example, self-driving cars, drones, and autonomous mobile robots in indoor and outdoor environments. + +In this project, we provide many algorithms, sample codes, +and documentations for autonomous navigation. + +#. :ref:`Localization` +#. :ref:`Mapping` +#. :ref:`SLAM` +#. :ref:`Path planning` +#. :ref:`Path tracking` + + + +.. _`Manipulation`: + +Manipulation +^^^^^^^^^^^^^^^^^^^^^^^^ + +#. :ref:`Arm Navigation` + +.. _`Robot type specific technologies`: + +Robot type specific technologies +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +#. :ref:`Aerial Navigation` +#. :ref:`Bipedal` diff --git a/docs/modules/2_localization/localization_main.rst b/docs/modules/2_localization/localization_main.rst index 22cbd094da..770a234b69 100644 --- a/docs/modules/2_localization/localization_main.rst +++ b/docs/modules/2_localization/localization_main.rst @@ -1,4 +1,4 @@ -.. _localization: +.. _`Localization`: Localization ============ diff --git a/docs/modules/3_mapping/mapping_main.rst b/docs/modules/3_mapping/mapping_main.rst index 825b08d3ec..34a3744893 100644 --- a/docs/modules/3_mapping/mapping_main.rst +++ b/docs/modules/3_mapping/mapping_main.rst @@ -1,4 +1,4 @@ -.. _mapping: +.. _`Mapping`: Mapping ======= diff --git a/docs/modules/4_slam/slam_main.rst b/docs/modules/4_slam/slam_main.rst index dec04f253a..98211986c2 100644 --- a/docs/modules/4_slam/slam_main.rst +++ b/docs/modules/4_slam/slam_main.rst @@ -1,4 +1,4 @@ -.. _slam: +.. _`SLAM`: SLAM ==== diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst index 65fbfdbc3d..a0f9c30a3d 100644 --- a/docs/modules/5_path_planning/path_planning_main.rst +++ b/docs/modules/5_path_planning/path_planning_main.rst @@ -1,4 +1,4 @@ -.. _path_planning: +.. _`Path Planning`: Path Planning ============= diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst index d7a895b562..130a2340c1 100644 --- a/docs/modules/6_path_tracking/path_tracking_main.rst +++ b/docs/modules/6_path_tracking/path_tracking_main.rst @@ -1,4 +1,4 @@ -.. _path_tracking: +.. _`Path Tracking`: Path Tracking ============= diff --git a/docs/modules/7_arm_navigation/arm_navigation_main.rst b/docs/modules/7_arm_navigation/arm_navigation_main.rst index bbbc872c58..7acd3ee7d3 100644 --- a/docs/modules/7_arm_navigation/arm_navigation_main.rst +++ b/docs/modules/7_arm_navigation/arm_navigation_main.rst @@ -1,4 +1,4 @@ -.. _arm_navigation: +.. _`Arm Navigation`: Arm Navigation ============== diff --git a/docs/modules/8_aerial_navigation/aerial_navigation_main.rst b/docs/modules/8_aerial_navigation/aerial_navigation_main.rst index b2ccb071af..7f76689770 100644 --- a/docs/modules/8_aerial_navigation/aerial_navigation_main.rst +++ b/docs/modules/8_aerial_navigation/aerial_navigation_main.rst @@ -1,4 +1,4 @@ -.. _aerial_navigation: +.. _`Aerial Navigation`: Aerial Navigation ================= diff --git a/docs/modules/9_bipedal/bipedal_main.rst b/docs/modules/9_bipedal/bipedal_main.rst index fc5b933055..dc387dc4e8 100644 --- a/docs/modules/9_bipedal/bipedal_main.rst +++ b/docs/modules/9_bipedal/bipedal_main.rst @@ -1,4 +1,4 @@ -.. _bipedal: +.. _`Bipedal`: Bipedal ================= From f343573a7bb9b99fd8fcfc72cef7336ccf859c28 Mon Sep 17 00:00:00 2001 From: Aglargil <34728006+Aglargil@users.noreply.github.com> Date: Thu, 20 Feb 2025 18:09:30 +0800 Subject: [PATCH 817/909] Update move_to_pose for cases where alpha > pi/2 or alpha < -pi/2 (#1168) * Update move_to_pose for cases where alpha > pi/2 or alpha < -pi/2 * Update move_to_pose * Add move_to_pose test * Update move_to_pose --- Control/move_to_pose/move_to_pose.py | 90 +++++++++++++++++++--------- tests/test_move_to_pose.py | 74 ++++++++++++++++++++++- 2 files changed, 134 insertions(+), 30 deletions(-) diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py index 279ba0625b..34736a2e21 100644 --- a/Control/move_to_pose/move_to_pose.py +++ b/Control/move_to_pose/move_to_pose.py @@ -5,6 +5,7 @@ Author: Daniel Ingram (daniel-s-ingram) Atsushi Sakai (@Atsushi_twi) Seied Muhammad Yazdian (@Muhammad-Yazdian) + Wang Zheng (@Aglargil) P. I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7 @@ -12,8 +13,13 @@ import matplotlib.pyplot as plt import numpy as np -from random import random +import sys +import pathlib + +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) from utils.angle import angle_mod +from random import random + class PathFinderController: """ @@ -70,14 +76,20 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal): # [-pi, pi] to prevent unstable behavior e.g. difference going # from 0 rad to 2*pi rad with slight turn + # Ref: The velocity v always has a constant sign which depends on the initial value of α. rho = np.hypot(x_diff, y_diff) - alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta) - beta = angle_mod(theta_goal - theta - alpha) v = self.Kp_rho * rho - w = self.Kp_alpha * alpha - self.Kp_beta * beta + alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta) + beta = angle_mod(theta_goal - theta - alpha) if alpha > np.pi / 2 or alpha < -np.pi / 2: + # recalculate alpha to make alpha in the range of [-pi/2, pi/2] + alpha = angle_mod(np.arctan2(-y_diff, -x_diff) - theta) + beta = angle_mod(theta_goal - theta - alpha) + w = self.Kp_alpha * alpha - self.Kp_beta * beta v = -v + else: + w = self.Kp_alpha * alpha - self.Kp_beta * beta return rho, v, w @@ -85,6 +97,7 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal): # simulation parameters controller = PathFinderController(9, 15, 3) dt = 0.01 +MAX_SIM_TIME = 5 # seconds, robot will stop moving when time exceeds this value # Robot specifications MAX_LINEAR_SPEED = 15 @@ -101,18 +114,19 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal): x_diff = x_goal - x y_diff = y_goal - y - x_traj, y_traj = [], [] + x_traj, y_traj, v_traj, w_traj = [x], [y], [0], [0] rho = np.hypot(x_diff, y_diff) - while rho > 0.001: + t = 0 + while rho > 0.001 and t < MAX_SIM_TIME: + t += dt x_traj.append(x) y_traj.append(y) x_diff = x_goal - x y_diff = y_goal - y - rho, v, w = controller.calc_control_command( - x_diff, y_diff, theta, theta_goal) + rho, v, w = controller.calc_control_command(x_diff, y_diff, theta, theta_goal) if abs(v) > MAX_LINEAR_SPEED: v = np.sign(v) * MAX_LINEAR_SPEED @@ -120,18 +134,35 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal): if abs(w) > MAX_ANGULAR_SPEED: w = np.sign(w) * MAX_ANGULAR_SPEED + v_traj.append(v) + w_traj.append(w) + theta = theta + w * dt x = x + v * np.cos(theta) * dt y = y + v * np.sin(theta) * dt if show_animation: # pragma: no cover plt.cla() - plt.arrow(x_start, y_start, np.cos(theta_start), - np.sin(theta_start), color='r', width=0.1) - plt.arrow(x_goal, y_goal, np.cos(theta_goal), - np.sin(theta_goal), color='g', width=0.1) + plt.arrow( + x_start, + y_start, + np.cos(theta_start), + np.sin(theta_start), + color="r", + width=0.1, + ) + plt.arrow( + x_goal, + y_goal, + np.cos(theta_goal), + np.sin(theta_goal), + color="g", + width=0.1, + ) plot_vehicle(x, y, theta, x_traj, y_traj) + return x_traj, y_traj, v_traj, w_traj + def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover # Corners of triangular vehicle when pointing to the right (0 radians) @@ -144,16 +175,16 @@ def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover p2 = np.matmul(T, p2_i) p3 = np.matmul(T, p3_i) - plt.plot([p1[0], p2[0]], [p1[1], p2[1]], 'k-') - plt.plot([p2[0], p3[0]], [p2[1], p3[1]], 'k-') - plt.plot([p3[0], p1[0]], [p3[1], p1[1]], 'k-') + plt.plot([p1[0], p2[0]], [p1[1], p2[1]], "k-") + plt.plot([p2[0], p3[0]], [p2[1], p3[1]], "k-") + plt.plot([p3[0], p1[0]], [p3[1], p1[1]], "k-") - plt.plot(x_traj, y_traj, 'b--') + plt.plot(x_traj, y_traj, "b--") # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect( - 'key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + "key_release_event", lambda event: [exit(0) if event.key == "escape" else None] + ) plt.xlim(0, 20) plt.ylim(0, 20) @@ -162,15 +193,16 @@ def plot_vehicle(x, y, theta, x_traj, y_traj): # pragma: no cover def transformation_matrix(x, y, theta): - return np.array([ - [np.cos(theta), -np.sin(theta), x], - [np.sin(theta), np.cos(theta), y], - [0, 0, 1] - ]) + return np.array( + [ + [np.cos(theta), -np.sin(theta), x], + [np.sin(theta), np.cos(theta), y], + [0, 0, 1], + ] + ) def main(): - for i in range(5): x_start = 20.0 * random() y_start = 20.0 * random() @@ -178,10 +210,14 @@ def main(): x_goal = 20 * random() y_goal = 20 * random() theta_goal = 2 * np.pi * random() - np.pi - print(f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n") - print(f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n") + print( + f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n" + ) + print( + f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n" + ) move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py index 8bc11a8d24..94c3ec1102 100644 --- a/tests/test_move_to_pose.py +++ b/tests/test_move_to_pose.py @@ -1,13 +1,81 @@ +import itertools +import numpy as np import conftest # Add root path to sys.path from Control.move_to_pose import move_to_pose as m -def test_1(): +def test_random(): m.show_animation = False m.main() -def test_2(): +def test_stability(): + """ + This unit test tests the move_to_pose.py program for stability + """ + m.show_animation = False + x_start = 5 + y_start = 5 + theta_start = 0 + x_goal = 1 + y_goal = 4 + theta_goal = 0 + _, _, v_traj, w_traj = m.move_to_pose( + x_start, y_start, theta_start, x_goal, y_goal, theta_goal + ) + + def v_is_change(current, previous): + return abs(current - previous) > m.MAX_LINEAR_SPEED + + def w_is_change(current, previous): + return abs(current - previous) > m.MAX_ANGULAR_SPEED + + # Check if the speed is changing too much + window_size = 10 + count_threshold = 4 + v_change = [v_is_change(v_traj[i], v_traj[i - 1]) for i in range(1, len(v_traj))] + w_change = [w_is_change(w_traj[i], w_traj[i - 1]) for i in range(1, len(w_traj))] + for i in range(len(v_change) - window_size + 1): + v_window = v_change[i : i + window_size] + w_window = w_change[i : i + window_size] + + v_unstable = sum(v_window) > count_threshold + w_unstable = sum(w_window) > count_threshold + + assert not v_unstable, ( + f"v_unstable in window [{i}, {i + window_size}], unstable count: {sum(v_window)}" + ) + assert not w_unstable, ( + f"w_unstable in window [{i}, {i + window_size}], unstable count: {sum(w_window)}" + ) + + +def test_reach_goal(): + """ + This unit test tests the move_to_pose.py program for reaching the goal + """ + m.show_animation = False + x_start = 5 + y_start = 5 + theta_start_list = [0, np.pi / 2, np.pi, 3 * np.pi / 2] + x_goal_list = [0, 5, 10] + y_goal_list = [0, 5, 10] + theta_goal = 0 + for theta_start, x_goal, y_goal in itertools.product( + theta_start_list, x_goal_list, y_goal_list + ): + x_traj, y_traj, _, _ = m.move_to_pose( + x_start, y_start, theta_start, x_goal, y_goal, theta_goal + ) + x_diff = x_goal - x_traj[-1] + y_diff = y_goal - y_traj[-1] + rho = np.hypot(x_diff, y_diff) + assert rho < 0.001, ( + f"start:[{x_start}, {y_start}, {theta_start}], goal:[{x_goal}, {y_goal}, {theta_goal}], rho: {rho} is too large" + ) + + +def test_max_speed(): """ This unit test tests the move_to_pose.py program for a MAX_LINEAR_SPEED and MAX_ANGULAR_SPEED @@ -18,5 +86,5 @@ def test_2(): m.main() -if __name__ == '__main__': +if __name__ == "__main__": conftest.run_this_test(__file__) From 64779298ffa77c918c63f9206b694f0d8d439c71 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 21 Feb 2025 21:40:21 +0900 Subject: [PATCH 818/909] =?UTF-8?q?refactor:=20rename=20files=20and=20upda?= =?UTF-8?q?te=20references=20for=20inverted=20pendulum=20an=E2=80=A6=20(#1?= =?UTF-8?q?171)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * refactor: rename files and update references for inverted pendulum and path tracking modules * refactor: rename inverted pendulum control files and update type check references * refactor: update import statements to use consistent casing for InvertedPendulum module --- Control/move_to_pose/__init__.py | 0 .../inverted_pendulum_lqr_control.py | 0 .../inverted_pendulum_mpc_control.py | 0 {Control => PathTracking/move_to_pose}/__init__.py | 0 .../move_to_pose/move_to_pose.py | 0 .../move_to_pose/move_to_pose_robot.py | 0 docs/index_main.rst | 2 +- docs/modules/10_control/control_main.rst | 12 ------------ .../inverted-pendulum.png | Bin .../inverted_pendulum_main.rst} | 6 ++++-- docs/modules/11_utils/utils_main.rst | 2 +- docs/modules/12_appendix/appendix_main.rst | 2 +- .../technologies_for_robotics_main.rst | 10 ++++++++++ .../move_to_a_pose_control_main.rst | 0 docs/modules/6_path_tracking/path_tracking_main.rst | 1 + tests/test_inverted_pendulum_lqr_control.py | 2 +- tests/test_inverted_pendulum_mpc_control.py | 2 +- tests/test_move_to_pose.py | 2 +- tests/test_move_to_pose_robot.py | 2 +- tests/test_mypy_type_check.py | 2 +- 20 files changed, 23 insertions(+), 22 deletions(-) delete mode 100644 Control/move_to_pose/__init__.py rename {Control/inverted_pendulum => InvertedPendulum}/inverted_pendulum_lqr_control.py (100%) rename {Control/inverted_pendulum => InvertedPendulum}/inverted_pendulum_mpc_control.py (100%) rename {Control => PathTracking/move_to_pose}/__init__.py (100%) rename {Control => PathTracking}/move_to_pose/move_to_pose.py (100%) rename {Control => PathTracking}/move_to_pose/move_to_pose_robot.py (100%) delete mode 100644 docs/modules/10_control/control_main.rst rename docs/modules/{10_control/inverted_pendulum_control => 10_inverted_pendulum}/inverted-pendulum.png (100%) rename docs/modules/{10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst => 10_inverted_pendulum/inverted_pendulum_main.rst} (97%) rename docs/modules/{10_control => 6_path_tracking}/move_to_a_pose_control/move_to_a_pose_control_main.rst (100%) diff --git a/Control/move_to_pose/__init__.py b/Control/move_to_pose/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py b/InvertedPendulum/inverted_pendulum_lqr_control.py similarity index 100% rename from Control/inverted_pendulum/inverted_pendulum_lqr_control.py rename to InvertedPendulum/inverted_pendulum_lqr_control.py diff --git a/Control/inverted_pendulum/inverted_pendulum_mpc_control.py b/InvertedPendulum/inverted_pendulum_mpc_control.py similarity index 100% rename from Control/inverted_pendulum/inverted_pendulum_mpc_control.py rename to InvertedPendulum/inverted_pendulum_mpc_control.py diff --git a/Control/__init__.py b/PathTracking/move_to_pose/__init__.py similarity index 100% rename from Control/__init__.py rename to PathTracking/move_to_pose/__init__.py diff --git a/Control/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py similarity index 100% rename from Control/move_to_pose/move_to_pose.py rename to PathTracking/move_to_pose/move_to_pose.py diff --git a/Control/move_to_pose/move_to_pose_robot.py b/PathTracking/move_to_pose/move_to_pose_robot.py similarity index 100% rename from Control/move_to_pose/move_to_pose_robot.py rename to PathTracking/move_to_pose/move_to_pose_robot.py diff --git a/docs/index_main.rst b/docs/index_main.rst index c1e8d22d32..75805d1184 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -43,7 +43,7 @@ this graph shows GitHub star history of this project: modules/7_arm_navigation/arm_navigation modules/8_aerial_navigation/aerial_navigation modules/9_bipedal/bipedal - modules/10_control/control + modules/10_inverted_pendulum/inverted_pendulum modules/11_utils/utils modules/12_appendix/appendix diff --git a/docs/modules/10_control/control_main.rst b/docs/modules/10_control/control_main.rst deleted file mode 100644 index cee2aa9e8e..0000000000 --- a/docs/modules/10_control/control_main.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. _control: - -Control -================= - -.. toctree:: - :maxdepth: 2 - :caption: Contents - - inverted_pendulum_control/inverted_pendulum_control - move_to_a_pose_control/move_to_a_pose_control - diff --git a/docs/modules/10_control/inverted_pendulum_control/inverted-pendulum.png b/docs/modules/10_inverted_pendulum/inverted-pendulum.png similarity index 100% rename from docs/modules/10_control/inverted_pendulum_control/inverted-pendulum.png rename to docs/modules/10_inverted_pendulum/inverted-pendulum.png diff --git a/docs/modules/10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst similarity index 97% rename from docs/modules/10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst rename to docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst index e41729fd61..048cbea9ac 100644 --- a/docs/modules/10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst +++ b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst @@ -1,5 +1,7 @@ -Inverted Pendulum Control ------------------------------ +.. _`Inverted Pendulum`: + +Inverted Pendulum +------------------ An inverted pendulum on a cart consists of a mass :math:`m` at the top of a pole of length :math:`l` pivoted on a horizontally moving base as shown in the adjacent. diff --git a/docs/modules/11_utils/utils_main.rst b/docs/modules/11_utils/utils_main.rst index ff79a26205..95c982b077 100644 --- a/docs/modules/11_utils/utils_main.rst +++ b/docs/modules/11_utils/utils_main.rst @@ -1,4 +1,4 @@ -.. _utils: +.. _`utils`: Utilities ========== diff --git a/docs/modules/12_appendix/appendix_main.rst b/docs/modules/12_appendix/appendix_main.rst index a55389e1e6..d0b9eeea3a 100644 --- a/docs/modules/12_appendix/appendix_main.rst +++ b/docs/modules/12_appendix/appendix_main.rst @@ -1,4 +1,4 @@ -.. _appendix: +.. _`Appendix`: Appendix ============== diff --git a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst index c77997a138..0ed51e961b 100644 --- a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst +++ b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst @@ -56,3 +56,13 @@ Robot type specific technologies #. :ref:`Aerial Navigation` #. :ref:`Bipedal` +#. :ref:`Inverted Pendulum` + + +Additional Information +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +#. :ref:`utils` +#. :ref:`Appendix` + + diff --git a/docs/modules/10_control/move_to_a_pose_control/move_to_a_pose_control_main.rst b/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst similarity index 100% rename from docs/modules/10_control/move_to_a_pose_control/move_to_a_pose_control_main.rst rename to docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst index 130a2340c1..d98e324583 100644 --- a/docs/modules/6_path_tracking/path_tracking_main.rst +++ b/docs/modules/6_path_tracking/path_tracking_main.rst @@ -16,3 +16,4 @@ Path tracking is the ability of a robot to follow the reference path generated b lqr_speed_and_steering_control/lqr_speed_and_steering_control model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control cgmres_nmpc/cgmres_nmpc + move_to_a_pose_control/move_to_a_pose_control diff --git a/tests/test_inverted_pendulum_lqr_control.py b/tests/test_inverted_pendulum_lqr_control.py index cbbabb93b1..62afda71c3 100644 --- a/tests/test_inverted_pendulum_lqr_control.py +++ b/tests/test_inverted_pendulum_lqr_control.py @@ -1,5 +1,5 @@ import conftest -from Control.inverted_pendulum import inverted_pendulum_lqr_control as m +from InvertedPendulum import inverted_pendulum_lqr_control as m def test_1(): diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py index 800aefd7d5..94859c2e0a 100644 --- a/tests/test_inverted_pendulum_mpc_control.py +++ b/tests/test_inverted_pendulum_mpc_control.py @@ -1,6 +1,6 @@ import conftest -from Control.inverted_pendulum import inverted_pendulum_mpc_control as m +from InvertedPendulum import inverted_pendulum_mpc_control as m def test1(): diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py index 94c3ec1102..e06d801555 100644 --- a/tests/test_move_to_pose.py +++ b/tests/test_move_to_pose.py @@ -1,7 +1,7 @@ import itertools import numpy as np import conftest # Add root path to sys.path -from Control.move_to_pose import move_to_pose as m +from PathTracking.move_to_pose import move_to_pose as m def test_random(): diff --git a/tests/test_move_to_pose_robot.py b/tests/test_move_to_pose_robot.py index a93b44d198..7a82f98556 100644 --- a/tests/test_move_to_pose_robot.py +++ b/tests/test_move_to_pose_robot.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from Control.move_to_pose import move_to_pose as m +from PathTracking.move_to_pose import move_to_pose as m def test_1(): diff --git a/tests/test_mypy_type_check.py b/tests/test_mypy_type_check.py index 07afb40afd..6b933c1011 100644 --- a/tests/test_mypy_type_check.py +++ b/tests/test_mypy_type_check.py @@ -7,12 +7,12 @@ "AerialNavigation", "ArmNavigation", "Bipedal", - "Control", "Localization", "Mapping", "PathPlanning", "PathTracking", "SLAM", + "InvertedPendulum" ] From 6e13e8292aad80661093603ed262c6f0bdcb4137 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Feb 2025 08:07:42 +0900 Subject: [PATCH 819/909] build(deps): bump ruff from 0.9.6 to 0.9.7 in /requirements (#1173) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.6 to 0.9.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.6...0.9.7) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 8176364c29..b439ea4266 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.4 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.9.6 # For unit test +ruff == 0.9.7 # For unit test From 0c8ff11645e6804db18bec6bd918e03ed03454f7 Mon Sep 17 00:00:00 2001 From: Jonathan Schwartz Date: Tue, 25 Feb 2025 06:53:36 -0500 Subject: [PATCH 820/909] Space-Time AStar (#1170) * wip - sketch out obstacles * move to correct path * better animation * clean up * use np to sample points * implemented time-based A* * cleaning up Grid + adding new obstacle arrangement * added unit test * formatting p1 * format STA* file * remove newlines by docstrings * linter * working on typehints * fix linter errors * lint some more * appease AppVeyor * dataclasses are :fire: * back to @total_ordering * trailing whitespace * add docs page on SpaceTimeA* * docs lint * remove trailing newlines in doc * address comments * Update docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst --------- Co-authored-by: Atsushi Sakai --- .../GridWithDynamicObstacles.py | 273 ++++++++++++++++++ .../TimeBasedPathPlanning/SpaceTimeAStar.py | 220 ++++++++++++++ .../TimeBasedPathPlanning/__init__.py | 0 .../5_path_planning/path_planning_main.rst | 1 + .../time_based_grid_search_main.rst | 22 ++ tests/test_space_time_astar.py | 33 +++ 6 files changed, 549 insertions(+) create mode 100644 PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py create mode 100644 PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py create mode 100644 PathPlanning/TimeBasedPathPlanning/__init__.py create mode 100644 docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst create mode 100644 tests/test_space_time_astar.py diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py new file mode 100644 index 0000000000..7b0190d023 --- /dev/null +++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py @@ -0,0 +1,273 @@ +""" +This file implements a grid with a 3d reservation matrix with dimensions for x, y, and time. There +is also infrastructure to generate dynamic obstacles that move around the grid. The obstacles' paths +are stored in the reservation matrix on creation. +""" +import numpy as np +import matplotlib.pyplot as plt +from enum import Enum +from dataclasses import dataclass + +@dataclass(order=True) +class Position: + x: int + y: int + + def as_ndarray(self) -> np.ndarray: + return np.array([self.x, self.y]) + + def __add__(self, other): + if isinstance(other, Position): + return Position(self.x + other.x, self.y + other.y) + raise NotImplementedError( + f"Addition not supported for Position and {type(other)}" + ) + + def __sub__(self, other): + if isinstance(other, Position): + return Position(self.x - other.x, self.y - other.y) + raise NotImplementedError( + f"Subtraction not supported for Position and {type(other)}" + ) + + +class ObstacleArrangement(Enum): + # Random obstacle positions and movements + RANDOM = 0 + # Obstacles start in a line in y at center of grid and move side-to-side in x + ARRANGEMENT1 = 1 + + +class Grid: + # Set in constructor + grid_size: np.ndarray + reservation_matrix: np.ndarray + obstacle_paths: list[list[Position]] = [] + # Obstacles will never occupy these points. Useful to avoid impossible scenarios + obstacle_avoid_points: list[Position] = [] + + # Number of time steps in the simulation + time_limit: int + + # Logging control + verbose = False + + def __init__( + self, + grid_size: np.ndarray, + num_obstacles: int = 40, + obstacle_avoid_points: list[Position] = [], + obstacle_arrangement: ObstacleArrangement = ObstacleArrangement.RANDOM, + time_limit: int = 100, + ): + self.obstacle_avoid_points = obstacle_avoid_points + self.time_limit = time_limit + self.grid_size = grid_size + self.reservation_matrix = np.zeros((grid_size[0], grid_size[1], self.time_limit)) + + if num_obstacles > self.grid_size[0] * self.grid_size[1]: + raise Exception("Number of obstacles is greater than grid size!") + + if obstacle_arrangement == ObstacleArrangement.RANDOM: + self.obstacle_paths = self.generate_dynamic_obstacles(num_obstacles) + elif obstacle_arrangement == ObstacleArrangement.ARRANGEMENT1: + self.obstacle_paths = self.obstacle_arrangement_1(num_obstacles) + + for i, path in enumerate(self.obstacle_paths): + obs_idx = i + 1 # avoid using 0 - that indicates free space in the grid + for t, position in enumerate(path): + # Reserve old & new position at this time step + if t > 0: + self.reservation_matrix[path[t - 1].x, path[t - 1].y, t] = obs_idx + self.reservation_matrix[position.x, position.y, t] = obs_idx + + """ + Generate dynamic obstacles that move around the grid. Initial positions and movements are random + """ + def generate_dynamic_obstacles(self, obs_count: int) -> list[list[Position]]: + obstacle_paths = [] + for _ in (0, obs_count): + # Sample until a free starting space is found + initial_position = self.sample_random_position() + while not self.valid_obstacle_position(initial_position, 0): + initial_position = self.sample_random_position() + + positions = [initial_position] + if self.verbose: + print("Obstacle initial position: ", initial_position) + + # Encourage obstacles to mostly stay in place - too much movement leads to chaotic planning scenarios + # that are not fun to watch + weights = [0.05, 0.05, 0.05, 0.05, 0.8] + diffs = [ + Position(0, 1), + Position(0, -1), + Position(1, 0), + Position(-1, 0), + Position(0, 0), + ] + + for t in range(1, self.time_limit - 1): + sampled_indices = np.random.choice( + len(diffs), size=5, replace=False, p=weights + ) + rand_diffs = [diffs[i] for i in sampled_indices] + + valid_position = None + for diff in rand_diffs: + new_position = positions[-1] + diff + + if not self.valid_obstacle_position(new_position, t): + continue + + valid_position = new_position + break + + # Impossible situation for obstacle - stay in place + # -> this can happen if the oaths of other obstacles this one + if valid_position is None: + valid_position = positions[-1] + + positions.append(valid_position) + + obstacle_paths.append(positions) + + return obstacle_paths + + """ + Generate a line of obstacles in y at the center of the grid that move side-to-side in x + Bottom half start moving right, top half start moving left. If `obs_count` is less than the length of + the grid, only the first `obs_count` obstacles will be generated. + """ + def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]: + obstacle_paths = [] + half_grid_x = self.grid_size[0] // 2 + half_grid_y = self.grid_size[1] // 2 + + for y_idx in range(0, min(obs_count, self.grid_size[1])): + moving_right = y_idx < half_grid_y + position = Position(half_grid_x, y_idx) + path = [position] + + for t in range(1, self.time_limit - 1): + # sit in place every other time step + if t % 2 == 0: + path.append(position) + continue + + # first check if we should switch direction (at edge of grid) + if (moving_right and position.x == self.grid_size[0] - 1) or ( + not moving_right and position.x == 0 + ): + moving_right = not moving_right + # step in direction + position = Position( + position.x + (1 if moving_right else -1), position.y + ) + path.append(position) + + obstacle_paths.append(path) + + return obstacle_paths + + """ + Check if the given position is valid at time t + + input: + position (Position): (x, y) position + t (int): time step + + output: + bool: True if position/time combination is valid, False otherwise + """ + def valid_position(self, position: Position, t: int) -> bool: + # Check if new position is in grid + if not self.inside_grid_bounds(position): + return False + + # Check if new position is not occupied at time t + return self.reservation_matrix[position.x, position.y, t] == 0 + + """ + Returns True if the given position is valid at time t and is not in the set of obstacle_avoid_points + """ + def valid_obstacle_position(self, position: Position, t: int) -> bool: + return ( + self.valid_position(position, t) + and position not in self.obstacle_avoid_points + ) + + """ + Returns True if the given position is within the grid's boundaries + """ + def inside_grid_bounds(self, position: Position) -> bool: + return ( + position.x >= 0 + and position.x < self.grid_size[0] + and position.y >= 0 + and position.y < self.grid_size[1] + ) + + """ + Sample a random position that is within the grid's boundaries + + output: + Position: (x, y) position + """ + def sample_random_position(self) -> Position: + return Position( + np.random.randint(0, self.grid_size[0]), + np.random.randint(0, self.grid_size[1]), + ) + + """ + Returns a tuple of (x_positions, y_positions) of the obstacles at time t + """ + def get_obstacle_positions_at_time(self, t: int) -> tuple[list[int], list[int]]: + x_positions = [] + y_positions = [] + for obs_path in self.obstacle_paths: + x_positions.append(obs_path[t].x) + y_positions.append(obs_path[t].y) + return (x_positions, y_positions) + + +show_animation = True + + +def main(): + grid = Grid( + np.array([11, 11]), + num_obstacles=10, + obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1, + ) + + if not show_animation: + return + + fig = plt.figure(figsize=(8, 7)) + ax = fig.add_subplot( + autoscale_on=False, + xlim=(0, grid.grid_size[0] - 1), + ylim=(0, grid.grid_size[1] - 1), + ) + ax.set_aspect("equal") + ax.grid() + ax.set_xticks(np.arange(0, 11, 1)) + ax.set_yticks(np.arange(0, 11, 1)) + (obs_points,) = ax.plot([], [], "ro", ms=15) + + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + "key_release_event", lambda event: [exit(0) if event.key == "escape" else None] + ) + + for i in range(0, grid.time_limit - 1): + obs_positions = grid.get_obstacle_positions_at_time(i) + obs_points.set_data(obs_positions[0], obs_positions[1]) + plt.pause(0.2) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py new file mode 100644 index 0000000000..3b3613d695 --- /dev/null +++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py @@ -0,0 +1,220 @@ +""" +Space-time A* Algorithm + This script demonstrates the Space-time A* algorithm for path planning in a grid world with moving obstacles. + This algorithm is different from normal 2D A* in one key way - the cost (often notated as g(n)) is + the number of time steps it took to get to a given node, instead of the number of cells it has + traversed. This ensures the path is time-optimal, while respescting any dynamic obstacles in the environment. + + Reference: https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf +""" + +import numpy as np +import matplotlib.pyplot as plt +from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import ( + Grid, + ObstacleArrangement, + Position, +) +import heapq +from collections.abc import Generator +import random +from dataclasses import dataclass +from functools import total_ordering + + +# Seed randomness for reproducibility +RANDOM_SEED = 50 +random.seed(RANDOM_SEED) +np.random.seed(RANDOM_SEED) + +@dataclass() +# Note: Total_ordering is used instead of adding `order=True` to the @dataclass decorator because +# this class needs to override the __lt__ and __eq__ methods to ignore parent_index. Parent +# index is just used to track the path found by the algorithm, and has no effect on the quality +# of a node. +@total_ordering +class Node: + position: Position + time: int + heuristic: int + parent_index: int + + """ + This is what is used to drive node expansion. The node with the lowest value is expanded next. + This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic) + """ + def __lt__(self, other: object): + if not isinstance(other, Node): + return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}") + return (self.time + self.heuristic) < (other.time + other.heuristic) + + def __eq__(self, other: object): + if not isinstance(other, Node): + return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}") + return self.position == other.position and self.time == other.time + + +class NodePath: + path: list[Node] + positions_at_time: dict[int, Position] = {} + + def __init__(self, path: list[Node]): + self.path = path + for node in path: + self.positions_at_time[node.time] = node.position + + """ + Get the position of the path at a given time + """ + def get_position(self, time: int) -> Position | None: + return self.positions_at_time.get(time) + + """ + Time stamp of the last node in the path + """ + def goal_reached_time(self) -> int: + return self.path[-1].time + + def __repr__(self): + repr_string = "" + for i, node in enumerate(self.path): + repr_string += f"{i}: {node}\n" + return repr_string + + +class SpaceTimeAStar: + grid: Grid + start: Position + goal: Position + + def __init__(self, grid: Grid, start: Position, goal: Position): + self.grid = grid + self.start = start + self.goal = goal + + def plan(self, verbose: bool = False) -> NodePath: + open_set: list[Node] = [] + heapq.heappush( + open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1) + ) + + expanded_set: list[Node] = [] + while open_set: + expanded_node: Node = heapq.heappop(open_set) + if verbose: + print("Expanded node:", expanded_node) + + if expanded_node.time + 1 >= self.grid.time_limit: + if verbose: + print(f"\tSkipping node that is past time limit: {expanded_node}") + continue + + if expanded_node.position == self.goal: + print(f"Found path to goal after {len(expanded_set)} expansions") + path = [] + path_walker: Node = expanded_node + while True: + path.append(path_walker) + if path_walker.parent_index == -1: + break + path_walker = expanded_set[path_walker.parent_index] + + # reverse path so it goes start -> goal + path.reverse() + return NodePath(path) + + expanded_idx = len(expanded_set) + expanded_set.append(expanded_node) + + for child in self.generate_successors(expanded_node, expanded_idx, verbose): + heapq.heappush(open_set, child) + + raise Exception("No path found") + + """ + Generate possible successors of the provided `parent_node` + """ + def generate_successors( + self, parent_node: Node, parent_node_idx: int, verbose: bool + ) -> Generator[Node, None, None]: + diffs = [ + Position(0, 0), + Position(1, 0), + Position(-1, 0), + Position(0, 1), + Position(0, -1), + ] + for diff in diffs: + new_pos = parent_node.position + diff + if self.grid.valid_position(new_pos, parent_node.time + 1): + new_node = Node( + new_pos, + parent_node.time + 1, + self.calculate_heuristic(new_pos), + parent_node_idx, + ) + if verbose: + print("\tNew successor node: ", new_node) + yield new_node + + def calculate_heuristic(self, position) -> int: + diff = self.goal - position + return abs(diff.x) + abs(diff.y) + + +show_animation = True +verbose = False + +def main(): + start = Position(1, 11) + goal = Position(19, 19) + grid_side_length = 21 + grid = Grid( + np.array([grid_side_length, grid_side_length]), + num_obstacles=40, + obstacle_avoid_points=[start, goal], + obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1, + ) + + planner = SpaceTimeAStar(grid, start, goal) + path = planner.plan(verbose) + + if verbose: + print(f"Path: {path}") + + if not show_animation: + return + + fig = plt.figure(figsize=(10, 7)) + ax = fig.add_subplot( + autoscale_on=False, + xlim=(0, grid.grid_size[0] - 1), + ylim=(0, grid.grid_size[1] - 1), + ) + ax.set_aspect("equal") + ax.grid() + ax.set_xticks(np.arange(0, grid_side_length, 1)) + ax.set_yticks(np.arange(0, grid_side_length, 1)) + + (start_and_goal,) = ax.plot([], [], "mD", ms=15, label="Start and Goal") + start_and_goal.set_data([start.x, goal.x], [start.y, goal.y]) + (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles") + (path_points,) = ax.plot([], [], "bo", ms=10, label="Path Found") + ax.legend(bbox_to_anchor=(1.05, 1)) + + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + "key_release_event", lambda event: [exit(0) if event.key == "escape" else None] + ) + + for i in range(0, path.goal_reached_time()): + obs_positions = grid.get_obstacle_positions_at_time(i) + obs_points.set_data(obs_positions[0], obs_positions[1]) + path_position = path.get_position(i) + path_points.set_data([path_position.x], [path_position.y]) + plt.pause(0.2) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/PathPlanning/TimeBasedPathPlanning/__init__.py b/PathPlanning/TimeBasedPathPlanning/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst index a0f9c30a3d..0c84a19c22 100644 --- a/docs/modules/5_path_planning/path_planning_main.rst +++ b/docs/modules/5_path_planning/path_planning_main.rst @@ -12,6 +12,7 @@ Path planning is the ability of a robot to search feasible and efficient path to dynamic_window_approach/dynamic_window_approach bugplanner/bugplanner grid_base_search/grid_base_search + time_based_grid_search/time_based_grid_search model_predictive_trajectory_generator/model_predictive_trajectory_generator state_lattice_planner/state_lattice_planner prm_planner/prm_planner diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst new file mode 100644 index 0000000000..0c26badec7 --- /dev/null +++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst @@ -0,0 +1,22 @@ +Time based grid search +---------------------- + +Space-time astar +~~~~~~~~~~~~~~~~~~~~~~ + +This is an extension of A* algorithm that supports planning around dynamic obstacles. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar/path_animation.gif + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar/path_animation2.gif + +The key difference of this algorithm compared to vanilla A* is that the cost and heuristic are now time-based instead of distance-based. +Using a time-based cost and heuristic ensures the path found is optimal in terms of time to reach the goal. + +The cost is the amount of time it takes to reach a given node, and the heuristic is the minimum amount of time it could take to reach the goal from that node, disregarding all obstacles. +For a simple scenario where the robot can move 1 cell per time step and stop and go as it pleases, the heuristic for time is equivalent to the heuristic for distance. + +References: +~~~~~~~~~~~ + +- `Cooperative Pathfinding `__ diff --git a/tests/test_space_time_astar.py b/tests/test_space_time_astar.py new file mode 100644 index 0000000000..5290738eb4 --- /dev/null +++ b/tests/test_space_time_astar.py @@ -0,0 +1,33 @@ +from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import ( + Grid, + ObstacleArrangement, + Position, +) +from PathPlanning.TimeBasedPathPlanning import SpaceTimeAStar as m +import numpy as np +import conftest + + +def test_1(): + start = Position(1, 11) + goal = Position(19, 19) + grid_side_length = 21 + grid = Grid( + np.array([grid_side_length, grid_side_length]), + obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1, + ) + + m.show_animation = False + planner = m.SpaceTimeAStar(grid, start, goal) + + path = planner.plan(False) + + # path should have 28 entries + assert len(path.path) == 31 + + # path should end at the goal + assert path.path[-1].position == goal + + +if __name__ == "__main__": + conftest.run_this_test(__file__) From 67a3ca7138384b4a013974dbf22383252f83686e Mon Sep 17 00:00:00 2001 From: Aglargil <34728006+Aglargil@users.noreply.github.com> Date: Fri, 28 Feb 2025 19:30:24 +0800 Subject: [PATCH 821/909] add state machine (#1172) * add state machine state_machine test state_machine_update * add state machine test/doc * state machine update * state machine generate_plantuml() can show diagram by using https://www.plantuml.com/plantuml/ --- .../StateMachine/robot_behavior_case.py | 111 +++++++ MissionPlanning/StateMachine/state_machine.py | 294 ++++++++++++++++++ docs/index_main.rst | 1 + .../mission_planning_main.rst | 12 + .../state_machine/robot_behavior_case.png | Bin 0 -> 28565 bytes .../state_machine/state_machine_main.rst | 74 +++++ tests/test_state_machine.py | 51 +++ 7 files changed, 543 insertions(+) create mode 100644 MissionPlanning/StateMachine/robot_behavior_case.py create mode 100644 MissionPlanning/StateMachine/state_machine.py create mode 100644 docs/modules/13_mission_planning/mission_planning_main.rst create mode 100644 docs/modules/13_mission_planning/state_machine/robot_behavior_case.png create mode 100644 docs/modules/13_mission_planning/state_machine/state_machine_main.rst create mode 100644 tests/test_state_machine.py diff --git a/MissionPlanning/StateMachine/robot_behavior_case.py b/MissionPlanning/StateMachine/robot_behavior_case.py new file mode 100644 index 0000000000..03ee60ae9f --- /dev/null +++ b/MissionPlanning/StateMachine/robot_behavior_case.py @@ -0,0 +1,111 @@ +""" +A case study of robot behavior using state machine + +author: Wang Zheng (@Aglargil) +""" + +from state_machine import StateMachine + + +class Robot: + def __init__(self): + self.battery = 100 + self.task_progress = 0 + + # Initialize state machine + self.machine = StateMachine("robot_sm", self) + + # Add state transition rules + self.machine.add_transition( + src_state="patrolling", + event="detect_task", + dst_state="executing_task", + guard=None, + action=None, + ) + + self.machine.add_transition( + src_state="executing_task", + event="task_complete", + dst_state="patrolling", + guard=None, + action="https://github.com/reset_task", + ) + + self.machine.add_transition( + src_state="executing_task", + event="low_battery", + dst_state="returning_to_base", + guard="is_battery_low", + ) + + self.machine.add_transition( + src_state="returning_to_base", + event="reach_base", + dst_state="charging", + guard=None, + action=None, + ) + + self.machine.add_transition( + src_state="charging", + event="charge_complete", + dst_state="patrolling", + guard=None, + action="https://github.com/battery_full", + ) + + # Set initial state + self.machine.set_current_state("patrolling") + + def is_battery_low(self): + """Battery level check condition""" + return self.battery < 30 + + def reset_task(self): + """Reset task progress""" + self.task_progress = 0 + print("[Action] Task progress has been reset") + + # Modify state entry callback naming convention (add state_ prefix) + def on_enter_executing_task(self): + print("\n------ Start Executing Task ------") + print(f"Current battery: {self.battery}%") + while self.machine.get_current_state().name == "executing_task": + self.task_progress += 10 + self.battery -= 25 + print( + f"Task progress: {self.task_progress}%, Remaining battery: {self.battery}%" + ) + + if self.task_progress >= 100: + self.machine.process("task_complete") + break + elif self.is_battery_low(): + self.machine.process("low_battery") + break + + def on_enter_returning_to_base(self): + print("\nLow battery, returning to charging station...") + self.machine.process("reach_base") + + def on_enter_charging(self): + print("\n------ Charging ------") + self.battery = 100 + print("Charging complete!") + self.machine.process("charge_complete") + + +# Keep the test section structure the same, only modify the trigger method +if __name__ == "__main__": + robot = Robot() + print(robot.machine.generate_plantuml()) + + print(f"Initial state: {robot.machine.get_current_state().name}") + print("------------") + + # Trigger task detection event + robot.machine.process("detect_task") + + print("\n------------") + print(f"Final state: {robot.machine.get_current_state().name}") diff --git a/MissionPlanning/StateMachine/state_machine.py b/MissionPlanning/StateMachine/state_machine.py new file mode 100644 index 0000000000..de72f0f451 --- /dev/null +++ b/MissionPlanning/StateMachine/state_machine.py @@ -0,0 +1,294 @@ +""" +State Machine + +author: Wang Zheng (@Aglargil) + +Ref: + +- [State Machine] +(https://en.wikipedia.org/wiki/Finite-state_machine) +""" + +import string +from urllib.request import urlopen, Request +from base64 import b64encode +from zlib import compress +from io import BytesIO +from collections.abc import Callable +from matplotlib.image import imread +from matplotlib import pyplot as plt + + +def deflate_and_encode(plantuml_text): + """ + zlib compress the plantuml text and encode it for the plantuml server. + + Ref: https://plantuml.com/en/text-encoding + """ + plantuml_alphabet = ( + string.digits + string.ascii_uppercase + string.ascii_lowercase + "-_" + ) + base64_alphabet = ( + string.ascii_uppercase + string.ascii_lowercase + string.digits + "+/" + ) + b64_to_plantuml = bytes.maketrans( + base64_alphabet.encode("utf-8"), plantuml_alphabet.encode("utf-8") + ) + zlibbed_str = compress(plantuml_text.encode("utf-8")) + compressed_string = zlibbed_str[2:-4] + return b64encode(compressed_string).translate(b64_to_plantuml).decode("utf-8") + + +class State: + def __init__(self, name, on_enter=None, on_exit=None): + self.name = name + self.on_enter = on_enter + self.on_exit = on_exit + + def enter(self): + print(f"entering <{self.name}>") + if self.on_enter: + self.on_enter() + + def exit(self): + print(f"exiting <{self.name}>") + if self.on_exit: + self.on_exit() + + +class StateMachine: + def __init__(self, name: str, model=object): + """Initialize the state machine. + + Args: + name (str): Name of the state machine. + model (object, optional): Model object used to automatically look up callback functions + for states and transitions: + State callbacks: Automatically searches for 'on_enter_' and 'on_exit_' methods. + Transition callbacks: When action or guard parameters are strings, looks up corresponding methods in the model. + + Example: + >>> class MyModel: + ... def on_enter_idle(self): + ... print("Entering idle state") + ... def on_exit_idle(self): + ... print("Exiting idle state") + ... def can_start(self): + ... return True + ... def on_start(self): + ... print("Starting operation") + >>> model = MyModel() + >>> machine = StateMachine("my_machine", model) + """ + self._name = name + self._states = {} + self._events = {} + self._transition_table = {} + self._model = model + self._state: State = None + + def _register_event(self, event: str): + self._events[event] = event + + def _get_state(self, name): + return self._states[name] + + def _get_event(self, name): + return self._events[name] + + def _has_event(self, event: str): + return event in self._events + + def add_transition( + self, + src_state: str | State, + event: str, + dst_state: str | State, + guard: str | Callable = None, + action: str | Callable = None, + ) -> None: + """Add a transition to the state machine. + + Args: + src_state (str | State): The source state where the transition begins. + Can be either a state name or a State object. + event (str): The event that triggers this transition. + dst_state (str | State): The destination state where the transition ends. + Can be either a state name or a State object. + guard (str | Callable, optional): Guard condition for the transition. + If callable: Function that returns bool. + If str: Name of a method in the model class. + If returns True: Transition proceeds. + If returns False: Transition is skipped. + action (str | Callable, optional): Action to execute during transition. + If callable: Function to execute. + If str: Name of a method in the model class. + Executed after guard passes and before entering new state. + + Example: + >>> machine.add_transition( + ... src_state="idle", + ... event="start", + ... dst_state="running", + ... guard="can_start", + ... action="https://github.com/on_start" + ... ) + """ + # Convert string parameters to objects if necessary + self.register_state(src_state) + self._register_event(event) + self.register_state(dst_state) + + def get_state_obj(state): + return state if isinstance(state, State) else self._get_state(state) + + def get_callable(func): + return func if callable(func) else getattr(self._model, func, None) + + src_state_obj = get_state_obj(src_state) + dst_state_obj = get_state_obj(dst_state) + + guard_func = get_callable(guard) if guard else None + action_func = get_callable(action) if action else None + self._transition_table[(src_state_obj.name, event)] = ( + dst_state_obj, + guard_func, + action_func, + ) + + def state_transition(self, src_state: State, event: str): + if (src_state.name, event) not in self._transition_table: + raise ValueError( + f"|{self._name}| invalid transition: <{src_state.name}> : [{event}]" + ) + + dst_state, guard, action = self._transition_table[(src_state.name, event)] + + def call_guard(guard): + if callable(guard): + return guard() + else: + return True + + def call_action(action): + if callable(action): + action() + + if call_guard(guard): + call_action(action) + if src_state.name != dst_state.name: + print( + f"|{self._name}| transitioning from <{src_state.name}> to <{dst_state.name}>" + ) + src_state.exit() + self._state = dst_state + dst_state.enter() + else: + print( + f"|{self._name}| skipping transition from <{src_state.name}> to <{dst_state.name}> because guard failed" + ) + + def register_state(self, state: str | State, on_enter=None, on_exit=None): + """Register a state in the state machine. + + Args: + state (str | State): The state to register. Can be either a string (state name) + or a State object. + on_enter (Callable, optional): Callback function to be executed when entering the state. + If state is a string and on_enter is None, it will look for + a method named 'on_enter_' in the model. + on_exit (Callable, optional): Callback function to be executed when exiting the state. + If state is a string and on_exit is None, it will look for + a method named 'on_exit_' in the model. + Example: + >>> machine.register_state("idle", on_enter=on_enter_idle, on_exit=on_exit_idle) + >>> machine.register_state(State("running", on_enter=on_enter_running, on_exit=on_exit_running)) + """ + if isinstance(state, str): + if on_enter is None: + on_enter = getattr(self._model, "on_enter_" + state, None) + if on_exit is None: + on_exit = getattr(self._model, "on_exit_" + state, None) + self._states[state] = State(state, on_enter, on_exit) + return + + self._states[state.name] = state + + def set_current_state(self, state: State | str): + if isinstance(state, str): + self._state = self._get_state(state) + else: + self._state = state + + def get_current_state(self): + return self._state + + def process(self, event: str) -> None: + """Process an event in the state machine. + + Args: + event: Event name. + + Example: + >>> machine.process("start") + """ + if self._state is None: + raise ValueError("State machine is not initialized") + + if self._has_event(event): + self.state_transition(self._state, event) + else: + raise ValueError(f"Invalid event: {event}") + + def generate_plantuml(self) -> str: + """Generate PlantUML state diagram representation of the state machine. + + Returns: + str: PlantUML state diagram code. + """ + if self._state is None: + raise ValueError("State machine is not initialized") + + plant_uml = ["@startuml"] + plant_uml.append("[*] --> " + self._state.name) + + # Generate transitions + for (src_state, event), ( + dst_state, + guard, + action, + ) in self._transition_table.items(): + transition = f"{src_state} --> {dst_state.name} : {event}" + + # Add guard and action if present + conditions = [] + if guard: + guard_name = guard.__name__ if callable(guard) else guard + conditions.append(f"[{guard_name}]") + if action: + action_name = action.__name__ if callable(action) else action + conditions.append(f"/ {action_name}") + + if conditions: + transition += "\\n" + " ".join(conditions) + + plant_uml.append(transition) + + plant_uml.append("@enduml") + plant_uml_text = "\n".join(plant_uml) + + try: + url = f"http://www.plantuml.com/plantuml/img/%7Bdeflate_and_encode(plant_uml_text)%7D" + headers = {"User-Agent": "Mozilla/5.0"} + request = Request(url, headers=headers) + + with urlopen(request) as response: + content = response.read() + + plt.imshow(imread(BytesIO(content), format="png")) + plt.axis("off") + plt.show() + except Exception as e: + print(f"Error showing PlantUML: {e}") + + return plant_uml_text diff --git a/docs/index_main.rst b/docs/index_main.rst index 75805d1184..65634f32e8 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -44,6 +44,7 @@ this graph shows GitHub star history of this project: modules/8_aerial_navigation/aerial_navigation modules/9_bipedal/bipedal modules/10_inverted_pendulum/inverted_pendulum + modules/13_mission_planning/mission_planning modules/11_utils/utils modules/12_appendix/appendix diff --git a/docs/modules/13_mission_planning/mission_planning_main.rst b/docs/modules/13_mission_planning/mission_planning_main.rst new file mode 100644 index 0000000000..385e62f68e --- /dev/null +++ b/docs/modules/13_mission_planning/mission_planning_main.rst @@ -0,0 +1,12 @@ +.. _`Mission Planning`: + +Mission Planning +================ + +Mission planning includes tools such as finite state machines and behavior trees used to describe robot behavior and high level task planning. + +.. toctree:: + :maxdepth: 2 + :caption: Contents + + state_machine/state_machine diff --git 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z=`|EF^@!11J2<9Z~a^g6|v((gRxH|Auzk@&Dq3Xi+`vP@dF~&)MV&5@%a87 zFvSRwJ^h*g^(*vv*=0+AHh~5L4_N;r&~Z_HLj(L=vKeAR*B@XlS{~}au1{4Pran_-JnU<`Wc6pEQc6ZeqlX*;%&>F3|Gj%a7P2h*# zuye&uI|~YtlSbg_C6LFl@@*oDP_hyPWqYxEA*5g~PlA*$c5;XzGA<)USSsY%tK(<( VMQeA>!#)E3qv`LZ-qN)T{cpp^WxxOc literal 0 HcmV?d00001 diff --git a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst new file mode 100644 index 0000000000..abaece1b11 --- /dev/null +++ b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst @@ -0,0 +1,74 @@ +State Machine +------------- + +A state machine is a model used to describe the transitions of an object between different states. It clearly shows how an object changes state based on events and may trigger corresponding actions. + +Core Concepts +~~~~~~~~~~~~~ + +- **State**: A distinct mode or condition of the system (e.g. "Idle", "Running"). Managed by State class with optional on_enter/on_exit callbacks +- **Event**: A trigger signal that may cause state transitions (e.g. "start", "stop") +- **Transition**: A state change path from source to destination state triggered by an event +- **Action**: An operation executed during transition (before entering new state) +- **Guard**: A precondition that must be satisfied to allow transition + +API +~~~ + +.. autoclass:: MissionPlanning.StateMachine.state_machine.StateMachine + :members: add_transition, process, register_state + :special-members: __init__ + +PlantUML Support +~~~~~~~~~~~~~~~~ + +The ``generate_plantuml()`` method creates diagrams showing: + +- Current state (marked with [*] arrow) +- All possible transitions +- Guard conditions in [brackets] +- Actions prefixed with / + +Example +~~~~~~~ + +state machine diagram: ++++++++++++++++++++++++ +.. image:: robot_behavior_case.png + +state transition table: ++++++++++++++++++++++++ +.. list-table:: State Transitions + :header-rows: 1 + :widths: 20 15 20 20 20 + + * - Source State + - Event + - Target State + - Guard + - Action + * - patrolling + - detect_task + - executing_task + - + - + * - executing_task + - task_complete + - patrolling + - + - reset_task + * - executing_task + - low_battery + - returning_to_base + - is_battery_low + - + * - returning_to_base + - reach_base + - charging + - + - + * - charging + - charge_complete + - patrolling + - + - \ No newline at end of file diff --git a/tests/test_state_machine.py b/tests/test_state_machine.py new file mode 100644 index 0000000000..e36a8120fd --- /dev/null +++ b/tests/test_state_machine.py @@ -0,0 +1,51 @@ +import conftest + +from MissionPlanning.StateMachine.state_machine import StateMachine + + +def test_transition(): + sm = StateMachine("state_machine") + sm.add_transition(src_state="idle", event="start", dst_state="running") + sm.set_current_state("idle") + sm.process("start") + assert sm.get_current_state().name == "running" + + +def test_guard(): + class Model: + def can_start(self): + return False + + sm = StateMachine("state_machine", Model()) + sm.add_transition( + src_state="idle", event="start", dst_state="running", guard="can_start" + ) + sm.set_current_state("idle") + sm.process("start") + assert sm.get_current_state().name == "idle" + + +def test_action(): + class Model: + def on_start(self): + self.start_called = True + + model = Model() + sm = StateMachine("state_machine", model) + sm.add_transition( + src_state="idle", event="start", dst_state="running", action="https://github.com/on_start" + ) + sm.set_current_state("idle") + sm.process("start") + assert model.start_called + + +def test_plantuml(): + sm = StateMachine("state_machine") + sm.add_transition(src_state="idle", event="start", dst_state="running") + sm.set_current_state("idle") + assert sm.generate_plantuml() + + +if __name__ == "__main__": + conftest.run_this_test(__file__) From 346037a6e2362ed17870928ccf26992b60b0e54c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Mar 2025 07:26:47 +0900 Subject: [PATCH 822/909] build(deps): bump pytest from 8.3.4 to 8.3.5 in /requirements (#1178) Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.4 to 8.3.5. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/8.3.4...8.3.5) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b439ea4266..b46a0e41f1 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 2.2.3 scipy == 1.15.2 matplotlib == 3.10.0 cvxpy == 1.5.3 -pytest == 8.3.4 # For unit test +pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test ruff == 0.9.7 # For unit test From cd09abd5e00ca29d15f42c539121cf62751ecd52 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Mar 2025 12:21:33 +0900 Subject: [PATCH 823/909] build(deps): bump ruff from 0.9.7 to 0.9.9 in /requirements (#1179) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.7 to 0.9.9. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.7...0.9.9) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b46a0e41f1..3e2a1e7c7c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.9.7 # For unit test +ruff == 0.9.9 # For unit test From 5f3be9bccd6366493ea8d159ceeae26d55ed5250 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Mar 2025 12:52:57 +0900 Subject: [PATCH 824/909] build(deps): bump matplotlib from 3.10.0 to 3.10.1 in /requirements (#1181) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.10.0 to 3.10.1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.10.0...v3.10.1) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3e2a1e7c7c..552bf8482b 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 2.2.3 scipy == 1.15.2 -matplotlib == 3.10.0 +matplotlib == 3.10.1 cvxpy == 1.5.3 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test From 30a61add126e2c21c035d50a7a448ed8eb06afb0 Mon Sep 17 00:00:00 2001 From: Surya Singh <133056660+spnsingh@users.noreply.github.com> Date: Fri, 7 Mar 2025 09:01:37 -0500 Subject: [PATCH 825/909] bug: fix typo on line 6 of SpaceTimeAStar.py (#1182) * bug: fix typo on line 6 of SpaceTimeAStar.py * bug: removed extra line return on line 11 of SpaceTimeAStar.py --- PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py index 3b3613d695..a7aed41869 100644 --- a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py +++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py @@ -3,7 +3,7 @@ This script demonstrates the Space-time A* algorithm for path planning in a grid world with moving obstacles. This algorithm is different from normal 2D A* in one key way - the cost (often notated as g(n)) is the number of time steps it took to get to a given node, instead of the number of cells it has - traversed. This ensures the path is time-optimal, while respescting any dynamic obstacles in the environment. + traversed. This ensures the path is time-optimal, while respecting any dynamic obstacles in the environment. Reference: https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf """ From fc160179c06cb4b57f821d83a9bdcf192379bd72 Mon Sep 17 00:00:00 2001 From: Aglargil <34728006+Aglargil@users.noreply.github.com> Date: Sat, 8 Mar 2025 18:26:23 +0800 Subject: [PATCH 826/909] feat: add behavior tree (#1177) * feat: add behavior tree * feat: add behavior tree test * feat: add behavior tree doc * feat: add behavior tree update --- MissionPlanning/BehaviorTree/behavior_tree.py | 690 ++++++++++++++++++ .../BehaviorTree/robot_behavior_case.py | 247 +++++++ .../BehaviorTree/robot_behavior_tree.xml | 57 ++ .../behavior_tree/behavior_tree_main.rst | 104 +++ .../behavior_tree/robot_behavior_case.svg | 22 + .../mission_planning_main.rst | 1 + tests/test_behavior_tree.py | 207 ++++++ 7 files changed, 1328 insertions(+) create mode 100644 MissionPlanning/BehaviorTree/behavior_tree.py create mode 100644 MissionPlanning/BehaviorTree/robot_behavior_case.py create mode 100644 MissionPlanning/BehaviorTree/robot_behavior_tree.xml create mode 100644 docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst create mode 100644 docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg create mode 100644 tests/test_behavior_tree.py diff --git a/MissionPlanning/BehaviorTree/behavior_tree.py b/MissionPlanning/BehaviorTree/behavior_tree.py new file mode 100644 index 0000000000..59f4c713f1 --- /dev/null +++ b/MissionPlanning/BehaviorTree/behavior_tree.py @@ -0,0 +1,690 @@ +""" +Behavior Tree + +author: Wang Zheng (@Aglargil) + +Ref: + +- [Behavior Tree](https://en.wikipedia.org/wiki/Behavior_tree_(artificial_intelligence,_robotics_and_control)) +""" + +import time +import xml.etree.ElementTree as ET +from enum import Enum + + +class Status(Enum): + SUCCESS = "success" + FAILURE = "failure" + RUNNING = "running" + + +class NodeType(Enum): + CONTROL_NODE = "ControlNode" + ACTION_NODE = "ActionNode" + DECORATOR_NODE = "DecoratorNode" + + +class Node: + """ + Base class for all nodes in a behavior tree. + """ + + def __init__(self, name): + self.name = name + self.status = None + + def tick(self) -> Status: + """ + Tick the node. + + Returns: + Status: The status of the node. + """ + raise ValueError("Node is not implemented") + + def tick_and_set_status(self) -> Status: + """ + Tick the node and set the status. + + Returns: + Status: The status of the node. + """ + self.status = self.tick() + return self.status + + def reset(self): + """ + Reset the node. + """ + self.status = None + + def reset_children(self): + """ + Reset the children of the node. + """ + pass + + +class ControlNode(Node): + """ + Base class for all control nodes in a behavior tree. + + Control nodes manage the execution flow of their child nodes according to specific rules. + They typically have multiple children and determine which children to execute and in what order. + """ + + def __init__(self, name): + super().__init__(name) + self.children = [] + self.type = NodeType.CONTROL_NODE + + def not_set_children_raise_error(self): + if len(self.children) == 0: + raise ValueError("Children are not set") + + def reset_children(self): + for child in self.children: + child.reset() + + +class SequenceNode(ControlNode): + """ + Executes child nodes in sequence until one fails or all succeed. + + Returns: + - Returns FAILURE if any child returns FAILURE + - Returns SUCCESS when all children have succeeded + - Returns RUNNING when a child is still running or when moving to the next child + + Example: + .. code-block:: xml + + + + + + """ + + def __init__(self, name): + super().__init__(name) + self.current_child_index = 0 + + def tick(self) -> Status: + self.not_set_children_raise_error() + + if self.current_child_index >= len(self.children): + self.reset_children() + return Status.SUCCESS + status = self.children[self.current_child_index].tick_and_set_status() + if status == Status.FAILURE: + self.reset_children() + return Status.FAILURE + elif status == Status.SUCCESS: + self.current_child_index += 1 + return Status.RUNNING + elif status == Status.RUNNING: + return Status.RUNNING + else: + raise ValueError("Unknown status") + + +class SelectorNode(ControlNode): + """ + Executes child nodes in sequence until one succeeds or all fail. + + Returns: + - Returns SUCCESS if any child returns SUCCESS + - Returns FAILURE when all children have failed + - Returns RUNNING when a child is still running or when moving to the next child + + Examples: + .. code-block:: xml + + + + + + """ + + def __init__(self, name): + super().__init__(name) + self.current_child_index = 0 + + def tick(self) -> Status: + self.not_set_children_raise_error() + + if self.current_child_index >= len(self.children): + self.reset_children() + return Status.FAILURE + status = self.children[self.current_child_index].tick_and_set_status() + if status == Status.SUCCESS: + self.reset_children() + return Status.SUCCESS + elif status == Status.FAILURE: + self.current_child_index += 1 + return Status.RUNNING + elif status == Status.RUNNING: + return Status.RUNNING + else: + raise ValueError("Unknown status") + + +class WhileDoElseNode(ControlNode): + """ + Conditional execution node with three parts: condition, do, and optional else. + + Returns: + First executes the condition node (child[0]) + If condition succeeds, executes do node (child[1]) and returns RUNNING + If condition fails, executes else node (child[2]) if present and returns result of else node + If condition fails and there is no else node, returns SUCCESS + + Example: + .. code-block:: xml + + + + + + + """ + + def __init__(self, name): + super().__init__(name) + + def tick(self) -> Status: + if len(self.children) != 3 and len(self.children) != 2: + raise ValueError("WhileDoElseNode must have exactly 3 or 2 children") + + condition_node = self.children[0] + do_node = self.children[1] + else_node = self.children[2] if len(self.children) == 3 else None + + condition_status = condition_node.tick_and_set_status() + if condition_status == Status.SUCCESS: + do_node.tick_and_set_status() + return Status.RUNNING + elif condition_status == Status.FAILURE: + if else_node is not None: + else_status = else_node.tick_and_set_status() + if else_status == Status.SUCCESS: + self.reset_children() + return Status.SUCCESS + elif else_status == Status.FAILURE: + self.reset_children() + return Status.FAILURE + elif else_status == Status.RUNNING: + return Status.RUNNING + else: + raise ValueError("Unknown status") + else: + self.reset_children() + return Status.SUCCESS + else: + raise ValueError("Unknown status") + + +class ActionNode(Node): + """ + Base class for all action nodes in a behavior tree. + + Action nodes are responsible for performing specific tasks or actions. + They do not have children and are typically used to execute logic or operations. + """ + + def __init__(self, name): + super().__init__(name) + self.type = NodeType.ACTION_NODE + + +class SleepNode(ActionNode): + """ + Sleep node that sleeps for a specified duration. + + Returns: + Returns SUCCESS after the specified duration has passed + Returns RUNNING if the duration has not yet passed + + Example: + .. code-block:: xml + + + """ + + def __init__(self, name, duration): + super().__init__(name) + self.duration = duration + self.start_time = None + + def tick(self) -> Status: + if self.start_time is None: + self.start_time = time.time() + if time.time() - self.start_time > self.duration: + return Status.SUCCESS + return Status.RUNNING + + +class EchoNode(ActionNode): + """ + Echo node that prints a message to the console. + + Returns: + Returns SUCCESS after the message has been printed + + Example: + .. code-block:: xml + + + """ + + def __init__(self, name, message): + super().__init__(name) + self.message = message + + def tick(self) -> Status: + print(self.name, self.message) + return Status.SUCCESS + + +class DecoratorNode(Node): + """ + Base class for all decorator nodes in a behavior tree. + + Decorator nodes modify the behavior of their child node. + They must have a single child and can alter the status of the child node. + """ + + def __init__(self, name): + super().__init__(name) + self.type = NodeType.DECORATOR_NODE + self.child = None + + def not_set_child_raise_error(self): + if self.child is None: + raise ValueError("Child is not set") + + def reset_children(self): + self.child.reset() + + +class InverterNode(DecoratorNode): + """ + Inverter node that inverts the status of its child node. + + Returns: + - Returns SUCCESS if the child returns FAILURE + - Returns FAILURE if the child returns SUCCESS + - Returns RUNNING if the child returns RUNNING + + Examples: + .. code-block:: xml + + + + + """ + + def __init__(self, name): + super().__init__(name) + + def tick(self) -> Status: + self.not_set_child_raise_error() + status = self.child.tick_and_set_status() + return Status.SUCCESS if status == Status.FAILURE else Status.FAILURE + + +class TimeoutNode(DecoratorNode): + """ + Timeout node that fails if the child node takes too long to execute + + Returns: + - FAILURE: If the timeout duration has been exceeded + - Child's status: Otherwise, passes through the status of the child node + + Example: + .. code-block:: xml + + + + + """ + + def __init__(self, name, timeout): + super().__init__(name) + self.timeout = timeout + self.start_time = None + + def tick(self) -> Status: + self.not_set_child_raise_error() + if self.start_time is None: + self.start_time = time.time() + if time.time() - self.start_time > self.timeout: + return Status.FAILURE + print(f"{self.name} is running") + return self.child.tick_and_set_status() + + +class DelayNode(DecoratorNode): + """ + Delay node that delays the execution of its child node for a specified duration. + + Returns: + - Returns RUNNING if the duration has not yet passed + - Returns child's status after the duration has passed + + Example: + .. code-block:: xml + + + + + """ + + def __init__(self, name, delay): + super().__init__(name) + self.delay = delay + self.start_time = None + + def tick(self) -> Status: + self.not_set_child_raise_error() + if self.start_time is None: + self.start_time = time.time() + if time.time() - self.start_time > self.delay: + return self.child.tick_and_set_status() + return Status.RUNNING + + +class ForceSuccessNode(DecoratorNode): + """ + ForceSuccess node that always returns SUCCESS. + + Returns: + - Returns RUNNING if the child returns RUNNING + - Returns SUCCESS if the child returns SUCCESS or FAILURE + """ + + def __init__(self, name): + super().__init__(name) + + def tick(self) -> Status: + self.not_set_child_raise_error() + status = self.child.tick_and_set_status() + if status == Status.FAILURE: + return Status.SUCCESS + return status + + +class ForceFailureNode(DecoratorNode): + """ + ForceFailure node that always returns FAILURE. + + Returns: + - Returns RUNNING if the child returns RUNNING + - Returns FAILURE if the child returns SUCCESS or FAILURE + """ + + def __init__(self, name): + super().__init__(name) + + def tick(self) -> Status: + self.not_set_child_raise_error() + status = self.child.tick_and_set_status() + if status == Status.SUCCESS: + return Status.FAILURE + return status + + +class BehaviorTree: + """ + Behavior tree class that manages the execution of a behavior tree. + """ + + def __init__(self, root): + self.root = root + + def tick(self): + """ + Tick once on the behavior tree. + """ + self.root.tick_and_set_status() + + def reset(self): + """ + Reset the behavior tree. + """ + self.root.reset() + + def tick_while_running(self, interval=None, enable_print=True): + """ + Tick the behavior tree while it is running. + + Args: + interval (float, optional): The interval between ticks. Defaults to None. + enable_print (bool, optional): Whether to print the behavior tree. Defaults to True. + """ + while self.root.tick_and_set_status() == Status.RUNNING: + if enable_print: + self.print_tree() + if interval is not None: + time.sleep(interval) + if enable_print: + self.print_tree() + + def to_text(self, root, indent=0): + """ + Recursively convert the behavior tree to a text representation. + """ + current_text = "" + if root.status == Status.RUNNING: + # yellow + current_text = "\033[93m" + root.name + "\033[0m" + elif root.status == Status.SUCCESS: + # green + current_text = "\033[92m" + root.name + "\033[0m" + elif root.status == Status.FAILURE: + # red + current_text = "\033[91m" + root.name + "\033[0m" + else: + current_text = root.name + if root.type == NodeType.CONTROL_NODE: + current_text = " " * indent + "[" + current_text + "]\n" + for child in root.children: + current_text += self.to_text(child, indent + 2) + elif root.type == NodeType.DECORATOR_NODE: + current_text = " " * indent + "(" + current_text + ")\n" + current_text += self.to_text(root.child, indent + 2) + elif root.type == NodeType.ACTION_NODE: + current_text = " " * indent + "<" + current_text + ">\n" + return current_text + + def print_tree(self): + """ + Print the behavior tree. + + Node print format: + Action: + Decorator: (Decorator) + Control: [Control] + + Node status colors: + Yellow: RUNNING + Green: SUCCESS + Red: FAILURE + """ + text = self.to_text(self.root) + text = text.strip() + print("\033[94m" + "Behavior Tree" + "\033[0m") + print(text) + print("\033[94m" + "Behavior Tree" + "\033[0m") + + +class BehaviorTreeFactory: + """ + Factory class for creating behavior trees from XML strings. + """ + + def __init__(self): + self.node_builders = {} + # Control nodes + self.register_node_builder( + "Sequence", + lambda node: SequenceNode(node.attrib.get("name", SequenceNode.__name__)), + ) + self.register_node_builder( + "Selector", + lambda node: SelectorNode(node.attrib.get("name", SelectorNode.__name__)), + ) + self.register_node_builder( + "WhileDoElse", + lambda node: WhileDoElseNode( + node.attrib.get("name", WhileDoElseNode.__name__) + ), + ) + # Decorator nodes + self.register_node_builder( + "Inverter", + lambda node: InverterNode(node.attrib.get("name", InverterNode.__name__)), + ) + self.register_node_builder( + "Timeout", + lambda node: TimeoutNode( + node.attrib.get("name", SelectorNode.__name__), + float(node.attrib["sec"]), + ), + ) + self.register_node_builder( + "Delay", + lambda node: DelayNode( + node.attrib.get("name", DelayNode.__name__), + float(node.attrib["sec"]), + ), + ) + self.register_node_builder( + "ForceSuccess", + lambda node: ForceSuccessNode( + node.attrib.get("name", ForceSuccessNode.__name__) + ), + ) + self.register_node_builder( + "ForceFailure", + lambda node: ForceFailureNode( + node.attrib.get("name", ForceFailureNode.__name__) + ), + ) + # Action nodes + self.register_node_builder( + "Sleep", + lambda node: SleepNode( + node.attrib.get("name", SleepNode.__name__), + float(node.attrib["sec"]), + ), + ) + self.register_node_builder( + "Echo", + lambda node: EchoNode( + node.attrib.get("name", EchoNode.__name__), + node.attrib["message"], + ), + ) + + def register_node_builder(self, node_name, builder): + """ + Register a builder for a node + + Args: + node_name (str): The name of the node. + builder (function): The builder function. + + Example: + .. code-block:: python + + factory = BehaviorTreeFactory() + factory.register_node_builder( + "MyNode", + lambda node: MyNode( + node.attrib.get("name", MyNode.__name__), + node.attrib["my_param"], + ), + ) + """ + self.node_builders[node_name] = builder + + def build_node(self, node): + """ + Build a node from an XML element. + + Args: + node (Element): The XML element to build the node from. + + Returns: + BehaviorTree Node: the built node + """ + if node.tag in self.node_builders: + root = self.node_builders[node.tag](node) + if root.type == NodeType.CONTROL_NODE: + if len(node) <= 0: + raise ValueError(f"{root.name} Control node must have children") + for child in node: + root.children.append(self.build_node(child)) + elif root.type == NodeType.DECORATOR_NODE: + if len(node) != 1: + raise ValueError( + f"{root.name} Decorator node must have exactly one child" + ) + root.child = self.build_node(node[0]) + elif root.type == NodeType.ACTION_NODE: + if len(node) != 0: + raise ValueError(f"{root.name} Action node must have no children") + return root + else: + raise ValueError(f"Unknown node type: {node.tag}") + + def build_tree(self, xml_string): + """ + Build a behavior tree from an XML string. + + Args: + xml_string (str): The XML string containing the behavior tree. + + Returns: + BehaviorTree: The behavior tree. + """ + xml_tree = ET.fromstring(xml_string) + root = self.build_node(xml_tree) + return BehaviorTree(root) + + def build_tree_from_file(self, file_path): + """ + Build a behavior tree from a file. + + Args: + file_path (str): The path to the file containing the behavior tree. + + Returns: + BehaviorTree: The behavior tree. + """ + with open(file_path) as file: + xml_string = file.read() + return self.build_tree(xml_string) + + +xml_string = """ + + + + + + + + """ + + +def main(): + factory = BehaviorTreeFactory() + tree = factory.build_tree(xml_string) + tree.tick_while_running() + + +if __name__ == "__main__": + main() diff --git a/MissionPlanning/BehaviorTree/robot_behavior_case.py b/MissionPlanning/BehaviorTree/robot_behavior_case.py new file mode 100644 index 0000000000..6c39aa76b2 --- /dev/null +++ b/MissionPlanning/BehaviorTree/robot_behavior_case.py @@ -0,0 +1,247 @@ +""" +Robot Behavior Tree Case + +This file demonstrates how to use a behavior tree to control robot behavior. +""" + +from behavior_tree import ( + BehaviorTreeFactory, + Status, + ActionNode, +) +import time +import random +import os + + +class CheckBatteryNode(ActionNode): + """ + Node to check robot battery level + + If battery level is below threshold, returns FAILURE, otherwise returns SUCCESS + """ + + def __init__(self, name, threshold=20): + super().__init__(name) + self.threshold = threshold + self.battery_level = 100 # Initial battery level is 100% + + def tick(self): + # Simulate battery level decreasing + self.battery_level -= random.randint(1, 5) + print(f"Current battery level: {self.battery_level}%") + + if self.battery_level <= self.threshold: + return Status.FAILURE + return Status.SUCCESS + + +class ChargeBatteryNode(ActionNode): + """ + Node to charge the robot's battery + """ + + def __init__(self, name, charge_rate=10): + super().__init__(name) + self.charge_rate = charge_rate + self.charging_time = 0 + + def tick(self): + # Simulate charging process + if self.charging_time == 0: + print("Starting to charge...") + + self.charging_time += 1 + charge_amount = self.charge_rate * self.charging_time + + if charge_amount >= 100: + print("Charging complete! Battery level: 100%") + self.charging_time = 0 + return Status.SUCCESS + else: + print(f"Charging in progress... Battery level: {min(charge_amount, 100)}%") + return Status.RUNNING + + +class MoveToPositionNode(ActionNode): + """ + Node to move to a specified position + """ + + def __init__(self, name, position, move_duration=2): + super().__init__(name) + self.position = position + self.move_duration = move_duration + self.start_time = None + + def tick(self): + if self.start_time is None: + self.start_time = time.time() + print(f"Starting movement to position {self.position}") + + elapsed_time = time.time() - self.start_time + + if elapsed_time >= self.move_duration: + print(f"Arrived at position {self.position}") + self.start_time = None + return Status.SUCCESS + else: + print( + f"Moving to position {self.position}... {int(elapsed_time / self.move_duration * 100)}% complete" + ) + return Status.RUNNING + + +class DetectObstacleNode(ActionNode): + """ + Node to detect obstacles + """ + + def __init__(self, name, obstacle_probability=0.3): + super().__init__(name) + self.obstacle_probability = obstacle_probability + + def tick(self): + # Use random probability to simulate obstacle detection + if random.random() < self.obstacle_probability: + print("Obstacle detected!") + return Status.SUCCESS + else: + print("No obstacle detected") + return Status.FAILURE + + +class AvoidObstacleNode(ActionNode): + """ + Node to avoid obstacles + """ + + def __init__(self, name, avoid_duration=1.5): + super().__init__(name) + self.avoid_duration = avoid_duration + self.start_time = None + + def tick(self): + if self.start_time is None: + self.start_time = time.time() + print("Starting obstacle avoidance...") + + elapsed_time = time.time() - self.start_time + + if elapsed_time >= self.avoid_duration: + print("Obstacle avoidance complete") + self.start_time = None + return Status.SUCCESS + else: + print("Avoiding obstacle...") + return Status.RUNNING + + +class PerformTaskNode(ActionNode): + """ + Node to perform a specific task + """ + + def __init__(self, name, task_name, task_duration=3): + super().__init__(name) + self.task_name = task_name + self.task_duration = task_duration + self.start_time = None + + def tick(self): + if self.start_time is None: + self.start_time = time.time() + print(f"Starting task: {self.task_name}") + + elapsed_time = time.time() - self.start_time + + if elapsed_time >= self.task_duration: + print(f"Task complete: {self.task_name}") + self.start_time = None + return Status.SUCCESS + else: + print( + f"Performing task: {self.task_name}... {int(elapsed_time / self.task_duration * 100)}% complete" + ) + return Status.RUNNING + + +def create_robot_behavior_tree(): + """ + Create robot behavior tree + """ + + factory = BehaviorTreeFactory() + + # Register custom nodes + factory.register_node_builder( + "CheckBattery", + lambda node: CheckBatteryNode( + node.attrib.get("name", "CheckBattery"), + int(node.attrib.get("threshold", "20")), + ), + ) + + factory.register_node_builder( + "ChargeBattery", + lambda node: ChargeBatteryNode( + node.attrib.get("name", "ChargeBattery"), + int(node.attrib.get("charge_rate", "10")), + ), + ) + + factory.register_node_builder( + "MoveToPosition", + lambda node: MoveToPositionNode( + node.attrib.get("name", "MoveToPosition"), + node.attrib.get("position", "Unknown Position"), + float(node.attrib.get("move_duration", "2")), + ), + ) + + factory.register_node_builder( + "DetectObstacle", + lambda node: DetectObstacleNode( + node.attrib.get("name", "DetectObstacle"), + float(node.attrib.get("obstacle_probability", "0.3")), + ), + ) + + factory.register_node_builder( + "AvoidObstacle", + lambda node: AvoidObstacleNode( + node.attrib.get("name", "AvoidObstacle"), + float(node.attrib.get("avoid_duration", "1.5")), + ), + ) + + factory.register_node_builder( + "PerformTask", + lambda node: PerformTaskNode( + node.attrib.get("name", "PerformTask"), + node.attrib.get("task_name", "Unknown Task"), + float(node.attrib.get("task_duration", "3")), + ), + ) + # Read XML from file + xml_path = os.path.join(os.path.dirname(__file__), "robot_behavior_tree.xml") + return factory.build_tree_from_file(xml_path) + + +def main(): + """ + Main function: Create and run the robot behavior tree + """ + print("Creating robot behavior tree...") + tree = create_robot_behavior_tree() + + print("\nStarting robot behavior tree execution...\n") + # Run for a period of time or until completion + + tree.tick_while_running(interval=0.01) + + print("\nBehavior tree execution complete!") + + +if __name__ == "__main__": + main() diff --git a/MissionPlanning/BehaviorTree/robot_behavior_tree.xml b/MissionPlanning/BehaviorTree/robot_behavior_tree.xml new file mode 100644 index 0000000000..0bca76a3ff --- /dev/null +++ b/MissionPlanning/BehaviorTree/robot_behavior_tree.xml @@ -0,0 +1,57 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst new file mode 100644 index 0000000000..ae3e16da81 --- /dev/null +++ b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst @@ -0,0 +1,104 @@ +Behavior Tree +------------- + +Behavior Tree is a modular, hierarchical decision model that is widely used in robot control, and game development. +It present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state. +Behavior Tree have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863) + +Core Concepts +~~~~~~~~~~~~~ + +Control Node +++++++++++++ + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ControlNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.SequenceNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.SelectorNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.WhileDoElseNode + +Action Node +++++++++++++ + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ActionNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.EchoNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.SleepNode + +Decorator Node +++++++++++++++ + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.DecoratorNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.InverterNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.TimeoutNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.DelayNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ForceSuccessNode + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ForceFailureNode + +Behavior Tree Factory ++++++++++++++++++++++ + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.BehaviorTreeFactory + :members: + +Behavior Tree ++++++++++++++ + +.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.BehaviorTree + :members: + +Example +~~~~~~~ + +Visualize the behavior tree by `xml-tree-visual `_. + +.. image:: ./robot_behavior_case.svg + +Print the behavior tree + +.. code-block:: text + + Behavior Tree + [Robot Main Controller] + [Battery Management] + (Low Battery Detection) + + + + [Patrol Task] + + [Move to Position A] + + [Obstacle Handling A] + [Obstacle Present] + + + + + [Move to Position B] + (Short Wait) + + + (Limited Time Obstacle Handling) + [Obstacle Present] + + + + [Conditional Move to C] + + [Perform Position C Task] + + (Ensure Completion) + + + + + Behavior Tree diff --git a/docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg b/docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg new file mode 100644 index 0000000000..a3d43aed52 --- /dev/null +++ b/docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg @@ -0,0 +1,22 @@ +Selectorname: Robot Main ControllerSequencename: Battery ManagementSequencename: Patrol TaskInvertername: Low Battery DetectionEchoname: Low Battery Warningmessage: Battery level low!Charging neededChargeBatteryname: Charge Batterycharge_rate: 20Echoname: Start Taskmessage: Starting patrol taskSequencename: Move to Position ASequencename: Move to Position BWhileDoElsename: Conditional Move to CEchoname: Complete Patrolmessage: Patrol taskcompleted, returning tocharging stationMoveToPositionname: Return to ChargingStationposition: Charging Stationmove_duration: 4CheckBatteryname: Check Batterythreshold: 30MoveToPositionname: Move to Aposition: Amove_duration: 2Selectorname: Obstacle Handling APerformTaskname: Position A Tasktask_name: Check Device Statustask_duration: 2Delayname: Short Waitsec: 1MoveToPositionname: Move to Bposition: Bmove_duration: 3Timeoutname: Limited Time ObstacleHandlingsec: 2PerformTaskname: Position B Tasktask_name: Data Collectiontask_duration: 2.5CheckBatteryname: Check Sufficient Batterythreshold: 50Sequencename: Perform Position C TaskEchoname: Skip Position Cmessage: Insufficient power,skipping position C taskSequencename: Obstacle PresentEchoname: No Obstaclemessage: Path clearEchoname: Prepare Movementmessage: Preparing to move tonext positionSequencename: Obstacle PresentMoveToPositionname: Move to Cposition: Cmove_duration: 2.5ForceSuccessname: Ensure CompletionDetectObstaclename: Detect Obstacleobstacle_probability: 0.3AvoidObstaclename: Avoid Obstacleavoid_duration: 1.5DetectObstaclename: Detect Obstacleobstacle_probability: 0.4AvoidObstaclename: Avoid Obstacleavoid_duration: 1.8PerformTaskname: Position C Tasktask_name: EnvironmentMonitoringtask_duration: 2 \ No newline at end of file diff --git a/docs/modules/13_mission_planning/mission_planning_main.rst b/docs/modules/13_mission_planning/mission_planning_main.rst index 385e62f68e..c35eacd8d5 100644 --- a/docs/modules/13_mission_planning/mission_planning_main.rst +++ b/docs/modules/13_mission_planning/mission_planning_main.rst @@ -10,3 +10,4 @@ Mission planning includes tools such as finite state machines and behavior trees :caption: Contents state_machine/state_machine + behavior_tree/behavior_tree diff --git a/tests/test_behavior_tree.py b/tests/test_behavior_tree.py new file mode 100644 index 0000000000..0898690448 --- /dev/null +++ b/tests/test_behavior_tree.py @@ -0,0 +1,207 @@ +import pytest +import conftest + +from MissionPlanning.BehaviorTree.behavior_tree import ( + BehaviorTreeFactory, + Status, + ActionNode, +) + + +def test_sequence_node1(): + xml_string = """ + + + + + + + + """ + bt_factory = BehaviorTreeFactory() + bt = bt_factory.build_tree(xml_string) + bt.tick() + assert bt.root.status == Status.RUNNING + assert bt.root.children[0].status == Status.SUCCESS + assert bt.root.children[1].status is None + assert bt.root.children[2].status is None + bt.tick() + bt.tick() + assert bt.root.status == Status.FAILURE + assert bt.root.children[0].status is None + assert bt.root.children[1].status is None + assert bt.root.children[2].status is None + + +def test_sequence_node2(): + xml_string = """ + + + + + + + + """ + bt_factory = BehaviorTreeFactory() + bt = bt_factory.build_tree(xml_string) + bt.tick_while_running() + assert bt.root.status == Status.SUCCESS + assert bt.root.children[0].status is None + assert bt.root.children[1].status is None + assert bt.root.children[2].status is None + + +def test_selector_node1(): + xml_string = """ + + + + + + + + """ + bt_factory = BehaviorTreeFactory() + bt = bt_factory.build_tree(xml_string) + bt.tick() + assert bt.root.status == Status.RUNNING + assert bt.root.children[0].status == Status.FAILURE + assert bt.root.children[1].status is None + assert bt.root.children[2].status is None + bt.tick() + assert bt.root.status == Status.SUCCESS + assert bt.root.children[0].status is None + assert bt.root.children[1].status is None + assert bt.root.children[2].status is None + + +def test_selector_node2(): + xml_string = """ + + + + + + + + + """ + bt_factory = BehaviorTreeFactory() + bt = bt_factory.build_tree(xml_string) + bt.tick_while_running() + assert bt.root.status == Status.FAILURE + assert bt.root.children[0].status is None + assert bt.root.children[1].status is None + + +def test_while_do_else_node(): + xml_string = """ + + + + + + """ + + class CountNode(ActionNode): + def __init__(self, name, count_threshold): + super().__init__(name) + self.count = 0 + self.count_threshold = count_threshold + + def tick(self): + self.count += 1 + if self.count >= self.count_threshold: + return Status.FAILURE + else: + return Status.SUCCESS + + bt_factory = BehaviorTreeFactory() + bt_factory.register_node_builder( + "Count", + lambda node: CountNode( + node.attrib.get("name", CountNode.__name__), + int(node.attrib["count_threshold"]), + ), + ) + bt = bt_factory.build_tree(xml_string) + bt.tick() + assert bt.root.status == Status.RUNNING + assert bt.root.children[0].status == Status.SUCCESS + assert bt.root.children[1].status is Status.SUCCESS + assert bt.root.children[2].status is None + bt.tick() + assert bt.root.status == Status.RUNNING + assert bt.root.children[0].status == Status.SUCCESS + assert bt.root.children[1].status is Status.SUCCESS + assert bt.root.children[2].status is None + bt.tick() + assert bt.root.status == Status.SUCCESS + assert bt.root.children[0].status is None + assert bt.root.children[1].status is None + assert bt.root.children[2].status is None + + +def test_node_children(): + # ControlNode Must have children + xml_string = """ + + + """ + bt_factory = BehaviorTreeFactory() + with pytest.raises(ValueError): + bt_factory.build_tree(xml_string) + + # DecoratorNode Must have child + xml_string = """ + + + """ + with pytest.raises(ValueError): + bt_factory.build_tree(xml_string) + + # DecoratorNode Must have only one child + xml_string = """ + + + + + """ + with pytest.raises(ValueError): + bt_factory.build_tree(xml_string) + + # ActionNode Must have no children + xml_string = """ + + + + """ + with pytest.raises(ValueError): + bt_factory.build_tree(xml_string) + + # WhileDoElse Must have exactly 2 or 3 children + xml_string = """ + + + + """ + with pytest.raises(ValueError): + bt = bt_factory.build_tree(xml_string) + bt.tick() + + xml_string = """ + + + + + + + """ + with pytest.raises(ValueError): + bt = bt_factory.build_tree(xml_string) + bt.tick() + + +if __name__ == "__main__": + conftest.run_this_test(__file__) From e7f893ef1aa503df04c83ec6355d18dd05d36394 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 9 Mar 2025 20:21:24 +0900 Subject: [PATCH 827/909] feat: add linkcode_resolve function for enhanced documentation linking (#1185) * feat: add linkcode_resolve function for enhanced documentation linking * feat: enhance documentation linking with linkcode and relative path resolution --- docs/conf.py | 47 +++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 45 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 919fa9ac76..eeabab11b1 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -13,6 +13,8 @@ # import os import sys +from pathlib import Path + sys.path.insert(0, os.path.abspath('../')) @@ -41,7 +43,7 @@ 'matplotlib.sphinxext.plot_directive', 'sphinx.ext.autodoc', 'sphinx.ext.mathjax', - 'sphinx.ext.viewcode', + 'sphinx.ext.linkcode', 'sphinx.ext.napoleon', 'sphinx.ext.imgconverter', 'IPython.sphinxext.ipython_console_highlighting', @@ -184,4 +186,45 @@ ] -# -- Extension configuration ------------------------------------------------- +# -- linkcode setting ------------------------------------------------- + +import inspect +import os +import sys +import functools + +GITHUB_REPO = "https://github.com/AtsushiSakai/PythonRobotics" +GITHUB_BRANCH = "master" + + +def linkcode_resolve(domain, info): + if domain != "py": + return None + + modname = info["module"] + fullname = info["fullname"] + + try: + module = __import__(modname, fromlist=[fullname]) + obj = functools.reduce(getattr, fullname.split("."), module) + except (ImportError, AttributeError): + return None + + try: + srcfile = inspect.getsourcefile(obj) + srcfile = get_relative_path_from_parent(srcfile, "PythonRobotics") + lineno = inspect.getsourcelines(obj)[1] + except Exception: + return None + + return f"{GITHUB_REPO}/blob/{GITHUB_BRANCH}/{srcfile}#L{lineno}" + + +def get_relative_path_from_parent(file_path: str, parent_dir: str): + path = Path(file_path).resolve() + + try: + parent_path = next(p for p in path.parents if p.name == parent_dir) + return str(path.relative_to(parent_path)) + except StopIteration: + raise ValueError(f"Parent directory '{parent_dir}' not found in {file_path}") From 73ebcd85dc967dbb160e29ee4c529b7a64f11e2b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 12 Mar 2025 21:26:09 +0900 Subject: [PATCH 828/909] build(deps): bump ruff from 0.9.9 to 0.9.10 in /requirements (#1186) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.9 to 0.9.10. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.9...0.9.10) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 552bf8482b..cf6dea3a46 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.9.9 # For unit test +ruff == 0.9.10 # For unit test From 1308e76424723eb68a4a864117e60c0bbd054df1 Mon Sep 17 00:00:00 2001 From: Jonathan Schwartz Date: Thu, 13 Mar 2025 10:24:53 -0400 Subject: [PATCH 829/909] Add expanded node set to SpaceTime AStar (#1183) * speed up spacetime astar * forgot to include hash impl on Position * add condition to test on node expansions * remove heuristic from Node __hash__ impl * update rst with note about optimization --- .../GridWithDynamicObstacles.py | 3 ++ .../TimeBasedPathPlanning/SpaceTimeAStar.py | 51 +++++++++++++------ .../time_based_grid_search_main.rst | 17 +++++++ tests/test_space_time_astar.py | 1 + 4 files changed, 57 insertions(+), 15 deletions(-) diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py index 7b0190d023..7416ff2f08 100644 --- a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py +++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py @@ -30,6 +30,9 @@ def __sub__(self, other): f"Subtraction not supported for Position and {type(other)}" ) + def __hash__(self): + return hash((self.x, self.y)) + class ObstacleArrangement(Enum): # Random obstacle positions and movements diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py index a7aed41869..c4e2802d37 100644 --- a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py +++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py @@ -20,7 +20,7 @@ import random from dataclasses import dataclass from functools import total_ordering - +import time # Seed randomness for reproducibility RANDOM_SEED = 50 @@ -48,11 +48,17 @@ def __lt__(self, other: object): return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}") return (self.time + self.heuristic) < (other.time + other.heuristic) + """ + Note: cost and heuristic are not included in eq or hash, since they will always be the same + for a given (position, time) pair. Including either cost or heuristic would be redundant. + """ def __eq__(self, other: object): if not isinstance(other, Node): return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}") return self.position == other.position and self.time == other.time + def __hash__(self): + return hash((self.position, self.time)) class NodePath: path: list[Node] @@ -86,6 +92,8 @@ class SpaceTimeAStar: grid: Grid start: Position goal: Position + # Used to evaluate solutions + expanded_node_count: int = -1 def __init__(self, grid: Grid, start: Position, goal: Position): self.grid = grid @@ -98,7 +106,8 @@ def plan(self, verbose: bool = False) -> NodePath: open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1) ) - expanded_set: list[Node] = [] + expanded_list: list[Node] = [] + expanded_set: set[Node] = set() while open_set: expanded_node: Node = heapq.heappop(open_set) if verbose: @@ -110,23 +119,25 @@ def plan(self, verbose: bool = False) -> NodePath: continue if expanded_node.position == self.goal: - print(f"Found path to goal after {len(expanded_set)} expansions") + print(f"Found path to goal after {len(expanded_list)} expansions") path = [] path_walker: Node = expanded_node while True: path.append(path_walker) if path_walker.parent_index == -1: break - path_walker = expanded_set[path_walker.parent_index] + path_walker = expanded_list[path_walker.parent_index] # reverse path so it goes start -> goal path.reverse() + self.expanded_node_count = len(expanded_set) return NodePath(path) - expanded_idx = len(expanded_set) - expanded_set.append(expanded_node) + expanded_idx = len(expanded_list) + expanded_list.append(expanded_node) + expanded_set.add(expanded_node) - for child in self.generate_successors(expanded_node, expanded_idx, verbose): + for child in self.generate_successors(expanded_node, expanded_idx, verbose, expanded_set): heapq.heappush(open_set, child) raise Exception("No path found") @@ -135,7 +146,7 @@ def plan(self, verbose: bool = False) -> NodePath: Generate possible successors of the provided `parent_node` """ def generate_successors( - self, parent_node: Node, parent_node_idx: int, verbose: bool + self, parent_node: Node, parent_node_idx: int, verbose: bool, expanded_set: set[Node] ) -> Generator[Node, None, None]: diffs = [ Position(0, 0), @@ -146,13 +157,17 @@ def generate_successors( ] for diff in diffs: new_pos = parent_node.position + diff + new_node = Node( + new_pos, + parent_node.time + 1, + self.calculate_heuristic(new_pos), + parent_node_idx, + ) + + if new_node in expanded_set: + continue + if self.grid.valid_position(new_pos, parent_node.time + 1): - new_node = Node( - new_pos, - parent_node.time + 1, - self.calculate_heuristic(new_pos), - parent_node_idx, - ) if verbose: print("\tNew successor node: ", new_node) yield new_node @@ -166,9 +181,12 @@ def calculate_heuristic(self, position) -> int: verbose = False def main(): - start = Position(1, 11) + start = Position(1, 5) goal = Position(19, 19) grid_side_length = 21 + + start_time = time.time() + grid = Grid( np.array([grid_side_length, grid_side_length]), num_obstacles=40, @@ -179,6 +197,9 @@ def main(): planner = SpaceTimeAStar(grid, start, goal) path = planner.plan(verbose) + runtime = time.time() - start_time + print(f"Planning took: {runtime:.5f} seconds") + if verbose: print(f"Path: {path}") diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst index 0c26badec7..48dc1289c2 100644 --- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst +++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst @@ -16,6 +16,23 @@ Using a time-based cost and heuristic ensures the path found is optimal in terms The cost is the amount of time it takes to reach a given node, and the heuristic is the minimum amount of time it could take to reach the goal from that node, disregarding all obstacles. For a simple scenario where the robot can move 1 cell per time step and stop and go as it pleases, the heuristic for time is equivalent to the heuristic for distance. +One optimization that was added in `this PR `__ was to add an expanded set to the algorithm. The algorithm will not expand nodes that are already in that set. This greatly reduces the number of node expansions needed to find a path, since no duplicates are expanded. It also helps to reduce the amount of memory the algorithm uses. + +Before:: + + Found path to goal after 204490 expansions + Planning took: 1.72464 seconds + Memory usage (RSS): 68.19 MB + + +After:: + + Found path to goal after 2348 expansions + Planning took: 0.01550 seconds + Memory usage (RSS): 64.85 MB + +When starting at (1, 11) in the structured obstacle arrangement (second of the two gifs above). + References: ~~~~~~~~~~~ diff --git a/tests/test_space_time_astar.py b/tests/test_space_time_astar.py index 5290738eb4..390c7732dc 100644 --- a/tests/test_space_time_astar.py +++ b/tests/test_space_time_astar.py @@ -28,6 +28,7 @@ def test_1(): # path should end at the goal assert path.path[-1].position == goal + assert planner.expanded_node_count < 1000 if __name__ == "__main__": conftest.run_this_test(__file__) From 41187d67b11f06265431b87dcf0b9da679345249 Mon Sep 17 00:00:00 2001 From: Zichao Yang Date: Sun, 16 Mar 2025 21:40:11 +0800 Subject: [PATCH 830/909] fix "ModuleNotFoundError: No module named 'utils'" (#1188) --- .../lqr_speed_steer_control/lqr_speed_steer_control.py | 4 ++-- PathTracking/lqr_steer_control/lqr_steer_control.py | 4 ++-- PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 7 +++++-- PathTracking/stanley_controller/stanley_controller.py | 4 ++-- 4 files changed, 11 insertions(+), 8 deletions(-) diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py index d85fa98a84..5831d02d30 100644 --- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py +++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py @@ -11,9 +11,9 @@ import numpy as np import scipy.linalg as la import pathlib -from utils.angle import angle_mod -sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +from utils.angle import angle_mod from PathPlanning.CubicSpline import cubic_spline_planner # === Parameters ===== diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py index 461d6e3722..3c066917ff 100644 --- a/PathTracking/lqr_steer_control/lqr_steer_control.py +++ b/PathTracking/lqr_steer_control/lqr_steer_control.py @@ -11,9 +11,9 @@ import numpy as np import sys import pathlib -from utils.angle import angle_mod -sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +from utils.angle import angle_mod from PathPlanning.CubicSpline import cubic_spline_planner Kp = 1.0 # speed proportional gain diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py index 1702bd47dd..fd04fb6d17 100644 --- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py +++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py @@ -8,11 +8,14 @@ import matplotlib.pyplot as plt import math import numpy as np -from utils.angle import angle_mod - +import sys +import pathlib from scipy import interpolate from scipy import optimize +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +from utils.angle import angle_mod + Kp = 1.0 # speed proportional gain # steering control parameter KTH = 1.0 diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index 08aa049395..bc98175f17 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -13,9 +13,9 @@ import matplotlib.pyplot as plt import sys import pathlib -from utils.angle import angle_mod -sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) +from utils.angle import angle_mod from PathPlanning.CubicSpline import cubic_spline_planner k = 0.5 # control gain From aa61a6ea570e013d346c40874f8a33890ec66d28 Mon Sep 17 00:00:00 2001 From: Jonathan Schwartz Date: Mon, 17 Mar 2025 09:01:07 -0400 Subject: [PATCH 831/909] Safe Interval Path Planner (#1184) * it works and is WAY faster than a* * some bug fixes from testing different scenarios * add some docs & address todos * add sipp test * spiff up comments revert changes in speed-up * explain what the removal is doing * linting * fix docs build * docs formatting * revert change to file (maybe linter did it?) * point at gifs in gifs repo * use raw githubusercontent gif links * change formatting on planner results * format output differently * proper formatting final * missing underline * revert unintended change * grammar + add descriptions for gifs * missing :: * add title to gifs section * dont use sections for sub-sections * constent a* spelling * Update PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update tests/test_safe_interval_path_planner.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst Co-authored-by: Atsushi Sakai * Update PathPlanning/TimeBasedPathPlanning/SafeInterval.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * addressing comments * revert np.full change --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Atsushi Sakai --- .../GridWithDynamicObstacles.py | 57 +++- .../TimeBasedPathPlanning/SafeInterval.py | 303 ++++++++++++++++++ .../time_based_grid_search_main.rst | 43 ++- tests/test_safe_interval_path_planner.py | 33 ++ 4 files changed, 434 insertions(+), 2 deletions(-) create mode 100644 PathPlanning/TimeBasedPathPlanning/SafeInterval.py create mode 100644 tests/test_safe_interval_path_planner.py diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py index 7416ff2f08..2a47023f8c 100644 --- a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py +++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py @@ -33,6 +33,10 @@ def __sub__(self, other): def __hash__(self): return hash((self.x, self.y)) +@dataclass +class Interval: + start_time: int + end_time: int class ObstacleArrangement(Enum): # Random obstacle positions and movements @@ -40,6 +44,14 @@ class ObstacleArrangement(Enum): # Obstacles start in a line in y at center of grid and move side-to-side in x ARRANGEMENT1 = 1 +""" +Generates a 2d numpy array with lists for elements. +""" +def empty_2d_array_of_lists(x: int, y: int) -> np.ndarray: + arr = np.empty((x, y), dtype=object) + # assign each element individually - np.full creates references to the same list + arr[:] = [[[] for _ in range(y)] for _ in range(x)] + return arr class Grid: # Set in constructor @@ -89,7 +101,7 @@ def __init__( """ def generate_dynamic_obstacles(self, obs_count: int) -> list[list[Position]]: obstacle_paths = [] - for _ in (0, obs_count): + for _ in range(0, obs_count): # Sample until a free starting space is found initial_position = self.sample_random_position() while not self.valid_obstacle_position(initial_position, 0): @@ -234,6 +246,49 @@ def get_obstacle_positions_at_time(self, t: int) -> tuple[list[int], list[int]]: y_positions.append(obs_path[t].y) return (x_positions, y_positions) + """ + Returns safe intervals for each cell. + """ + def get_safe_intervals(self) -> np.ndarray: + intervals = empty_2d_array_of_lists(self.grid_size[0], self.grid_size[1]) + for x in range(intervals.shape[0]): + for y in range(intervals.shape[1]): + intervals[x, y] = self.get_safe_intervals_at_cell(Position(x, y)) + + return intervals + + """ + Generate the safe intervals for a given cell. The intervals will be in order of start time. + ex: Interval (2, 3) will be before Interval (4, 5) + """ + def get_safe_intervals_at_cell(self, cell: Position) -> list[Interval]: + vals = self.reservation_matrix[cell.x, cell.y, :] + # Find where the array is zero + zero_mask = (vals == 0) + + # Identify transitions between zero and nonzero elements + diff = np.diff(zero_mask.astype(int)) + + # Start indices: where zeros begin (1 after a nonzero) + start_indices = np.where(diff == 1)[0] + 1 + + # End indices: where zeros stop (just before a nonzero) + end_indices = np.where(diff == -1)[0] + + # Handle edge cases if the array starts or ends with zeros + if zero_mask[0]: # If the first element is zero, add index 0 to start_indices + start_indices = np.insert(start_indices, 0, 0) + if zero_mask[-1]: # If the last element is zero, add the last index to end_indices + end_indices = np.append(end_indices, len(vals) - 1) + + # Create pairs of (first zero, last zero) + intervals = [Interval(int(start), int(end)) for start, end in zip(start_indices, end_indices)] + + # Remove intervals where a cell is only free for one time step. Those intervals not provide enough time to + # move into and out of the cell each take 1 time step, and the cell is considered occupied during + # both the time step when it is entering the cell, and the time step when it is leaving the cell. + intervals = [interval for interval in intervals if interval.start_time != interval.end_time] + return intervals show_animation = True diff --git a/PathPlanning/TimeBasedPathPlanning/SafeInterval.py b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py new file mode 100644 index 0000000000..7fea10c67f --- /dev/null +++ b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py @@ -0,0 +1,303 @@ +""" +Safe interval path planner + This script implements a safe-interval path planner for a 2d grid with dynamic obstacles. It is faster than + SpaceTime A* because it reduces the number of redundant node expansions by pre-computing regions of adjacent + time steps that are safe ("safe intervals") at each position. This allows the algorithm to skip expanding nodes + that are in intervals that have already been visited earlier. + + Reference: https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf +""" + +import numpy as np +import matplotlib.pyplot as plt +from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import ( + Grid, + Interval, + ObstacleArrangement, + Position, + empty_2d_array_of_lists, +) +import heapq +import random +from dataclasses import dataclass +from functools import total_ordering +import time + +@dataclass() +# Note: Total_ordering is used instead of adding `order=True` to the @dataclass decorator because +# this class needs to override the __lt__ and __eq__ methods to ignore parent_index. The Parent +# index and interval member variables are just used to track the path found by the algorithm, +# and has no effect on the quality of a node. +@total_ordering +class Node: + position: Position + time: int + heuristic: int + parent_index: int + interval: Interval + + """ + This is what is used to drive node expansion. The node with the lowest value is expanded next. + This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic) + """ + def __lt__(self, other: object): + if not isinstance(other, Node): + return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}") + return (self.time + self.heuristic) < (other.time + other.heuristic) + + """ + Equality only cares about position and time. Heuristic and interval will always be the same for a given + (position, time) pairing, so they are not considered in equality. + """ + def __eq__(self, other: object): + if not isinstance(other, Node): + return NotImplemented + return self.position == other.position and self.time == other.time + +@dataclass +class EntryTimeAndInterval: + entry_time: int + interval: Interval + +class NodePath: + path: list[Node] + positions_at_time: dict[int, Position] = {} + + def __init__(self, path: list[Node]): + self.path = path + for (i, node) in enumerate(path): + if i > 0: + # account for waiting in interval at previous node + prev_node = path[i-1] + for t in range(prev_node.time, node.time): + self.positions_at_time[t] = prev_node.position + + self.positions_at_time[node.time] = node.position + + """ + Get the position of the path at a given time + """ + def get_position(self, time: int) -> Position | None: + return self.positions_at_time.get(time) + + """ + Time stamp of the last node in the path + """ + def goal_reached_time(self) -> int: + return self.path[-1].time + + def __repr__(self): + repr_string = "" + for i, node in enumerate(self.path): + repr_string += f"{i}: {node}\n" + return repr_string + + +class SafeIntervalPathPlanner: + grid: Grid + start: Position + goal: Position + + def __init__(self, grid: Grid, start: Position, goal: Position): + self.grid = grid + self.start = start + self.goal = goal + + # Seed randomness for reproducibility + RANDOM_SEED = 50 + random.seed(RANDOM_SEED) + np.random.seed(RANDOM_SEED) + + """ + Generate a plan given the loaded problem statement. Raises an exception if it fails to find a path. + Arguments: + verbose (bool): set to True to print debug information + """ + def plan(self, verbose: bool = False) -> NodePath: + + safe_intervals = self.grid.get_safe_intervals() + + open_set: list[Node] = [] + first_node_interval = safe_intervals[self.start.x, self.start.y][0] + heapq.heappush( + open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1, first_node_interval) + ) + + expanded_list: list[Node] = [] + visited_intervals = empty_2d_array_of_lists(self.grid.grid_size[0], self.grid.grid_size[1]) + while open_set: + expanded_node: Node = heapq.heappop(open_set) + if verbose: + print("Expanded node:", expanded_node) + + if expanded_node.time + 1 >= self.grid.time_limit: + if verbose: + print(f"\tSkipping node that is past time limit: {expanded_node}") + continue + + if expanded_node.position == self.goal: + print(f"Found path to goal after {len(expanded_list)} expansions") + path = [] + path_walker: Node = expanded_node + while True: + path.append(path_walker) + if path_walker.parent_index == -1: + break + path_walker = expanded_list[path_walker.parent_index] + + # reverse path so it goes start -> goal + path.reverse() + return NodePath(path) + + expanded_idx = len(expanded_list) + expanded_list.append(expanded_node) + entry_time_and_node = EntryTimeAndInterval(expanded_node.time, expanded_node.interval) + add_entry_to_visited_intervals_array(entry_time_and_node, visited_intervals, expanded_node) + + for child in self.generate_successors(expanded_node, expanded_idx, safe_intervals, visited_intervals): + heapq.heappush(open_set, child) + + raise Exception("No path found") + + """ + Generate list of possible successors of the provided `parent_node` that are worth expanding + """ + def generate_successors( + self, parent_node: Node, parent_node_idx: int, intervals: np.ndarray, visited_intervals: np.ndarray + ) -> list[Node]: + new_nodes = [] + diffs = [ + Position(0, 0), + Position(1, 0), + Position(-1, 0), + Position(0, 1), + Position(0, -1), + ] + for diff in diffs: + new_pos = parent_node.position + diff + if not self.grid.inside_grid_bounds(new_pos): + continue + + current_interval = parent_node.interval + + new_cell_intervals: list[Interval] = intervals[new_pos.x, new_pos.y] + for interval in new_cell_intervals: + # if interval starts after current ends, break + # assumption: intervals are sorted by start time, so all future intervals will hit this condition as well + if interval.start_time > current_interval.end_time: + break + + # if interval ends before current starts, skip + if interval.end_time < current_interval.start_time: + continue + + # if we have already expanded a node in this interval with a <= starting time, skip + better_node_expanded = False + for visited in visited_intervals[new_pos.x, new_pos.y]: + if interval == visited.interval and visited.entry_time <= parent_node.time + 1: + better_node_expanded = True + break + if better_node_expanded: + continue + + # We know there is a node worth expanding. Generate successor at the earliest possible time the + # new interval can be entered + for possible_t in range(max(parent_node.time + 1, interval.start_time), min(current_interval.end_time, interval.end_time)): + if self.grid.valid_position(new_pos, possible_t): + new_nodes.append(Node( + new_pos, + # entry is max of interval start and parent node time + 1 (get there as soon as possible) + max(interval.start_time, parent_node.time + 1), + self.calculate_heuristic(new_pos), + parent_node_idx, + interval, + )) + # break because all t's after this will make nodes with a higher cost, the same heuristic, and are in the same interval + break + + return new_nodes + + """ + Calculate the heuristic for a given position - Manhattan distance to the goal + """ + def calculate_heuristic(self, position) -> int: + diff = self.goal - position + return abs(diff.x) + abs(diff.y) + + +""" +Adds a new entry to the visited intervals array. If the entry is already present, the entry time is updated if the new +entry time is better. Otherwise, the entry is added to `visited_intervals` at the position of `expanded_node`. +""" +def add_entry_to_visited_intervals_array(entry_time_and_interval: EntryTimeAndInterval, visited_intervals: np.ndarray, expanded_node: Node): + # if entry is present, update entry time if better + for existing_entry_and_interval in visited_intervals[expanded_node.position.x, expanded_node.position.y]: + if existing_entry_and_interval.interval == entry_time_and_interval.interval: + existing_entry_and_interval.entry_time = min(existing_entry_and_interval.entry_time, entry_time_and_interval.entry_time) + + # Otherwise, append + visited_intervals[expanded_node.position.x, expanded_node.position.y].append(entry_time_and_interval) + + +show_animation = True +verbose = False + +def main(): + start = Position(1, 18) + goal = Position(19, 19) + grid_side_length = 21 + + start_time = time.time() + + grid = Grid( + np.array([grid_side_length, grid_side_length]), + num_obstacles=250, + obstacle_avoid_points=[start, goal], + obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1, + # obstacle_arrangement=ObstacleArrangement.RANDOM, + ) + + planner = SafeIntervalPathPlanner(grid, start, goal) + path = planner.plan(verbose) + runtime = time.time() - start_time + print(f"Planning took: {runtime:.5f} seconds") + + if verbose: + print(f"Path: {path}") + + if not show_animation: + return + + fig = plt.figure(figsize=(10, 7)) + ax = fig.add_subplot( + autoscale_on=False, + xlim=(0, grid.grid_size[0] - 1), + ylim=(0, grid.grid_size[1] - 1), + ) + ax.set_aspect("equal") + ax.grid() + ax.set_xticks(np.arange(0, grid_side_length, 1)) + ax.set_yticks(np.arange(0, grid_side_length, 1)) + + (start_and_goal,) = ax.plot([], [], "mD", ms=15, label="Start and Goal") + start_and_goal.set_data([start.x, goal.x], [start.y, goal.y]) + (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles") + (path_points,) = ax.plot([], [], "bo", ms=10, label="Path Found") + ax.legend(bbox_to_anchor=(1.05, 1)) + + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + "key_release_event", lambda event: [exit(0) if event.key == "escape" else None] + ) + + for i in range(0, path.goal_reached_time() + 1): + obs_positions = grid.get_obstacle_positions_at_time(i) + obs_points.set_data(obs_positions[0], obs_positions[1]) + path_position = path.get_position(i) + path_points.set_data([path_position.x], [path_position.y]) + plt.pause(0.2) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst index 48dc1289c2..04001d4504 100644 --- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst +++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst @@ -33,7 +33,48 @@ After:: When starting at (1, 11) in the structured obstacle arrangement (second of the two gifs above). -References: + +Safe Interval Path Planning +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The safe interval path planning algorithm is described in this paper: + +`SIPP: Safe Interval Path Planning for Dynamic Environments `__ + +It is faster than space-time A* because it pre-computes the intervals of time that are unoccupied in each cell. This allows it to reduce the number of successor node it generates by avoiding nodes within the same interval. + +**Comparison with SpaceTime A*:** + +Arrangement 1 starting at (1, 18) + +SafeInterval planner:: + + Found path to goal after 322 expansions + Planning took: 0.00730 seconds + +SpaceTime A*:: + + Found path to goal after 2717154 expansions + Planning took: 20.51330 seconds + +**Benchmarking the Safe Interval Path Planner:** + +250 random obstacles:: + + Found path to goal after 764 expansions + Planning took: 0.60596 seconds + +.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/SafeIntervalPathPlanner/path_animation.gif + +Arrangement 1 starting at (1, 18):: + + Found path to goal after 322 expansions + Planning took: 0.00730 seconds + +.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/SafeIntervalPathPlanner/path_animation2.gif + +References ~~~~~~~~~~~ - `Cooperative Pathfinding `__ +- `SIPP: Safe Interval Path Planning for Dynamic Environments `__ diff --git a/tests/test_safe_interval_path_planner.py b/tests/test_safe_interval_path_planner.py new file mode 100644 index 0000000000..f1fbf90d2a --- /dev/null +++ b/tests/test_safe_interval_path_planner.py @@ -0,0 +1,33 @@ +from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import ( + Grid, + ObstacleArrangement, + Position, +) +from PathPlanning.TimeBasedPathPlanning import SafeInterval as m +import numpy as np +import conftest + + +def test_1(): + start = Position(1, 11) + goal = Position(19, 19) + grid_side_length = 21 + grid = Grid( + np.array([grid_side_length, grid_side_length]), + obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1, + ) + + m.show_animation = False + planner = m.SafeIntervalPathPlanner(grid, start, goal) + + path = planner.plan(False) + + # path should have 31 entries + assert len(path.path) == 31 + + # path should end at the goal + assert path.path[-1].position == goal + + +if __name__ == "__main__": + conftest.run_this_test(__file__) From 19fc48d60fea057f6c14365c502db4d18490c3a8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Mar 2025 20:22:31 +0900 Subject: [PATCH 832/909] build(deps): bump numpy from 2.2.3 to 2.2.4 in /requirements (#1189) Bumps [numpy](https://github.com/numpy/numpy) from 2.2.3 to 2.2.4. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v2.2.3...v2.2.4) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index cf6dea3a46..3ea4197736 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 2.2.3 +numpy == 2.2.4 scipy == 1.15.2 matplotlib == 3.10.1 cvxpy == 1.5.3 From 764ba21f86fc0d5c2ae5b613449ca81ea6406036 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Mar 2025 20:29:12 +0900 Subject: [PATCH 833/909] build(deps): bump ruff from 0.9.10 to 0.11.0 in /requirements (#1191) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.10 to 0.11.0. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.9.10...0.11.0) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3ea4197736..5438678f87 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.9.10 # For unit test +ruff == 0.11.0 # For unit test From 1c1596be21f015b3b2c3db6489b93cd94d6eca01 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 28 Apr 2025 08:51:09 +0900 Subject: [PATCH 834/909] docs: enhance contribution guide with instructions for linking code in GitHub (#1202) --- docs/_static/img/source_link_1.png | Bin 0 -> 87987 bytes docs/_static/img/source_link_2.png | Bin 0 -> 237071 bytes .../3_how_to_contribute_main.rst | 13 +++++++++++++ 3 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Documentations related codes should be in the python script as the header comments of the script or docstrings of each function. +Also, each document should have a link to the code in Github. +You can easily add the link by using the `.. autoclass::`, `.. autofunction::`, and `.. automodule` by Sphinx's `autodoc`_ module. + +Using this `autodoc`_ module, the generated documentations have the link to the code in Github like: + +.. image:: /_static/img/source_link_1.png + +When you click the link, you will jump to the source code in Github like: + +.. image:: /_static/img/source_link_2.png + + .. _`submit a pull request`: @@ -210,5 +222,6 @@ Current Major Sponsors: .. _`1Password`: https://github.com/1Password/for-open-source .. _`matplotrecorder`: https://github.com/AtsushiSakai/matplotrecorder .. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs +.. _`autodoc`: https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html From f466f25f5328e019bd1e0ccc62b176ff2fc72187 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 28 Apr 2025 08:53:35 +0900 Subject: [PATCH 835/909] build(deps): bump ruff from 0.11.0 to 0.11.6 in /requirements (#1201) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.0 to 0.11.6. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.11.0...0.11.6) --- updated-dependencies: - dependency-name: ruff dependency-version: 0.11.6 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5438678f87..f3e6c3dc51 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.5.3 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.11.0 # For unit test +ruff == 0.11.6 # For unit test From af0442d358b1deca5710c0c6812424b12b39f7e7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 1 May 2025 13:08:29 +0900 Subject: [PATCH 836/909] build(deps): update cvxpy version from 1.5.3 to 1.6.5 in requirements (#1203) * build(deps): update cvxpy version from 1.5.3 to 1.6.5 in requirements * Add ECOS solver and improve solver handling for stability Added ECOS to requirements and enhanced compatibility with cvxpy solvers by specifying 'order' for matrix reshaping. Updated solver configurations in rocket landing and pendulum control for consistency and reliability. Improved test behavior by enforcing stricter warning handling in pytest. --- .../rocket_powered_landing/rocket_powered_landing.py | 9 +++++---- InvertedPendulum/inverted_pendulum_mpc_control.py | 2 +- requirements/requirements.txt | 3 ++- tests/conftest.py | 2 +- 4 files changed, 9 insertions(+), 7 deletions(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 239f3629c1..1918dc1cee 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -31,6 +31,7 @@ W_DELTA_SIGMA = 1e-1 # difference in flight time W_NU = 1e5 # virtual control +print(cvxpy.installed_solvers()) solver = 'ECOS' verbose_solver = False @@ -462,11 +463,11 @@ def __init__(self, m, K): # x_t+1 = A_*x_t+B_*U_t+C_*U_T+1*S_*sigma+zbar+nu constraints += [ self.var['X'][:, k + 1] == - cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x)) @ + cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x), order='F') @ self.var['X'][:, k] + - cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u)) @ + cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u), order='F') @ self.var['U'][:, k] + - cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u)) @ + cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u), order='F') @ self.var['U'][:, k + 1] + self.par['S_bar'][:, k] * self.var['sigma'] + self.par['z_bar'][:, k] + @@ -536,7 +537,7 @@ def solve(self, **kwargs): with warnings.catch_warnings(): # For User warning from solver warnings.simplefilter('ignore') self.prob.solve(verbose=verbose_solver, - solver=solver) + solver=solver) except cvxpy.SolverError: error = True diff --git a/InvertedPendulum/inverted_pendulum_mpc_control.py b/InvertedPendulum/inverted_pendulum_mpc_control.py index 9a5fa2ab41..c45dde8acc 100644 --- a/InvertedPendulum/inverted_pendulum_mpc_control.py +++ b/InvertedPendulum/inverted_pendulum_mpc_control.py @@ -91,7 +91,7 @@ def mpc_control(x0): prob = cvxpy.Problem(cvxpy.Minimize(cost), constr) start = time.time() - prob.solve(verbose=False) + prob.solve(verbose=False, solver=cvxpy.CLARABEL) elapsed_time = time.time() - start print(f"calc time:{elapsed_time:.6f} [sec]") diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f3e6c3dc51..e9c68be7e6 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,8 @@ numpy == 2.2.4 scipy == 1.15.2 matplotlib == 3.10.1 -cvxpy == 1.5.3 +cvxpy == 1.6.5 +ecos == 2.0.14 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test diff --git a/tests/conftest.py b/tests/conftest.py index 3485fe8150..b707b22d00 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -10,4 +10,4 @@ def run_this_test(file): - pytest.main([os.path.abspath(file)]) + pytest.main(args=["-W", "error", "-Werror", "--pythonwarnings=error", os.path.abspath(file)]) From 1f729cb8cbd5075871bb52c8fb31df2732ad6641 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 1 May 2025 13:59:15 +0900 Subject: [PATCH 837/909] build(deps): bump ruff from 0.11.6 to 0.11.7 in /requirements (#1205) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.6 to 0.11.7. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.11.6...0.11.7) --- updated-dependencies: - dependency-name: ruff dependency-version: 0.11.7 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e9c68be7e6..88cbb21f81 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -6,4 +6,4 @@ ecos == 2.0.14 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.11.6 # For unit test +ruff == 0.11.7 # For unit test From a2c42c3d6837658edb734e9f16ab64c1bd2662e7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 1 May 2025 21:53:12 +0900 Subject: [PATCH 838/909] Update Python version to 3.13 across the project (#1208) * Update Python version to 3.13 across the project Upgraded the required Python version to 3.13 in configurations, CI workflows, documentation, and environment files. This ensures compatibility with the latest Python release and maintains consistency across all project components. * Update Python version to 3.13 across the project Upgraded the required Python version to 3.13 in configurations, CI workflows, documentation, and environment files. This ensures compatibility with the latest Python release and maintains consistency across all project components. --- .circleci/config.yml | 2 +- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .github/workflows/Windows_CI.yml | 2 +- README.md | 2 +- appveyor.yml | 2 +- docs/modules/0_getting_started/3_how_to_contribute_main.rst | 2 +- requirements/environment.yml | 2 +- ruff.toml | 4 ++-- 9 files changed, 10 insertions(+), 10 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 9f803ece4a..f6eff674de 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,7 +6,7 @@ orbs: jobs: build_doc: docker: - - image: cimg/python:3.12 + - image: cimg/python:3.13 steps: - checkout - run: diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 7b3dc14751..907d36452d 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: [ '3.12' ] + python-version: [ '3.13' ] name: Python ${{ matrix.python-version }} CI diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 5ea15ac72e..03db65f43d 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: macos-latest strategy: matrix: - python-version: [ '3.12' ] + python-version: [ '3.13' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index b9c8dea649..5cb19699b2 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: windows-latest strategy: matrix: - python-version: [ '3.12' ] + python-version: [ '3.13' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v4 diff --git a/README.md b/README.md index 9e605435ce..6d04c90bc8 100644 --- a/README.md +++ b/README.md @@ -102,7 +102,7 @@ or this paper for more details: For running each sample code: -- [Python 3.12.x](https://www.python.org/) +- [Python 3.13.x](https://www.python.org/) - [NumPy](https://numpy.org/) diff --git a/appveyor.yml b/appveyor.yml index 05ad8a2820..72d89acf11 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -8,7 +8,7 @@ environment: CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd" matrix: - - PYTHON_DIR: C:\Python310-x64 + - PYTHON_DIR: C:\Python313-x64 branches: only: diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst index 30e3001f36..0325aaacae 100644 --- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst +++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst @@ -26,7 +26,7 @@ to understand the philosophies of this project. Check your Python version. --------------------------- -We only accept a PR for Python 3.12.x or higher. +We only accept a PR for Python 3.13.x or higher. We will not accept a PR for Python 2.x. diff --git a/requirements/environment.yml b/requirements/environment.yml index afbb3fb8ce..023a3d75bf 100644 --- a/requirements/environment.yml +++ b/requirements/environment.yml @@ -2,7 +2,7 @@ name: python_robotics channels: - conda-forge dependencies: - - python=3.12 + - python=3.13 - pip - scipy - numpy diff --git a/ruff.toml b/ruff.toml index 5823ca3db7..d76b715a06 100644 --- a/ruff.toml +++ b/ruff.toml @@ -5,8 +5,8 @@ ignore = ["E501", "E741", "E402"] exclude = [ ] -# Assume Python 3.11 -target-version = "py312" +# Assume Python 3.13 +target-version = "py313" [per-file-ignores] From 22cbee49218a1f8acb07ad488416db3c952c19d6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Fri, 2 May 2025 10:01:19 +0900 Subject: [PATCH 839/909] Standardize "Ref:" to "Reference" across files (#1210) Updated all instances of "Ref:" to "Reference" for consistency in both code and documentation. This change improves clarity and aligns with standard terminology practices. --- .../rocket_powered_landing.py | 2 +- .../two_joint_arm_to_point_control.py | 2 +- .../ensemble_kalman_filter.py | 2 +- Mapping/DistanceMap/distance_map.py | 2 +- .../rectangle_fitting/rectangle_fitting.py | 2 +- MissionPlanning/BehaviorTree/behavior_tree.py | 2 +- MissionPlanning/StateMachine/state_machine.py | 4 +- PathPlanning/ElasticBands/elastic_bands.py | 2 +- .../Eta3SplinePath/eta3_spline_path.py | 2 +- .../cartesian_frenet_converter.py | 2 +- .../frenet_optimal_trajectory.py | 2 +- .../potential_field_planning.py | 2 +- .../quintic_polynomials_planner.py | 2 +- .../state_lattice_planner.py | 2 +- PathTracking/cgmres_nmpc/cgmres_nmpc.py | 2 +- PathTracking/move_to_pose/move_to_pose.py | 2 +- .../stanley_controller/stanley_controller.py | 2 +- README.md | 42 ++--- .../Kalmanfilter_basics_2_main.rst | 2 +- .../12_appendix/Kalmanfilter_basics_main.rst | 2 +- .../steering_motion_model_main.rst | 2 +- ...samble_kalman_filter_localization_main.rst | 5 + ...tended_kalman_filter_localization_main.rst | 168 +++++++++--------- .../histogram_filter_localization_main.rst | 7 +- .../particle_filter_localization_main.rst | 8 +- ...cented_kalman_filter_localization_main.rst | 8 +- .../modules/4_slam/ekf_slam/ekf_slam_main.rst | 2 +- .../4_slam/graph_slam/graph_slam_main.rst | 2 +- .../bezier_path/bezier_path_main.rst | 2 +- .../eta3_spline/eta3_spline_main.rst | 2 +- .../frenet_frame_path_main.rst | 2 +- .../grid_base_search_main.rst | 6 +- ...l_predictive_trajectory_generator_main.rst | 2 +- .../prm_planner/prm_planner_main.rst | 2 +- .../quintic_polynomials_planner_main.rst | 2 +- .../reeds_shepp_path_main.rst | 2 +- docs/modules/5_path_planning/rrt/rrt_main.rst | 8 +- .../state_lattice_planner_main.rst | 2 +- .../vrm_planner/vrm_planner_main.rst | 2 +- .../lqr_speed_and_steering_control_main.rst | 2 +- .../lqr_steering_control_main.rst | 2 +- .../pure_pursuit_tracking_main.rst | 2 +- .../rear_wheel_feedback_control_main.rst | 2 +- .../stanley_control/stanley_control_main.rst | 2 +- users_comments.md | 2 +- 45 files changed, 173 insertions(+), 155 deletions(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 1918dc1cee..e8ba8fa220 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -5,7 +5,7 @@ author: Sven Niederberger Atsushi Sakai -Ref: +Reference: - Python implementation of 'Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time' paper by Michael Szmuk and Behcet Acıkmese. diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index c2227f18e3..09969c30be 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -5,7 +5,7 @@ Author: Daniel Ingram (daniel-s-ingram) Atsushi Sakai (@Atsushi_twi) -Ref: P. I. Corke, "Robotics, Vision & Control", Springer 2017, +Reference: P. I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7 p102 - [Robotics, Vision and Control] (https://link.springer.com/book/10.1007/978-3-642-20144-8) diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py index 2bab3b49c1..e8a988a270 100644 --- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py +++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py @@ -4,7 +4,7 @@ author: Ryohei Sasaki(rsasaki0109) -Ref: +Reference: Ensemble Kalman filtering (https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135) diff --git a/Mapping/DistanceMap/distance_map.py b/Mapping/DistanceMap/distance_map.py index 0dcc7380c5..0f96c9e8c6 100644 --- a/Mapping/DistanceMap/distance_map.py +++ b/Mapping/DistanceMap/distance_map.py @@ -3,7 +3,7 @@ author: Wang Zheng (@Aglargil) -Ref: +Reference: - [Distance Map] (https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf) diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index 177f078871..7902619666 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -4,7 +4,7 @@ author: Atsushi Sakai (@Atsushi_twi) -Ref: +Reference: - Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners - The Robotics Institute Carnegie Mellon University https://www.ri.cmu.edu/publications/ diff --git a/MissionPlanning/BehaviorTree/behavior_tree.py b/MissionPlanning/BehaviorTree/behavior_tree.py index 59f4c713f1..9ad886aafb 100644 --- a/MissionPlanning/BehaviorTree/behavior_tree.py +++ b/MissionPlanning/BehaviorTree/behavior_tree.py @@ -3,7 +3,7 @@ author: Wang Zheng (@Aglargil) -Ref: +Reference: - [Behavior Tree](https://en.wikipedia.org/wiki/Behavior_tree_(artificial_intelligence,_robotics_and_control)) """ diff --git a/MissionPlanning/StateMachine/state_machine.py b/MissionPlanning/StateMachine/state_machine.py index de72f0f451..454759236e 100644 --- a/MissionPlanning/StateMachine/state_machine.py +++ b/MissionPlanning/StateMachine/state_machine.py @@ -3,7 +3,7 @@ author: Wang Zheng (@Aglargil) -Ref: +Reference: - [State Machine] (https://en.wikipedia.org/wiki/Finite-state_machine) @@ -23,7 +23,7 @@ def deflate_and_encode(plantuml_text): """ zlib compress the plantuml text and encode it for the plantuml server. - Ref: https://plantuml.com/en/text-encoding + Reference https://plantuml.com/en/text-encoding """ plantuml_alphabet = ( string.digits + string.ascii_uppercase + string.ascii_lowercase + "-_" diff --git a/PathPlanning/ElasticBands/elastic_bands.py b/PathPlanning/ElasticBands/elastic_bands.py index 785f822d14..77d4e6e399 100644 --- a/PathPlanning/ElasticBands/elastic_bands.py +++ b/PathPlanning/ElasticBands/elastic_bands.py @@ -3,7 +3,7 @@ author: Wang Zheng (@Aglargil) -Ref: +Reference: - [Elastic Bands: Connecting Path Planning and Control] (http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf) diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py index dc07d3c84b..3f685e512f 100644 --- a/PathPlanning/Eta3SplinePath/eta3_spline_path.py +++ b/PathPlanning/Eta3SplinePath/eta3_spline_path.py @@ -5,7 +5,7 @@ author: Joe Dinius, Ph.D (https://jwdinius.github.io) Atsushi Sakai (@Atsushi_twi) -Ref: +Reference: - [eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots] (https://ieeexplore.ieee.org/document/4339545/) diff --git a/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py index 4cc8650c89..482712ceaf 100644 --- a/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py +++ b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py @@ -4,7 +4,7 @@ author: Wang Zheng (@Aglargil) -Ref: +Reference: - [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame] (https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf) diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index 248894c1c6..4b82fb70fd 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -4,7 +4,7 @@ author: Atsushi Sakai (@Atsushi_twi) -Ref: +Reference: - [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame] (https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf) diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py index 8f136b5ee3..603a9d16cf 100644 --- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py +++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py @@ -4,7 +4,7 @@ author: Atsushi Sakai (@Atsushi_twi) -Ref: +Reference: https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf """ diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py index fdc181afab..86f9f662da 100644 --- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py +++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py @@ -4,7 +4,7 @@ author: Atsushi Sakai (@Atsushi_twi) -Ref: +Reference: - [Local Path planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/) diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py index 7f8e725e0a..05f8df78f8 100644 --- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py +++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py @@ -8,7 +8,7 @@ https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning /ModelPredictiveTrajectoryGenerator/lookup_table_generator.py -Ref: +Reference: - State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py index a582c9da81..ee40e73504 100644 --- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py +++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py @@ -4,7 +4,7 @@ author Atsushi Sakai (@Atsushi_twi) -Ref: +Reference: Shunichi09/nonlinear_control: Implementing the nonlinear model predictive control, sliding mode control https://github.com/Shunichi09/PythonLinearNonlinearControl diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py index 34736a2e21..faf1264953 100644 --- a/PathTracking/move_to_pose/move_to_pose.py +++ b/PathTracking/move_to_pose/move_to_pose.py @@ -76,7 +76,7 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal): # [-pi, pi] to prevent unstable behavior e.g. difference going # from 0 rad to 2*pi rad with slight turn - # Ref: The velocity v always has a constant sign which depends on the initial value of α. + # The velocity v always has a constant sign which depends on the initial value of α. rho = np.hypot(x_diff, y_diff) v = self.Kp_rho * rho diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py index bc98175f17..01c2ec0229 100644 --- a/PathTracking/stanley_controller/stanley_controller.py +++ b/PathTracking/stanley_controller/stanley_controller.py @@ -4,7 +4,7 @@ author: Atsushi Sakai (@Atsushi_twi) -Ref: +Reference: - [Stanley: The robot that won the DARPA grand challenge](http://isl.ecst.csuchico.edu/DOCS/darpa2005/DARPA%202005%20Stanley.pdf) - [Autonomous Automobile Path Tracking](https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf) diff --git a/README.md b/README.md index 6d04c90bc8..05fd0262df 100644 --- a/README.md +++ b/README.md @@ -168,7 +168,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation EKF pic -Ref: +Reference - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html) @@ -186,7 +186,7 @@ It is assumed that the robot can measure a distance from landmarks (RFID). These measurements are used for PF localization. -Ref: +Reference - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) @@ -207,7 +207,7 @@ The filter integrates speed input and range observations from RFID for localizat Initial position is not needed. -Ref: +Reference - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) @@ -256,7 +256,7 @@ It can calculate a rotation matrix, and a translation vector between points and ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif) -Ref: +Reference - [Introduction to Mobile Robotics: Iterative Closest Point Algorithm](https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf) @@ -275,7 +275,7 @@ Black points are landmarks, blue crosses are estimated landmark positions by Fas ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif) -Ref: +Reference - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/) @@ -321,7 +321,7 @@ This is a 2D grid based the shortest path planning with D star algorithm. The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm. -Ref: +Reference - [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*) @@ -346,7 +346,7 @@ This is a 2D grid based path planning with Potential Field algorithm. In the animation, the blue heat map shows potential value on each grid. -Ref: +Reference - [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf) @@ -362,7 +362,7 @@ This script is a path planning code with state lattice planning. This code uses the model predictive trajectory generator to solve boundary problem. -Ref: +Reference - [Optimal rough terrain trajectory generation for wheeled mobile robots](https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328) @@ -390,7 +390,7 @@ Cyan crosses means searched points with Dijkstra method, The red line is the final path of PRM. -Ref: +Reference - [Probabilistic roadmap \- Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_roadmap) @@ -406,7 +406,7 @@ This is a path planning code with RRT\* Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. -Ref: +Reference - [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416) @@ -426,7 +426,7 @@ A double integrator motion model is used for LQR local planner. ![LQR_RRT](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif) -Ref: +Reference - [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](https://lis.csail.mit.edu/pubs/perez-icra12.pdf) @@ -441,7 +441,7 @@ Motion planning with quintic polynomials. It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials. -Ref: +Reference - [Local Path Planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/) @@ -451,7 +451,7 @@ A sample code with Reeds Shepp path planning. ![RSPlanning](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true) -Ref: +Reference - [15.3.2 Reeds\-Shepp Curves](http://planning.cs.uiuc.edu/node822.html) @@ -477,7 +477,7 @@ The cyan line is the target course and black crosses are obstacles. The red line is the predicted path. -Ref: +Reference - [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame](https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf) @@ -492,7 +492,7 @@ This is a simulation of moving to a pose control ![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif) -Ref: +Reference - [P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102](https://link.springer.com/book/10.1007/978-3-642-20144-8) @@ -503,7 +503,7 @@ Path tracking simulation with Stanley steering control and PID speed control. ![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif) -Ref: +Reference - [Stanley: The robot that won the DARPA grand challenge](http://robots.stanford.edu/papers/thrun.stanley05.pdf) @@ -517,7 +517,7 @@ Path tracking simulation with rear wheel feedback steering control and PID speed ![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif) -Ref: +Reference - [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446) @@ -528,7 +528,7 @@ Path tracking simulation with LQR speed and steering control. ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif) -Ref: +Reference - [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](https://ieeexplore.ieee.org/document/5940562/) @@ -539,7 +539,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri MPC pic -Ref: +Reference - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html) @@ -551,7 +551,7 @@ A motion planning and path tracking simulation with NMPC of C-GMRES ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif) -Ref: +Reference - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.html) @@ -591,7 +591,7 @@ This is a 3d trajectory generation simulation for a rocket powered landing. ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif) -Ref: +Reference - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.html) diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst index 9ae6fc5bcb..b7ff84e6f6 100644 --- a/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst +++ b/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst @@ -331,7 +331,7 @@ are vectors and matrices, but the concepts are exactly the same: - Use the process model to predict the next state (the prior) - Form an estimate part way between the measurement and the prior -References: +Reference ~~~~~~~~~~~ 1. Roger Labbe’s diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst index 6548377e07..a1d99d47ef 100644 --- a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst +++ b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst @@ -552,7 +552,7 @@ a given (X,Y) value. .. image:: Kalmanfilter_basics_files/Kalmanfilter_basics_28_1.png -References: +Reference ~~~~~~~~~~~ 1. Roger Labbe’s diff --git a/docs/modules/12_appendix/steering_motion_model_main.rst b/docs/modules/12_appendix/steering_motion_model_main.rst index 6e444b7909..c697123fa2 100644 --- a/docs/modules/12_appendix/steering_motion_model_main.rst +++ b/docs/modules/12_appendix/steering_motion_model_main.rst @@ -91,7 +91,7 @@ the target minimum velocity :math:`v_{min}` can be calculated as follows: :math:`v_{min} = \frac{d_{t+1}+d_{t}}{\Delta t} = \frac{d_{t+1}+d_{t}}{(\kappa_{t+1}-\kappa_{t})}\frac{tan\dot{\delta}_{max}}{WB}` -References: +Reference ~~~~~~~~~~~ - `Vehicle Dynamics and Control `_ diff --git a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst index 2543d1186a..214e645d10 100644 --- a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst +++ b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst @@ -5,3 +5,8 @@ Ensamble Kalman Filter Localization This is a sensor fusion localization with Ensamble Kalman Filter(EnKF). +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Localization.ensemble_kalman_filter.ensemble_kalman_filter.enkf_localization + diff --git a/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst b/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst index da214b6de5..adb41e5e40 100644 --- a/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst +++ b/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst @@ -23,28 +23,40 @@ is estimated trajectory with EKF. The red ellipse is estimated covariance ellipse with EKF. -Code: `PythonRobotics/extended_kalman_filter.py at master · -AtsushiSakai/PythonRobotics `__ +Code Link +~~~~~~~~~~~~~ -Kalman Filter with Speed Scale Factor Correction -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. autofunction:: Localization.extended_kalman_filter.extended_kalman_filter.ekf_estimation +Extended Kalman Filter algorithm +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. image:: ekf_with_velocity_correction_1_0.png - :width: 600px +Localization process using Extended Kalman Filter:EKF is -This is a Extended kalman filter (EKF) localization with velocity correction. +=== Predict === -This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear. +:math:`x_{Pred} = Fx_t+Bu_t` -Code: `PythonRobotics/extended_kalman_ekf_with_velocity_correctionfilter.py -AtsushiSakai/PythonRobotics `__ +:math:`P_{Pred} = J_f P_t J_f^T + Q` -Filter design -~~~~~~~~~~~~~ +=== Update === -Position Estimation Kalman Filter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +:math:`z_{Pred} = Hx_{Pred}` + +:math:`y = z - z_{Pred}` + +:math:`S = J_g P_{Pred}.J_g^T + R` + +:math:`K = P_{Pred}.J_g^T S^{-1}` + +:math:`x_{t+1} = x_{Pred} + Ky` + +:math:`P_{t+1} = ( I - K J_g) P_{Pred}` + + + +Filter design +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In this simulation, the robot has a state vector includes 4 states at time :math:`t`. @@ -82,27 +94,9 @@ In the code, “observation” function generates the input and observation vector with noise `code `__ -Kalman Filter with Speed Scale Factor Correction -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In this simulation, the robot has a state vector includes 5 states at -time :math:`t`. - -.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t] - -x, y are a 2D x-y position, :math:`\phi` is orientation, v is -velocity, and s is a scale factor of velocity. - -In the code, “xEst” means the state vector. -`code `__ - -The rest is the same as the Position Estimation Kalman Filter. Motion Model -~~~~~~~~~~~~ - -Position Estimation Kalman Filter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +~~~~~~~~~~~~~~~~~ The robot model is @@ -137,9 +131,61 @@ Its Jacobian matrix is :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v \sin(\phi) \Delta t & \cos(\phi) \Delta t\\ 0 & 1 & v \cos(\phi) \Delta t & \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} \end{equation*}` +Observation Model +~~~~~~~~~~~~~~~~~ + +The robot can get x-y position information from GPS. + +So GPS Observation model is + +.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t + +where + +:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}` + +The observation function states that + +:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}` + +Its Jacobian matrix is + +:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}` + +:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}` + Kalman Filter with Speed Scale Factor Correction ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +This is a Extended kalman filter (EKF) localization with velocity correction. + +This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear. + + +In this simulation, the robot has a state vector includes 5 states at +time :math:`t`. + +.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t] + +x, y are a 2D x-y position, :math:`\phi` is orientation, v is +velocity, and s is a scale factor of velocity. + +In the code, “xEst” means the state vector. +`code `__ + +The rest is the same as the Position Estimation Kalman Filter. + +.. image:: ekf_with_velocity_correction_1_0.png + :width: 600px + +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.extended_kalman_filter.ekf_with_velocity_correction.ekf_estimation + + +Motion Model +~~~~~~~~~~~~ The robot model is @@ -178,33 +224,7 @@ Its Jacobian matrix is Observation Model ~~~~~~~~~~~~~~~~~ -Position Estimation Kalman Filter -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The robot can get x-y position infomation from GPS. - -So GPS Observation model is - -.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t - -where - -:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}` - -The observation function states that - -:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}` - -Its Jacobian matrix is - -:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}` - -:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}` - -Kalman Filter with Speed Scale Factor Correction -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The robot can get x-y position infomation from GPS. +The robot can get x-y position information from GPS. So GPS Observation model is @@ -225,32 +245,8 @@ Its Jacobian matrix is :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 & 0\\ \end{bmatrix} \end{equation*}` -Extended Kalman Filter -~~~~~~~~~~~~~~~~~~~~~~ - -Localization process using Extended Kalman Filter:EKF is - -=== Predict === - -:math:`x_{Pred} = Fx_t+Bu_t` - -:math:`P_{Pred} = J_f P_t J_f^T + Q` - -=== Update === - -:math:`z_{Pred} = Hx_{Pred}` - -:math:`y = z - z_{Pred}` - -:math:`S = J_g P_{Pred}.J_g^T + R` - -:math:`K = P_{Pred}.J_g^T S^{-1}` - -:math:`x_{t+1} = x_{Pred} + Ky` - -:math:`P_{t+1} = ( I - K J_g) P_{Pred}` -Ref: -~~~~ +Reference +^^^^^^^^^^^ - `PROBABILISTIC-ROBOTICS.ORG `__ diff --git a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst index fafd578333..3a175b1316 100644 --- a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst +++ b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst @@ -16,6 +16,11 @@ The filter uses speed input and range observations from RFID for localization. Initial position information is not needed. +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.histogram_filter.histogram_filter.histogram_filter_localization + Filtering algorithm ~~~~~~~~~~~~~~~~~~~~ @@ -107,7 +112,7 @@ There are two ways to calculate the final positions: -References: +Reference ~~~~~~~~~~~ - `_PROBABILISTIC ROBOTICS: `_ diff --git a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst index 20a9eb58fc..d648d8e080 100644 --- a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst +++ b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst @@ -15,6 +15,12 @@ It is assumed that the robot can measure a distance from landmarks This measurements are used for PF localization. +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.particle_filter.particle_filter.pf_localization + + How to calculate covariance matrix from particles ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -30,7 +36,7 @@ The covariance matrix :math:`\Xi` from particle information is calculated by the - :math:`\mu_j` is the :math:`j` th mean state of particles. -References: +Reference ~~~~~~~~~~~ - `_PROBABILISTIC ROBOTICS: `_ diff --git a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst index bb6b5b664b..a7a5aab61b 100644 --- a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst +++ b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst @@ -7,7 +7,13 @@ This is a sensor fusion localization with Unscented Kalman Filter(UKF). The lines and points are same meaning of the EKF simulation. -References: +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: Localization.unscented_kalman_filter.unscented_kalman_filter.ukf_estimation + + +Reference ~~~~~~~~~~~ - `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization `_ diff --git a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst index b27971225e..a1486ffe1e 100644 --- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst +++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst @@ -578,7 +578,7 @@ reckoning and control functions are passed along here as well. .. image:: ekf_slam_1_0.png -References: +Reference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - `PROBABILISTIC ROBOTICS `_ diff --git a/docs/modules/4_slam/graph_slam/graph_slam_main.rst b/docs/modules/4_slam/graph_slam/graph_slam_main.rst index 987eed385c..a2ef513527 100644 --- a/docs/modules/4_slam/graph_slam/graph_slam_main.rst +++ b/docs/modules/4_slam/graph_slam/graph_slam_main.rst @@ -17,7 +17,7 @@ The black stars are landmarks for graph edge generation. .. include:: graphSLAM_formulation.rst .. include:: graphSLAM_SE2_example.rst -References: +Reference ~~~~~~~~~~~ - `A Tutorial on Graph-Based SLAM `_ diff --git a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst index d306a85352..9d9b3de709 100644 --- a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst +++ b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst @@ -13,7 +13,7 @@ You can get different Beizer course: .. image:: Figure_2.png -Ref: +Reference - `Continuous Curvature Path Generation Based on Bezier Curves for Autonomous diff --git a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst index ffc3cc6451..82e0a71044 100644 --- a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst +++ b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst @@ -7,7 +7,7 @@ Eta^3 Spline path planning This is a path planning with Eta^3 spline. -Ref: +Reference - `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots `__ diff --git a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst index 38efaf2b53..371d536e3f 100644 --- a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst +++ b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst @@ -35,7 +35,7 @@ Low Speed and Merging and Stopping Scenario This scenario illustrates the trajectory planning at low speeds with merging and stopping behaviors. -Ref: +Reference - `Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst index 1644ed00cc..bf82c9f391 100644 --- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst +++ b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst @@ -63,7 +63,7 @@ This is a 2D grid based shortest path planning with D star algorithm. The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm. -Ref: +Reference - `D* search Wikipedia `__ @@ -74,7 +74,7 @@ This is a 2D grid based path planning and replanning with D star lite algorithm. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif -Ref: +Reference - `Improved Fast Replanning for Robot Navigation in Unknown Terrain `_ @@ -88,7 +88,7 @@ This is a 2D grid based path planning with Potential Field algorithm. In the animation, the blue heat map shows potential value on each grid. -Ref: +Reference - `Robotic Motion Planning:Potential Functions `__ diff --git a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst index 463363ddcf..1c5df1c9cc 100644 --- a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst +++ b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst @@ -16,7 +16,7 @@ Lookup table generation sample .. image:: lookup_table.png -Ref: +Reference - `Optimal rough terrain trajectory generation for wheeled mobile robots `__ diff --git a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst index 0628719176..f816c5c1b9 100644 --- a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst +++ b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst @@ -13,7 +13,7 @@ Cyan crosses means searched points with Dijkstra method, The red line is the final path of PRM. -Ref: +Reference - `Probabilistic roadmap - Wikipedia `__ diff --git a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst index 0412b3c9b3..66c3001c05 100644 --- a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst +++ b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst @@ -97,7 +97,7 @@ Each velocity and acceleration boundary condition can be calculated with each or :math:`v_{xe}=v_ecos(\theta_e), v_{ye}=v_esin(\theta_e)` -References: +Reference ~~~~~~~~~~~ - `Local Path Planning And Motion Control For Agv In diff --git a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst index ff377eb91b..a4a5d28e01 100644 --- a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst +++ b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst @@ -380,7 +380,7 @@ Hence, we have: - :math:`v = (t - φ)` -Ref: +Reference - `15.3.2 Reeds-Shepp Curves `__ diff --git a/docs/modules/5_path_planning/rrt/rrt_main.rst b/docs/modules/5_path_planning/rrt/rrt_main.rst index e5f351a7ba..da3a4957a5 100644 --- a/docs/modules/5_path_planning/rrt/rrt_main.rst +++ b/docs/modules/5_path_planning/rrt/rrt_main.rst @@ -53,7 +53,7 @@ This is a path planning code with Informed RRT*. The cyan ellipse is the heuristic sampling domain of Informed RRT*. -Ref: +Reference - `Informed RRT\*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal @@ -68,7 +68,7 @@ Batch Informed RRT\* This is a path planning code with Batch Informed RRT*. -Ref: +Reference - `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric @@ -87,7 +87,7 @@ In this code, pure-pursuit algorithm is used for steering control, PID is used for speed control. -Ref: +Reference - `Motion Planning in Complex Environments using Closed-loop Prediction `__ @@ -109,7 +109,7 @@ A double integrator motion model is used for LQR local planner. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif -Ref: +Reference - `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension diff --git a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst index d5e7ed17d1..bf89ac11ae 100644 --- a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst +++ b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst @@ -22,7 +22,7 @@ Lane sampling .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif -Ref: +Reference - `Optimal rough terrain trajectory generation for wheeled mobile robots `__ diff --git a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst index 92e729ab29..65e0e53465 100644 --- a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst +++ b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst @@ -11,7 +11,7 @@ Cyan crosses mean searched points with Dijkstra method, The red line is the final path of Vornoi Road-Map. -Ref: +Reference - `Robotic Motion Planning `__ diff --git a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst index ded187e972..568ef9a0df 100644 --- a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst @@ -134,7 +134,7 @@ Simulation results -References: +Reference ~~~~~~~~~~~ - `Towards fully autonomous driving: Systems and algorithms `__ diff --git a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst index baca7a33fc..831423f364 100644 --- a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst @@ -113,7 +113,7 @@ The optimal control input `u` can be calculated as: where `K` is the feedback gain matrix obtained by solving the Riccati equation. -References: +Reference ~~~~~~~~~~~ - `ApolloAuto/apollo: An open autonomous driving platform `_ diff --git a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst index 5c7bcef85f..d7354cf8fb 100644 --- a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst +++ b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst @@ -9,7 +9,7 @@ speed control. The red line is a target course, the green cross means the target point for pure pursuit control, the blue line is the tracking. -References: +Reference ~~~~~~~~~~~ - `A Survey of Motion Planning and Control Techniques for Self-driving diff --git a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst index 70875fdc6c..d18cd6fbf7 100644 --- a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst +++ b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst @@ -6,7 +6,7 @@ PID speed control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif -References: +Reference ~~~~~~~~~~~ - `A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles `__ diff --git a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst index fe325b0102..69089ac33b 100644 --- a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst +++ b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst @@ -6,7 +6,7 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif -References: +Reference ~~~~~~~~~~~ - `Stanley: The robot that won the DARPA grand diff --git a/users_comments.md b/users_comments.md index a2f798eac4..1674f21377 100644 --- a/users_comments.md +++ b/users_comments.md @@ -10,7 +10,7 @@ This is an electric wheelchair control demo by [Katsushun89](https://github.com/ ![1](https://github.com/AtsushiSakai/PythonRoboticsGifs/blob/master/Users/WHILL_Model_CR_with_move_to_a_pose/WHLL_Model_CR_with_move_to_a_pose.gif) -Ref: +Reference: - [toioと同じように動くWHILL Model CR (in Japanese)](https://qiita.com/KatsuShun89/items/68fde7544ecfe36096d2) From 5392fcff4d555421b4417827dc67c611ad14cc06 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 3 May 2025 09:25:12 +0900 Subject: [PATCH 840/909] Refactor module links and improve code documentation. (#1211) * Refactor module links and improve code documentation. Updated documentation to rename "API" sections to "Code Link" for clarity and consistency. Enhanced docstrings for `circle_fitting` and `kmeans_clustering` functions, improving parameter descriptions and adding return value details. Fixed typos in function and file names in the ray casting grid map module. * Fix import typo in ray casting grid map test module. Corrected the import statement in the test file by updating the module's name to `ray_casting_grid_map` for consistency with the source file. This ensures proper functionality of the test suite. --- Mapping/circle_fitting/circle_fitting.py | 33 +++++++++++++++---- .../kmeans_clustering/kmeans_clustering.py | 27 ++++++++++++++- .../ray_casting_grid_map.py} | 4 +-- .../circle_fitting/circle_fitting_main.rst | 4 +++ .../distance_map/distance_map_main.rst | 4 +-- .../gaussian_grid_map_main.rst | 6 ++++ .../k_means_object_clustering_main.rst | 6 ++++ .../lidar_to_grid_map_tutorial_main.rst | 6 ++++ .../normal_vector_estimation_main.rst | 8 ++--- .../point_cloud_sampling_main.rst | 8 ++--- .../ray_casting_grid_map_main.rst | 7 +++- .../rectangle_fitting_main.rst | 4 +-- ...id_map.py => test_ray_casting_grid_map.py} | 2 +- 13 files changed, 96 insertions(+), 23 deletions(-) rename Mapping/{raycasting_grid_map/raycasting_grid_map.py => ray_casting_grid_map/ray_casting_grid_map.py} (96%) rename tests/{test_raycasting_grid_map.py => test_ray_casting_grid_map.py} (71%) diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py index 2eba550127..b5714b507c 100644 --- a/Mapping/circle_fitting/circle_fitting.py +++ b/Mapping/circle_fitting/circle_fitting.py @@ -16,12 +16,33 @@ def circle_fitting(x, y): """ - Circle Fitting with least squared - input: point x-y positions - output cxe x center position - cye y center position - re radius of circle - error: prediction error + Fits a circle to a given set of points using a least-squares approach. + + This function calculates the center (x, y) and radius of a circle that best fits + the given set of points in a two-dimensional plane. It minimizes the squared + errors between the circle and the provided points and returns the calculated + center coordinates, radius, and the fitting error. + + Raises + ------ + ValueError + If the input lists x and y do not contain the same number of elements. + + Parameters + ---------- + x : list[float] + The x-coordinates of the points. + y : list[float] + The y-coordinates of the points. + + Returns + ------- + tuple[float, float, float, float] + A tuple containing: + - The x-coordinate of the center of the fitted circle (float). + - The y-coordinate of the center of the fitted circle (float). + - The radius of the fitted circle (float). + - The fitting error as a deviation metric (float). """ sumx = sum(x) diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py index e18960e990..cee01e5ad5 100644 --- a/Mapping/kmeans_clustering/kmeans_clustering.py +++ b/Mapping/kmeans_clustering/kmeans_clustering.py @@ -17,12 +17,37 @@ def kmeans_clustering(rx, ry, nc): + """ + Performs k-means clustering on the given dataset, iteratively adjusting cluster centroids + until convergence within a defined threshold or reaching the maximum number of + iterations. + + The implementation initializes clusters, calculates initial centroids, and refines the + clusters through iterative updates to optimize the cost function based on minimum + distance between datapoints and centroids. + + Arguments: + rx: List[float] + The x-coordinates of the dataset points to be clustered. + ry: List[float] + The y-coordinates of the dataset points to be clustered. + nc: int + The number of clusters to group the data into. + + Returns: + Clusters + An instance containing the final cluster assignments and centroids after + convergence. + + Raises: + None + + """ clusters = Clusters(rx, ry, nc) clusters.calc_centroid() pre_cost = float("inf") for loop in range(MAX_LOOP): - print("loop:", loop) cost = clusters.update_clusters() clusters.calc_centroid() diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/ray_casting_grid_map/ray_casting_grid_map.py similarity index 96% rename from Mapping/raycasting_grid_map/raycasting_grid_map.py rename to Mapping/ray_casting_grid_map/ray_casting_grid_map.py index 8ce37b925b..c7e73f0630 100644 --- a/Mapping/raycasting_grid_map/raycasting_grid_map.py +++ b/Mapping/ray_casting_grid_map/ray_casting_grid_map.py @@ -48,7 +48,7 @@ def atan_zero_to_twopi(y, x): return angle -def precasting(minx, miny, xw, yw, xyreso, yawreso): +def pre_casting(minx, miny, xw, yw, xyreso, yawreso): precast = [[] for i in range(int(round((math.pi * 2.0) / yawreso)) + 1)] @@ -81,7 +81,7 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso): pmap = [[0.0 for i in range(yw)] for i in range(xw)] - precast = precasting(minx, miny, xw, yw, xyreso, yawreso) + precast = pre_casting(minx, miny, xw, yw, xyreso, yawreso) for (x, y) in zip(ox, oy): diff --git a/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst b/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst index 1892d1f8f7..e243529a9c 100644 --- a/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst +++ b/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst @@ -11,3 +11,7 @@ The red crosses are observations from a ranging sensor. The red circle is the estimated object shape using circle fitting. +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.circle_fitting.circle_fitting.circle_fitting diff --git a/docs/modules/3_mapping/distance_map/distance_map_main.rst b/docs/modules/3_mapping/distance_map/distance_map_main.rst index 0ef9e3022f..ec60e752c9 100644 --- a/docs/modules/3_mapping/distance_map/distance_map_main.rst +++ b/docs/modules/3_mapping/distance_map/distance_map_main.rst @@ -14,8 +14,8 @@ The algorithm is demonstrated on a simple 2D grid with obstacles: .. image:: distance_map.png -API -~~~ +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autofunction:: Mapping.DistanceMap.distance_map.compute_sdf diff --git a/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst b/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst index b0f112a871..50033d2a20 100644 --- a/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst +++ b/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst @@ -6,3 +6,9 @@ Gaussian grid map This is a 2D Gaussian grid mapping example. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif + +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.gaussian_grid_map.gaussian_grid_map.generate_gaussian_grid_map + diff --git a/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst b/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst index e098ca5409..0ece604009 100644 --- a/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst +++ b/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst @@ -4,3 +4,9 @@ k-means object clustering This is a 2D object clustering with k-means algorithm. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif + +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.kmeans_clustering.kmeans_clustering.kmeans_clustering + diff --git a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst index 1f62179efd..29f5878e48 100644 --- a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst +++ b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst @@ -196,3 +196,9 @@ Let’s use this flood fill on real data: .. image:: lidar_to_grid_map_tutorial_14_1.png +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.lidar_to_grid_map.lidar_to_grid_map.main + + diff --git a/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst b/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst index a4d1bf0df2..68a19e1534 100644 --- a/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst +++ b/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst @@ -25,8 +25,8 @@ This is an example of normal vector calculation: .. figure:: normal_vector_calc.png -API -===== +Code Link +========== .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.calc_normal_vector @@ -67,8 +67,8 @@ This is an example of RANSAC based normal vector estimation: .. figure:: ransac_normal_vector_estimation.png -API -===== +Code Link +========== .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.ransac_normal_vector_estimation diff --git a/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst b/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst index cbb5652f56..8cb08d4b78 100644 --- a/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst +++ b/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst @@ -27,8 +27,8 @@ This method determines which each point is in a grid, and replaces the point clouds that are in the same Voxel with their average to reduce the number of points. -API -===== +Code Link +========== .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling @@ -61,8 +61,8 @@ Although this method does not have good performance comparing the Farthest distance sample where each point is distributed farther from each other, this is suitable for real-time processing because of its fast computation time. -API -===== +Code Link +========== .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling diff --git a/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst b/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst index cc5a1a1c5b..bee2f64193 100644 --- a/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst +++ b/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst @@ -3,4 +3,9 @@ Ray casting grid map This is a 2D ray casting grid mapping example. -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif \ No newline at end of file +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif + +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: Mapping.ray_casting_grid_map.ray_casting_grid_map.generate_ray_casting_grid_map diff --git a/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst b/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst index b6ced1dc1d..06d85efe61 100644 --- a/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst +++ b/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst @@ -57,8 +57,8 @@ This evaluation function uses the squreed distances between the edges of the rec Calculating the squared error is the same as calculating the variance. The smaller this variance, the more it signifies that the points fit within the rectangle. -API -~~~~~~ +Code Link +~~~~~~~~~~~ .. autoclass:: Mapping.rectangle_fitting.rectangle_fitting.LShapeFitting :members: diff --git a/tests/test_raycasting_grid_map.py b/tests/test_ray_casting_grid_map.py similarity index 71% rename from tests/test_raycasting_grid_map.py rename to tests/test_ray_casting_grid_map.py index f08ae9277e..2d192c9310 100644 --- a/tests/test_raycasting_grid_map.py +++ b/tests/test_ray_casting_grid_map.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from Mapping.raycasting_grid_map import raycasting_grid_map as m +from Mapping.ray_casting_grid_map import ray_casting_grid_map as m def test1(): From e1cdb24ecfd68ff4eaf51626e5a3f4854da588a4 Mon Sep 17 00:00:00 2001 From: Yuri Harada <55676955+NightzDev@users.noreply.github.com> Date: Fri, 2 May 2025 21:51:20 -0300 Subject: [PATCH 841/909] docs: rewrite internal sensors section with detailed descriptions and references (#1197) * docs: rewrite and enhance internal sensors documentation * Update docs/modules/12_appendix/internal_sensors_main.rst --------- Co-authored-by: Atsushi Sakai --- .../12_appendix/internal_sensors_main.rst | 41 +++++++++++++++---- 1 file changed, 34 insertions(+), 7 deletions(-) diff --git a/docs/modules/12_appendix/internal_sensors_main.rst b/docs/modules/12_appendix/internal_sensors_main.rst index 18f209098e..fa2594a2bf 100644 --- a/docs/modules/12_appendix/internal_sensors_main.rst +++ b/docs/modules/12_appendix/internal_sensors_main.rst @@ -3,32 +3,59 @@ Internal Sensors for Robots ============================ -This project, `PythonRobotics`, focuses on algorithms, so hardware is not included. -However, having basic knowledge of hardware in robotics is also important for understanding algorithms. -Therefore, we will provide an overview. +This project, `PythonRobotics`, focuses on algorithms, so hardware is not included. However, having basic knowledge of hardware in robotics is also important for understanding algorithms. Therefore, we will provide an overview. Introduction ------------- +------------- + +In robotic systems, internal sensors play a crucial role in monitoring the robot’s internal state, such as orientation, acceleration, angular velocity, altitude, and temperature. These sensors provide essential feedback that supports control, localization, and safety mechanisms. While external sensors perceive the environment, internal sensors give the robot self-awareness of its own motion and condition. Understanding the principles and characteristics of these sensors is vital to fully utilize their information within algorithms and decision-making systems. This section outlines the main internal sensors used in robotics. Global Navigation Satellite System (GNSS) -------------------------------------------- +----------------------------------------- + +GNSS is a satellite-based navigation system that provides global positioning and time information by analyzing signals from multiple satellites. It is commonly used in outdoor environments for absolute positioning. Although GNSS offers global coverage without infrastructure dependency, its performance is limited indoors or in obstructed areas, and it suffers from low update rates and susceptibility to signal noise. It is widely used in outdoor navigation for drones, vehicles, and delivery robots. Gyroscope ---------- +A gyroscope measures angular velocity around the robot’s axes, enabling orientation and attitude estimation through detection of the Coriolis effect. Gyroscopes are compact, cost-effective, and provide high update rates, but they are prone to drift and require calibration or sensor fusion for long-term accuracy. These sensors are essential in drones, balancing robots, and IMU-based systems for motion tracking. + Accelerometer --------------- +--------------- + +An accelerometer measures linear acceleration along one or more axes, typically by detecting mass displacement due to motion. It is small, affordable, and useful for detecting movement, tilt, or vibration. However, accelerometers are limited by noisy output and cannot independently determine position without integration and fusion. They are commonly applied in wearable robotics, step counters, and vibration sensing. Magnetometer -------------- +A magnetometer measures the direction and intensity of magnetic fields, enabling heading estimation based on Earth’s magnetic field. It is lightweight and useful for orientation, especially in GPS-denied environments, though it is sensitive to interference from electronics and requires calibration. Magnetometers are often used in conjunction with IMUs for navigation and directional awareness. + Inertial Measurement Unit (IMU) -------------------------------- +An IMU integrates a gyroscope, accelerometer, and sometimes a magnetometer to estimate a robot's motion and orientation through sensor fusion techniques such as Kalman filters. IMUs are compact and provide high-frequency data, which is essential for localization and navigation in GPS-denied areas. Nonetheless, they accumulate drift over time and require complex filtering to maintain accuracy. IMUs are found in drones, mobile robots, and motion tracking systems. + Pressure Sensor ------------------ +---------------- + +Pressure sensors detect atmospheric or fluid pressure by measuring the force exerted on a diaphragm. They are effective for estimating altitude and monitoring environmental conditions, especially in drones and underwater robots. Although cost-effective and efficient, their accuracy may degrade due to temperature variation and limitations in low-altitude resolution. Temperature Sensor -------------------- +Temperature sensors monitor environmental or internal component temperatures, using changes in resistance or voltage depending on sensor type (e.g., RTD or thermocouple). They are simple and reliable for safety and thermal regulation, though they may respond slowly and be affected by nearby electronics. Common applications include battery and motor monitoring, safety systems, and ambient sensing. + +References +---------- +- *Introduction to Autonomous Mobile Robots*, Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza +- *Probabilistic Robotics*, Sebastian Thrun, Wolfram Burgard, Dieter Fox +- Wikipedia articles: + + - `Inertial Measurement Unit (IMU) `_ + - `Accelerometer `_ + - `Gyroscope `_ + - `Magnetometer `_ + - `Pressure sensor `_ + - `Temperature sensor `_ +- `Adafruit Sensor Guides `_ \ No newline at end of file From d2fe5ae8f0e414f394c00570771c07807d167e68 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 4 May 2025 09:50:49 +0900 Subject: [PATCH 842/909] Add code links to SLAM module documentation (#1212) This commit updates SLAM module documentation files to include direct code links for better navigation. It also adjusts imports in `test_iterative_closest_point.py` to reflect updated module structure and adds a safety check for the directory in `runtests.sh`. --- .../icp_matching.py} | 0 docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst | 5 +++++ docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst | 6 ++++++ docs/modules/4_slam/ekf_slam/ekf_slam_main.rst | 6 ++++++ docs/modules/4_slam/graph_slam/graph_slam_main.rst | 6 ++++++ .../iterative_closest_point_matching_main.rst | 8 +++++++- runtests.sh | 1 + tests/test_iterative_closest_point.py | 2 +- 8 files changed, 32 insertions(+), 2 deletions(-) rename SLAM/{iterative_closest_point/iterative_closest_point.py => ICPMatching/icp_matching.py} (100%) diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/ICPMatching/icp_matching.py similarity index 100% rename from SLAM/iterative_closest_point/iterative_closest_point.py rename to SLAM/ICPMatching/icp_matching.py diff --git a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst index a2aa521216..b6bafa0982 100644 --- a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst +++ b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst @@ -22,6 +22,11 @@ The red points are particles of FastSLAM. Black points are landmarks, blue crosses are estimated landmark positions by FastSLAM. +Code Link +~~~~~~~~~~~~ + +.. autofunction:: SLAM.FastSLAM1.fast_slam1.fast_slam1 + Introduction ~~~~~~~~~~~~ diff --git a/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst b/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst index 9e79b496a3..59ed3b9f75 100644 --- a/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst +++ b/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst @@ -7,6 +7,12 @@ The animation has the same meanings as one of FastSLAM 1.0. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif +Code Link +~~~~~~~~~~~ + +.. autofunction:: SLAM.FastSLAM2.fast_slam2.fast_slam2 + + References ~~~~~~~~~~ diff --git a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst index a1486ffe1e..3967a7ae4d 100644 --- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst +++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst @@ -21,6 +21,12 @@ This is a simulation of EKF SLAM. - Blue line: ground truth - Red line: EKF SLAM position estimation +Code Link +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. autofunction:: SLAM.EKFSLAM.ekf_slam.ekf_slam + + Introduction ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/4_slam/graph_slam/graph_slam_main.rst b/docs/modules/4_slam/graph_slam/graph_slam_main.rst index a2ef513527..2ef17e4179 100644 --- a/docs/modules/4_slam/graph_slam/graph_slam_main.rst +++ b/docs/modules/4_slam/graph_slam/graph_slam_main.rst @@ -13,6 +13,12 @@ The black stars are landmarks for graph edge generation. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif +Code Link +~~~~~~~~~~~~ + +.. autofunction:: SLAM.GraphBasedSLAM.graph_based_slam.graph_based_slam + + .. include:: graphSLAM_doc.rst .. include:: graphSLAM_formulation.rst .. include:: graphSLAM_SE2_example.rst diff --git a/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst b/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst index a30b1fc99b..772fe62889 100644 --- a/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst +++ b/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst @@ -10,7 +10,13 @@ points to points. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif +Code Link +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. autofunction:: SLAM.ICPMatching.icp_matching.icp_matching + + References -~~~~~~~~~~ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - `Introduction to Mobile Robotics: Iterative Closest Point Algorithm `_ diff --git a/runtests.sh b/runtests.sh index 12d1b80453..c4460c8aa7 100755 --- a/runtests.sh +++ b/runtests.sh @@ -1,4 +1,5 @@ #!/usr/bin/env bash +cd "$(dirname "$0")" || exit 1 echo "Run test suites! " # === pytest based test runner === diff --git a/tests/test_iterative_closest_point.py b/tests/test_iterative_closest_point.py index 3e726b5649..cdfa89cc55 100644 --- a/tests/test_iterative_closest_point.py +++ b/tests/test_iterative_closest_point.py @@ -1,5 +1,5 @@ import conftest -from SLAM.iterative_closest_point import iterative_closest_point as m +from SLAM.ICPMatching import icp_matching as m def test_1(): From a38da41bafaf82bb56d4491d94a0095c1019ac4a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 4 May 2025 20:32:11 +0900 Subject: [PATCH 843/909] Add code links to documentation and fix naming inconsistencies (#1213) Added references to related Python functions in documentation for better navigation and usability. Corrected inconsistencies in module and test names to align with their respective directories and improve clarity. --- .../rear_wheel_feedback_control.py} | 0 .../stanley_control.py} | 0 .../10_inverted_pendulum/inverted_pendulum_main.rst | 12 ++++++++++++ .../behavior_tree/behavior_tree_main.rst | 2 +- .../state_machine/state_machine_main.rst | 4 ++-- .../cgmres_nmpc/cgmres_nmpc_main.rst | 5 +++++ .../lqr_speed_and_steering_control_main.rst | 7 ++++++- .../lqr_steering_control_main.rst | 6 +++++- ...l_predictive_speed_and_steering_control_main.rst | 13 ++++++------- .../move_to_a_pose_main.rst} | 8 +++++++- docs/modules/6_path_tracking/path_tracking_main.rst | 2 +- .../pure_pursuit_tracking_main.rst | 6 ++++++ .../rear_wheel_feedback_control_main.rst | 6 ++++++ .../stanley_control/stanley_control_main.rst | 6 ++++++ .../n_joint_arm_to_point_control_main.rst | 7 +++++++ .../obstacle_avoidance_arm_navigation_main.rst | 7 ++++++- .../7_arm_navigation/planar_two_link_ik_main.rst | 6 ++++++ .../drone_3d_trajectory_following_main.rst | 5 +++++ .../rocket_powered_landing_main.rst | 8 ++++++++ .../bipedal_planner/bipedal_planner_main.rst | 5 +++++ tests/test_rear_wheel_feedback.py | 2 +- tests/test_stanley_controller.py | 2 +- 22 files changed, 102 insertions(+), 17 deletions(-) rename PathTracking/{rear_wheel_feedback/rear_wheel_feedback.py => rear_wheel_feedback_control/rear_wheel_feedback_control.py} (100%) rename PathTracking/{stanley_controller/stanley_controller.py => stanley_control/stanley_control.py} (100%) rename docs/modules/6_path_tracking/{move_to_a_pose_control/move_to_a_pose_control_main.rst => move_to_a_pose/move_to_a_pose_main.rst} (97%) diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback_control/rear_wheel_feedback_control.py similarity index 100% rename from PathTracking/rear_wheel_feedback/rear_wheel_feedback.py rename to PathTracking/rear_wheel_feedback_control/rear_wheel_feedback_control.py diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_control/stanley_control.py similarity index 100% rename from PathTracking/stanley_controller/stanley_controller.py rename to PathTracking/stanley_control/stanley_control.py diff --git a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst index 048cbea9ac..58dc0f2e57 100644 --- a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst +++ b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst @@ -89,6 +89,12 @@ and :math:`P` is the unique positive definite solution to the discrete time .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation_lqr.gif +Code Link +^^^^^^^^^^^ + +.. autofunction:: InvertedPendulum.inverted_pendulum_lqr_control.main + + MPC control ~~~~~~~~~~~~~~~~~~~~~~~~~~~ The MPC controller minimize this cost function defined as: @@ -101,3 +107,9 @@ subject to: - Initial state .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif + +Code Link +^^^^^^^^^^^ + +.. autofunction:: InvertedPendulum.inverted_pendulum_mpc_control.main + diff --git a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst index ae3e16da81..22849f7c54 100644 --- a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst +++ b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst @@ -5,7 +5,7 @@ Behavior Tree is a modular, hierarchical decision model that is widely used in r It present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state. Behavior Tree have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863) -Core Concepts +Code Link ~~~~~~~~~~~~~ Control Node diff --git a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst index abaece1b11..3f516d46a9 100644 --- a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst +++ b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst @@ -12,8 +12,8 @@ Core Concepts - **Action**: An operation executed during transition (before entering new state) - **Guard**: A precondition that must be satisfied to allow transition -API -~~~ +Code Link +~~~~~~~~~~~ .. autoclass:: MissionPlanning.StateMachine.state_machine.StateMachine :members: add_transition, process, register_state diff --git a/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst b/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst index 23f1218a94..914a4555ff 100644 --- a/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst +++ b/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst @@ -17,6 +17,11 @@ Nonlinear Model Predictive Control with C-GMRES .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif :alt: gif +Code Link +~~~~~~~~~~~~ + +.. autofunction:: PathTracking.cgmres_nmpc.cgmres_nmpc.NMPCControllerCGMRES + Mathematical Formulation ~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst index 568ef9a0df..b0ba9e0d45 100644 --- a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst @@ -7,7 +7,12 @@ Path tracking simulation with LQR speed and steering control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif -`[Code Link] `_ + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.lqr_speed_steer_control.lqr_speed_steer_control.lqr_speed_steering_control + Overview ~~~~~~~~ diff --git a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst index 831423f364..fc8f2a33aa 100644 --- a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst +++ b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst @@ -8,7 +8,11 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif -`[Code Link] `_ +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.lqr_steer_control.lqr_steer_control.lqr_steering_control + Overview ~~~~~~~~ diff --git a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst index de55b545ba..76a6819a46 100644 --- a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst +++ b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst @@ -5,13 +5,6 @@ Model predictive speed and steering control .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif?raw=true :alt: Model predictive speed and steering control - Model predictive speed and steering control - -code: - -`PythonRobotics/model_predictive_speed_and_steer_control.py at master · -AtsushiSakai/PythonRobotics `__ - This is a path tracking simulation using model predictive control (MPC). The MPC controller controls vehicle speed and steering base on @@ -22,6 +15,12 @@ This code uses cvxpy as an optimization modeling tool. - `Welcome to CVXPY 1.0 — CVXPY 1.0.6 documentation `__ +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.model_predictive_speed_and_steer_control.model_predictive_speed_and_steer_control.iterative_linear_mpc_control + + MPC modeling ~~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst similarity index 97% rename from docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst rename to docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst index 77ec682a30..19bb0e4c14 100644 --- a/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst +++ b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst @@ -3,7 +3,13 @@ Move to a Pose Control In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below. -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/move_to_pose/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.move_to_pose.move_to_pose.move_to_pose + Position Control of non-Holonomic Systems ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst index d98e324583..742cc807e6 100644 --- a/docs/modules/6_path_tracking/path_tracking_main.rst +++ b/docs/modules/6_path_tracking/path_tracking_main.rst @@ -16,4 +16,4 @@ Path tracking is the ability of a robot to follow the reference path generated b lqr_speed_and_steering_control/lqr_speed_and_steering_control model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control cgmres_nmpc/cgmres_nmpc - move_to_a_pose_control/move_to_a_pose_control + move_to_a_pose/move_to_a_pose diff --git a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst index d7354cf8fb..ff6749bbbe 100644 --- a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst +++ b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst @@ -9,6 +9,12 @@ speed control. The red line is a target course, the green cross means the target point for pure pursuit control, the blue line is the tracking. +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.pure_pursuit.pure_pursuit.pure_pursuit_steer_control + + Reference ~~~~~~~~~~~ diff --git a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst index d18cd6fbf7..56d0db63ad 100644 --- a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst +++ b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst @@ -6,6 +6,12 @@ PID speed control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.rear_wheel_feedback_control.rear_wheel_feedback_control.rear_wheel_feedback_control + + Reference ~~~~~~~~~~~ - `A Survey of Motion Planning and Control Techniques for Self-driving diff --git a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst index 69089ac33b..3c491804f6 100644 --- a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst +++ b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst @@ -6,6 +6,12 @@ control. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathTracking.stanley_control.stanley_control.stanley_control + + Reference ~~~~~~~~~~~ diff --git a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst index cc6279f681..56900acde1 100644 --- a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst +++ b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst @@ -11,3 +11,10 @@ plotting area. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif In this simulation N = 10, however, you can change it. + + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: ArmNavigation.n_joint_arm_to_point_control.n_joint_arm_to_point_control.main + diff --git a/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst b/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst index 899a64a5cd..4433ab531b 100644 --- a/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst +++ b/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst @@ -3,4 +3,9 @@ Arm navigation with obstacle avoidance Arm navigation with obstacle avoidance simulation. -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif \ No newline at end of file +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: ArmNavigation.arm_obstacle_navigation.arm_obstacle_navigation.main diff --git a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst index 2da2b0dc30..5b2712eb48 100644 --- a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst +++ b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst @@ -11,6 +11,12 @@ This is a interactive simulation. You can set the goal position of the end effector with left-click on the plotting area. +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: ArmNavigation.two_joint_arm_to_point_control.two_joint_arm_to_point_control.main + + Inverse Kinematics for a Planar Two-Link Robotic Arm ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst b/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst index c3bdc33cdc..1805bb3f6d 100644 --- a/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst +++ b/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst @@ -4,3 +4,8 @@ Drone 3d trajectory following This is a 3d trajectory following simulation for a quadrotor. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: AerialNavigation.drone_3d_trajectory_following.drone_3d_trajectory_following.quad_sim diff --git a/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst b/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst index 6c90d2b44e..4bf5117500 100644 --- a/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst +++ b/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst @@ -1,6 +1,14 @@ Rocket powered landing ----------------------------- +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: AerialNavigation.rocket_powered_landing.rocket_powered_landing.main + + Simulation ~~~~~~~~~~ diff --git a/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst b/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst index 2ee5971e7a..6253715cc1 100644 --- a/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst +++ b/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst @@ -4,3 +4,8 @@ Bipedal Planner Bipedal Walking with modifying designated footsteps .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Bipedal/bipedal_planner/animation.gif + +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: Bipedal.bipedal_planner.bipedal_planner.BipedalPlanner diff --git a/tests/test_rear_wheel_feedback.py b/tests/test_rear_wheel_feedback.py index 895eb188b3..eccde52358 100644 --- a/tests/test_rear_wheel_feedback.py +++ b/tests/test_rear_wheel_feedback.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from PathTracking.rear_wheel_feedback import rear_wheel_feedback as m +from PathTracking.rear_wheel_feedback_control import rear_wheel_feedback_control as m def test1(): diff --git a/tests/test_stanley_controller.py b/tests/test_stanley_controller.py index a1d8073764..bd590cb51a 100644 --- a/tests/test_stanley_controller.py +++ b/tests/test_stanley_controller.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -from PathTracking.stanley_controller import stanley_controller as m +from PathTracking.stanley_control import stanley_control as m def test1(): From 73e1c0bebccf4f4fc67ae59ada53b783a8533096 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 5 May 2025 17:29:45 +0900 Subject: [PATCH 844/909] Add "Code Link" sections and rename classes for consistency (#1214) This commit adds "Code Link" sections to documentation across various path planning modules, linking to relevant class and function APIs. Additionally, several class renaming changes were made, such as `Dijkstra` to `DijkstraPlanner` and `eta3_trajectory` to `Eta3SplineTrajectory`, to enhance naming consistency. Minor fixes include file restructuring and image renaming for the RRT module. --- ..._rrtstar.py => batch_informed_rrt_star.py} | 0 PathPlanning/Dijkstra/dijkstra.py | 4 +- .../eta3_spline_trajectory.py | 14 ++--- .../bezier_path/bezier_path_main.rst | 9 ++- .../bspline_path/bspline_path_main.rst | 8 +-- .../bugplanner/bugplanner_main.rst | 8 +++ .../catmull_rom_spline_main.rst | 4 +- .../clothoid_path/clothoid_path_main.rst | 5 ++ .../coverage_path/coverage_path_main.rst | 21 +++++++ .../cubic_spline/cubic_spline_main.rst | 8 +-- .../dubins_path/dubins_path_main.rst | 2 +- .../dynamic_window_approach_main.rst | 12 +++- .../elastic_bands/elastic_bands_main.rst | 7 ++- .../eta3_spline/eta3_spline_main.rst | 7 +++ .../frenet_frame_path_main.rst | 6 ++ .../grid_base_search_main.rst | 50 ++++++++++++++++ .../hybridastar/hybridastar_main.rst | 5 ++ .../lqr_path/lqr_path_main.rst | 5 ++ ...l_predictive_trajectory_generator_main.rst | 7 +++ .../prm_planner/prm_planner_main.rst | 7 +++ .../quintic_polynomials_planner_main.rst | 5 ++ .../reeds_shepp_path_main.rst | 7 +++ .../closed_loop_rrt_star_car/Figure_1.png | Bin .../closed_loop_rrt_star_car/Figure_3.png | Bin .../closed_loop_rrt_star_car/Figure_4.png | Bin .../closed_loop_rrt_star_car/Figure_5.png | Bin docs/modules/5_path_planning/rrt/rrt_main.rst | 54 ++++++++++++++++++ docs/modules/5_path_planning/rrt/rrt_star.rst | 6 ++ .../state_lattice_planner_main.rst | 17 ++++++ .../time_based_grid_search_main.rst | 9 +++ .../visibility_road_map_planner_main.rst | 7 ++- .../vrm_planner/vrm_planner_main.rst | 6 ++ tests/test_batch_informed_rrt_star.py | 2 +- 33 files changed, 277 insertions(+), 25 deletions(-) rename PathPlanning/BatchInformedRRTStar/{batch_informed_rrtstar.py => batch_informed_rrt_star.py} (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_1.png (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_3.png (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_4.png (100%) rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_5.png (100%) diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrt_star.py similarity index 100% rename from PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py rename to PathPlanning/BatchInformedRRTStar/batch_informed_rrt_star.py diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index 8a585e4b18..f5a4703910 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -12,7 +12,7 @@ show_animation = True -class Dijkstra: +class DijkstraPlanner: def __init__(self, ox, oy, resolution, robot_radius): """ @@ -246,7 +246,7 @@ def main(): plt.grid(True) plt.axis("equal") - dijkstra = Dijkstra(ox, oy, grid_size, robot_radius) + dijkstra = DijkstraPlanner(ox, oy, grid_size, robot_radius) rx, ry = dijkstra.planning(sx, sy, gx, gy) if show_animation: # pragma: no cover diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py index a73797dacb..e72d33261e 100644 --- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py +++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py @@ -29,7 +29,7 @@ def __init__(self, actual_vel, max_vel): self.message = f'Actual velocity {actual_vel} does not equal desired max velocity {max_vel}!' -class eta3_trajectory(Eta3Path): +class Eta3SplineTrajectory(Eta3Path): """ eta3_trajectory @@ -300,8 +300,8 @@ def test1(max_vel=0.5): trajectory_segments.append(Eta3PathSegment( start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa)) - traj = eta3_trajectory(trajectory_segments, - max_vel=max_vel, max_accel=0.5) + traj = Eta3SplineTrajectory(trajectory_segments, + max_vel=max_vel, max_accel=0.5) # interpolate at several points along the path times = np.linspace(0, traj.total_time, 101) @@ -334,8 +334,8 @@ def test2(max_vel=0.5): trajectory_segments.append(Eta3PathSegment( start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa)) - traj = eta3_trajectory(trajectory_segments, - max_vel=max_vel, max_accel=0.5) + traj = Eta3SplineTrajectory(trajectory_segments, + max_vel=max_vel, max_accel=0.5) # interpolate at several points along the path times = np.linspace(0, traj.total_time, 101) @@ -400,8 +400,8 @@ def test3(max_vel=2.0): start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa)) # construct the whole path - traj = eta3_trajectory(trajectory_segments, - max_vel=max_vel, max_accel=0.5, max_jerk=1) + traj = Eta3SplineTrajectory(trajectory_segments, + max_vel=max_vel, max_accel=0.5, max_jerk=1) # interpolate at several points along the path times = np.linspace(0, traj.total_time, 1001) diff --git a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst index 9d9b3de709..19fb89a1b1 100644 --- a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst +++ b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst @@ -13,8 +13,15 @@ You can get different Beizer course: .. image:: Figure_2.png +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathPlanning.BezierPath.bezier_path.calc_4points_bezier_path + + Reference +~~~~~~~~~~~~~~~ - `Continuous Curvature Path Generation Based on Bezier Curves for Autonomous - Vehicles ` + Vehicles `__ diff --git a/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst b/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst index e734352e34..00e5ef2fdb 100644 --- a/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst +++ b/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst @@ -105,8 +105,8 @@ The default spline degree is 3, so curvature changes smoothly. .. image:: interp_and_curvature.png -API -++++ +Code link +++++++++++ .. autofunction:: PathPlanning.BSplinePath.bspline_path.interpolate_b_spline_path @@ -133,8 +133,8 @@ The default spline degree is 3, so curvature changes smoothly. .. image:: approx_and_curvature.png -API -++++ +Code Link +++++++++++ .. autofunction:: PathPlanning.BSplinePath.bspline_path.approximate_b_spline_path diff --git a/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst b/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst index 81c91c0313..e1cd2fe353 100644 --- a/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst +++ b/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst @@ -5,4 +5,12 @@ This is a 2D planning with Bug algorithm. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathPlanning.BugPlanning.bug.main + +Reference +~~~~~~~~~~~~ + - `ECE452 Bug Algorithms `_ diff --git a/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst b/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst index 72e558c486..fa2a2ff72b 100644 --- a/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst +++ b/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst @@ -88,8 +88,8 @@ Catmull-Rom Spline API This section provides an overview of the functions used for Catmull-Rom spline path planning. -API -++++ +Code Link +++++++++++ .. autofunction:: PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path.catmull_rom_point diff --git a/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst b/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst index 1e8cee694a..16d0ec03c1 100644 --- a/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst +++ b/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst @@ -73,6 +73,11 @@ The final clothoid path can be calculated with the path parameters and Fresnel i &y(s)=y_{0}+\int_{0}^{s} \sin \left(\frac{1}{2} \kappa^{\prime} \tau^{2}+\kappa \tau+\vartheta_{0}\right) \mathrm{d} \tau \end{aligned} +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: PathPlanning.ClothoidPath.clothoid_path_planner.generate_clothoid_path + References ~~~~~~~~~~ diff --git a/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst b/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst index 0a688b5ed2..eaa876c80b 100644 --- a/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst +++ b/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst @@ -8,6 +8,11 @@ This is a 2D grid based sweep coverage path planner simulation: .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.GridBasedSweepCPP.grid_based_sweep_coverage_path_planner.planning + Spiral Spanning Tree ~~~~~~~~~~~~~~~~~~~~ @@ -17,6 +22,14 @@ This is a 2D grid based spiral spanning tree coverage path planner simulation: .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation2.gif .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation3.gif +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.SpiralSpanningTreeCPP.spiral_spanning_tree_coverage_path_planner.main + +Reference ++++++++++++++ + - `Spiral-STC: An On-Line Coverage Algorithm of Grid Environments by a Mobile Robot `_ @@ -29,6 +42,14 @@ This is a 2D grid based wavefront coverage path planner simulation: .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.WavefrontCPP.wavefront_coverage_path_planner.wavefront + +Reference ++++++++++++++ + - `Planning paths of complete coverage of an unstructured environment by a mobile robot `_ diff --git a/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst b/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst index 33471f7c85..a110217a2e 100644 --- a/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst +++ b/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst @@ -171,8 +171,8 @@ the second derivative by: These equations can be calculated by differentiating the cubic polynomial. -API -=== +Code Link +========== This is the 1D cubic spline class API: @@ -199,8 +199,8 @@ Curvature of each point can be also calculated analytically by: :math:`\kappa=\frac{y^{\prime \prime} x^{\prime}-x^{\prime \prime} y^{\prime}}{\left(x^{\prime2}+y^{\prime2}\right)^{\frac{2}{3}}}` -API -=== +Code Link +========== .. autoclass:: PathPlanning.CubicSpline.cubic_spline_planner.CubicSpline2D :members: diff --git a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst b/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst index 12172fb51e..5a3d14464b 100644 --- a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst +++ b/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst @@ -62,7 +62,7 @@ You can generate a path from these information and the maximum curvature informa A path type which has minimum course length among 6 types is selected, and then a path is constructed based on the selected type and its distances. -API +Code Link ~~~~~~~~~~~~~~~~~~~~ .. autofunction:: PathPlanning.DubinsPath.dubins_path_planner.plan_dubins_path diff --git a/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst b/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst index d645838597..ac5322df94 100644 --- a/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst +++ b/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst @@ -5,7 +5,17 @@ Dynamic Window Approach This is a 2D navigation sample code with Dynamic Window Approach. +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif + +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.DynamicWindowApproach.dynamic_window_approach.dwa_control + + +Reference +~~~~~~~~~~~~ + - `The Dynamic Window Approach to Collision Avoidance `__ -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst index 8a3e517105..d0109d4ec3 100644 --- a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst +++ b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst @@ -5,6 +5,11 @@ This is a path planning with Elastic Bands. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ElasticBands/animation.gif +Code Link ++++++++++++++ + +.. autoclass:: PathPlanning.ElasticBands.elastic_bands.ElasticBands + Core Concept ~~~~~~~~~~~~ @@ -69,6 +74,6 @@ Dynamic Path Maintenance - Remove redundant nodes if adjacent nodes are too close References -~~~~~~~~~~~~~~~~~~~~~~~ ++++++++++++++ - `Elastic Bands: Connecting Path Planning and Control `__ diff --git a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst index 82e0a71044..9ee343e8a7 100644 --- a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst +++ b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst @@ -7,7 +7,14 @@ Eta^3 Spline path planning This is a path planning with Eta^3 spline. +Code Link +~~~~~~~~~~~~~~~ + +.. autoclass:: PathPlanning.Eta3SplineTrajectory.eta3_spline_trajectory.Eta3SplineTrajectory + + Reference +~~~~~~~~~~~~~~~ - `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots `__ diff --git a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst index 371d536e3f..0f84d381ea 100644 --- a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst +++ b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst @@ -7,6 +7,12 @@ The cyan line is the target course and black crosses are obstacles. The red line is predicted path. +Code Link +~~~~~~~~~~~~~~ + +.. autofunction:: PathPlanning.FrenetOptimalTrajectory.frenet_optimal_trajectory.main + + High Speed and Velocity Keeping Scenario ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst index bf82c9f391..c4aa6882aa 100644 --- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst +++ b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst @@ -10,6 +10,12 @@ This is a 2D grid based path planning with Breadth first search algorithm. In the animation, cyan points are searched nodes. +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.BreadthFirstSearch.breadth_first_search.BreadthFirstSearchPlanner + + Depth First Search ~~~~~~~~~~~~~~~~~~~~ @@ -19,6 +25,12 @@ This is a 2D grid based path planning with Depth first search algorithm. In the animation, cyan points are searched nodes. +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.DepthFirstSearch.depth_first_search.DepthFirstSearchPlanner + + .. _dijkstra: Dijkstra algorithm @@ -30,6 +42,12 @@ This is a 2D grid based shortest path planning with Dijkstra's algorithm. In the animation, cyan points are searched nodes. +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.Dijkstra.dijkstra.DijkstraPlanner + + .. _a*-algorithm: A\* algorithm @@ -43,6 +61,12 @@ In the animation, cyan points are searched nodes. Its heuristic is 2D Euclid distance. +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.AStar.a_star.AStarPlanner + + Bidirectional A\* algorithm ~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -52,6 +76,12 @@ This is a 2D grid based shortest path planning with bidirectional A star algorit In the animation, cyan points are searched nodes. +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.BidirectionalAStar.bidirectional_a_star.BidirectionalAStarPlanner + + .. _D*-algorithm: D\* algorithm @@ -63,7 +93,14 @@ This is a 2D grid based shortest path planning with D star algorithm. The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm. +Code Link ++++++++++++++ + +.. autoclass:: PathPlanning.DStar.dstar.Dstar + + Reference +++++++++++++ - `D* search Wikipedia `__ @@ -74,7 +111,13 @@ This is a 2D grid based path planning and replanning with D star lite algorithm. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif +Code Link ++++++++++++++ + +.. autoclass:: PathPlanning.DStarLite.d_star_lite.DStarLite + Reference +++++++++++++ - `Improved Fast Replanning for Robot Navigation in Unknown Terrain `_ @@ -88,7 +131,14 @@ This is a 2D grid based path planning with Potential Field algorithm. In the animation, the blue heat map shows potential value on each grid. +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.PotentialFieldPlanning.potential_field_planning.potential_field_planning + + Reference +++++++++++++ - `Robotic Motion Planning:Potential Functions `__ diff --git a/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst b/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst index a9876fa3d4..36f340e0c2 100644 --- a/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst +++ b/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst @@ -4,3 +4,8 @@ Hybrid a star This is a simple vehicle model based hybrid A\* path planner. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/HybridAStar/animation.gif + +Code Link ++++++++++++++ + +.. autofunction:: PathPlanning.HybridAStar.hybrid_a_star.hybrid_a_star_planning diff --git a/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst b/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst index 788442ff63..1eb1e4d840 100644 --- a/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst +++ b/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst @@ -4,3 +4,8 @@ LQR based path planning A sample code using LQR based path planning for double integrator model. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true + +Code Link ++++++++++++++ + +.. autoclass:: PathPlanning.LQRPlanner.lqr_planner.LQRPlanner diff --git a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst index 1c5df1c9cc..76472a6792 100644 --- a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst +++ b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst @@ -6,6 +6,12 @@ generator. This algorithm is used for state lattice planner. +Code Link +~~~~~~~~~~~~~ + +.. autofunction:: PathPlanning.ModelPredictiveTrajectoryGenerator.trajectory_generator.optimize_trajectory + + Path optimization sample ~~~~~~~~~~~~~~~~~~~~~~~~ @@ -17,6 +23,7 @@ Lookup table generation sample .. image:: lookup_table.png Reference +~~~~~~~~~~~~ - `Optimal rough terrain trajectory generation for wheeled mobile robots `__ diff --git a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst index f816c5c1b9..d58d1e2633 100644 --- a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst +++ b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst @@ -13,7 +13,14 @@ Cyan crosses means searched points with Dijkstra method, The red line is the final path of PRM. +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathPlanning.ProbabilisticRoadMap.probabilistic_road_map.prm_planning + + Reference +~~~~~~~~~~~ - `Probabilistic roadmap - Wikipedia `__ diff --git a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst index 66c3001c05..c7bc3fb55c 100644 --- a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst +++ b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst @@ -9,6 +9,11 @@ Motion planning with quintic polynomials. It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials. +Code Link +~~~~~~~~~~~~~~~ + +.. autofunction:: PathPlanning.QuinticPolynomialsPlanner.quintic_polynomials_planner.quintic_polynomials_planner + Quintic polynomials for one dimensional robot motion diff --git a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst index a4a5d28e01..4dd54d7c97 100644 --- a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst +++ b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst @@ -5,6 +5,12 @@ A sample code with Reeds Shepp path planning. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true +Code Link +============== + +.. autofunction:: PathPlanning.ReedsSheppPath.reeds_shepp_path_planning.reeds_shepp_path_planning + + Mathematical Description of Individual Path Types ================================================= Here is an overview of mathematical derivations of formulae for individual path types. @@ -381,6 +387,7 @@ Hence, we have: Reference +============= - `15.3.2 Reeds-Shepp Curves `__ diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_1.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_1.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_1.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_1.png diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_3.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_3.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_3.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_3.png diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_4.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_4.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_4.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_4.png diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_5.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_5.png similarity index 100% rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_5.png rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_5.png diff --git a/docs/modules/5_path_planning/rrt/rrt_main.rst b/docs/modules/5_path_planning/rrt/rrt_main.rst index da3a4957a5..1bd5497f4c 100644 --- a/docs/modules/5_path_planning/rrt/rrt_main.rst +++ b/docs/modules/5_path_planning/rrt/rrt_main.rst @@ -14,6 +14,12 @@ This is a simple path planning code with Rapidly-Exploring Random Trees Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.RRT.rrt.RRT + + .. include:: rrt_star.rst @@ -24,6 +30,12 @@ RRT with dubins path Path planning for a car robot with RRT and dubins path planner. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.RRTDubins.rrt_dubins.RRTDubins + + .. _rrt*-with-dubins-path: RRT\* with dubins path @@ -33,6 +45,12 @@ RRT\* with dubins path Path planning for a car robot with RRT\* and dubins path planner. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.RRTStarDubins.rrt_star_dubins.RRTStarDubins + + .. _rrt*-with-reeds-sheep-path: RRT\* with reeds-sheep path @@ -42,6 +60,12 @@ RRT\* with reeds-sheep path Path planning for a car robot with RRT\* and reeds sheep path planner. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.RRTStarReedsShepp.rrt_star_reeds_shepp.RRTStarReedsShepp + + .. _informed-rrt*: Informed RRT\* @@ -53,7 +77,14 @@ This is a path planning code with Informed RRT*. The cyan ellipse is the heuristic sampling domain of Informed RRT*. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.InformedRRTStar.informed_rrt_star.InformedRRTStar + + Reference +^^^^^^^^^^ - `Informed RRT\*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal @@ -68,12 +99,20 @@ Batch Informed RRT\* This is a path planning code with Batch Informed RRT*. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.BatchInformedRRTStar.batch_informed_rrt_star.BITStar + + Reference +^^^^^^^^^^^ - `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs `__ + .. _closed-loop-rrt*: Closed Loop RRT\* @@ -87,7 +126,14 @@ In this code, pure-pursuit algorithm is used for steering control, PID is used for speed control. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.ClosedLoopRRTStar.closed_loop_rrt_star_car.ClosedLoopRRTStar + + Reference +^^^^^^^^^^^^ - `Motion Planning in Complex Environments using Closed-loop Prediction `__ @@ -98,6 +144,7 @@ Reference - `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning Using Closed-loop Prediction `__ + .. _lqr-rrt*: LQR-RRT\* @@ -109,7 +156,14 @@ A double integrator motion model is used for LQR local planner. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.LQRRRTStar.lqr_rrt_star.LQRRRTStar + + Reference +~~~~~~~~~~~~~ - `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension diff --git a/docs/modules/5_path_planning/rrt/rrt_star.rst b/docs/modules/5_path_planning/rrt/rrt_star.rst index 6deb6b9172..960b9976d5 100644 --- a/docs/modules/5_path_planning/rrt/rrt_star.rst +++ b/docs/modules/5_path_planning/rrt/rrt_star.rst @@ -7,6 +7,12 @@ This is a path planning code with RRT\* Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. +Code Link +^^^^^^^^^^ + +.. autoclass:: PathPlanning.RRTStar.rrt_star.RRTStar + + Simulation ^^^^^^^^^^ diff --git a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst index bf89ac11ae..9f8cc0c50f 100644 --- a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst +++ b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst @@ -12,17 +12,34 @@ Uniform polar sampling .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif +Code Link +^^^^^^^^^^^^^ + +.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_uniform_polar_states + + Biased polar sampling ~~~~~~~~~~~~~~~~~~~~~ .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif +Code Link +^^^^^^^^^^^^^ +.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_biased_polar_states + + Lane sampling ~~~~~~~~~~~~~ .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif +Code Link +^^^^^^^^^^^^^ +.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_lane_states + + Reference +~~~~~~~~~~~~~~~ - `Optimal rough terrain trajectory generation for wheeled mobile robots `__ diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst index 04001d4504..9517e95b31 100644 --- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst +++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst @@ -33,6 +33,10 @@ After:: When starting at (1, 11) in the structured obstacle arrangement (second of the two gifs above). +Code Link +^^^^^^^^^^^^^ +.. autoclass:: PathPlanning.TimeBasedPathPlanning.SpaceTimeAStar.SpaceTimeAStar + Safe Interval Path Planning ~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -73,6 +77,11 @@ Arrangement 1 starting at (1, 18):: .. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/SafeIntervalPathPlanner/path_animation2.gif +Code Link +^^^^^^^^^^^^^ +.. autoclass:: PathPlanning.TimeBasedPathPlanning.SafeInterval.SafeIntervalPathPlanner + + References ~~~~~~~~~~~ diff --git a/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst b/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst index 3c9b7c008c..aac96d6e19 100644 --- a/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst +++ b/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst @@ -13,6 +13,11 @@ red crosses are visibility nodes, and blue lines area collision free visibility The red line is the final path searched by dijkstra algorithm frm the visibility graphs. +Code Link +~~~~~~~~~~~~ +.. autoclass:: PathPlanning.VisibilityRoadMap.visibility_road_map.VisibilityRoadMap + + Algorithms ~~~~~~~~~~ @@ -64,7 +69,7 @@ The red line is searched path in the figure: You can find the details of Dijkstra algorithm in :ref:`dijkstra`. References -^^^^^^^^^^ +~~~~~~~~~~~~ - `Visibility graph - Wikipedia `_ diff --git a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst index 65e0e53465..a9a41aab74 100644 --- a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst +++ b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst @@ -11,7 +11,13 @@ Cyan crosses mean searched points with Dijkstra method, The red line is the final path of Vornoi Road-Map. +Code Link +~~~~~~~~~~~~~~~ +.. autoclass:: PathPlanning.VoronoiRoadMap.voronoi_road_map.VoronoiRoadMapPlanner + + Reference +~~~~~~~~~~~~ - `Robotic Motion Planning `__ diff --git a/tests/test_batch_informed_rrt_star.py b/tests/test_batch_informed_rrt_star.py index 3ad78bdb23..cf0a9827a8 100644 --- a/tests/test_batch_informed_rrt_star.py +++ b/tests/test_batch_informed_rrt_star.py @@ -1,7 +1,7 @@ import random import conftest -from PathPlanning.BatchInformedRRTStar import batch_informed_rrtstar as m +from PathPlanning.BatchInformedRRTStar import batch_informed_rrt_star as m def test_1(): From 0c82dde2be24e5f3955018caa061750d7404978d Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 6 May 2025 07:22:51 +0900 Subject: [PATCH 845/909] build(deps): bump ruff from 0.11.7 to 0.11.8 in /requirements (#1215) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.7 to 0.11.8. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](https://github.com/astral-sh/ruff/compare/0.11.7...0.11.8) --- updated-dependencies: - dependency-name: ruff dependency-version: 0.11.8 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 88cbb21f81..e78b9b8d51 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -6,4 +6,4 @@ ecos == 2.0.14 pytest == 8.3.5 # For unit test pytest-xdist == 3.6.1 # For unit test mypy == 1.15.0 # For unit test -ruff == 0.11.7 # For unit test +ruff == 0.11.8 # For unit test From 292c9dc613d1b0ace3d25ca8a0295e19549516f2 Mon Sep 17 00:00:00 2001 From: atm <72185056+sutatoruta@users.noreply.github.com> Date: Sat, 10 May 2025 09:34:19 +0900 Subject: [PATCH 846/909] fix broken links (#1216) --- .github/pull_request_template.md | 2 +- README.md | 12 ++++++------ users_comments.md | 2 +- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index b6ac52efa2..c0d9f7eab2 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1,7 @@

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@@ line-length = 88 select = ["F", "E", "W", "UP"] -ignore = ["E501", "E741"] +ignore = ["E501", "E741", "E402"] exclude = [ ] diff --git a/tests/test_normal_vector_estimation.py b/tests/test_normal_vector_estimation.py new file mode 100644 index 0000000000..7612f22aa7 --- /dev/null +++ b/tests/test_normal_vector_estimation.py @@ -0,0 +1,19 @@ +import conftest # Add root path to sys.path +from Mapping.normal_vector_estimation import normal_vector_estimation as m +import random + +random.seed(12345) + + +def test_1(): + m.show_animation = False + m.main1() + + +def test_2(): + m.show_animation = False + m.main2() + + +if __name__ == '__main__': + conftest.run_this_test(__file__) diff --git a/utils/plot.py b/utils/plot.py index 3499694821..d4bbe29ffd 100644 --- a/utils/plot.py +++ b/utils/plot.py @@ -4,6 +4,10 @@ import math import matplotlib.pyplot as plt import numpy as np +from mpl_toolkits.mplot3d import art3d +from matplotlib.patches import FancyArrowPatch +from mpl_toolkits.mplot3d.proj3d import proj_transform +from mpl_toolkits.mplot3d import Axes3D def plot_arrow(x, y, yaw, arrow_length=1.0, @@ -84,3 +88,78 @@ def plot_curvature(x_list, y_list, heading_list, curvature, plt.plot(cx, cy, c, label=label) for ix, iy, icx, icy in zip(x_list, y_list, cx, cy): plt.plot([ix, icx], [iy, icy], c) + + +class Arrow3D(FancyArrowPatch): + + def __init__(self, x, y, z, dx, dy, dz, *args, **kwargs): + super().__init__((0, 0), (0, 0), *args, **kwargs) + self._xyz = (x, y, z) + self._dxdydz = (dx, dy, dz) + + def draw(self, renderer): + x1, y1, z1 = self._xyz + dx, dy, dz = self._dxdydz + x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz) + + xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M) + self.set_positions((xs[0], ys[0]), (xs[1], ys[1])) + super().draw(renderer) + + def do_3d_projection(self, renderer=None): + x1, y1, z1 = self._xyz + dx, dy, dz = self._dxdydz + x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz) + + xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M) + self.set_positions((xs[0], ys[0]), (xs[1], ys[1])) + + return np.min(zs) + + +def _arrow3D(ax, x, y, z, dx, dy, dz, *args, **kwargs): + '''Add an 3d arrow to an `Axes3D` instance.''' + arrow = Arrow3D(x, y, z, dx, dy, dz, *args, **kwargs) + ax.add_artist(arrow) + + +def plot_3d_vector_arrow(ax, p1, p2): + setattr(Axes3D, 'arrow3D', _arrow3D) + ax.arrow3D(p1[0], p1[1], p1[2], + p2[0]-p1[0], p2[1]-p1[1], p2[2]-p1[2], + mutation_scale=20, + arrowstyle="-|>", + ) + + + +def plot_triangle(p1, p2, p3, ax): + ax.add_collection3d(art3d.Poly3DCollection([[p1, p2, p3]], color='b')) + + +def set_equal_3d_axis(ax, x_lims, y_lims, z_lims): + """Helper function to set equal axis + + Args: + ax (Axes3DSubplot): matplotlib 3D axis, created by + `ax = fig.add_subplot(projection='3d')` + x_lims (np.array): array containing min and max value of x + y_lims (np.array): array containing min and max value of y + z_lims (np.array): array containing min and max value of z + """ + x_lims = np.asarray(x_lims) + y_lims = np.asarray(y_lims) + z_lims = np.asarray(z_lims) + # compute max required range + max_range = np.array([x_lims.max() - x_lims.min(), + y_lims.max() - y_lims.min(), + z_lims.max() - z_lims.min()]).max() / 2.0 + # compute mid-point along each axis + mid_x = (x_lims.max() + x_lims.min()) * 0.5 + mid_y = (y_lims.max() + y_lims.min()) * 0.5 + mid_z = (z_lims.max() + z_lims.min()) * 0.5 + + # set limits to axis + ax.set_xlim(mid_x - max_range, mid_x + max_range) + ax.set_ylim(mid_y - max_range, mid_y + max_range) + ax.set_zlim(mid_z - max_range, mid_z + max_range) From 5b14e91bc46a1bb7a32c736c534498e723ab24bc Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 14 Mar 2023 21:41:34 +0900 Subject: [PATCH 537/909] Bump mypy from 1.0.1 to 1.1.1 in /requirements (#803) Bumps [mypy](https://github.com/python/mypy) from 1.0.1 to 1.1.1. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v1.0.1...v1.1.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 35798d7e86..8ed129f345 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.7.1 cvxpy == 1.3.0 pytest == 7.2.2 # For unit test pytest-xdist == 3.2.0 # For unit test -mypy == 1.0.1 # For unit test +mypy == 1.1.1 # For unit test ruff == 0.0.254 # For unit test From 9b70a6e1c22f68f8702e013962256a52b56e2d02 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Mar 2023 08:25:06 +0900 Subject: [PATCH 538/909] Bump ruff from 0.0.254 to 0.0.255 in /requirements (#805) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.254 to 0.0.255. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.254...v0.0.255) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 8ed129f345..3efc1c87fc 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.0 pytest == 7.2.2 # For unit test pytest-xdist == 3.2.0 # For unit test mypy == 1.1.1 # For unit test -ruff == 0.0.254 # For unit test +ruff == 0.0.255 # For unit test From 3dbf2a4eb5b6ddaa113548d126429475128631ae Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 15 Mar 2023 10:36:48 +0900 Subject: [PATCH 539/909] Bump pytest-xdist from 3.2.0 to 3.2.1 in /requirements (#804) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.2.0 to 3.2.1. - [Release notes](https://github.com/pytest-dev/pytest-xdist/releases) - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.2.0...v3.2.1) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3efc1c87fc..fc90ceb9d5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.0 pytest == 7.2.2 # For unit test -pytest-xdist == 3.2.0 # For unit test +pytest-xdist == 3.2.1 # For unit test mypy == 1.1.1 # For unit test ruff == 0.0.255 # For unit test From 1ce148e4be6e0c2cb619bc2c865f41b55a32dcc0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Mar 2023 07:11:27 +0900 Subject: [PATCH 540/909] Bump cvxpy from 1.3.0 to 1.3.1 in /requirements (#806) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.3.0 to 1.3.1. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.3.0...v1.3.1) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index fc90ceb9d5..001836e72e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.24.2 scipy == 1.10.1 matplotlib == 3.7.1 -cvxpy == 1.3.0 +cvxpy == 1.3.1 pytest == 7.2.2 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.1.1 # For unit test From 53eae53b5a78a08b7ce4c6ffeed727c1d6a0ab2e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 21 Mar 2023 07:11:48 +0900 Subject: [PATCH 541/909] Bump ruff from 0.0.255 to 0.0.257 in /requirements (#807) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.255 to 0.0.257. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.255...v0.0.257) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 001836e72e..59a34902f4 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.2.2 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.1.1 # For unit test -ruff == 0.0.255 # For unit test +ruff == 0.0.257 # For unit test From 7d1365d08d272aef76d7cb88a8044711bacf3461 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Wed, 22 Mar 2023 22:47:12 +0900 Subject: [PATCH 542/909] Clean up README badge. (#808) --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 4ad19353e3..bfde2f9295 100644 --- a/README.md +++ b/README.md @@ -3,10 +3,9 @@ # PythonRobotics ![GitHub_Action_Linux_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Linux_CI/badge.svg) ![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg) +![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg) [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics) [![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics) -[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python) -[![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics) Python codes for robotics algorithm. From 5093342d35b914c125f1a857d254083887574444 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 28 Mar 2023 07:36:24 +0900 Subject: [PATCH 543/909] Bump ruff from 0.0.257 to 0.0.259 in /requirements (#809) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.257 to 0.0.259. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.257...v0.0.259) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 59a34902f4..bc27a2cb7d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.2.2 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.1.1 # For unit test -ruff == 0.0.257 # For unit test +ruff == 0.0.259 # For unit test From 32ecd546a45587e7634c0d321d3441057e1ebdf0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sat, 8 Apr 2023 19:15:17 +0900 Subject: [PATCH 544/909] Bump ruff from 0.0.259 to 0.0.260 in /requirements (#813) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.259 to 0.0.260. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.259...v0.0.260) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index bc27a2cb7d..c8b9725def 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.2.2 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.1.1 # For unit test -ruff == 0.0.259 # For unit test +ruff == 0.0.260 # For unit test From f97fc0a26da22ac3eb51472cca5b2858bf9067fb Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Apr 2023 08:17:07 +0900 Subject: [PATCH 545/909] Bump pytest from 7.2.2 to 7.3.0 in /requirements (#814) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.2.2 to 7.3.0. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.2.2...7.3.0) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index c8b9725def..4019b31e0c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.24.2 scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 -pytest == 7.2.2 # For unit test +pytest == 7.3.0 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.1.1 # For unit test ruff == 0.0.260 # For unit test From cc6245cdcfcd6e35f5cb789a7aafe7fe57fe05c0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Apr 2023 09:34:13 +0900 Subject: [PATCH 546/909] Bump ruff from 0.0.260 to 0.0.261 in /requirements (#815) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.260 to 0.0.261. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.260...v0.0.261) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4019b31e0c..ad78be529a 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.3.0 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.1.1 # For unit test -ruff == 0.0.260 # For unit test +ruff == 0.0.261 # For unit test From 90189c4711eba64d5ce902170277b9887a5f7cf5 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Apr 2023 12:20:02 +0900 Subject: [PATCH 547/909] Bump mypy from 1.1.1 to 1.2.0 in /requirements (#816) Bumps [mypy](https://github.com/python/mypy) from 1.1.1 to 1.2.0. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v1.1.1...v1.2.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ad78be529a..9d43d13eb2 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.7.1 cvxpy == 1.3.1 pytest == 7.3.0 # For unit test pytest-xdist == 3.2.1 # For unit test -mypy == 1.1.1 # For unit test +mypy == 1.2.0 # For unit test ruff == 0.0.261 # For unit test From 2394e776f628cc0bba35b176e74e65345bb870b2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Apr 2023 14:56:19 +1000 Subject: [PATCH 548/909] Bump numpy from 1.24.2 to 1.24.3 in /requirements (#820) Bumps [numpy](https://github.com/numpy/numpy) from 1.24.2 to 1.24.3. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.24.2...v1.24.3) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9d43d13eb2..9f88bd9bfb 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.24.2 +numpy == 1.24.3 scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 From 1efac6795ad75e7f28a04b049deab7038712efd9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 26 Apr 2023 21:23:42 +0900 Subject: [PATCH 549/909] Bump ruff from 0.0.261 to 0.0.263 in /requirements (#822) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.261 to 0.0.263. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.261...v0.0.263) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9f88bd9bfb..e05a591dc5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.3.0 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.2.0 # For unit test -ruff == 0.0.261 # For unit test +ruff == 0.0.263 # For unit test From 28004c7b9c6cb0050e9b6e01a7c284ea631e9c9a Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 26 Apr 2023 21:49:36 +0900 Subject: [PATCH 550/909] Bump pytest from 7.3.0 to 7.3.1 in /requirements (#818) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.3.0 to 7.3.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.3.0...7.3.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e05a591dc5..501723248f 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.24.3 scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 -pytest == 7.3.0 # For unit test +pytest == 7.3.1 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.2.0 # For unit test ruff == 0.0.263 # For unit test From 80124f20e814a03baa440481999847bdc850be62 Mon Sep 17 00:00:00 2001 From: RyderCRD <39330401+RyderCRD@users.noreply.github.com> Date: Thu, 27 Apr 2023 22:04:39 +0800 Subject: [PATCH 551/909] Fix the rewire() of rrt* path planning (#550) (#812) * Update rrt_star_reeds_shepp.py to fix #550 Add step_size attribute to RRTStarReedsShepp, and a method set_random_seed() to set the random seed, with two test cases. * Update rewire() of rrt_star.py Update rewire() of rrt_star.py to fix #550. Since the old version didn't assign path_yaw of edge_node to near_node, the old rewire() doesn't fit rrt_star_reeds_shepp.py * Update reeds_shepp_path_planning.py Limit the range of phi for the sake of planning speed, and simplify the the calculation process in straight_left_straight(). * Update reeds_shepp_path_planning.py * Remove unnecessary cost calculation Cost of edge_node is calculated in line 221, and self.node_list[i] is replaced to edge_node, so no need to update. * Update reeds_shepp_path_planning.py --- PathPlanning/RRTStar/rrt_star.py | 12 +++--- .../RRTStarReedsShepp/rrt_star_reeds_shepp.py | 14 ++++--- .../reeds_shepp_path_planning.py | 13 +++---- tests/test_rrt_star_reeds_shepp.py | 37 +++++++++++++++++++ 4 files changed, 56 insertions(+), 20 deletions(-) diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index f875d3a76d..b2884fe21b 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -225,13 +225,11 @@ def rewire(self, new_node, near_inds): improved_cost = near_node.cost > edge_node.cost if no_collision and improved_cost: - near_node.x = edge_node.x - near_node.y = edge_node.y - near_node.cost = edge_node.cost - near_node.path_x = edge_node.path_x - near_node.path_y = edge_node.path_y - near_node.parent = edge_node.parent - self.propagate_cost_to_leaves(new_node) + for node in self.node_list: + if node.parent == self.node_list[i]: + node.parent = edge_node + self.node_list[i] = edge_node + self.propagate_cost_to_leaves(self.node_list[i]) def calc_new_cost(self, from_node, to_node): d, _ = self.calc_distance_and_angle(from_node, to_node) diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py index 66185017a0..c4c3e7c8a8 100644 --- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py +++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py @@ -36,7 +36,7 @@ def __init__(self, x, y, yaw): self.path_yaw = [] def __init__(self, start, goal, obstacle_list, rand_area, - max_iter=200, + max_iter=200, step_size=0.2, connect_circle_dist=50.0, robot_radius=0.0 ): @@ -55,6 +55,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, self.min_rand = rand_area[0] self.max_rand = rand_area[1] self.max_iter = max_iter + self.step_size = step_size self.obstacle_list = obstacle_list self.connect_circle_dist = connect_circle_dist self.robot_radius = robot_radius @@ -63,6 +64,9 @@ def __init__(self, start, goal, obstacle_list, rand_area, self.goal_yaw_th = np.deg2rad(1.0) self.goal_xy_th = 0.5 + def set_random_seed(self, seed): + random.seed(seed) + def planning(self, animation=True, search_until_max_iter=True): """ planning @@ -147,8 +151,8 @@ def plot_start_goal_arrow(self): def steer(self, from_node, to_node): px, py, pyaw, mode, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning( - from_node.x, from_node.y, from_node.yaw, - to_node.x, to_node.y, to_node.yaw, self.curvature) + from_node.x, from_node.y, from_node.yaw, to_node.x, + to_node.y, to_node.yaw, self.curvature, self.step_size) if not px: return None @@ -169,8 +173,8 @@ def steer(self, from_node, to_node): def calc_new_cost(self, from_node, to_node): _, _, _, _, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning( - from_node.x, from_node.y, from_node.yaw, - to_node.x, to_node.y, to_node.yaw, self.curvature) + from_node.x, from_node.y, from_node.yaw, to_node.x, + to_node.y, to_node.yaw, self.curvature, self.step_size) if not course_lengths: return float("inf") diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py index 1577d048d3..c55d2629e5 100644 --- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py +++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py @@ -53,17 +53,14 @@ def mod2pi(x): def straight_left_straight(x, y, phi): phi = mod2pi(phi) - if y > 0.0 and 0.0 < phi < math.pi * 0.99: + # only take phi in (0.01*math.pi, 0.99*math.pi) for the sake of speed. + # phi in (0, 0.01*math.pi) will make test2() in test_rrt_star_reeds_shepp.py + # extremely time-consuming, since the value of xd, t will be very large. + if math.pi * 0.01 < phi < math.pi * 0.99 and y != 0: xd = - y / math.tan(phi) + x t = xd - math.tan(phi / 2.0) u = phi - v = math.hypot(x - xd, y) - math.tan(phi / 2.0) - return True, t, u, v - elif y < 0.0 < phi < math.pi * 0.99: - xd = - y / math.tan(phi) + x - t = xd - math.tan(phi / 2.0) - u = phi - v = -math.hypot(x - xd, y) - math.tan(phi / 2.0) + v = np.sign(y) * math.hypot(x - xd, y) - math.tan(phi / 2.0) return True, t, u, v return False, 0.0, 0.0, 0.0 diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py index cb6b586b33..20d4916f96 100644 --- a/tests/test_rrt_star_reeds_shepp.py +++ b/tests/test_rrt_star_reeds_shepp.py @@ -6,6 +6,43 @@ def test1(): m.show_animation = False m.main(max_iter=5) +obstacleList = [ + (5, 5, 1), + (4, 6, 1), + (4, 8, 1), + (4, 10, 1), + (6, 5, 1), + (7, 5, 1), + (8, 6, 1), + (8, 8, 1), + (8, 10, 1) +] # [x,y,size(radius)] + +start = [0.0, 0.0, m.np.deg2rad(0.0)] +goal = [6.0, 7.0, m.np.deg2rad(90.0)] + +def test2(): + step_size = 0.2 + rrt_star_reeds_shepp = m.RRTStarReedsShepp(start, goal, + obstacleList, [-2.0, 15.0], + max_iter=100, step_size=step_size) + rrt_star_reeds_shepp.set_random_seed(seed=8) + path = rrt_star_reeds_shepp.planning(animation=False) + for i in range(len(path)-1): + # + 0.00000000000001 for acceptable errors arising from the planning process + assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001 + +def test3(): + step_size = 20 + rrt_star_reeds_shepp = m.RRTStarReedsShepp(start, goal, + obstacleList, [-2.0, 15.0], + max_iter=100, step_size=step_size) + rrt_star_reeds_shepp.set_random_seed(seed=8) + path = rrt_star_reeds_shepp.planning(animation=False) + for i in range(len(path)-1): + # + 0.00000000000001 for acceptable errors arising from the planning process + assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001 + if __name__ == '__main__': conftest.run_this_test(__file__) From 033e301498a12a039f95a990fa9b831c5015eaf3 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 2 May 2023 20:54:45 +0900 Subject: [PATCH 552/909] Cleanup cvxpy related codes (#823) * add normal_vector_estimation_main.rst * add normal_vector_estimation_main.rst * cleanup cvxpy related codes * cleanup cvxpy related codes --- .../rocket_powered_landing.py | 17 +++++++++-------- .../model_predictive_speed_and_steer_control.py | 6 ++++-- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py index 37dca15b98..239f3629c1 100644 --- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py +++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py @@ -12,13 +12,12 @@ - EmbersArc/SuccessiveConvexificationFreeFinalTime: Implementation of "Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time" https://github.com/EmbersArc/SuccessiveConvexificationFreeFinalTime """ - +import warnings from time import time import numpy as np from scipy.integrate import odeint import cvxpy import matplotlib.pyplot as plt -from mpl_toolkits import mplot3d # Trajectory points K = 50 @@ -123,7 +122,7 @@ def set_random_initial_state(self, rng): rng.uniform(-20, 20))) def f_func(self, x, u): - m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[ + m, _, _, _, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[ 2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13] ux, uy, uz = u[0], u[1], u[2] @@ -148,7 +147,7 @@ def f_func(self, x, u): ]) def A_func(self, x, u): - m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[ + m, _, _, _, _, _, _, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[ 2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13] ux, uy, uz = u[0], u[1], u[2] @@ -176,7 +175,7 @@ def A_func(self, x, u): [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) def B_func(self, x, u): - m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[ + m, _, _, _, _, _, _, q0, q1, q2, q3, _, _, _ = x[0], x[1], x[ 2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13] ux, uy, uz = u[0], u[1], u[2] @@ -534,7 +533,9 @@ def get_variable(self, name): def solve(self, **kwargs): error = False try: - self.prob.solve(verbose=verbose_solver, + with warnings.catch_warnings(): # For User warning from solver + warnings.simplefilter('ignore') + self.prob.solve(verbose=verbose_solver, solver=solver) except cvxpy.SolverError: error = True @@ -569,10 +570,10 @@ def axis3d_equal(X, Y, Z, ax): def plot_animation(X, U): # pragma: no cover fig = plt.figure() - ax = fig.gca(projection='3d') + ax = fig.add_subplot(projection='3d') # for stopping simulation with the esc key. fig.canvas.mpl_connect('key_release_event', - lambda event: [exit(0) if event.key == 'escape' else None]) + lambda event: [exit(0) if event.key == 'escape' else None]) for k in range(K): plt.cla() diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py index 59e69aa999..3015313198 100644 --- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py +++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py @@ -225,8 +225,9 @@ def predict_motion(x0, oa, od, xref): def iterative_linear_mpc_control(xref, x0, dref, oa, od): """ - MPC contorl with updating operational point iteraitvely + MPC control with updating operational point iteratively """ + ox, oy, oyaw, ov = None, None, None, None if oa is None or od is None: oa = [0.0] * T @@ -406,10 +407,11 @@ def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state): oa, odelta, ox, oy, oyaw, ov = iterative_linear_mpc_control( xref, x0, dref, oa, odelta) + di, ai = 0.0, 0.0 if odelta is not None: di, ai = odelta[0], oa[0] + state = update_state(state, ai, di) - state = update_state(state, ai, di) time = time + DT x.append(state.x) From b877a9d5ca15b794a72221c33d52d13bf3bf8434 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 14 May 2023 17:15:10 +0900 Subject: [PATCH 553/909] Bump ruff from 0.0.263 to 0.0.265 in /requirements (#826) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.263 to 0.0.265. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.263...v0.0.265) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 501723248f..4522f6c7ca 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.3.1 # For unit test pytest-xdist == 3.2.1 # For unit test mypy == 1.2.0 # For unit test -ruff == 0.0.263 # For unit test +ruff == 0.0.265 # For unit test From 18a7d1b75871d9f12fe5fd20a93f5c50f059e614 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 May 2023 07:27:24 +0900 Subject: [PATCH 554/909] Bump mypy from 1.2.0 to 1.3.0 in /requirements (#835) Bumps [mypy](https://github.com/python/mypy) from 1.2.0 to 1.3.0. - [Commits](https://github.com/python/mypy/compare/v1.2.0...v1.3.0) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4522f6c7ca..30c5330b90 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.7.1 cvxpy == 1.3.1 pytest == 7.3.1 # For unit test pytest-xdist == 3.2.1 # For unit test -mypy == 1.2.0 # For unit test +mypy == 1.3.0 # For unit test ruff == 0.0.265 # For unit test From 57943ae6f598ca14a0aadfe706bd7762c94ff36e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 May 2023 20:08:06 +0900 Subject: [PATCH 555/909] Bump pytest-xdist from 3.2.1 to 3.3.0 in /requirements (#837) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.2.1 to 3.3.0. - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.2.1...v3.3.0) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 30c5330b90..616e3c3fc0 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 pytest == 7.3.1 # For unit test -pytest-xdist == 3.2.1 # For unit test +pytest-xdist == 3.3.0 # For unit test mypy == 1.3.0 # For unit test ruff == 0.0.265 # For unit test From 29e0bab4e7ab380624c4c6f7fe068e67a5c71718 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 16 May 2023 20:39:26 +0900 Subject: [PATCH 556/909] Bump ruff from 0.0.265 to 0.0.267 in /requirements (#836) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.265 to 0.0.267. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.265...v0.0.267) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 616e3c3fc0..6efd6fcb98 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.3.1 # For unit test pytest-xdist == 3.3.0 # For unit test mypy == 1.3.0 # For unit test -ruff == 0.0.265 # For unit test +ruff == 0.0.267 # For unit test From dd6d046bf9b6458a8bcdd947045806af8c86e788 Mon Sep 17 00:00:00 2001 From: Erkam Kavak <70749779+erkamkavak@users.noreply.github.com> Date: Tue, 16 May 2023 15:42:18 +0300 Subject: [PATCH 557/909] fix added for checking if a neighbor is inside openList (#832) --- PathPlanning/HybridAStar/hybrid_a_star.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index b87fe706c2..03db0dc167 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -311,7 +311,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution): neighbor_index = calc_index(neighbor, config) if neighbor_index in closedList: continue - if neighbor not in openList \ + if neighbor_index not in openList \ or openList[neighbor_index].cost > neighbor.cost: heapq.heappush( pq, (calc_cost(neighbor, h_dp, config), From 874ebff495b088e0aee48ad29e352548dee65e8d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Yangshen=E2=9A=A1Deng?= Date: Tue, 16 May 2023 20:43:57 +0800 Subject: [PATCH 558/909] fix: set rrt_star to find the shortest path (#834) * fix: set rrt_star to find the shortest path * fix: code style white space --- PathPlanning/RRTStar/rrt_star.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index b2884fe21b..dcb1a066eb 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -163,9 +163,13 @@ def search_best_goal_node(self): if not safe_goal_inds: return None - min_cost = min([self.node_list[i].cost for i in safe_goal_inds]) - for i in safe_goal_inds: - if self.node_list[i].cost == min_cost: + safe_goal_costs = [self.node_list[i].cost + + self.calc_dist_to_goal(self.node_list[i].x, self.node_list[i].y) + for i in safe_goal_inds] + + min_cost = min(safe_goal_costs) + for i, cost in zip(safe_goal_inds, safe_goal_costs): + if cost == min_cost: return i return None From 7be69c13cce0f518d3ce5521b4bafcc56bce1445 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 May 2023 07:23:34 +0900 Subject: [PATCH 559/909] Bump pytest-xdist from 3.3.0 to 3.3.1 in /requirements (#838) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.3.0 to 3.3.1. - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.3.0...v3.3.1) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6efd6fcb98..389107f0bb 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 pytest == 7.3.1 # For unit test -pytest-xdist == 3.3.0 # For unit test +pytest-xdist == 3.3.1 # For unit test mypy == 1.3.0 # For unit test ruff == 0.0.267 # For unit test From 390c1cdd9fce482f13280724d762c9f9000baaba Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 23 May 2023 12:42:52 +0900 Subject: [PATCH 560/909] Bump ruff from 0.0.267 to 0.0.269 in /requirements (#839) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.267 to 0.0.269. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.267...v0.0.269) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 389107f0bb..649b331e1d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.3.1 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.3.0 # For unit test -ruff == 0.0.267 # For unit test +ruff == 0.0.269 # For unit test From ca82a4b1dff20dba0c852f4e0ac41501cba6e9ab Mon Sep 17 00:00:00 2001 From: Faizan Alam <51821426+Faizan-Alam-1@users.noreply.github.com> Date: Sat, 27 May 2023 17:00:02 +0530 Subject: [PATCH 561/909] Add copy button in the documentation code snippets (#840) --- docs/conf.py | 1 + docs/doc_requirements.txt | 1 + 2 files changed, 2 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index e7c64d07b2..0cbf3471f9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -45,6 +45,7 @@ 'sphinx.ext.napoleon', 'sphinx.ext.imgconverter', 'IPython.sphinxext.ipython_console_highlighting', + 'sphinx_copybutton', ] # Add any paths that contain templates here, relative to this directory. diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index c01b596539..04c30e6037 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -2,3 +2,4 @@ sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 8.10.0 # For sphinx documentation sphinxcontrib-napoleon == 0.7 # For auto doc +sphinx-copybutton From 5ddf70615ea35c366199ef6837ea88eba94ba6c8 Mon Sep 17 00:00:00 2001 From: Faizan Alam <51821426+Faizan-Alam-1@users.noreply.github.com> Date: Sun, 28 May 2023 13:52:35 +0530 Subject: [PATCH 562/909] Add dark mode feature in documentation (#842) --- docs/conf.py | 3 ++- docs/doc_requirements.txt | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 0cbf3471f9..df9b8fce22 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -46,6 +46,7 @@ 'sphinx.ext.imgconverter', 'IPython.sphinxext.ipython_console_highlighting', 'sphinx_copybutton', + 'sphinx_rtd_dark_mode', ] # Add any paths that contain templates here, relative to this directory. @@ -86,7 +87,7 @@ if on_rtd: html_theme = 'default' else: - html_theme = 'sphinx_rtd_theme' + html_theme = 'sphinx_rtd_light_them' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index 04c30e6037..a8cd2bde40 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -3,3 +3,4 @@ sphinx_rtd_theme == 1.0.0 IPython == 8.10.0 # For sphinx documentation sphinxcontrib-napoleon == 0.7 # For auto doc sphinx-copybutton +sphinx-rtd-dark-mode \ No newline at end of file From e4fe460e02f9f29ea2553e579e7599a652ca5207 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 30 May 2023 07:56:26 +0900 Subject: [PATCH 563/909] Bump ruff from 0.0.269 to 0.0.270 in /requirements (#844) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.269 to 0.0.270. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.269...v0.0.270) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 649b331e1d..4499e715a9 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.3.1 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.3.0 # For unit test -ruff == 0.0.269 # For unit test +ruff == 0.0.270 # For unit test From 6bade5000dca75b91cc9de94f797e5d558082855 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Jun 2023 20:46:56 +0900 Subject: [PATCH 564/909] Bump ruff from 0.0.270 to 0.0.272 in /requirements (#849) Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.270 to 0.0.272. - [Release notes](https://github.com/charliermarsh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.270...v0.0.272) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4499e715a9..93a1b8bba1 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.3.1 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.3.0 # For unit test -ruff == 0.0.270 # For unit test +ruff == 0.0.272 # For unit test From 3be53ab0e0802e908544e274278ed0390ea98597 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 13 Jun 2023 20:47:31 +0900 Subject: [PATCH 565/909] Bump pytest from 7.3.1 to 7.3.2 in /requirements (#850) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.3.1 to 7.3.2. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.3.1...7.3.2) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 93a1b8bba1..470180c1f7 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.24.3 scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 -pytest == 7.3.1 # For unit test +pytest == 7.3.2 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.3.0 # For unit test ruff == 0.0.272 # For unit test From c06e4909c865cf3912cdee4610ab91afcd3b111c Mon Sep 17 00:00:00 2001 From: Yichao <111082392+YichaoDeng@users.noreply.github.com> Date: Mon, 19 Jun 2023 17:02:16 +0800 Subject: [PATCH 566/909] fix: [ValueError] setting an array element with a sequence in [fast_slam2] (#851) --- SLAM/FastSLAM2/fast_slam2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 7cd708df81..4c6c103325 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -405,8 +405,8 @@ def main(): for iz in range(len(z[:, 0])): landmark_id = int(z[2, iz]) - plt.plot([xEst[0], RFID[landmark_id, 0]], [ - xEst[1], RFID[landmark_id, 1]], "-k") + plt.plot([xEst[0][0], RFID[landmark_id, 0]], [ + xEst[1][0], RFID[landmark_id, 1]], "-k") for i in range(N_PARTICLE): plt.plot(particles[i].x, particles[i].y, ".r") From 94a8072b11fb2198b03ae193c4a3499d7a004a0f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Jun 2023 07:01:39 +0900 Subject: [PATCH 567/909] Bump mypy from 1.3.0 to 1.4.1 in /requirements (#854) Bumps [mypy](https://github.com/python/mypy) from 1.3.0 to 1.4.1. - [Commits](https://github.com/python/mypy/compare/v1.3.0...v1.4.1) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 470180c1f7..bbd3764998 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.7.1 cvxpy == 1.3.1 pytest == 7.3.2 # For unit test pytest-xdist == 3.3.1 # For unit test -mypy == 1.3.0 # For unit test +mypy == 1.4.1 # For unit test ruff == 0.0.272 # For unit test From a0bb72e000bfcbc2442303c0bc39e4e6696f793f Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Jun 2023 07:20:25 +0900 Subject: [PATCH 568/909] Bump pytest from 7.3.2 to 7.4.0 in /requirements (#857) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.3.2 to 7.4.0. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.3.2...7.4.0) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index bbd3764998..580a4ba731 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.24.3 scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 -pytest == 7.3.2 # For unit test +pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test ruff == 0.0.272 # For unit test From 81c75f75ab987645c733952bbf277d349fefcec2 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Jun 2023 13:25:12 +0900 Subject: [PATCH 569/909] Bump ruff from 0.0.272 to 0.0.275 in /requirements (#856) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.272 to 0.0.275. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.272...v0.0.275) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 580a4ba731..9db6898463 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.1 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.272 # For unit test +ruff == 0.0.275 # For unit test From d7fdcade2f0a86cbe263d31530667fadc9ec1e61 Mon Sep 17 00:00:00 2001 From: neriya shulman <45920392+neriyashul@users.noreply.github.com> Date: Fri, 30 Jun 2023 15:13:09 +0300 Subject: [PATCH 570/909] Fix broken links in README.md for ipynb files (#859) --- README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index bfde2f9295..bc0198f78e 100644 --- a/README.md +++ b/README.md @@ -161,7 +161,9 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation EKF pic -Documentation: [Notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb) +Ref: + +- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html) ## Particle filter localization @@ -532,7 +534,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri Ref: -- [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb) +- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html) - [Real\-time Model Predictive Control \(MPC\), ACADO, Python \| Work\-is\-Playing](http://grauonline.de/wordpress/?page_id=3244) @@ -544,7 +546,7 @@ A motion planning and path tracking simulation with NMPC of C-GMRES Ref: -- [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb) +- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.html) # Arm Navigation @@ -584,7 +586,7 @@ This is a 3d trajectory generation simulation for a rocket powered landing. Ref: -- [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb) +- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.html) # Bipedal From 67edaf8b026bee1c502b09d92249c10ce760e22a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 1 Jul 2023 08:29:34 +0900 Subject: [PATCH 571/909] Enhance rectangle fitting docs (#848) * enhance rectangle fitting docs * enhance rectangle fitting docs * Update rectangle_fitting_main.rst * Update rectangle_fitting_main.rst * Update rectangle_fitting_main.rst * Update rectangle_fitting_main.rst * Update rectangle_fitting_main.rst * Update rectangle_fitting_main.rst * Update rectangle_fitting_main.rst * Update rectangle_fitting_main.rst * enhance rectangle fitting docs --- .../rectangle_fitting/rectangle_fitting.py | 108 +++++++++++------- .../rectangle_fitting_main.rst | 62 ++++++++++ .../modules/slam/FastSLAM1/FastSLAM1_main.rst | 2 +- 3 files changed, 128 insertions(+), 44 deletions(-) diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index f54ada1e3a..177f078871 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -30,6 +30,11 @@ class LShapeFitting: + """ + LShapeFitting class. You can use this class by initializing the class and + changing the parameters, and then calling the fitting method. + + """ class Criteria(Enum): AREA = 1 @@ -37,15 +42,35 @@ class Criteria(Enum): VARIANCE = 3 def __init__(self): - # Parameters + """ + Default parameter settings + """ + #: Fitting criteria parameter self.criteria = self.Criteria.VARIANCE - self.min_dist_of_closeness_criteria = 0.01 # [m] - self.d_theta_deg_for_search = 1.0 # [deg] - self.R0 = 3.0 # [m] range segmentation param - self.Rd = 0.001 # [m] range segmentation param + #: Minimum distance for closeness criteria parameter [m] + self.min_dist_of_closeness_criteria = 0.01 + #: Angle difference parameter [deg] + self.d_theta_deg_for_search = 1.0 + #: Range segmentation parameter [m] + self.R0 = 3.0 + #: Range segmentation parameter [m] + self.Rd = 0.001 def fitting(self, ox, oy): + """ + Fitting L-shape model to object points + + Parameters + ---------- + ox : x positions of range points from an object + oy : y positions of range points from an object + + Returns + ------- + rects: Fitting rectangles + id_sets: id sets of each cluster + """ # step1: Adaptive Range Segmentation id_sets = self._adoptive_range_segmentation(ox, oy) @@ -60,56 +85,53 @@ def fitting(self, ox, oy): @staticmethod def _calc_area_criterion(c1, c2): - c1_max = max(c1) - c2_max = max(c2) - c1_min = min(c1) - c2_min = min(c2) - + c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2) alpha = -(c1_max - c1_min) * (c2_max - c2_min) - return alpha def _calc_closeness_criterion(self, c1, c2): - c1_max = max(c1) - c2_max = max(c2) - c1_min = min(c1) - c2_min = min(c2) + c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2) # Vectorization - D1 = np.minimum(c1_max - c1, c1 - c1_min) - D2 = np.minimum(c2_max - c2, c2 - c2_min) - d = np.maximum(np.minimum(D1, D2), self.min_dist_of_closeness_criteria) + d1 = np.minimum(c1_max - c1, c1 - c1_min) + d2 = np.minimum(c2_max - c2, c2 - c2_min) + d = np.maximum(np.minimum(d1, d2), self.min_dist_of_closeness_criteria) beta = (1.0 / d).sum() return beta @staticmethod def _calc_variance_criterion(c1, c2): - c1_max = max(c1) - c2_max = max(c2) - c1_min = min(c1) - c2_min = min(c2) + c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2) # Vectorization - D1 = np.minimum(c1_max - c1, c1 - c1_min) - D2 = np.minimum(c2_max - c2, c2 - c2_min) - E1 = D1[D1 < D2] - E2 = D2[D1 >= D2] - V1 = - np.var(E1) if len(E1) > 0 else 0. - V2 = - np.var(E2) if len(E2) > 0 else 0. - gamma = V1 + V2 + d1 = np.minimum(c1_max - c1, c1 - c1_min) + d2 = np.minimum(c2_max - c2, c2 - c2_min) + e1 = d1[d1 < d2] + e2 = d2[d1 >= d2] + v1 = - np.var(e1) if len(e1) > 0 else 0. + v2 = - np.var(e2) if len(e2) > 0 else 0. + gamma = v1 + v2 return gamma + @staticmethod + def _find_min_max(c1, c2): + c1_max = max(c1) + c2_max = max(c2) + c1_min = min(c1) + c2_min = min(c2) + return c1_max, c1_min, c2_max, c2_min + def _rectangle_search(self, x, y): - X = np.array([x, y]).T + xy = np.array([x, y]).T d_theta = np.deg2rad(self.d_theta_deg_for_search) min_cost = (-float('inf'), None) for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta): - c = X @ rot_mat_2d(theta) + c = xy @ rot_mat_2d(theta) c1 = c[:, 0] c2 = c[:, 1] @@ -129,8 +151,8 @@ def _rectangle_search(self, x, y): sin_s = np.sin(min_cost[1]) cos_s = np.cos(min_cost[1]) - c1_s = X @ np.array([cos_s, sin_s]).T - c2_s = X @ np.array([-sin_s, cos_s]).T + c1_s = xy @ np.array([cos_s, sin_s]).T + c2_s = xy @ np.array([-sin_s, cos_s]).T rect = RectangleData() rect.a[0] = cos_s @@ -151,28 +173,28 @@ def _rectangle_search(self, x, y): def _adoptive_range_segmentation(self, ox, oy): # Setup initial cluster - S = [] + segment_list = [] for i, _ in enumerate(ox): - C = set() - R = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]]) + c = set() + r = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]]) for j, _ in enumerate(ox): d = np.hypot(ox[i] - ox[j], oy[i] - oy[j]) - if d <= R: - C.add(j) - S.append(C) + if d <= r: + c.add(j) + segment_list.append(c) # Merge cluster while True: no_change = True - for (c1, c2) in list(itertools.permutations(range(len(S)), 2)): - if S[c1] & S[c2]: - S[c1] = (S[c1] | S.pop(c2)) + for (c1, c2) in list(itertools.permutations(range(len(segment_list)), 2)): + if segment_list[c1] & segment_list[c2]: + segment_list[c1] = (segment_list[c1] | segment_list.pop(c2)) no_change = False break if no_change: break - return S + return segment_list class RectangleData: diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst index c0949ac4c3..50a50141b2 100644 --- a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst +++ b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst @@ -5,3 +5,65 @@ This is an object shape recognition using rectangle fitting. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/rectangle_fitting/animation.gif +This example code is based on this paper algorithm: + +- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University `_ + +The algorithm consists of 2 steps as below. + +Step1: Adaptive range segmentation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In the first step, all range data points are segmented into some clusters. + +We calculate the distance between each range data and the nearest range data, and if this distance is below a certain threshold, it is judged to be in the same cluster. +This distance threshold is determined in proportion to the distance from the sensor. +This is taking advantage of the general model of distance sensors, which tends to have sparser data distribution as the distance from the sensor increases. + +The threshold range is calculated by: + +.. math:: r_{th} = R_0 + R_d * r_{origin} + +where + +- :math:`r_{th}`: Threashold range +- :math:`R_0, R_d`: Constant parameters +- :math:`r_{origin}`: Distance from the sensor for a range data. + +Step2: Rectangle search +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In the second step, for each cluster calculated in the previous step, rectangular fittings will be applied. +In this rectangular fitting, each cluster's distance data is rotated at certain angle intervals. +It is evaluated by one of the three evaluation functions below, then best angle parameter one is selected as the rectangle shape. + +1. Rectangle Area Minimization criteria +========================================= + +This evaluation function calculates the area of the smallest rectangle that includes all the points, derived from the difference between the maximum and minimum values on the x-y axis for all distance data points. +This allows for fitting a rectangle in a direction that encompasses as much of the smallest rectangular shape as possible. + + +2. Closeness criteria +====================== + +This evaluation function uses the distances between the top and bottom vertices on the right side of the rectangle and each point in the distance data as evaluation values. +If there are points on the rectangle edges, this evaluation value decreases. + +3. Variance criteria +======================= + +This evaluation function uses the squreed distances between the edges of the rectangle (horizontal and vertical) and each point. +Calculating the squared error is the same as calculating the variance. +The smaller this variance, the more it signifies that the points fit within the rectangle. + +API +~~~~~~ + +.. autoclass:: Mapping.rectangle_fitting.rectangle_fitting.LShapeFitting + :members: + +References +~~~~~~~~~~ + +- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University `_ diff --git a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst index f19527c453..a2aa521216 100644 --- a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst +++ b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst @@ -65,7 +65,7 @@ represent the initial uncertainty. At each time step we do: The following equations and code snippets we can see how the particles distribution evolves in case we provide only the control :math:`(v,w)`, -which are the linear and angular velocity repsectively. +which are the linear and angular velocity respectively. :math:`\begin{equation*} F= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix} \end{equation*}` From 4d714706312735ce46a118bb6782c2aef0ba333f Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 1 Jul 2023 15:30:32 +0900 Subject: [PATCH 572/909] upgrade numpy to 1.25.0 and fix bugs (#861) --- .../drone_3d_trajectory_following.py | 13 ++++--- .../two_joint_arm_to_point_control.py | 11 +++--- .../inverted_pendulum_lqr_control.py | 6 +-- .../inverted_pendulum_mpc_control.py | 6 +-- .../histogram_filter/histogram_filter.py | 5 ++- .../unscented_kalman_filter.py | 4 +- Mapping/circle_fitting/circle_fitting.py | 6 +-- .../batch_informed_rrtstar.py | 6 +-- .../InformedRRTStar/informed_rrt_star.py | 4 +- .../lookup_table_generator.py | 2 +- .../motion_model.py | 39 +++++++++++-------- .../trajectory_generator.py | 6 +-- .../state_lattice_planner.py | 2 +- SLAM/FastSLAM1/fast_slam1.py | 2 +- SLAM/FastSLAM2/fast_slam2.py | 2 +- SLAM/GraphBasedSLAM/graph_based_slam.py | 6 +-- requirements/requirements.txt | 2 +- tests/test_codestyle.py | 2 +- 18 files changed, 66 insertions(+), 58 deletions(-) diff --git a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py index e00b3fff48..029e82be62 100644 --- a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py +++ b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py @@ -8,7 +8,6 @@ import numpy as np from Quadrotor import Quadrotor from TrajectoryGenerator import TrajectoryGenerator -from mpl_toolkits.mplot3d import Axes3D show_animation = True @@ -128,7 +127,7 @@ def calculate_position(c, t): Calculates a position given a set of quintic coefficients and a time. Args - c: List of coefficients generated by a quintic polynomial + c: List of coefficients generated by a quintic polynomial trajectory generator. t: Time at which to calculate the position @@ -143,7 +142,7 @@ def calculate_velocity(c, t): Calculates a velocity given a set of quintic coefficients and a time. Args - c: List of coefficients generated by a quintic polynomial + c: List of coefficients generated by a quintic polynomial trajectory generator. t: Time at which to calculate the velocity @@ -158,7 +157,7 @@ def calculate_acceleration(c, t): Calculates an acceleration given a set of quintic coefficients and a time. Args - c: List of coefficients generated by a quintic polynomial + c: List of coefficients generated by a quintic polynomial trajectory generator. t: Time at which to calculate the acceleration @@ -168,7 +167,7 @@ def calculate_acceleration(c, t): return 20 * c[0] * t**3 + 12 * c[1] * t**2 + 6 * c[2] * t + 2 * c[3] -def rotation_matrix(roll, pitch, yaw): +def rotation_matrix(roll_array, pitch_array, yaw): """ Calculates the ZYX rotation matrix. @@ -180,6 +179,8 @@ def rotation_matrix(roll, pitch, yaw): Returns 3x3 rotation matrix as NumPy array """ + roll = roll_array[0] + pitch = pitch_array[0] return np.array( [[cos(yaw) * cos(pitch), -sin(yaw) * cos(roll) + cos(yaw) * sin(pitch) * sin(roll), sin(yaw) * sin(roll) + cos(yaw) * sin(pitch) * cos(roll)], [sin(yaw) * cos(pitch), cos(yaw) * cos(roll) + sin(yaw) * sin(pitch) * @@ -190,7 +191,7 @@ def rotation_matrix(roll, pitch, yaw): def main(): """ - Calculates the x, y, z coefficients for the four segments + Calculates the x, y, z coefficients for the four segments of the trajectory """ x_coeffs = [[], [], [], []] diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py index 012a7f72f1..66d0ebeb23 100644 --- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py +++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py @@ -14,9 +14,10 @@ import matplotlib.pyplot as plt import numpy as np +import math -# Similation parameters +# Simulation parameters Kp = 15 dt = 0.01 @@ -50,15 +51,15 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0): else: theta2_goal = np.arccos( (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2)) - tmp = np.math.atan2(l2 * np.sin(theta2_goal), + tmp = math.atan2(l2 * np.sin(theta2_goal), (l1 + l2 * np.cos(theta2_goal))) - theta1_goal = np.math.atan2(y, x) - tmp + theta1_goal = math.atan2(y, x) - tmp if theta1_goal < 0: theta2_goal = -theta2_goal - tmp = np.math.atan2(l2 * np.sin(theta2_goal), + tmp = math.atan2(l2 * np.sin(theta2_goal), (l1 + l2 * np.cos(theta2_goal))) - theta1_goal = np.math.atan2(y, x) - tmp + theta1_goal = math.atan2(y, x) - tmp theta1 = theta1 + Kp * ang_diff(theta1_goal, theta1) * dt theta2 = theta2 + Kp * ang_diff(theta2_goal, theta2) * dt diff --git a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py index fb68c91c45..c4380530b8 100644 --- a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py +++ b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py @@ -50,14 +50,14 @@ def main(): if show_animation: plt.clf() - px = float(x[0]) - theta = float(x[2]) + px = float(x[0, 0]) + theta = float(x[2, 0]) plot_cart(px, theta) plt.xlim([-5.0, 2.0]) plt.pause(0.001) print("Finish") - print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]") + print(f"x={float(x[0, 0]):.2f} [m] , theta={math.degrees(x[2, 0]):.2f} [deg]") if show_animation: plt.show() diff --git a/Control/inverted_pendulum/inverted_pendulum_mpc_control.py b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py index 6b966b13b4..9a5fa2ab41 100644 --- a/Control/inverted_pendulum/inverted_pendulum_mpc_control.py +++ b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py @@ -55,14 +55,14 @@ def main(): if show_animation: plt.clf() - px = float(x[0]) - theta = float(x[2]) + px = float(x[0, 0]) + theta = float(x[2, 0]) plot_cart(px, theta) plt.xlim([-5.0, 2.0]) plt.pause(0.001) print("Finish") - print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]") + print(f"x={float(x[0, 0]):.2f} [m] , theta={math.degrees(x[2, 0]):.2f} [deg]") if show_animation: plt.show() diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py index a4aeb419cf..17cfc2e14c 100644 --- a/Localization/histogram_filter/histogram_filter.py +++ b/Localization/histogram_filter/histogram_filter.py @@ -15,6 +15,7 @@ import math import matplotlib.pyplot as plt +import matplotlib as mpl import numpy as np from scipy.ndimage import gaussian_filter from scipy.stats import norm @@ -114,7 +115,7 @@ def motion_model(x, u): def draw_heat_map(data, mx, my): max_value = max([max(i_data) for i_data in data]) plt.grid(False) - plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues")) + plt.pcolor(mx, my, data, vmax=max_value, cmap=mpl.colormaps["Blues"]) plt.axis("equal") @@ -194,7 +195,7 @@ def motion_update(grid_map, u, yaw): y_shift = grid_map.dy // grid_map.xy_resolution if abs(x_shift) >= 1.0 or abs(y_shift) >= 1.0: # map should be shifted - grid_map = map_shift(grid_map, int(x_shift), int(y_shift)) + grid_map = map_shift(grid_map, int(x_shift[0]), int(y_shift[0])) grid_map.dx -= x_shift * grid_map.xy_resolution grid_map.dy -= y_shift * grid_map.xy_resolution diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py index dbcdeef015..4af748ec71 100644 --- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py +++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py @@ -69,8 +69,8 @@ def motion_model(x, u): [0, 0, 1.0, 0], [0, 0, 0, 0]]) - B = np.array([[DT * math.cos(x[2]), 0], - [DT * math.sin(x[2]), 0], + B = np.array([[DT * math.cos(x[2, 0]), 0], + [DT * math.sin(x[2, 0]), 0], [0.0, DT], [1.0, 0.0]]) diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py index c331d56796..2eba550127 100644 --- a/Mapping/circle_fitting/circle_fitting.py +++ b/Mapping/circle_fitting/circle_fitting.py @@ -40,9 +40,9 @@ def circle_fitting(x, y): T = np.linalg.inv(F).dot(G) - cxe = float(T[0] / -2) - cye = float(T[1] / -2) - re = math.sqrt(cxe**2 + cye**2 - T[2]) + cxe = float(T[0, 0] / -2) + cye = float(T[1, 0] / -2) + re = math.sqrt(cxe**2 + cye**2 - T[2, 0]) error = sum([np.hypot(cxe - ix, cye - iy) - re for (ix, iy) in zip(x, y)]) diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py index b5eabbb39b..92c3a58761 100644 --- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py +++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py @@ -23,7 +23,7 @@ show_animation = True -class RTree(object): +class RTree: # Class to represent the explicit tree created # while sampling through the state space @@ -132,7 +132,7 @@ def node_id_to_real_world_coord(self, nid): self.node_id_to_grid_coord(nid)) -class BITStar(object): +class BITStar: def __init__(self, start, goal, obstacleList, randArea, eta=2.0, @@ -189,7 +189,7 @@ def setup_planning(self): [(self.start[1] + self.goal[1]) / 2.0], [0]]) a1 = np.array([[(self.goal[0] - self.start[0]) / cMin], [(self.goal[1] - self.start[1]) / cMin], [0]]) - eTheta = math.atan2(a1[1], a1[0]) + eTheta = math.atan2(a1[1, 0], a1[0, 0]) # first column of identity matrix transposed id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3) M = np.dot(a1, id1_t) diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py index 4f0ec31988..b6f9d234dd 100644 --- a/PathPlanning/InformedRRTStar/informed_rrt_star.py +++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py @@ -58,7 +58,7 @@ def informed_rrt_star_search(self, animation=True): a1 = np.array([[(self.goal.x - self.start.x) / c_min], [(self.goal.y - self.start.y) / c_min], [0]]) - e_theta = math.atan2(a1[1], a1[0]) + e_theta = math.atan2(a1[1, 0], a1[0, 0]) # first column of identity matrix transposed id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3) m = a1 @ id1_t @@ -136,7 +136,7 @@ def choose_parent(self, new_node, near_inds): def find_near_nodes(self, new_node): n_node = len(self.node_list) - r = 50.0 * math.sqrt((math.log(n_node) / n_node)) + r = 50.0 * math.sqrt(math.log(n_node) / n_node) d_list = [(node.x - new_node.x) ** 2 + (node.y - new_node.y) ** 2 for node in self.node_list] near_inds = [d_list.index(i) for i in d_list if i <= r ** 2] diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py index 6cb226adef..4c3b32f280 100644 --- a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py +++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py @@ -81,7 +81,7 @@ def generate_lookup_table(): if x is not None: print("find good path") lookup_table.append( - [x[-1], y[-1], yaw[-1], float(p[0]), float(p[1]), float(p[2])]) + [x[-1], y[-1], yaw[-1], float(p[0, 0]), float(p[1, 0]), float(p[2, 0])]) print("finish lookup table generation") diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py index f6c78d3bc1..531bf91ef5 100644 --- a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py +++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py @@ -1,10 +1,10 @@ import math import numpy as np -import scipy.interpolate +from scipy.interpolate import interp1d # motion parameter L = 1.0 # wheel base -ds = 0.1 # course distanse +ds = 0.1 # course distance v = 10.0 / 3.6 # velocity [m/s] @@ -22,7 +22,6 @@ def pi_2_pi(angle): def update(state, v, delta, dt, L): - state.v = v state.x = state.x + state.v * math.cos(state.yaw) * dt state.y = state.y + state.v * math.sin(state.yaw) * dt @@ -33,18 +32,20 @@ def update(state, v, delta, dt, L): def generate_trajectory(s, km, kf, k0): - n = s / ds time = s / v # [s] - - if isinstance(time, type(np.array([]))): time = time[0] - if isinstance(km, type(np.array([]))): km = km[0] - if isinstance(kf, type(np.array([]))): kf = kf[0] - + + if isinstance(time, type(np.array([]))): + time = time[0] + if isinstance(km, type(np.array([]))): + km = km[0] + if isinstance(kf, type(np.array([]))): + kf = kf[0] + tk = np.array([0.0, time / 2.0, time]) kk = np.array([k0, km, kf]) t = np.arange(0.0, time, time / n) - fkp = scipy.interpolate.interp1d(tk, kk, kind="quadratic") + fkp = interp1d(tk, kk, kind="quadratic") kp = [fkp(ti) for ti in t] dt = float(time / n) @@ -64,18 +65,22 @@ def generate_trajectory(s, km, kf, k0): def generate_last_state(s, km, kf, k0): - n = s / ds time = s / v # [s] - - if isinstance(time, type(np.array([]))): time = time[0] - if isinstance(km, type(np.array([]))): km = km[0] - if isinstance(kf, type(np.array([]))): kf = kf[0] - + + if isinstance(n, type(np.array([]))): + n = n[0] + if isinstance(time, type(np.array([]))): + time = time[0] + if isinstance(km, type(np.array([]))): + km = km[0] + if isinstance(kf, type(np.array([]))): + kf = kf[0] + tk = np.array([0.0, time / 2.0, time]) kk = np.array([k0, km, kf]) t = np.arange(0.0, time, time / n) - fkp = scipy.interpolate.interp1d(tk, kk, kind="quadratic") + fkp = interp1d(tk, kk, kind="quadratic") kp = [fkp(ti) for ti in t] dt = time / n diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py index a44e1ac956..97c0ad8b80 100644 --- a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py +++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py @@ -106,7 +106,7 @@ def show_trajectory(target, xc, yc): # pragma: no cover def optimize_trajectory(target, k0, p): for i in range(max_iter): - xc, yc, yawc = motion_model.generate_trajectory(p[0], p[1], p[2], k0) + xc, yc, yawc = motion_model.generate_trajectory(p[0, 0], p[1, 0], p[2, 0], k0) dc = np.array(calc_diff(target, xc, yc, yawc)).reshape(3, 1) cost = np.linalg.norm(dc) @@ -135,7 +135,7 @@ def optimize_trajectory(target, k0, p): return xc, yc, yawc, p -def test_optimize_trajectory(): # pragma: no cover +def optimize_trajectory_demo(): # pragma: no cover # target = motion_model.State(x=5.0, y=2.0, yaw=np.deg2rad(00.0)) target = motion_model.State(x=5.0, y=2.0, yaw=np.deg2rad(90.0)) @@ -155,7 +155,7 @@ def test_optimize_trajectory(): # pragma: no cover def main(): # pragma: no cover print(__file__ + " start!!") - test_optimize_trajectory() + optimize_trajectory_demo() if __name__ == '__main__': diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py index e26fb8288c..f4b1461b7a 100644 --- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py +++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py @@ -70,7 +70,7 @@ def generate_path(target_states, k0): if x is not None: print("find good path") result.append( - [x[-1], y[-1], yaw[-1], float(p[0]), float(p[1]), float(p[2])]) + [x[-1], y[-1], yaw[-1], float(p[0, 0]), float(p[1, 0]), float(p[2, 0])]) print("finish path generation") return result diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py index c4beaba257..7b89f52df4 100644 --- a/SLAM/FastSLAM1/fast_slam1.py +++ b/SLAM/FastSLAM1/fast_slam1.py @@ -194,7 +194,7 @@ def compute_weight(particle, z, Q_cov): print("singular") return 1.0 - num = math.exp(-0.5 * dx.T @ invS @ dx) + num = np.exp(-0.5 * (dx.T @ invS @ dx))[0, 0] den = 2.0 * math.pi * math.sqrt(np.linalg.det(Sf)) w = num / den diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py index 4c6c103325..aa77aa956a 100644 --- a/SLAM/FastSLAM2/fast_slam2.py +++ b/SLAM/FastSLAM2/fast_slam2.py @@ -193,7 +193,7 @@ def compute_weight(particle, z, Q_cov): except np.linalg.linalg.LinAlgError: return 1.0 - num = math.exp(-0.5 * dz.T @ invS @ dz) + num = np.exp(-0.5 * dz.T @ invS @ dz)[0, 0] den = 2.0 * math.pi * math.sqrt(np.linalg.det(Sf)) w = num / den diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py index 5510a41110..4d66ef6732 100644 --- a/SLAM/GraphBasedSLAM/graph_based_slam.py +++ b/SLAM/GraphBasedSLAM/graph_based_slam.py @@ -117,9 +117,9 @@ def calc_edges(x_list, z_list): for iz2 in range(len(z_list[t2][:, 0])): if z_list[t1][iz1, 3] == z_list[t2][iz2, 3]: d1 = z_list[t1][iz1, 0] - angle1, phi1 = z_list[t1][iz1, 1], z_list[t1][iz1, 2] + angle1, _ = z_list[t1][iz1, 1], z_list[t1][iz1, 2] d2 = z_list[t2][iz2, 0] - angle2, phi2 = z_list[t2][iz2, 1], z_list[t2][iz2, 2] + angle2, _ = z_list[t2][iz2, 1], z_list[t2][iz2, 2] edge = calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1, angle1, d2, angle2, t1, t2) @@ -188,7 +188,7 @@ def graph_based_slam(x_init, hz): for i in range(nt): x_opt[0:3, i] += dx[i * 3:i * 3 + 3, 0] - diff = dx.T @ dx + diff = (dx.T @ dx)[0, 0] print("iteration: %d, diff: %f" % (itr + 1, diff)) if diff < 1.0e-5: break diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 9db6898463..4f226facb1 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.24.3 +numpy == 1.25.0 scipy == 1.10.1 matplotlib == 3.7.1 cvxpy == 1.3.1 diff --git a/tests/test_codestyle.py b/tests/test_codestyle.py index 2ad3816ea1..a7d11d270f 100644 --- a/tests/test_codestyle.py +++ b/tests/test_codestyle.py @@ -126,7 +126,7 @@ def test(): branch_commit = find_branch_point("origin/master") files = diff_files(branch_commit) print(files) - rc, errors = run_ruff(files, fix=False) + rc, errors = run_ruff(files, fix=True) if errors: print(errors) else: From e0b7ea5a0ace961fdada3bf524080208e36875ee Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 1 Jul 2023 18:40:03 +0900 Subject: [PATCH 573/909] upgrade scipy and cvxpy (#864) --- requirements/requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 4f226facb1..30ccc4765d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.25.0 -scipy == 1.10.1 +scipy == 1.11.1 matplotlib == 3.7.1 -cvxpy == 1.3.1 +cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test From 5264c68542306e901fc0585219ade6f6f573d2a9 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 2 Jul 2023 22:12:25 +0900 Subject: [PATCH 574/909] Update dependabot.yml (#868) --- .github/dependabot.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 07f1bc80e0..7d7e6723cb 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -6,3 +6,8 @@ updates: interval: weekly time: "20:00" open-pull-requests-limit: 10 + +- package-ecosystem: 'github-actions' + directory: '/' + schedule: + interval: 'weekly' From c14bdda136d76193e8fdf2f0322579c0e0c71571 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 2 Jul 2023 22:25:55 +0900 Subject: [PATCH 575/909] Update dependabot.yml --- .github/dependabot.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index 7d7e6723cb..c6fecb796f 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -7,7 +7,7 @@ updates: time: "20:00" open-pull-requests-limit: 10 -- package-ecosystem: 'github-actions' - directory: '/' +- package-ecosystem: github-actions + directory: "/" schedule: - interval: 'weekly' + interval: weekly From d5a0512568edbb12c7020e578d2b484ec48f8f3b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 2 Jul 2023 22:27:45 +0900 Subject: [PATCH 576/909] Update dependabot.yml --- .github/dependabot.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/dependabot.yml b/.github/dependabot.yml index c6fecb796f..6ffedf6f3b 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -8,6 +8,6 @@ updates: open-pull-requests-limit: 10 - package-ecosystem: github-actions - directory: "/" - schedule: - interval: weekly + directory: "/" + schedule: + interval: weekly From 64c7624760a450a7163723b37a3f2dd7da09a4e0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 2 Jul 2023 23:12:22 +0900 Subject: [PATCH 577/909] Bump actions/checkout from 2 to 3 (#869) Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v2...v3) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .github/workflows/Windows_CI.yml | 2 +- .github/workflows/codeql.yml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index a7b5aba5fe..a92d022e96 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -17,7 +17,7 @@ jobs: name: Python ${{ matrix.python-version }} CI steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - run: git fetch --prune --unshallow - name: Setup python diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 6f0849a43d..f983cbbab3 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -19,7 +19,7 @@ jobs: python-version: [ '3.11' ] name: Python ${{ matrix.python-version }} CI steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - run: git fetch --prune --unshallow - name: Update bash diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index ed267045ca..5ca29fc632 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -19,7 +19,7 @@ jobs: python-version: [ '3.11' ] name: Python ${{ matrix.python-version }} CI steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 - run: git fetch --prune --unshallow - name: Setup python diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index 148260a835..ad173f14bf 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -16,7 +16,7 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v2 + uses: actions/checkout@v3 with: # We must fetch at least the immediate parents so that if this is # a pull request then we can checkout the head. From 38639fd9c21999b1c45f0f2e3e0aa6e74d4c6ed8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 2 Jul 2023 23:12:44 +0900 Subject: [PATCH 578/909] Bump github/codeql-action from 1 to 2 (#870) Bumps [github/codeql-action](https://github.com/github/codeql-action) from 1 to 2. - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/github/codeql-action/compare/v1...v2) --- updated-dependencies: - dependency-name: github/codeql-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/codeql.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml index ad173f14bf..d33545c90f 100644 --- a/.github/workflows/codeql.yml +++ b/.github/workflows/codeql.yml @@ -24,7 +24,7 @@ jobs: # Initializes the CodeQL tools for scanning. - name: Initialize CodeQL - uses: github/codeql-action/init@v1 + uses: github/codeql-action/init@v2 with: config-file: ./.github/codeql/codeql-config.yml # Override language selection by uncommenting this and choosing your languages @@ -34,7 +34,7 @@ jobs: # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). # If this step fails, then you should remove it and run the build manually (see below) - name: Autobuild - uses: github/codeql-action/autobuild@v1 + uses: github/codeql-action/autobuild@v2 # ℹ️ Command-line programs to run using the OS shell. # 📚 https://git.io/JvXDl @@ -48,4 +48,4 @@ jobs: # make release - name: Perform CodeQL Analysis - uses: github/codeql-action/analyze@v1 + uses: github/codeql-action/analyze@v2 From 46d9809df367979c3d8bec8d44a63eb3e529fb23 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 2 Jul 2023 23:12:57 +0900 Subject: [PATCH 579/909] Bump actions/setup-python from 2 to 4 (#871) Bumps [actions/setup-python](https://github.com/actions/setup-python) from 2 to 4. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v2...v4) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/Linux_CI.yml | 2 +- .github/workflows/MacOS_CI.yml | 2 +- .github/workflows/Windows_CI.yml | 2 +- .github/workflows/gh-pages.yml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index a92d022e96..a152972753 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -21,7 +21,7 @@ jobs: - run: git fetch --prune --unshallow - name: Setup python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - name: Install dependencies diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index f983cbbab3..c8bfbbcb7e 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -26,7 +26,7 @@ jobs: run: brew install bash - name: Setup python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index 5ca29fc632..13fdcea8a1 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -23,7 +23,7 @@ jobs: - run: git fetch --prune --unshallow - name: Setup python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index bf46598633..d5af3b87c6 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -8,7 +8,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Setup python - uses: actions/setup-python@v3 + uses: actions/setup-python@v4 - name: Checkout uses: actions/checkout@master with: From 49dd1a93f37a47a7e5c91376947b49282cceb3da Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 4 Jul 2023 07:35:39 +0900 Subject: [PATCH 580/909] Bump ruff from 0.0.275 to 0.0.276 in /requirements (#872) Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.275 to 0.0.276. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md) - [Commits](https://github.com/astral-sh/ruff/compare/v0.0.275...v0.0.276) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 30ccc4765d..2fab6f69c8 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,4 +5,4 @@ cvxpy == 1.3.2 pytest == 7.4.0 # For unit test pytest-xdist == 3.3.1 # For unit test mypy == 1.4.1 # For unit test -ruff == 0.0.275 # For unit test +ruff == 0.0.276 # For unit test From 0fc769421f66a6403f750781dc46534d84e7c424 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 6 Jul 2023 23:12:43 +0900 Subject: [PATCH 581/909] Adding NDT mapping script and doc (#867) * Adding ndt mapping script and doc * Adding ndt mapping script and doc * Adding ndt mapping script and doc * Adding ndt mapping script and doc * Adding ndt mapping script and doc * Adding ndt mapping script and doc * Adding ndt mapping script and doc * Adding ndt mapping script and doc --- .../extended_kalman_filter.py | 28 +--- Mapping/grid_map_lib/grid_map_lib.py | 125 ++++++++++------ Mapping/ndt_map/ndt_map.py | 135 ++++++++++++++++++ .../grid_based_sweep_coverage_path_planner.py | 31 ++-- docs/modules/mapping/mapping_main.rst | 1 + .../mapping/ndt_map/grid_clustering.png | Bin 0 -> 33111 bytes docs/modules/mapping/ndt_map/ndt_map1.png | Bin 0 -> 23081 bytes docs/modules/mapping/ndt_map/ndt_map2.png | Bin 0 -> 21736 bytes docs/modules/mapping/ndt_map/ndt_map_main.rst | 53 +++++++ .../mapping/ndt_map/raw_observations.png | Bin 0 -> 18305 bytes tests/test_grid_map_lib.py | 11 ++ utils/plot.py | 71 ++++++++- 12 files changed, 372 insertions(+), 83 deletions(-) create mode 100644 Mapping/ndt_map/ndt_map.py create mode 100644 docs/modules/mapping/ndt_map/grid_clustering.png create mode 100644 docs/modules/mapping/ndt_map/ndt_map1.png create mode 100644 docs/modules/mapping/ndt_map/ndt_map2.png create mode 100644 docs/modules/mapping/ndt_map/ndt_map_main.rst create mode 100644 docs/modules/mapping/ndt_map/raw_observations.png diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py index c2fe8e3a87..d9ece6c6f3 100644 --- a/Localization/extended_kalman_filter/extended_kalman_filter.py +++ b/Localization/extended_kalman_filter/extended_kalman_filter.py @@ -7,13 +7,14 @@ """ import sys import pathlib + sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) import math import matplotlib.pyplot as plt import numpy as np -from utils.angle import rot_mat_2d +from utils.plot import plot_covariance_ellipse # Covariance for EKF simulation Q = np.diag([ @@ -135,29 +136,6 @@ def ekf_estimation(xEst, PEst, z, u): return xEst, PEst -def plot_covariance_ellipse(xEst, PEst): # pragma: no cover - Pxy = PEst[0:2, 0:2] - eigval, eigvec = np.linalg.eig(Pxy) - - if eigval[0] >= eigval[1]: - bigind = 0 - smallind = 1 - else: - bigind = 1 - smallind = 0 - - t = np.arange(0, 2 * math.pi + 0.1, 0.1) - a = math.sqrt(eigval[bigind]) - b = math.sqrt(eigval[smallind]) - x = [a * math.cos(it) for it in t] - y = [b * math.sin(it) for it in t] - angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind]) - fx = rot_mat_2d(angle) @ (np.array([x, y])) - px = np.array(fx[0, :] + xEst[0, 0]).flatten() - py = np.array(fx[1, :] + xEst[1, 0]).flatten() - plt.plot(px, py, "--r") - - def main(): print(__file__ + " start!!") @@ -202,7 +180,7 @@ def main(): hxDR[1, :].flatten(), "-k") plt.plot(hxEst[0, :].flatten(), hxEst[1, :].flatten(), "-r") - plot_covariance_ellipse(xEst, PEst) + plot_covariance_ellipse(xEst[0, 0], xEst[1, 0], PEst) plt.axis("equal") plt.grid(True) plt.pause(0.001) diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py index 85dd76e71d..d08d8ec5ba 100644 --- a/Mapping/grid_map_lib/grid_map_lib.py +++ b/Mapping/grid_map_lib/grid_map_lib.py @@ -5,22 +5,42 @@ author: Atsushi Sakai """ - +from functools import total_ordering import matplotlib.pyplot as plt import numpy as np +@total_ordering +class FloatGrid: + + def __init__(self, init_val=0.0): + self.data = init_val + + def get_float_data(self): + return self.data + + def __eq__(self, other): + if not isinstance(other, FloatGrid): + return NotImplemented + return self.get_float_data() == other.get_float_data() + + def __lt__(self, other): + if not isinstance(other, FloatGrid): + return NotImplemented + return self.get_float_data() < other.get_float_data() + + class GridMap: """ GridMap class """ def __init__(self, width, height, resolution, - center_x, center_y, init_val=0.0): + center_x, center_y, init_val=FloatGrid(0.0)): """__init__ :param width: number of grid for width - :param height: number of grid for heigt + :param height: number of grid for height :param resolution: grid resolution [m] :param center_x: center x position [m] :param center_y: center y position [m] @@ -35,8 +55,9 @@ def __init__(self, width, height, resolution, self.left_lower_x = self.center_x - self.width / 2.0 * self.resolution self.left_lower_y = self.center_y - self.height / 2.0 * self.resolution - self.ndata = self.width * self.height - self.data = [init_val] * self.ndata + self.n_data = self.width * self.height + self.data = [init_val] * self.n_data + self.data_type = type(init_val) def get_value_from_xy_index(self, x_ind, y_ind): """get_value_from_xy_index @@ -49,7 +70,7 @@ def get_value_from_xy_index(self, x_ind, y_ind): grid_ind = self.calc_grid_index_from_xy_index(x_ind, y_ind) - if 0 <= grid_ind < self.ndata: + if 0 <= grid_ind < self.n_data: return self.data[grid_ind] else: return None @@ -101,7 +122,7 @@ def set_value_from_xy_index(self, x_ind, y_ind, val): grid_ind = int(y_ind * self.width + x_ind) - if 0 <= grid_ind < self.ndata: + if 0 <= grid_ind < self.n_data and isinstance(val, self.data_type): self.data[grid_ind] = val return True # OK else: @@ -138,6 +159,27 @@ def calc_grid_index_from_xy_index(self, x_ind, y_ind): grid_ind = int(y_ind * self.width + x_ind) return grid_ind + def calc_xy_index_from_grid_index(self, grid_ind): + y_ind, x_ind = divmod(grid_ind, self.width) + return x_ind, y_ind + + def calc_grid_index_from_xy_pos(self, x_pos, y_pos): + """get_xy_index_from_xy_pos + + :param x_pos: x position [m] + :param y_pos: y position [m] + """ + x_ind = self.calc_xy_index_from_position( + x_pos, self.left_lower_x, self.width) + y_ind = self.calc_xy_index_from_position( + y_pos, self.left_lower_y, self.height) + + return self.calc_grid_index_from_xy_index(x_ind, y_ind) + + def calc_grid_central_xy_position_from_grid_index(self, grid_ind): + x_ind, y_ind = self.calc_xy_index_from_grid_index(grid_ind) + return self.calc_grid_central_xy_position_from_xy_index(x_ind, y_ind) + def calc_grid_central_xy_position_from_xy_index(self, x_ind, y_ind): x_pos = self.calc_grid_central_xy_position_from_index( x_ind, self.left_lower_x) @@ -156,39 +198,40 @@ def calc_xy_index_from_position(self, pos, lower_pos, max_index): else: return None - def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0): + def check_occupied_from_xy_index(self, x_ind, y_ind, occupied_val): - val = self.get_value_from_xy_index(xind, yind) + val = self.get_value_from_xy_index(x_ind, y_ind) if val is None or val >= occupied_val: return True else: return False - def expand_grid(self): - xinds, yinds = [], [] + def expand_grid(self, occupied_val=FloatGrid(1.0)): + x_inds, y_inds, values = [], [], [] for ix in range(self.width): for iy in range(self.height): - if self.check_occupied_from_xy_index(ix, iy): - xinds.append(ix) - yinds.append(iy) - - for (ix, iy) in zip(xinds, yinds): - self.set_value_from_xy_index(ix + 1, iy, val=1.0) - self.set_value_from_xy_index(ix, iy + 1, val=1.0) - self.set_value_from_xy_index(ix + 1, iy + 1, val=1.0) - self.set_value_from_xy_index(ix - 1, iy, val=1.0) - self.set_value_from_xy_index(ix, iy - 1, val=1.0) - self.set_value_from_xy_index(ix - 1, iy - 1, val=1.0) + if self.check_occupied_from_xy_index(ix, iy, occupied_val): + x_inds.append(ix) + y_inds.append(iy) + values.append(self.get_value_from_xy_index(ix, iy)) + + for (ix, iy, value) in zip(x_inds, y_inds, values): + self.set_value_from_xy_index(ix + 1, iy, val=value) + self.set_value_from_xy_index(ix, iy + 1, val=value) + self.set_value_from_xy_index(ix + 1, iy + 1, val=value) + self.set_value_from_xy_index(ix - 1, iy, val=value) + self.set_value_from_xy_index(ix, iy - 1, val=value) + self.set_value_from_xy_index(ix - 1, iy - 1, val=value) @staticmethod def check_inside_polygon(iox, ioy, x, y): - npoint = len(x) - 1 + n_point = len(x) - 1 inside = False - for i1 in range(npoint): - i2 = (i1 + 1) % (npoint + 1) + for i1 in range(n_point): + i2 = (i1 + 1) % (n_point + 1) if x[i1] >= x[i2]: min_x, max_x = x[i2], x[i1] @@ -211,27 +254,26 @@ def print_grid_map_info(self): print("center_y:", self.center_y) print("left_lower_x:", self.left_lower_x) print("left_lower_y:", self.left_lower_y) - print("ndata:", self.ndata) + print("n_data:", self.n_data) def plot_grid_map(self, ax=None): - - grid_data = np.reshape(np.array(self.data), (self.height, self.width)) + float_data_array = np.array([d.get_float_data() for d in self.data]) + grid_data = np.reshape(float_data_array, (self.height, self.width)) if not ax: fig, ax = plt.subplots() heat_map = ax.pcolor(grid_data, cmap="Blues", vmin=0.0, vmax=1.0) plt.axis("equal") - # plt.show() return heat_map -def test_polygon_set(): +def polygon_set_demo(): ox = [0.0, 4.35, 20.0, 50.0, 100.0, 130.0, 40.0] oy = [0.0, -4.15, -20.0, 0.0, 30.0, 60.0, 80.0] grid_map = GridMap(600, 290, 0.7, 60.0, 30.5) - grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False) + grid_map.set_value_from_polygon(ox, oy, FloatGrid(1.0), inside=False) grid_map.plot_grid_map() @@ -239,24 +281,27 @@ def test_polygon_set(): plt.grid(True) -def test_position_set(): +def position_set_demo(): grid_map = GridMap(100, 120, 0.5, 10.0, -0.5) - grid_map.set_value_from_xy_pos(10.1, -1.1, 1.0) - grid_map.set_value_from_xy_pos(10.1, -0.1, 1.0) - grid_map.set_value_from_xy_pos(10.1, 1.1, 1.0) - grid_map.set_value_from_xy_pos(11.1, 0.1, 1.0) - grid_map.set_value_from_xy_pos(10.1, 0.1, 1.0) - grid_map.set_value_from_xy_pos(9.1, 0.1, 1.0) + grid_map.set_value_from_xy_pos(10.1, -1.1, FloatGrid(1.0)) + grid_map.set_value_from_xy_pos(10.1, -0.1, FloatGrid(1.0)) + grid_map.set_value_from_xy_pos(10.1, 1.1, FloatGrid(1.0)) + grid_map.set_value_from_xy_pos(11.1, 0.1, FloatGrid(1.0)) + grid_map.set_value_from_xy_pos(10.1, 0.1, FloatGrid(1.0)) + grid_map.set_value_from_xy_pos(9.1, 0.1, FloatGrid(1.0)) grid_map.plot_grid_map() + plt.axis("equal") + plt.grid(True) + def main(): print("start!!") - test_position_set() - test_polygon_set() + position_set_demo() + polygon_set_demo() plt.show() diff --git a/Mapping/ndt_map/ndt_map.py b/Mapping/ndt_map/ndt_map.py new file mode 100644 index 0000000000..f4f3299662 --- /dev/null +++ b/Mapping/ndt_map/ndt_map.py @@ -0,0 +1,135 @@ +""" +Normal Distribution Transform (NDTGrid) mapping sample +""" +import matplotlib.pyplot as plt +import numpy as np +from collections import defaultdict + +from Mapping.grid_map_lib.grid_map_lib import GridMap +from utils.plot import plot_covariance_ellipse + + +class NDTMap: + """ + Normal Distribution Transform (NDT) map class + + :param ox: obstacle x position list + :param oy: obstacle y position list + :param resolution: grid resolution [m] + """ + + class NDTGrid: + """ + NDT grid + """ + + def __init__(self): + #: Number of points in the NDTGrid grid + self.n_points = 0 + #: Mean x position of points in the NDTGrid cell + self.mean_x = None + #: Mean y position of points in the NDTGrid cell + self.mean_y = None + #: Center x position of the NDT grid + self.center_grid_x = None + #: Center y position of the NDT grid + self.center_grid_y = None + #: Covariance matrix of the NDT grid + self.covariance = None + #: Eigen vectors of the NDT grid + self.eig_vec = None + #: Eigen values of the NDT grid + self.eig_values = None + + def __init__(self, ox, oy, resolution): + #: Minimum number of points in the NDT grid + self.min_n_points = 3 + #: Resolution of the NDT grid [m] + self.resolution = resolution + width = int((max(ox) - min(ox))/resolution) + 3 # rounding up + right and left margin + height = int((max(oy) - min(oy))/resolution) + 3 + center_x = np.mean(ox) + center_y = np.mean(oy) + self.ox = ox + self.oy = oy + #: NDT grid index map + self.grid_index_map = self._create_grid_index_map(ox, oy) + + #: NDT grid map. Each grid contains NDTGrid object + self._construct_grid_map(center_x, center_y, height, ox, oy, resolution, width) + + def _construct_grid_map(self, center_x, center_y, height, ox, oy, resolution, width): + self.grid_map = GridMap(width, height, resolution, center_x, center_y, self.NDTGrid()) + for grid_index, inds in self.grid_index_map.items(): + ndt = self.NDTGrid() + ndt.n_points = len(inds) + if ndt.n_points >= self.min_n_points: + ndt.mean_x = np.mean(ox[inds]) + ndt.mean_y = np.mean(oy[inds]) + ndt.center_grid_x, ndt.center_grid_y = \ + self.grid_map.calc_grid_central_xy_position_from_grid_index(grid_index) + ndt.covariance = np.cov(ox[inds], oy[inds]) + ndt.eig_values, ndt.eig_vec = np.linalg.eig(ndt.covariance) + self.grid_map.data[grid_index] = ndt + + def _create_grid_index_map(self, ox, oy): + grid_index_map = defaultdict(list) + for i in range(len(ox)): + grid_index = self.grid_map.calc_grid_index_from_xy_pos(ox[i], oy[i]) + grid_index_map[grid_index].append(i) + return grid_index_map + + +def create_dummy_observation_data(): + ox = [] + oy = [] + # left corridor + for y in range(-50, 50): + ox.append(-20.0) + oy.append(y) + # right corridor 1 + for y in range(-50, 0): + ox.append(20.0) + oy.append(y) + # right corridor 2 + for x in range(20, 50): + ox.append(x) + oy.append(0) + # right corridor 3 + for x in range(20, 50): + ox.append(x) + oy.append(x/2.0+10) + # right corridor 4 + for y in range(20, 50): + ox.append(20) + oy.append(y) + ox = np.array(ox) + oy = np.array(oy) + # Adding random noize + ox += np.random.rand(len(ox)) * 1.0 + oy += np.random.rand(len(ox)) * 1.0 + return ox, oy + + +def main(): + print(__file__ + " start!!") + + ox, oy = create_dummy_observation_data() + grid_resolution = 10.0 + ndt_map = NDTMap(ox, oy, grid_resolution) + + # plot raw observation + plt.plot(ox, oy, ".r") + + # plot grid clustering + [plt.plot(ox[inds], oy[inds], "x") for inds in ndt_map.grid_index_map.values()] + + # plot ndt grid map + [plot_covariance_ellipse(ndt.mean_x, ndt.mean_y, ndt.covariance, color="-k") for ndt in ndt_map.grid_map.data if ndt.n_points > 0] + + plt.axis("equal") + plt.show() + + +if __name__ == '__main__': + main() diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py index 31dc917566..ee192e9200 100644 --- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py +++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py @@ -13,7 +13,7 @@ sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) from utils.angle import rot_mat_2d -from Mapping.grid_map_lib.grid_map_lib import GridMap +from Mapping.grid_map_lib.grid_map_lib import GridMap, FloatGrid do_animation = True @@ -41,8 +41,7 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map): n_y_index = c_y_index # found safe grid - if not grid_map.check_occupied_from_xy_index(n_x_index, n_y_index, - occupied_val=0.5): + if not self.check_occupied(n_x_index, n_y_index, grid_map): return n_x_index, n_y_index else: # occupied next_c_x_index, next_c_y_index = self.find_safe_turning_grid( @@ -51,19 +50,20 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map): # moving backward next_c_x_index = -self.moving_direction + c_x_index next_c_y_index = c_y_index - if grid_map.check_occupied_from_xy_index(next_c_x_index, - next_c_y_index): + if self.check_occupied(next_c_x_index, next_c_y_index, grid_map): # moved backward, but the grid is occupied by obstacle return None, None else: # keep moving until end - while not grid_map.check_occupied_from_xy_index( - next_c_x_index + self.moving_direction, - next_c_y_index, occupied_val=0.5): + while not self.check_occupied(next_c_x_index + self.moving_direction, next_c_y_index, grid_map): next_c_x_index += self.moving_direction self.swap_moving_direction() return next_c_x_index, next_c_y_index + @staticmethod + def check_occupied(c_x_index, c_y_index, grid_map): + return grid_map.check_occupied_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5)) + def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map): for (d_x_ind, d_y_ind) in self.turing_window: @@ -72,17 +72,14 @@ def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map): next_y_ind = d_y_ind + c_y_index # found safe grid - if not grid_map.check_occupied_from_xy_index(next_x_ind, - next_y_ind, - occupied_val=0.5): + if not self.check_occupied(next_x_ind, next_y_ind, grid_map): return next_x_ind, next_y_ind return None, None def is_search_done(self, grid_map): for ix in self.x_indexes_goal_y: - if not grid_map.check_occupied_from_xy_index(ix, self.goal_y, - occupied_val=0.5): + if not self.check_occupied(ix, self.goal_y, grid_map): return False # all lower grid is occupied @@ -168,7 +165,7 @@ def search_free_grid_index_at_edge_y(grid_map, from_upper=False): for iy in x_range: for ix in y_range: - if not grid_map.check_occupied_from_xy_index(ix, iy): + if not SweepSearcher.check_occupied(ix, iy, grid_map): y_index = iy x_indexes.append(ix) if y_index: @@ -185,7 +182,7 @@ def setup_grid_map(ox, oy, resolution, sweep_direction, offset_grid=10): grid_map = GridMap(width, height, resolution, center_x, center_y) grid_map.print_grid_map_info() - grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False) + grid_map.set_value_from_polygon(ox, oy, FloatGrid(1.0), inside=False) grid_map.expand_grid() x_inds_goal_y = [] @@ -203,7 +200,7 @@ def setup_grid_map(ox, oy, resolution, sweep_direction, offset_grid=10): def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False): # search start grid c_x_index, c_y_index = sweep_searcher.search_start_grid(grid_map) - if not grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5): + if not grid_map.set_value_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5)): print("Cannot find start grid") return [], [] @@ -235,7 +232,7 @@ def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False): px.append(x) py.append(y) - grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5) + grid_map.set_value_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5)) if grid_search_animation: grid_map.plot_grid_map(ax=ax) diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst index 1c02c75cb2..a98acaaf50 100644 --- a/docs/modules/mapping/mapping_main.rst +++ b/docs/modules/mapping/mapping_main.rst @@ -7,6 +7,7 @@ Mapping :caption: Contents gaussian_grid_map/gaussian_grid_map + ndt_map/ndt_map ray_casting_grid_map/ray_casting_grid_map lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial point_cloud_sampling/point_cloud_sampling diff --git a/docs/modules/mapping/ndt_map/grid_clustering.png b/docs/modules/mapping/ndt_map/grid_clustering.png new file mode 100644 index 0000000000000000000000000000000000000000..c53590287af100b42c31afdb7610a825c4e3b589 GIT binary patch literal 33111 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b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst @@ -9,14 +9,106 @@ The red cross is true position, black points are RFID positions. The blue grid shows a position probability of histogram filter. -In this simulation, x,y are unknown, yaw is known. +In this simulation, we assume the robot's yaw orientation and RFID's positions are known, +but x,y positions are unknown. + +The filter uses speed input and range observations from RFID for localization. + +Initial position information is not needed. + +Filtering algorithm +~~~~~~~~~~~~~~~~~~~~ + +Histogram filter is a discrete Bayes filter in continuous space. + +It uses regular girds to manage probability of the robot existence. + +If a grid has higher probability, it means that the robot is likely to be there. + +In the simulation, we want to estimate x-y position, so we use 2D grid data. + +There are 4 steps for the histogram filter to estimate the probability distribution. + +Step1: Filter initialization +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Histogram filter does not need initial position information. + +In that case, we can initialize each grid probability as a same value. + +If we can use initial position information, we can set initial probabilities based on it. + +:ref:`gaussian_grid_map` might be useful when the initial position information is provided as gaussian distribution. + +Step2: Predict probability by motion +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In histogram filter, when a robot move to a next grid, +all probability information of each grid are shifted towards the movement direction. + +This process represents the change in the probability distribution as the robot moves. + +After the robot has moved, the probability distribution needs reflect +the estimation error due to the movement. + +For example, the position probability is peaky with observations: + +.. image:: histogram_filter_localization/1.png + :width: 400px + +But, the probability is getting uncertain without observations: + +.. image:: histogram_filter_localization/2.png + :width: 400px + + +The `gaussian filter `_ +is used in the simulation for adding noize. + +Step3: Update probability by observation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +In this step, all probabilities are updated by observations, +this is the update step of bayesian filter. + +The probability update formula is different by the used sensor model. + +This simulation uses range observation model. + +The probability of each grid is updated by this formula: + +.. math:: p_t=p_{t-1}*h(z) + +.. math:: h(z)=\frac{\exp \left(-(d - z)^{2} / 2\right)}{\sqrt{2 \pi}} + +- :math:`p_t` is the probability at the time `t`. + +- :math:`h(z)` is the observation probability with the observation `z`. + +- :math:`d` is the known distance from the RD-ID to the grid center. + +When the `d` is 3.0, the `h(z)` distribution is: + +.. image:: histogram_filter_localization/4.png + :width: 400px + +The observation probability distribution looks a circle when a RF-ID is observed: + +.. image:: histogram_filter_localization/3.png + :width: 400px + +Step4: Estimate position from probability +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +In each time step, we can calculate the final robot position from the current probability distribution. +There are two ways to calculate the final positions: + +1. Using the maximum probability grid position. + +2. Using the average of probability weighted grind position. -The filter integrates speed input and range observations from RFID for -localization. -Initial position is not needed. References: ~~~~~~~~~~~ -- `PROBABILISTIC ROBOTICS`_ +- `PROBABILISTIC ROBOTICS`_ +- `Robust Vehicle Localization in Urban Environments Using Probabilistic Maps `_ diff --git a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst index f78f2c95d8..b0f112a871 100644 --- a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst +++ b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst @@ -1,3 +1,5 @@ +.. _gaussian_grid_map: + Gaussian grid map ----------------- From 040e12dbcb88ac5a3cfa7097ceca8a5e5562ee6d Mon Sep 17 00:00:00 2001 From: Trung Kien Date: Sat, 29 Jan 2022 14:16:34 +0700 Subject: [PATCH 420/909] Add inverted_pendulum_lqr_control (#635) * Add inverted_pendulum_lqr_control * reorganize document of inverted pendulum control module * Refactor inverted_pendulum_lqr_control.py * Add doccument for inverted pendulum control * Corrected inverted pedulum LQR control doccument * Refactor inverted pendulum control by mpc and lqr * Add unit test for inverted_pendulum_lqr_control.py --- .../inverted_pendulum_lqr_control.py | 192 ++++++++++++++++++ .../inverted_pendulum_mpc_control.py | 30 ++- docs/modules/control/control_main.rst | 3 +- .../inverted-pendulum.png | Bin 0 -> 6127 bytes .../inverted_pendulum_control.rst | 97 +++++++++ .../inverted_pendulum_mpc_control.rst | 6 - tests/test_inverted_pendulum_lqr_control.py | 11 + tests/test_inverted_pendulum_mpc_control.py | 12 +- 8 files changed, 328 insertions(+), 23 deletions(-) create mode 100644 Control/inverted_pendulum/inverted_pendulum_lqr_control.py rename Control/{InvertedPendulumMPCControl => inverted_pendulum}/inverted_pendulum_mpc_control.py (84%) create mode 100644 docs/modules/control/inverted_pendulum_control/inverted-pendulum.png create mode 100644 docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst delete mode 100644 docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst create mode 100644 tests/test_inverted_pendulum_lqr_control.py diff --git a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py new file mode 100644 index 0000000000..fb68c91c45 --- /dev/null +++ b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py @@ -0,0 +1,192 @@ +""" +Inverted Pendulum LQR control +author: Trung Kien - letrungkien.k53.hut@gmail.com +""" + +import math +import time + +import matplotlib.pyplot as plt +import numpy as np +from numpy.linalg import inv, eig + +# Model parameters + +l_bar = 2.0 # length of bar +M = 1.0 # [kg] +m = 0.3 # [kg] +g = 9.8 # [m/s^2] + +nx = 4 # number of state +nu = 1 # number of input +Q = np.diag([0.0, 1.0, 1.0, 0.0]) # state cost matrix +R = np.diag([0.01]) # input cost matrix + +delta_t = 0.1 # time tick [s] +sim_time = 5.0 # simulation time [s] + +show_animation = True + + +def main(): + x0 = np.array([ + [0.0], + [0.0], + [0.3], + [0.0] + ]) + + x = np.copy(x0) + time = 0.0 + + while sim_time > time: + time += delta_t + + # calc control input + u = lqr_control(x) + + # simulate inverted pendulum cart + x = simulation(x, u) + + if show_animation: + plt.clf() + px = float(x[0]) + theta = float(x[2]) + plot_cart(px, theta) + plt.xlim([-5.0, 2.0]) + plt.pause(0.001) + + print("Finish") + print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]") + if show_animation: + plt.show() + + +def simulation(x, u): + A, B = get_model_matrix() + x = A @ x + B @ u + + return x + + +def solve_DARE(A, B, Q, R, maxiter=150, eps=0.01): + """ + Solve a discrete time_Algebraic Riccati equation (DARE) + """ + P = Q + + for i in range(maxiter): + Pn = A.T @ P @ A - A.T @ P @ B @ \ + inv(R + B.T @ P @ B) @ B.T @ P @ A + Q + if (abs(Pn - P)).max() < eps: + break + P = Pn + + return Pn + + +def dlqr(A, B, Q, R): + """ + Solve the discrete time lqr controller. + x[k+1] = A x[k] + B u[k] + cost = sum x[k].T*Q*x[k] + u[k].T*R*u[k] + # ref Bertsekas, p.151 + """ + + # first, try to solve the ricatti equation + P = solve_DARE(A, B, Q, R) + + # compute the LQR gain + K = inv(B.T @ P @ B + R) @ (B.T @ P @ A) + + eigVals, eigVecs = eig(A - B @ K) + return K, P, eigVals + + +def lqr_control(x): + A, B = get_model_matrix() + start = time.time() + K, _, _ = dlqr(A, B, Q, R) + u = -K @ x + elapsed_time = time.time() - start + print(f"calc time:{elapsed_time:.6f} [sec]") + return u + + +def get_numpy_array_from_matrix(x): + """ + get build-in list from matrix + """ + return np.array(x).flatten() + + +def get_model_matrix(): + A = np.array([ + [0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, m * g / M, 0.0], + [0.0, 0.0, 0.0, 1.0], + [0.0, 0.0, g * (M + m) / (l_bar * M), 0.0] + ]) + A = np.eye(nx) + delta_t * A + + B = np.array([ + [0.0], + [1.0 / M], + [0.0], + [1.0 / (l_bar * M)] + ]) + B = delta_t * B + + return A, B + + +def flatten(a): + return np.array(a).flatten() + + +def plot_cart(xt, theta): + cart_w = 1.0 + cart_h = 0.5 + radius = 0.1 + + cx = np.array([-cart_w / 2.0, cart_w / 2.0, cart_w / + 2.0, -cart_w / 2.0, -cart_w / 2.0]) + cy = np.array([0.0, 0.0, cart_h, cart_h, 0.0]) + cy += radius * 2.0 + + cx = cx + xt + + bx = np.array([0.0, l_bar * math.sin(-theta)]) + bx += xt + by = np.array([cart_h, l_bar * math.cos(-theta) + cart_h]) + by += radius * 2.0 + + angles = np.arange(0.0, math.pi * 2.0, math.radians(3.0)) + ox = np.array([radius * math.cos(a) for a in angles]) + oy = np.array([radius * math.sin(a) for a in angles]) + + rwx = np.copy(ox) + cart_w / 4.0 + xt + rwy = np.copy(oy) + radius + lwx = np.copy(ox) - cart_w / 4.0 + xt + lwy = np.copy(oy) + radius + + wx = np.copy(ox) + bx[-1] + wy = np.copy(oy) + by[-1] + + plt.plot(flatten(cx), flatten(cy), "-b") + plt.plot(flatten(bx), flatten(by), "-k") + plt.plot(flatten(rwx), flatten(rwy), "-k") + plt.plot(flatten(lwx), flatten(lwy), "-k") + plt.plot(flatten(wx), flatten(wy), "-k") + plt.title(f"x: {xt:.2f} , theta: {math.degrees(theta):.2f}") + + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) + + plt.axis("equal") + + +if __name__ == '__main__': + main() diff --git a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py similarity index 84% rename from Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py rename to Control/inverted_pendulum/inverted_pendulum_mpc_control.py index 3764f29558..6b966b13b4 100644 --- a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py +++ b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py @@ -17,14 +17,16 @@ m = 0.3 # [kg] g = 9.8 # [m/s^2] -Q = np.diag([0.0, 1.0, 1.0, 0.0]) -R = np.diag([0.01]) nx = 4 # number of state nu = 1 # number of input +Q = np.diag([0.0, 1.0, 1.0, 0.0]) # state cost matrix +R = np.diag([0.01]) # input cost matrix + T = 30 # Horizon length delta_t = 0.1 # time tick +sim_time = 5.0 # simulation time [s] -animation = True +show_animation = True def main(): @@ -36,8 +38,10 @@ def main(): ]) x = np.copy(x0) + time = 0.0 - for i in range(50): + while sim_time > time: + time += delta_t # calc control input opt_x, opt_delta_x, opt_theta, opt_delta_theta, opt_input = \ @@ -49,7 +53,7 @@ def main(): # simulate inverted pendulum cart x = simulation(x, u) - if animation: + if show_animation: plt.clf() px = float(x[0]) theta = float(x[2]) @@ -57,10 +61,14 @@ def main(): plt.xlim([-5.0, 2.0]) plt.pause(0.001) + print("Finish") + print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]") + if show_animation: + plt.show() + def simulation(x, u): A, B = get_model_matrix() - x = np.dot(A, x) + np.dot(B, u) return x @@ -85,7 +93,7 @@ def mpc_control(x0): start = time.time() prob.solve(verbose=False) elapsed_time = time.time() - start - print("calc time:{0} [sec]".format(elapsed_time)) + print(f"calc time:{elapsed_time:.6f} [sec]") if prob.status == cvxpy.OPTIMAL: ox = get_numpy_array_from_matrix(x.value[0, :]) @@ -165,8 +173,12 @@ def plot_cart(xt, theta): plt.plot(flatten(rwx), flatten(rwy), "-k") plt.plot(flatten(lwx), flatten(lwy), "-k") plt.plot(flatten(wx), flatten(wy), "-k") - plt.title("x:" + str(round(xt, 2)) + ",theta:" + - str(round(math.degrees(theta), 2))) + plt.title(f"x: {xt:.2f} , theta: {math.degrees(theta):.2f}") + + # for stopping simulation with the esc key. + plt.gcf().canvas.mpl_connect( + 'key_release_event', + lambda event: [exit(0) if event.key == 'escape' else None]) plt.axis("equal") diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst index 9eeba434d2..d6d36a726b 100644 --- a/docs/modules/control/control_main.rst +++ b/docs/modules/control/control_main.rst @@ -3,6 +3,7 @@ Control ================= -.. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst 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pendulum on a cart consists of a mass :math:`m` at the top of a pole of length :math:`l` pivoted on a +horizontally moving base as shown in the adjacent. + +The objective of the control system is to balance the inverted pendulum by applying a force to the cart that the pendulum is attached to. + +Modeling +~~~~~~~~~~~~ + +.. image:: inverted_pendulum_control/inverted-pendulum.png + :align: center + +- :math:`M`: mass of the cart +- :math:`m`: mass of the load on the top of the rod +- :math:`l`: length of the rod +- :math:`u`: force applied to the cart +- :math:`x`: cart position coordinate +- :math:`\theta`: pendulum angle from vertical + +Using Lagrange's equations: + +.. math:: + & (M + m)\ddot{x} - ml\ddot{\theta}cos{\theta} + ml\dot{\theta^2}\sin{\theta} = u \\ + & l\ddot{\theta} - g\sin{\theta} = \ddot{x}\cos{\theta} + +See this `link `__ for more details. + +So + +.. math:: + & \ddot{x} = \frac{m(gcos{\theta} - \dot{\theta}^2l)sin{\theta} + u}{M + m - mcos^2{\theta}} \\ + & \ddot{\theta} = \frac{g(M + m)sin{\theta} - \dot{\theta}^2lmsin{\theta}cos{\theta} + ucos{\theta}}{l(M + m - mcos^2{\theta})} + + +Linearlied model when :math:`\theta` small, :math:`cos{\theta} \approx 1`, :math:`sin{\theta} \approx \theta`, :math:`\dot{\theta}^2 \approx 0`. + +.. math:: + & \ddot{x} = \frac{gm}{M}\theta + \frac{1}{M}u\\ + & \ddot{\theta} = \frac{g(M + m)}{Ml}\theta + \frac{1}{Ml}u + +State space: + +.. math:: + & \dot{x} = Ax + Bu \\ + & y = Cx + Du + +where + +.. math:: + & x = [x, \dot{x}, \theta,\dot{\theta}]\\ + & A = \begin{bmatrix} 0 & 1 & 0 & 0\\0 & 0 & \frac{gm}{M} & 0\\0 & 0 & 0 & 1\\0 & 0 & \frac{g(M + m)}{Ml} & 0 \end{bmatrix}\\ + & B = \begin{bmatrix} 0 \\ \frac{1}{M} \\ 0 \\ \frac{1}{Ml} \end{bmatrix} + +If control only \theta + +.. math:: + & C = \begin{bmatrix} 0 & 0 & 1 & 0 \end{bmatrix}\\ + & D = [0] + +If control x and \theta + +.. math:: + & C = \begin{bmatrix} 1 & 0 & 0 & 0\\0 & 0 & 1 & 0 \end{bmatrix}\\ + & D = \begin{bmatrix} 0 \\ 0 \end{bmatrix} + +LQR control +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The LQR cotroller minimize this cost function defined as: + +.. math:: J = x^T Q x + u^T R u +the feedback control law that minimizes the value of the cost is: + +.. math:: u = -K x +where: + +.. math:: K = (B^T P B + R)^{-1} B^T P A +and :math:`P` is the unique positive definite solution to the discrete time `algebraic Riccati equation `__ (DARE): + +.. math:: P = A^T P A - A^T P B ( R + B^T P B )^{-1} B^T P A + Q + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation_lqr.gif + +MPC control +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The MPC cotroller minimize this cost function defined as: + +.. math:: J = x^T Q x + u^T R u + +subject to: + +- Linearlied Inverted Pendulum model +- Initial state + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif diff --git a/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst b/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst deleted file mode 100644 index 328f9f6b24..0000000000 --- a/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst +++ /dev/null @@ -1,6 +0,0 @@ -Inverted Pendulum MPC Control ------------------------------ - -Bipedal Walking with modifying designated footsteps - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif diff --git a/tests/test_inverted_pendulum_lqr_control.py b/tests/test_inverted_pendulum_lqr_control.py new file mode 100644 index 0000000000..cbbabb93b1 --- /dev/null +++ b/tests/test_inverted_pendulum_lqr_control.py @@ -0,0 +1,11 @@ +import conftest +from Control.inverted_pendulum import inverted_pendulum_lqr_control as m + + +def test_1(): + m.show_animation = False + m.main() + + +if __name__ == '__main__': + conftest.run_this_test(__file__) diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py index 9519b45ec9..800aefd7d5 100644 --- a/tests/test_inverted_pendulum_mpc_control.py +++ b/tests/test_inverted_pendulum_mpc_control.py @@ -1,13 +1,11 @@ import conftest -import sys -if 'cvxpy' in sys.modules: # pragma: no cover - from Control.InvertedPendulumMPCControl \ - import inverted_pendulum_mpc_control as m +from Control.inverted_pendulum import inverted_pendulum_mpc_control as m - def test1(): - m.show_animation = False - m.main() + +def test1(): + m.show_animation = False + m.main() if __name__ == '__main__': From 1d8c252fef3798d502dc3187e0c9a360a26fa1df Mon Sep 17 00:00:00 2001 From: Daniel Ingram Date: Sun, 6 Feb 2022 08:18:54 -0500 Subject: [PATCH 421/909] Clothoidal path planner (#551) * Add clothoidal path planner * Code quality/style fixes * Open up possibilities a bit by allowing control over theta1 values * Get line length under 80 chars for code style checks --- .../ClothoidalPaths/clothoidal_paths.py | 144 ++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 PathPlanning/ClothoidalPaths/clothoidal_paths.py diff --git a/PathPlanning/ClothoidalPaths/clothoidal_paths.py b/PathPlanning/ClothoidalPaths/clothoidal_paths.py new file mode 100644 index 0000000000..6fbf46ed2a --- /dev/null +++ b/PathPlanning/ClothoidalPaths/clothoidal_paths.py @@ -0,0 +1,144 @@ +""" +Clothoidal Path Planner +Author: Daniel Ingram (daniel-s-ingram) +Reference paper: https://www.researchgate.net/publication/237062806 +""" + +import matplotlib.pyplot as plt +import numpy as np +import scipy.integrate as integrate +from collections import namedtuple +from scipy.optimize import fsolve +from math import atan2, cos, hypot, pi, sin +from matplotlib import animation + +Point = namedtuple("Point", ["x", "y"]) + + +def draw_clothoids( + theta1_vals, + theta2_vals, + num_clothoids=75, + num_steps=100, + save_animation=False +): + p1 = Point(0, 0) + p2 = Point(10, 0) + clothoids = [] + for theta1 in theta1_vals: + for theta2 in theta2_vals: + clothoid = get_clothoid_points(p1, p2, theta1, theta2, num_steps) + clothoids.append(clothoid) + + fig = plt.figure(figsize=(10, 10)) + x_min, x_max, y_min, y_max = get_axes_limits(clothoids) + axes = plt.axes(xlim=(x_min, x_max), ylim=(y_min, y_max)) + + axes.plot(p1.x, p1.y, 'ro') + axes.plot(p2.x, p2.y, 'ro') + lines = [axes.plot([], [], 'b-')[0] for _ in range(len(clothoids))] + + def animate(i): + for line, clothoid in zip(lines, clothoids): + x = [p.x for p in clothoid[:i]] + y = [p.y for p in clothoid[:i]] + line.set_data(x, y) + + return lines + + anim = animation.FuncAnimation( + fig, + animate, + frames=num_steps, + interval=25, + blit=True + ) + if save_animation: + anim.save('clothoid.gif', fps=30, writer="imagemagick") + plt.show() + + +def get_clothoid_points(p1, p2, theta1, theta2, num_steps=100): + dx = p2.x - p1.x + dy = p2.y - p1.y + r = hypot(dx, dy) + + phi = atan2(dy, dx) + phi1 = normalize_angle(theta1 - phi) + phi2 = normalize_angle(theta2 - phi) + delta = phi2 - phi1 + + try: + A = solve_for_root(phi1, phi2, delta) + L = compute_length(r, phi1, delta, A) + curv = compute_curvature(delta, A, L) + curv_rate = compute_curvature_rate(A, L) + except Exception as e: + print(f"Failed to generate clothoid points: {e}") + return None + + points = [] + for s in np.linspace(0, L, num_steps): + try: + x = p1.x + s*X(curv_rate*s**2, curv*s, theta1) + y = p1.y + s*Y(curv_rate*s**2, curv*s, theta1) + points.append(Point(x, y)) + except Exception as e: + print(f"Skipping failed clothoid point: {e}") + + return points + + +def X(a, b, c): + return integrate.quad(lambda t: cos((a/2)*t**2 + b*t + c), 0, 1)[0] + + +def Y(a, b, c): + return integrate.quad(lambda t: sin((a/2)*t**2 + b*t + c), 0, 1)[0] + + +def solve_for_root(theta1, theta2, delta): + initial_guess = 3*(theta1 + theta2) + return fsolve(lambda x: Y(2*x, delta - x, theta1), [initial_guess]) + + +def compute_length(r, theta1, delta, A): + return r / X(2*A, delta - A, theta1) + + +def compute_curvature(delta, A, L): + return (delta - A) / L + + +def compute_curvature_rate(A, L): + return 2 * A / (L**2) + + +def normalize_angle(angle_rad): + return (angle_rad + pi) % (2 * pi) - pi + + +def get_axes_limits(clothoids): + x_vals = [p.x for clothoid in clothoids for p in clothoid] + y_vals = [p.y for clothoid in clothoids for p in clothoid] + + x_min = min(x_vals) + x_max = max(x_vals) + y_min = min(y_vals) + y_max = max(y_vals) + + x_offset = 0.1*(x_max - x_min) + y_offset = 0.1*(y_max - y_min) + + x_min = x_min - x_offset + x_max = x_max + x_offset + y_min = y_min - y_offset + y_max = y_max + y_offset + + return x_min, x_max, y_min, y_max + + +if __name__ == "__main__": + theta1_vals = [0] + theta2_vals = np.linspace(-pi, pi, 75) + draw_clothoids(theta1_vals, theta2_vals, save_animation=False) From a5feb75dcefc71802abfc369f23e278dd0329202 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Feb 2022 07:30:42 +0900 Subject: [PATCH 422/909] Bump numpy from 1.22.1 to 1.22.2 in /requirements (#639) Bumps [numpy](https://github.com/numpy/numpy) from 1.22.1 to 1.22.2. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.22.1...v1.22.2) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 90e98e4f5b..7d830a846d 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.22.1 +numpy == 1.22.2 scipy == 1.7.3 pandas == 1.4.0 matplotlib == 3.5.1 From 30fe42c7b43265eff12836122231d3283daf52a3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Feb 2022 07:31:49 +0900 Subject: [PATCH 423/909] Bump pytest from 6.2.5 to 7.0.0 in /requirements (#640) Bumps [pytest](https://github.com/pytest-dev/pytest) from 6.2.5 to 7.0.0. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/6.2.5...7.0.0) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 7d830a846d..b1b5e9c679 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,7 +3,7 @@ scipy == 1.7.3 pandas == 1.4.0 matplotlib == 3.5.1 cvxpy == 1.1.18 -pytest == 6.2.5 # For unit test +pytest == 7.0.0 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test From c7ee9337e9ce789741d163f548680249e3522fa1 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Feb 2022 07:25:28 +0900 Subject: [PATCH 424/909] Bump pytest from 7.0.0 to 7.0.1 in /requirements (#642) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.0.0 to 7.0.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.0.0...7.0.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b1b5e9c679..de00ea5370 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,7 +3,7 @@ scipy == 1.7.3 pandas == 1.4.0 matplotlib == 3.5.1 cvxpy == 1.1.18 -pytest == 7.0.0 # For unit test +pytest == 7.0.1 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.931 # For unit test flake8 == 4.0.1 # For unit test From e494ed6eaecb00fdf13fb361db947498c70d898c Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Feb 2022 07:25:48 +0900 Subject: [PATCH 425/909] Bump pandas from 1.4.0 to 1.4.1 in /requirements (#643) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.0 to 1.4.1. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.4.0...v1.4.1) --- updated-dependencies: - dependency-name: pandas dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index de00ea5370..fb62322132 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.22.2 scipy == 1.7.3 -pandas == 1.4.0 +pandas == 1.4.1 matplotlib == 3.5.1 cvxpy == 1.1.18 pytest == 7.0.1 # For unit test From 9da650e31bc7879ac2a5d59bf46415b0232377d6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 21 Feb 2022 17:19:22 +0900 Subject: [PATCH 426/909] Update LICENSE --- LICENSE | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/LICENSE b/LICENSE index 2867142fc7..c0c52a4321 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,7 @@ The MIT License (MIT) -Copyright (c) 2016 - 2021 Atsushi Sakai +Copyright (c) 2016 - 2022 Atsushi Sakai and other contributors: +https://github.com/AtsushiSakai/PythonRobotics/contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal From b1885a056490ef0904cea80aa3389811903dbe76 Mon Sep 17 00:00:00 2001 From: i-aki-y Date: Tue, 1 Mar 2022 21:33:31 +0900 Subject: [PATCH 427/909] Fix the broken gif link in the "Lidar to grid map" (#645) The link for gif animation is broken now. I think this is the right URL. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9aff69ab72..8972bae5fa 100644 --- a/README.md +++ b/README.md @@ -217,7 +217,7 @@ This is a 2D ray casting grid mapping example. This example shows how to convert a 2D range measurement to a grid map. -![2](Mapping/lidar_to_grid_map/animation.gif) +![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif) ## k-means object clustering From 5f4b62d87a0e9bed2d0279b32841d4bbe323fdf3 Mon Sep 17 00:00:00 2001 From: Hrushikesh Budhale Date: Sat, 12 Mar 2022 04:38:25 -0500 Subject: [PATCH 428/909] Fix missing 'delta t' in EKF documentation (#647) Reference issue typo What does this implement/fix? Update motion function in localization/EKF documentation. Add missing delta t. --- .../extended_kalman_filter_localization.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst index d2cd52cf12..a9c2939678 100644 --- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst +++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst @@ -89,7 +89,7 @@ This is implemented at The motion function is that -:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \\ w' \\ v' \end{bmatrix} = f(\textbf{x}, \textbf{u}) = \begin{bmatrix} x + v\cos(\phi)\Delta t \\ y + v\sin(\phi) \\ \phi + \omega \Delta t \\ v \end{bmatrix} \end{equation*}` +:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \\ w' \\ v' \end{bmatrix} = f(\textbf{x}, \textbf{u}) = \begin{bmatrix} x + v\cos(\phi)\Delta t \\ y + v\sin(\phi)\Delta t \\ \phi + \omega \Delta t \\ v \end{bmatrix} \end{equation*}` Its Jacobian matrix is From 9c5f981bd90b5871a4ac3c596f729689ad029067 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Mar 2022 07:13:46 +0900 Subject: [PATCH 429/909] Bump mypy from 0.931 to 0.941 in /requirements (#651) Bumps [mypy](https://github.com/python/mypy) from 0.931 to 0.941. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v0.931...v0.941) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index fb62322132..42ef7586f1 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,5 +5,5 @@ matplotlib == 3.5.1 cvxpy == 1.1.18 pytest == 7.0.1 # For unit test pytest-xdist == 2.5.0 # For unit test -mypy == 0.931 # For unit test +mypy == 0.941 # For unit test flake8 == 4.0.1 # For unit test From a03a07296cab0669ac664042e31d420f55ebed4b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Mar 2022 07:14:13 +0900 Subject: [PATCH 430/909] Bump numpy from 1.22.2 to 1.22.3 in /requirements (#650) Bumps [numpy](https://github.com/numpy/numpy) from 1.22.2 to 1.22.3. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt) - [Commits](https://github.com/numpy/numpy/compare/v1.22.2...v1.22.3) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 42ef7586f1..b173a6fab5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.22.2 +numpy == 1.22.3 scipy == 1.7.3 pandas == 1.4.1 matplotlib == 3.5.1 From 762d1a4d428fb60be68106359c6456b37c7cef20 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Mar 2022 07:51:50 +0900 Subject: [PATCH 431/909] Bump pytest from 7.0.1 to 7.1.0 in /requirements (#649) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.0.1 to 7.1.0. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.0.1...7.1.0) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b173a6fab5..2de832ce3e 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,7 +3,7 @@ scipy == 1.7.3 pandas == 1.4.1 matplotlib == 3.5.1 cvxpy == 1.1.18 -pytest == 7.0.1 # For unit test +pytest == 7.1.0 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.941 # For unit test flake8 == 4.0.1 # For unit test From a3753272043c0e9adb810c33f1386c26e1333719 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Mar 2022 08:21:29 +0900 Subject: [PATCH 432/909] Bump cvxpy from 1.1.18 to 1.2.0 in /requirements (#648) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.1.18 to 1.2.0. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.1.18...v1.2.0) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 2de832ce3e..6f6be66470 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.22.3 scipy == 1.7.3 pandas == 1.4.1 matplotlib == 3.5.1 -cvxpy == 1.1.18 +cvxpy == 1.2.0 pytest == 7.1.0 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.941 # For unit test From 62eee8162ef561234ef6d79943334b344daa0339 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Mar 2022 14:36:50 +0900 Subject: [PATCH 433/909] Bump scipy from 1.7.3 to 1.8.0 in /requirements (#641) Bumps [scipy](https://github.com/scipy/scipy) from 1.7.3 to 1.8.0. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.7.3...v1.8.0) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 6f6be66470..e8dfbcbba8 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.22.3 -scipy == 1.7.3 +scipy == 1.8.0 pandas == 1.4.1 matplotlib == 3.5.1 cvxpy == 1.2.0 From fb6965a34fc01633aaf441964ca174a4eff605c4 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Mar 2022 07:21:11 +0900 Subject: [PATCH 434/909] Bump pytest from 7.1.0 to 7.1.1 in /requirements (#652) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.0 to 7.1.1. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.1.0...7.1.1) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e8dfbcbba8..f671321704 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,7 +3,7 @@ scipy == 1.8.0 pandas == 1.4.1 matplotlib == 3.5.1 cvxpy == 1.2.0 -pytest == 7.1.0 # For unit test +pytest == 7.1.1 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.941 # For unit test flake8 == 4.0.1 # For unit test From 840248e38e47bb669f32075d9794cdbdd0248e26 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 29 Mar 2022 07:21:50 +0900 Subject: [PATCH 435/909] Bump mypy from 0.941 to 0.942 in /requirements (#653) Bumps [mypy](https://github.com/python/mypy) from 0.941 to 0.942. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v0.941...v0.942) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index f671321704..bdf1571465 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,5 +5,5 @@ matplotlib == 3.5.1 cvxpy == 1.2.0 pytest == 7.1.1 # For unit test pytest-xdist == 2.5.0 # For unit test -mypy == 0.941 # For unit test +mypy == 0.942 # For unit test flake8 == 4.0.1 # For unit test From 7ac2a17d23a840cf530ed9962a5236d1b25d6033 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 5 Apr 2022 07:25:46 +0900 Subject: [PATCH 436/909] Bump pandas from 1.4.1 to 1.4.2 in /requirements (#654) Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.1 to 1.4.2. - [Release notes](https://github.com/pandas-dev/pandas/releases) - [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md) - [Commits](https://github.com/pandas-dev/pandas/compare/v1.4.1...v1.4.2) --- updated-dependencies: - dependency-name: pandas dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index bdf1571465..050904a674 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.22.3 scipy == 1.8.0 -pandas == 1.4.1 +pandas == 1.4.2 matplotlib == 3.5.1 cvxpy == 1.2.0 pytest == 7.1.1 # For unit test From 38261ec67efd65256f44cd92b88dba58502fc2f7 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 10 Apr 2022 19:30:31 +0900 Subject: [PATCH 437/909] clean up clothoidal_paths.py (#638) * clean up clothoidal_paths.py * add unit tests for clothoid_paths * add unit tests for clothoid_paths * add unit tests for clothoid_paths * code clean up * code clean up * code clean up * code clean up * code clean up * code clean up * code clean up * code clean up --- .../ClothoidPath/clothoid_path_planner.py | 192 ++++++++++++++++++ .../ClothoidalPaths/clothoidal_paths.py | 144 ------------- .../inverted_pendulum_control.rst | 14 +- .../clothoid_path/clothoid_path.rst | 80 ++++++++ .../path_planning/path_planning_main.rst | 1 + tests/test_clothoidal_paths.py | 11 + 6 files changed, 293 insertions(+), 149 deletions(-) create mode 100644 PathPlanning/ClothoidPath/clothoid_path_planner.py delete mode 100644 PathPlanning/ClothoidalPaths/clothoidal_paths.py create mode 100644 docs/modules/path_planning/clothoid_path/clothoid_path.rst create mode 100644 tests/test_clothoidal_paths.py diff --git a/PathPlanning/ClothoidPath/clothoid_path_planner.py b/PathPlanning/ClothoidPath/clothoid_path_planner.py new file mode 100644 index 0000000000..5e5fc6e9a3 --- /dev/null +++ b/PathPlanning/ClothoidPath/clothoid_path_planner.py @@ -0,0 +1,192 @@ +""" +Clothoid Path Planner +Author: Daniel Ingram (daniel-s-ingram) + Atsushi Sakai (AtsushiSakai) +Reference paper: Fast and accurate G1 fitting of clothoid curves +https://www.researchgate.net/publication/237062806 +""" + +from collections import namedtuple +import matplotlib.pyplot as plt +import numpy as np +import scipy.integrate as integrate +from scipy.optimize import fsolve +from math import atan2, cos, hypot, pi, sin +from matplotlib import animation + +Point = namedtuple("Point", ["x", "y"]) + +show_animation = True + + +def generate_clothoid_paths(start_point, start_yaw_list, + goal_point, goal_yaw_list, + n_path_points): + """ + Generate clothoid path list. This function generate multiple clothoid paths + from multiple orientations(yaw) at start points to multiple orientations + (yaw) at goal point. + + :param start_point: Start point of the path + :param start_yaw_list: Orientation list at start point in radian + :param goal_point: Goal point of the path + :param goal_yaw_list: Orientation list at goal point in radian + :param n_path_points: number of path points + :return: clothoid path list + """ + clothoids = [] + for start_yaw in start_yaw_list: + for goal_yaw in goal_yaw_list: + clothoid = generate_clothoid_path(start_point, start_yaw, + goal_point, goal_yaw, + n_path_points) + clothoids.append(clothoid) + return clothoids + + +def generate_clothoid_path(start_point, start_yaw, + goal_point, goal_yaw, n_path_points): + """ + Generate a clothoid path list. + + :param start_point: Start point of the path + :param start_yaw: Orientation at start point in radian + :param goal_point: Goal point of the path + :param goal_yaw: Orientation at goal point in radian + :param n_path_points: number of path points + :return: a clothoid path + """ + dx = goal_point.x - start_point.x + dy = goal_point.y - start_point.y + r = hypot(dx, dy) + + phi = atan2(dy, dx) + phi1 = normalize_angle(start_yaw - phi) + phi2 = normalize_angle(goal_yaw - phi) + delta = phi2 - phi1 + + try: + # Step1: Solve g function + A = solve_g_for_root(phi1, phi2, delta) + + # Step2: Calculate path parameters + L = compute_path_length(r, phi1, delta, A) + curvature = compute_curvature(delta, A, L) + curvature_rate = compute_curvature_rate(A, L) + except Exception as e: + print(f"Failed to generate clothoid points: {e}") + return None + + # Step3: Construct a path with Fresnel integral + points = [] + for s in np.linspace(0, L, n_path_points): + try: + x = start_point.x + s * X(curvature_rate * s ** 2, curvature * s, + start_yaw) + y = start_point.y + s * Y(curvature_rate * s ** 2, curvature * s, + start_yaw) + points.append(Point(x, y)) + except Exception as e: + print(f"Skipping failed clothoid point: {e}") + + return points + + +def X(a, b, c): + return integrate.quad(lambda t: cos((a/2)*t**2 + b*t + c), 0, 1)[0] + + +def Y(a, b, c): + return integrate.quad(lambda t: sin((a/2)*t**2 + b*t + c), 0, 1)[0] + + +def solve_g_for_root(theta1, theta2, delta): + initial_guess = 3*(theta1 + theta2) + return fsolve(lambda A: Y(2*A, delta - A, theta1), [initial_guess]) + + +def compute_path_length(r, theta1, delta, A): + return r / X(2*A, delta - A, theta1) + + +def compute_curvature(delta, A, L): + return (delta - A) / L + + +def compute_curvature_rate(A, L): + return 2 * A / (L**2) + + +def normalize_angle(angle_rad): + return (angle_rad + pi) % (2 * pi) - pi + + +def get_axes_limits(clothoids): + x_vals = [p.x for clothoid in clothoids for p in clothoid] + y_vals = [p.y for clothoid in clothoids for p in clothoid] + + x_min = min(x_vals) + x_max = max(x_vals) + y_min = min(y_vals) + y_max = max(y_vals) + + x_offset = 0.1*(x_max - x_min) + y_offset = 0.1*(y_max - y_min) + + x_min = x_min - x_offset + x_max = x_max + x_offset + y_min = y_min - y_offset + y_max = y_max + y_offset + + return x_min, x_max, y_min, y_max + + +def draw_clothoids(start, goal, num_steps, clothoidal_paths, + save_animation=False): + + fig = plt.figure(figsize=(10, 10)) + x_min, x_max, y_min, y_max = get_axes_limits(clothoidal_paths) + axes = plt.axes(xlim=(x_min, x_max), ylim=(y_min, y_max)) + + axes.plot(start.x, start.y, 'ro') + axes.plot(goal.x, goal.y, 'ro') + lines = [axes.plot([], [], 'b-')[0] for _ in range(len(clothoidal_paths))] + + def animate(i): + for line, clothoid_path in zip(lines, clothoidal_paths): + x = [p.x for p in clothoid_path[:i]] + y = [p.y for p in clothoid_path[:i]] + line.set_data(x, y) + + return lines + + anim = animation.FuncAnimation( + fig, + animate, + frames=num_steps, + interval=25, + blit=True + ) + if save_animation: + anim.save('clothoid.gif', fps=30, writer="imagemagick") + plt.show() + + +def main(): + start_point = Point(0, 0) + start_orientation_list = [0.0] + goal_point = Point(10, 0) + goal_orientation_list = np.linspace(-pi, pi, 75) + num_path_points = 100 + clothoid_paths = generate_clothoid_paths( + start_point, start_orientation_list, + goal_point, goal_orientation_list, + num_path_points) + if show_animation: + draw_clothoids(start_point, goal_point, + num_path_points, clothoid_paths, + save_animation=False) + + +if __name__ == "__main__": + main() diff --git a/PathPlanning/ClothoidalPaths/clothoidal_paths.py b/PathPlanning/ClothoidalPaths/clothoidal_paths.py deleted file mode 100644 index 6fbf46ed2a..0000000000 --- a/PathPlanning/ClothoidalPaths/clothoidal_paths.py +++ /dev/null @@ -1,144 +0,0 @@ -""" -Clothoidal Path Planner -Author: Daniel Ingram (daniel-s-ingram) -Reference paper: https://www.researchgate.net/publication/237062806 -""" - -import matplotlib.pyplot as plt -import numpy as np -import scipy.integrate as integrate -from collections import namedtuple -from scipy.optimize import fsolve -from math import atan2, cos, hypot, pi, sin -from matplotlib import animation - -Point = namedtuple("Point", ["x", "y"]) - - -def draw_clothoids( - theta1_vals, - theta2_vals, - num_clothoids=75, - num_steps=100, - save_animation=False -): - p1 = Point(0, 0) - p2 = Point(10, 0) - clothoids = [] - for theta1 in theta1_vals: - for theta2 in theta2_vals: - clothoid = get_clothoid_points(p1, p2, theta1, theta2, num_steps) - clothoids.append(clothoid) - - fig = plt.figure(figsize=(10, 10)) - x_min, x_max, y_min, y_max = get_axes_limits(clothoids) - axes = plt.axes(xlim=(x_min, x_max), ylim=(y_min, y_max)) - - axes.plot(p1.x, p1.y, 'ro') - axes.plot(p2.x, p2.y, 'ro') - lines = [axes.plot([], [], 'b-')[0] for _ in range(len(clothoids))] - - def animate(i): - for line, clothoid in zip(lines, clothoids): - x = [p.x for p in clothoid[:i]] - y = [p.y for p in clothoid[:i]] - line.set_data(x, y) - - return lines - - anim = animation.FuncAnimation( - fig, - animate, - frames=num_steps, - interval=25, - blit=True - ) - if save_animation: - anim.save('clothoid.gif', fps=30, writer="imagemagick") - plt.show() - - -def get_clothoid_points(p1, p2, theta1, theta2, num_steps=100): - dx = p2.x - p1.x - dy = p2.y - p1.y - r = hypot(dx, dy) - - phi = atan2(dy, dx) - phi1 = normalize_angle(theta1 - phi) - phi2 = normalize_angle(theta2 - phi) - delta = phi2 - phi1 - - try: - A = solve_for_root(phi1, phi2, delta) - L = compute_length(r, phi1, delta, A) - curv = compute_curvature(delta, A, L) - curv_rate = compute_curvature_rate(A, L) - except Exception as e: - print(f"Failed to generate clothoid points: {e}") - return None - - points = [] - for s in np.linspace(0, L, num_steps): - try: - x = p1.x + s*X(curv_rate*s**2, curv*s, theta1) - y = p1.y + s*Y(curv_rate*s**2, curv*s, theta1) - points.append(Point(x, y)) - except Exception as e: - print(f"Skipping failed clothoid point: {e}") - - return points - - -def X(a, b, c): - return integrate.quad(lambda t: cos((a/2)*t**2 + b*t + c), 0, 1)[0] - - -def Y(a, b, c): - return integrate.quad(lambda t: sin((a/2)*t**2 + b*t + c), 0, 1)[0] - - -def solve_for_root(theta1, theta2, delta): - initial_guess = 3*(theta1 + theta2) - return fsolve(lambda x: Y(2*x, delta - x, theta1), [initial_guess]) - - -def compute_length(r, theta1, delta, A): - return r / X(2*A, delta - A, theta1) - - -def compute_curvature(delta, A, L): - return (delta - A) / L - - -def compute_curvature_rate(A, L): - return 2 * A / (L**2) - - -def normalize_angle(angle_rad): - return (angle_rad + pi) % (2 * pi) - pi - - -def get_axes_limits(clothoids): - x_vals = [p.x for clothoid in clothoids for p in clothoid] - y_vals = [p.y for clothoid in clothoids for p in clothoid] - - x_min = min(x_vals) - x_max = max(x_vals) - y_min = min(y_vals) - y_max = max(y_vals) - - x_offset = 0.1*(x_max - x_min) - y_offset = 0.1*(y_max - y_min) - - x_min = x_min - x_offset - x_max = x_max + x_offset - y_min = y_min - y_offset - y_max = y_max + y_offset - - return x_min, x_max, y_min, y_max - - -if __name__ == "__main__": - theta1_vals = [0] - theta2_vals = np.linspace(-pi, pi, 75) - draw_clothoids(theta1_vals, theta2_vals, save_animation=False) diff --git a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst index 5a447f7cd4..4c1f672ba0 100644 --- a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst +++ b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst @@ -34,7 +34,7 @@ So & \ddot{\theta} = \frac{g(M + m)sin{\theta} - \dot{\theta}^2lmsin{\theta}cos{\theta} + ucos{\theta}}{l(M + m - mcos^2{\theta})} -Linearlied model when :math:`\theta` small, :math:`cos{\theta} \approx 1`, :math:`sin{\theta} \approx \theta`, :math:`\dot{\theta}^2 \approx 0`. +Linearized model when :math:`\theta` small, :math:`cos{\theta} \approx 1`, :math:`sin{\theta} \approx \theta`, :math:`\dot{\theta}^2 \approx 0`. .. math:: & \ddot{x} = \frac{gm}{M}\theta + \frac{1}{M}u\\ @@ -68,16 +68,20 @@ If control x and \theta LQR control ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The LQR cotroller minimize this cost function defined as: +The LQR controller minimize this cost function defined as: .. math:: J = x^T Q x + u^T R u + the feedback control law that minimizes the value of the cost is: .. math:: u = -K x + where: .. math:: K = (B^T P B + R)^{-1} B^T P A -and :math:`P` is the unique positive definite solution to the discrete time `algebraic Riccati equation `__ (DARE): + +and :math:`P` is the unique positive definite solution to the discrete time +`algebraic Riccati equation `__ (DARE): .. math:: P = A^T P A - A^T P B ( R + B^T P B )^{-1} B^T P A + Q @@ -85,13 +89,13 @@ and :math:`P` is the unique positive definite solution to the discrete time `alg MPC control ~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The MPC cotroller minimize this cost function defined as: +The MPC controller minimize this cost function defined as: .. math:: J = x^T Q x + u^T R u subject to: -- Linearlied Inverted Pendulum model +- Linearized Inverted Pendulum model - Initial state .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif diff --git a/docs/modules/path_planning/clothoid_path/clothoid_path.rst b/docs/modules/path_planning/clothoid_path/clothoid_path.rst new file mode 100644 index 0000000000..1e8cee694a --- /dev/null +++ b/docs/modules/path_planning/clothoid_path/clothoid_path.rst @@ -0,0 +1,80 @@ +.. _clothoid-path-planning: + +Clothoid path planning +-------------------------- + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClothoidPath/animation1.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClothoidPath/animation2.gif +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClothoidPath/animation3.gif + +This is a clothoid path planning sample code. + +This can interpolate two 2D pose (x, y, yaw) with a clothoid path, +which its curvature is linearly continuous. +In other words, this is G1 Hermite interpolation with a single clothoid segment. + +This path planning algorithm as follows: + +Step1: Solve g function +~~~~~~~~~~~~~~~~~~~~~~~ + +Solve the g(A) function with a nonlinear optimization solver. + +.. math:: + + g(A):=Y(2A, \delta-A, \phi_{s}) + +Where + +* :math:`\delta`: the orientation difference between start and goal pose. +* :math:`\phi_{s}`: the orientation of the start pose. +* :math:`Y`: :math:`Y(a, b, c)=\int_{0}^{1} \sin \left(\frac{a}{2} \tau^{2}+b \tau+c\right) d \tau` + + +Step2: Calculate path parameters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We can calculate these path parameters using :math:`A`, + +:math:`L`: path length + +.. math:: + + L=\frac{R}{X\left(2 A, \delta-A, \phi_{s}\right)} + +where + +* :math:`R`: the distance between start and goal pose +* :math:`X`: :math:`X(a, b, c)=\int_{0}^{1} \cos \left(\frac{a}{2} \tau^{2}+b \tau+c\right) d \tau` + + +- :math:`\kappa`: curvature + +.. math:: + + \kappa=(\delta-A) / L + + +- :math:`\kappa'`: curvature rate + +.. math:: + + \kappa^{\prime}=2 A / L^{2} + + +Step3: Construct a path with Fresnel integral +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The final clothoid path can be calculated with the path parameters and Fresnel integrals. + +.. math:: + \begin{aligned} + &x(s)=x_{0}+\int_{0}^{s} \cos \left(\frac{1}{2} \kappa^{\prime} \tau^{2}+\kappa \tau+\vartheta_{0}\right) \mathrm{d} \tau \\ + &y(s)=y_{0}+\int_{0}^{s} \sin \left(\frac{1}{2} \kappa^{\prime} \tau^{2}+\kappa \tau+\vartheta_{0}\right) \mathrm{d} \tau + \end{aligned} + + +References +~~~~~~~~~~ + +- `Fast and accurate G1 fitting of clothoid curves `__ diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst index c3c5c389ec..5c70592afc 100644 --- a/docs/modules/path_planning/path_planning_main.rst +++ b/docs/modules/path_planning/path_planning_main.rst @@ -14,6 +14,7 @@ Path Planning .. include:: rrt/rrt.rst .. include:: cubic_spline/cubic_spline.rst .. include:: bspline_path/bspline_path.rst +.. include:: clothoid_path/clothoid_path.rst .. include:: eta3_spline/eta3_spline.rst .. include:: bezier_path/bezier_path.rst .. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst diff --git a/tests/test_clothoidal_paths.py b/tests/test_clothoidal_paths.py new file mode 100644 index 0000000000..0b038c0338 --- /dev/null +++ b/tests/test_clothoidal_paths.py @@ -0,0 +1,11 @@ +import conftest +from PathPlanning.ClothoidPath import clothoid_path_planner as m + + +def test_1(): + m.show_animation = False + m.main() + + +if __name__ == '__main__': + conftest.run_this_test(__file__) From af2061a1e784a6779bdc08976a43157ddae17884 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 11 Apr 2022 23:06:13 +0900 Subject: [PATCH 438/909] fix doc artifact link in CI (#660) --- PathPlanning/HybridAStar/car.py | 14 +++++++------- PathPlanning/HybridAStar/hybrid_a_star.py | 6 +++--- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py index cc7529b9c7..b378447fcd 100644 --- a/PathPlanning/HybridAStar/car.py +++ b/PathPlanning/HybridAStar/car.py @@ -12,14 +12,14 @@ import numpy as np from scipy.spatial.transform import Rotation as Rot -WB = 3. # rear to front wheel -W = 2. # width of car +WB = 3.0 # rear to front wheel +W = 2.0 # width of car LF = 3.3 # distance from rear to vehicle front end LB = 1.0 # distance from rear to vehicle back end MAX_STEER = 0.6 # [rad] maximum steering angle -W_BUBBLE_DIST = (LF - LB) / 2.0 -W_BUBBLE_R = sqrt(((LF + LB) / 2.0) ** 2 + 1) +BUBBLE_DIST = (LF - LB) / 2.0 # distance from rear to center of vehicle. +BUBBLE_R = np.hypot((LF + LB) / 2.0, W / 2.0) # bubble radius # vehicle rectangle vertices VRX = [LF, LF, -LB, -LB, LF] @@ -28,10 +28,10 @@ def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree): for i_x, i_y, i_yaw in zip(x_list, y_list, yaw_list): - cx = i_x + W_BUBBLE_DIST * cos(i_yaw) - cy = i_y + W_BUBBLE_DIST * sin(i_yaw) + cx = i_x + BUBBLE_DIST * cos(i_yaw) + cy = i_y + BUBBLE_DIST * sin(i_yaw) - ids = kd_tree.query_ball_point([cx, cy], W_BUBBLE_R) + ids = kd_tree.query_ball_point([cx, cy], BUBBLE_R) if not ids: continue diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py index 9679e3979b..4ef0c1dd21 100644 --- a/PathPlanning/HybridAStar/hybrid_a_star.py +++ b/PathPlanning/HybridAStar/hybrid_a_star.py @@ -20,7 +20,8 @@ try: from dynamic_programming_heuristic import calc_distance_heuristic import reeds_shepp_path_planning as rs - from car import move, check_car_collision, MAX_STEER, WB, plot_car + from car import move, check_car_collision, MAX_STEER, WB, plot_car,\ + BUBBLE_R except Exception: raise @@ -28,7 +29,6 @@ YAW_GRID_RESOLUTION = np.deg2rad(15.0) # [rad] MOTION_RESOLUTION = 0.1 # [m] path interpolate resolution N_STEER = 20 # number of steer command -VR = 1.0 # robot radius SB_COST = 100.0 # switch back penalty cost BACK_COST = 5.0 # backward penalty cost @@ -276,7 +276,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution): h_dp = calc_distance_heuristic( goal_node.x_list[-1], goal_node.y_list[-1], - ox, oy, xy_resolution, VR) + ox, oy, xy_resolution, BUBBLE_R) pq = [] openList[calc_index(start_node, config)] = start_node From b53fdf75f66ccb63b5cfaadaa81253d43f01805a Mon Sep 17 00:00:00 2001 From: Chris Mower Date: Tue, 19 Apr 2022 04:53:24 +0100 Subject: [PATCH 439/909] Add optional robot radius to RRT/RRTStar path planners (#655) * Add optional robot radius to RRT/RRTStar path planners. * update __init__ and check_collision to include radius * during animation, if a robot radius is given then it is drawn * Add test for robot radius * Correct import error * Correct missing robot_radius errors * Address "expected 2 blank lines, found 1" error * Address "line too long" errors * Add missing argument description. * Remove collision_check_with_xy and replace with check_collision * Fix "missing whitespace after ','" error * Update PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py Co-authored-by: Atsushi Sakai Co-authored-by: Atsushi Sakai --- .../closed_loop_rrt_star_car.py | 23 +++++++------------ PathPlanning/LQRRRTStar/lqr_rrt_star.py | 8 +++++-- PathPlanning/RRT/rrt.py | 18 +++++++++++---- PathPlanning/RRT/rrt_with_sobol_sampler.py | 20 +++++++++++----- PathPlanning/RRTDubins/rrt_dubins.py | 6 ++++- PathPlanning/RRTStar/rrt_star.py | 21 +++++++++++------ PathPlanning/RRTStarDubins/rrt_star_dubins.py | 8 +++++-- .../RRTStarReedsShepp/rrt_star_reeds_shepp.py | 11 ++++++--- tests/test_rrt_star.py | 13 +++++++++++ tests/test_rrt_star_reeds_shepp.py | 2 +- 10 files changed, 88 insertions(+), 42 deletions(-) diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py index c0c8c356e8..dc9a575694 100644 --- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py +++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py @@ -37,11 +37,13 @@ class ClosedLoopRRTStar(RRTStarReedsShepp): def __init__(self, start, goal, obstacle_list, rand_area, max_iter=200, - connect_circle_dist=50.0 + connect_circle_dist=50.0, + robot_radius=0.0 ): super().__init__(start, goal, obstacle_list, rand_area, max_iter=max_iter, connect_circle_dist=connect_circle_dist, + robot_radius=robot_radius ) self.target_speed = 10.0 / 3.6 @@ -127,7 +129,11 @@ def check_tracking_path_is_feasible(self, path): print("path is too long") find_goal = False - if not self.collision_check_with_xy(x, y, self.obstacle_list): + tmp_node = self.Node(x, y, 0) + tmp_node.path_x = x + tmp_node.path_y = y + if not self.check_collision( + tmp_node, self.obstacle_list, self.robot_radius): print("This path is collision") find_goal = False @@ -151,19 +157,6 @@ def get_goal_indexes(self): return fgoalinds - @staticmethod - def collision_check_with_xy(x, y, obstacle_list): - - for (ox, oy, size) in obstacle_list: - for (ix, iy) in zip(x, y): - dx = ox - ix - dy = oy - iy - d = dx * dx + dy * dy - if d <= size ** 2: - return False # collision - - return True # safe - def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), max_iter=100): print("Start" + __file__) diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py index 177131ac96..28d332aa00 100644 --- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py +++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py @@ -35,7 +35,8 @@ def __init__(self, start, goal, obstacle_list, rand_area, goal_sample_rate=10, max_iter=200, connect_circle_dist=50.0, - step_size=0.2 + step_size=0.2, + robot_radius=0.0, ): """ Setting Parameter @@ -44,6 +45,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, goal:Goal Position [x,y] obstacleList:obstacle Positions [[x,y,size],...] randArea:Random Sampling Area [min,max] + robot_radius: robot body modeled as circle with given radius """ self.start = self.Node(start[0], start[1]) @@ -58,6 +60,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, self.curvature = 1.0 self.goal_xy_th = 0.5 self.step_size = step_size + self.robot_radius = robot_radius self.lqr_planner = LQRPlanner() @@ -75,7 +78,8 @@ def planning(self, animation=True, search_until_max_iter=True): nearest_ind = self.get_nearest_node_index(self.node_list, rnd) new_node = self.steer(self.node_list[nearest_ind], rnd) - if self.check_collision(new_node, self.obstacle_list): + if self.check_collision( + new_node, self.obstacle_list, self.robot_radius): near_indexes = self.find_near_nodes(new_node) new_node = self.choose_parent(new_node, near_indexes) if new_node: diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py index dfbd4b1b7f..55092e6e00 100644 --- a/PathPlanning/RRT/rrt.py +++ b/PathPlanning/RRT/rrt.py @@ -50,7 +50,8 @@ def __init__(self, path_resolution=0.5, goal_sample_rate=5, max_iter=500, - play_area=None + play_area=None, + robot_radius=0.0, ): """ Setting Parameter @@ -60,6 +61,7 @@ def __init__(self, obstacleList:obstacle Positions [[x,y,size],...] randArea:Random Sampling Area [min,max] play_area:stay inside this area [xmin,xmax,ymin,ymax] + robot_radius: robot body modeled as circle with given radius """ self.start = self.Node(start[0], start[1]) @@ -76,6 +78,7 @@ def __init__(self, self.max_iter = max_iter self.obstacle_list = obstacle_list self.node_list = [] + self.robot_radius = robot_radius def planning(self, animation=True): """ @@ -93,7 +96,8 @@ def planning(self, animation=True): new_node = self.steer(nearest_node, rnd_node, self.expand_dis) if self.check_if_outside_play_area(new_node, self.play_area) and \ - self.check_collision(new_node, self.obstacle_list): + self.check_collision( + new_node, self.obstacle_list, self.robot_radius): self.node_list.append(new_node) if animation and i % 5 == 0: @@ -103,7 +107,8 @@ def planning(self, animation=True): self.node_list[-1].y) <= self.expand_dis: final_node = self.steer(self.node_list[-1], self.end, self.expand_dis) - if self.check_collision(final_node, self.obstacle_list): + if self.check_collision( + final_node, self.obstacle_list, self.robot_radius): return self.generate_final_course(len(self.node_list) - 1) if animation and i % 5: @@ -173,6 +178,8 @@ def draw_graph(self, rnd=None): lambda event: [exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") + if self.robot_radius > 0.0: + self.plot_circle(rnd.x, rnd.y, self.robot_radius, '-r') for node in self.node_list: if node.parent: plt.plot(node.path_x, node.path_y, "-g") @@ -225,7 +232,7 @@ def check_if_outside_play_area(node, play_area): return True # inside - ok @staticmethod - def check_collision(node, obstacleList): + def check_collision(node, obstacleList, robot_radius): if node is None: return False @@ -235,7 +242,7 @@ def check_collision(node, obstacleList): dy_list = [oy - y for y in node.path_y] d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)] - if min(d_list) <= size**2: + if min(d_list) <= (size+robot_radius)**2: return False # collision return True # safe @@ -262,6 +269,7 @@ def main(gx=6.0, gy=10.0): rand_area=[-2, 15], obstacle_list=obstacleList, # play_area=[0, 10, 0, 14] + robot_radius=0.8 ) path = rrt.planning(animation=show_animation) diff --git a/PathPlanning/RRT/rrt_with_sobol_sampler.py b/PathPlanning/RRT/rrt_with_sobol_sampler.py index b88e51c680..8045d8f764 100644 --- a/PathPlanning/RRT/rrt_with_sobol_sampler.py +++ b/PathPlanning/RRT/rrt_with_sobol_sampler.py @@ -62,7 +62,8 @@ def __init__(self, expand_dis=3.0, path_resolution=0.5, goal_sample_rate=5, - max_iter=500): + max_iter=500, + robot_radius=0.0): """ Setting Parameter @@ -70,6 +71,7 @@ def __init__(self, goal:Goal Position [x,y] obstacle_list:obstacle Positions [[x,y,size],...] randArea:Random Sampling Area [min,max] + robot_radius: robot body modeled as circle with given radius """ self.start = self.Node(start[0], start[1]) @@ -83,6 +85,7 @@ def __init__(self, self.obstacle_list = obstacle_list self.node_list = [] self.sobol_inter_ = 0 + self.robot_radius = robot_radius def planning(self, animation=True): """ @@ -99,7 +102,8 @@ def planning(self, animation=True): new_node = self.steer(nearest_node, rnd_node, self.expand_dis) - if self.check_collision(new_node, self.obstacle_list): + if self.check_collision( + new_node, self.obstacle_list, self.robot_radius): self.node_list.append(new_node) if animation and i % 5 == 0: @@ -109,7 +113,8 @@ def planning(self, animation=True): self.node_list[-1].y) <= self.expand_dis: final_node = self.steer(self.node_list[-1], self.end, self.expand_dis) - if self.check_collision(final_node, self.obstacle_list): + if self.check_collision( + final_node, self.obstacle_list, self.robot_radius): return self.generate_final_course(len(self.node_list) - 1) if animation and i % 5: @@ -183,6 +188,8 @@ def draw_graph(self, rnd=None): lambda event: [sys.exit(0) if event.key == 'escape' else None]) if rnd is not None: plt.plot(rnd.x, rnd.y, "^k") + if self.robot_radius >= 0.0: + self.plot_circle(rnd.x, rnd.y, self.robot_radius, '-r') for node in self.node_list: if node.parent: plt.plot(node.path_x, node.path_y, "-g") @@ -214,7 +221,7 @@ def get_nearest_node_index(node_list, rnd_node): return minind @staticmethod - def check_collision(node, obstacle_list): + def check_collision(node, obstacle_list, robot_radius): if node is None: return False @@ -224,7 +231,7 @@ def check_collision(node, obstacle_list): dy_list = [oy - y for y in node.path_y] d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)] - if min(d_list) <= size**2: + if min(d_list) <= (size+robot_radius)**2: return False # collision return True # safe @@ -249,7 +256,8 @@ def main(gx=6.0, gy=10.0): start=[0, 0], goal=[gx, gy], rand_area=[-2, 15], - obstacle_list=obstacle_list) + obstacle_list=obstacle_list, + robot_radius=0.8) path = rrt.planning(animation=show_animation) if path is None: diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py index 8ab06c7323..a43362b340 100644 --- a/PathPlanning/RRTDubins/rrt_dubins.py +++ b/PathPlanning/RRTDubins/rrt_dubins.py @@ -46,6 +46,7 @@ def __init__(self, x, y, yaw): def __init__(self, start, goal, obstacle_list, rand_area, goal_sample_rate=10, max_iter=200, + robot_radius=0.0 ): """ Setting Parameter @@ -54,6 +55,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, goal:Goal Position [x,y] obstacleList:obstacle Positions [[x,y,size],...] randArea:Random Sampling Area [min,max] + robot_radius: robot body modeled as circle with given radius """ self.start = self.Node(start[0], start[1], start[2]) @@ -67,6 +69,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, self.curvature = 1.0 # for dubins path self.goal_yaw_th = np.deg2rad(1.0) self.goal_xy_th = 0.5 + self.robot_radius = robot_radius def planning(self, animation=True, search_until_max_iter=True): """ @@ -82,7 +85,8 @@ def planning(self, animation=True, search_until_max_iter=True): nearest_ind = self.get_nearest_node_index(self.node_list, rnd) new_node = self.steer(self.node_list[nearest_ind], rnd) - if self.check_collision(new_node, self.obstacle_list): + if self.check_collision( + new_node, self.obstacle_list, self.robot_radius): self.node_list.append(new_node) if animation and i % 5 == 0: diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py index bef0086095..e2c05456de 100644 --- a/PathPlanning/RRTStar/rrt_star.py +++ b/PathPlanning/RRTStar/rrt_star.py @@ -42,7 +42,8 @@ def __init__(self, goal_sample_rate=20, max_iter=300, connect_circle_dist=50.0, - search_until_max_iter=False): + search_until_max_iter=False, + robot_radius=0.0): """ Setting Parameter @@ -53,7 +54,8 @@ def __init__(self, """ super().__init__(start, goal, obstacle_list, rand_area, expand_dis, - path_resolution, goal_sample_rate, max_iter) + path_resolution, goal_sample_rate, max_iter, + robot_radius=robot_radius) self.connect_circle_dist = connect_circle_dist self.goal_node = self.Node(goal[0], goal[1]) self.search_until_max_iter = search_until_max_iter @@ -77,7 +79,8 @@ def planning(self, animation=True): math.hypot(new_node.x-near_node.x, new_node.y-near_node.y) - if self.check_collision(new_node, self.obstacle_list): + if self.check_collision( + new_node, self.obstacle_list, self.robot_radius): near_inds = self.find_near_nodes(new_node) node_with_updated_parent = self.choose_parent( new_node, near_inds) @@ -128,7 +131,8 @@ def choose_parent(self, new_node, near_inds): for i in near_inds: near_node = self.node_list[i] t_node = self.steer(near_node, new_node) - if t_node and self.check_collision(t_node, self.obstacle_list): + if t_node and self.check_collision( + t_node, self.obstacle_list, self.robot_radius): costs.append(self.calc_new_cost(near_node, new_node)) else: costs.append(float("inf")) # the cost of collision node @@ -156,7 +160,8 @@ def search_best_goal_node(self): safe_goal_inds = [] for goal_ind in goal_inds: t_node = self.steer(self.node_list[goal_ind], self.goal_node) - if self.check_collision(t_node, self.obstacle_list): + if self.check_collision( + t_node, self.obstacle_list, self.robot_radius): safe_goal_inds.append(goal_ind) if not safe_goal_inds: @@ -219,7 +224,8 @@ def rewire(self, new_node, near_inds): continue edge_node.cost = self.calc_new_cost(new_node, near_node) - no_collision = self.check_collision(edge_node, self.obstacle_list) + no_collision = self.check_collision( + edge_node, self.obstacle_list, self.robot_radius) improved_cost = near_node.cost > edge_node.cost if no_collision and improved_cost: @@ -264,7 +270,8 @@ def main(): goal=[6, 10], rand_area=[-2, 15], obstacle_list=obstacle_list, - expand_dis=1) + expand_dis=1, + robot_radius=0.8) path = rrt_star.planning(animation=show_animation) if path is None: diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py index 26367eecea..21cc9b2bc5 100644 --- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py +++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py @@ -46,7 +46,8 @@ def __init__(self, x, y, yaw): def __init__(self, start, goal, obstacle_list, rand_area, goal_sample_rate=10, max_iter=200, - connect_circle_dist=50.0 + connect_circle_dist=50.0, + robot_radius=0.0, ): """ Setting Parameter @@ -55,6 +56,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, goal:Goal Position [x,y] obstacleList:obstacle Positions [[x,y,size],...] randArea:Random Sampling Area [min,max] + robot_radius: robot body modeled as circle with given radius """ self.start = self.Node(start[0], start[1], start[2]) @@ -69,6 +71,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, self.curvature = 1.0 # for dubins path self.goal_yaw_th = np.deg2rad(1.0) self.goal_xy_th = 0.5 + self.robot_radius = robot_radius def planning(self, animation=True, search_until_max_iter=True): """ @@ -84,7 +87,8 @@ def planning(self, animation=True, search_until_max_iter=True): nearest_ind = self.get_nearest_node_index(self.node_list, rnd) new_node = self.steer(self.node_list[nearest_ind], rnd) - if self.check_collision(new_node, self.obstacle_list): + if self.check_collision( + new_node, self.obstacle_list, self.robot_radius): near_indexes = self.find_near_nodes(new_node) new_node = self.choose_parent(new_node, near_indexes) if new_node: diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py index 6ea66dc729..323bc76be2 100644 --- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py +++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py @@ -45,7 +45,8 @@ def __init__(self, x, y, yaw): def __init__(self, start, goal, obstacle_list, rand_area, max_iter=200, - connect_circle_dist=50.0 + connect_circle_dist=50.0, + robot_radius=0.0 ): """ Setting Parameter @@ -54,6 +55,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, goal:Goal Position [x,y] obstacleList:obstacle Positions [[x,y,size],...] randArea:Random Sampling Area [min,max] + robot_radius: robot body modeled as circle with given radius """ self.start = self.Node(start[0], start[1], start[2]) @@ -63,6 +65,7 @@ def __init__(self, start, goal, obstacle_list, rand_area, self.max_iter = max_iter self.obstacle_list = obstacle_list self.connect_circle_dist = connect_circle_dist + self.robot_radius = robot_radius self.curvature = 1.0 self.goal_yaw_th = np.deg2rad(1.0) @@ -82,7 +85,8 @@ def planning(self, animation=True, search_until_max_iter=True): nearest_ind = self.get_nearest_node_index(self.node_list, rnd) new_node = self.steer(self.node_list[nearest_ind], rnd) - if self.check_collision(new_node, self.obstacle_list): + if self.check_collision( + new_node, self.obstacle_list, self.robot_radius): near_indexes = self.find_near_nodes(new_node) new_node = self.choose_parent(new_node, near_indexes) if new_node: @@ -117,7 +121,8 @@ def try_goal_path(self, node): if new_node is None: return - if self.check_collision(new_node, self.obstacle_list): + if self.check_collision( + new_node, self.obstacle_list, self.robot_radius): self.node_list.append(new_node) def draw_graph(self, rnd=None): diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py index 5327f4ca20..232995ecb4 100644 --- a/tests/test_rrt_star.py +++ b/tests/test_rrt_star.py @@ -19,5 +19,18 @@ def test_no_obstacle(): assert path is not None +def test_no_obstacle_and_robot_radius(): + obstacle_list = [] + + # Set Initial parameters + rrt_star = m.RRTStar(start=[0, 0], + goal=[6, 10], + rand_area=[-2, 15], + obstacle_list=obstacle_list, + robot_radius=0.8) + path = rrt_star.planning(animation=False) + assert path is not None + + if __name__ == '__main__': conftest.run_this_test(__file__) diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py index 0a4663056a..cb6b586b33 100644 --- a/tests/test_rrt_star_reeds_shepp.py +++ b/tests/test_rrt_star_reeds_shepp.py @@ -1,5 +1,5 @@ import conftest # Add root path to sys.path -import rrt_star_reeds_shepp as m +from PathPlanning.RRTStarReedsShepp import rrt_star_reeds_shepp as m def test1(): From 4f8c923589af8738b97fe821938ea5f51d55b096 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 26 Apr 2022 07:15:19 +0900 Subject: [PATCH 440/909] Bump pytest from 7.1.1 to 7.1.2 in /requirements (#661) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.1 to 7.1.2. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.1.1...7.1.2) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 050904a674..25d15e780c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,7 +3,7 @@ scipy == 1.8.0 pandas == 1.4.2 matplotlib == 3.5.1 cvxpy == 1.2.0 -pytest == 7.1.1 # For unit test +pytest == 7.1.2 # For unit test pytest-xdist == 2.5.0 # For unit test mypy == 0.942 # For unit test flake8 == 4.0.1 # For unit test From 28d95730448ff25bbe7ec6d2e7d9240800e2ac4a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 2 May 2022 21:01:46 +0900 Subject: [PATCH 441/909] fix doc artifact link in CI (#659) * fix doc artifact link in CI * fix doc artifact link in CI * fix doc artifact link in CI * fix doc artifact link in CI --- .circleci/artifact_path | 1 - .circleci/config.yml | 2 +- .github/workflows/circle_artifacts.yml | 13 +++++++++++++ .github/workflows/circleci-artifacts-redirector.yml | 2 +- 4 files changed, 15 insertions(+), 3 deletions(-) delete mode 100644 .circleci/artifact_path create mode 100644 .github/workflows/circle_artifacts.yml diff --git a/.circleci/artifact_path b/.circleci/artifact_path deleted file mode 100644 index d2c64880b1..0000000000 --- a/.circleci/artifact_path +++ /dev/null @@ -1 +0,0 @@ -0/html/index.html diff --git a/.circleci/config.yml b/.circleci/config.yml index 0a1962ad0b..8df4fab9e4 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,7 +6,7 @@ orbs: jobs: build_doc: docker: - - image: circleci/python:3.9 + - image: cimg/python:3.9 steps: - checkout - run: diff --git a/.github/workflows/circle_artifacts.yml b/.github/workflows/circle_artifacts.yml new file mode 100644 index 0000000000..56ac78ed15 --- /dev/null +++ b/.github/workflows/circle_artifacts.yml @@ -0,0 +1,13 @@ +on: [status] +jobs: + circleci_artifacts_redirector_job: + runs-on: ubuntu-20.04 + name: Run CircleCI artifacts redirector + steps: + - name: GitHub Action step + uses: larsoner/circleci-artifacts-redirector-action@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + artifact-path: 0/html-scipyorg/index.html + circleci-jobs: build_docs + job-title: build_doc artifact \ No newline at end of file diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml index 951e3a5947..d988d2c38e 100644 --- a/.github/workflows/circleci-artifacts-redirector.yml +++ b/.github/workflows/circleci-artifacts-redirector.yml @@ -6,7 +6,7 @@ jobs: name: Run CircleCI artifacts redirector steps: - name: run-circleci-artifacts-redirector - uses: larsoner/circleci-artifacts-redirector-action@0.2.0 + uses: larsoner/circleci-artifacts-redirector-action@0.3.1 with: repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/html/index.html From 163cc5ea58d68ee994f1546d118333dc01217552 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 2 May 2022 21:06:33 +0900 Subject: [PATCH 442/909] Update dubins_path.rst (#663) * Update dubins_path.rst * Update dubins_path.rst --- .../path_planning/dubins_path/dubins_path.rst | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path.rst index 5e14820f8f..6765a84617 100644 --- a/docs/modules/path_planning/dubins_path/dubins_path.rst +++ b/docs/modules/path_planning/dubins_path/dubins_path.rst @@ -5,7 +5,18 @@ A sample code for Dubins path planning. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True -Ref: +Dubins path +~~~~~~~~~~~~ +Dubims path is a analyrical path planning algorithm for a simple car model. -- `Dubins path - - Wikipedia `__ +It can generates a shortest path between 2D poses (x, y, yaw) with maximum curvture comstraint and tangent(yaw angle) constraint. + + + + + +Reference +~~~~~~~~~~~~~~~~~~~~ + +- `Dubins path - Wikipedia `__ +- `15.3.1 Dubins Curves `__ From 462a8cd6c0fd48434af126204bf5b1eca7ac7357 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 3 May 2022 07:23:49 +0900 Subject: [PATCH 443/909] Bump mypy from 0.942 to 0.950 in /requirements (#665) Bumps [mypy](https://github.com/python/mypy) from 0.942 to 0.950. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v0.942...v0.950) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 25d15e780c..46f56702ea 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -5,5 +5,5 @@ matplotlib == 3.5.1 cvxpy == 1.2.0 pytest == 7.1.2 # For unit test pytest-xdist == 2.5.0 # For unit test -mypy == 0.942 # For unit test +mypy == 0.950 # For unit test flake8 == 4.0.1 # For unit test From 32b545fe7c35b57f280cd9d570f62839886f2e4b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 7 May 2022 13:19:30 +0900 Subject: [PATCH 444/909] Enhance dubins path docs (#664) * Engance dubins path docs * Update dubins_path.rst * fix doc artifact link in CI * wip * wip * wip * Update dubins_path.rst * wip * wip * wip * wip * wip --- .circleci/config.yml | 1 + ...ath_planning.py => dubins_path_planner.py} | 150 ++++++++++-------- PathPlanning/HybridAStar/car.py | 2 +- PathPlanning/RRTDubins/rrt_dubins.py | 10 +- PathPlanning/RRTStarDubins/rrt_star_dubins.py | 10 +- docs/conf.py | 5 +- docs/doc_requirements.txt | 1 + .../modules/path_planning/dubins_path/RLR.jpg | Bin 0 -> 10220 bytes .../modules/path_planning/dubins_path/RSR.jpg | Bin 0 -> 10245 bytes .../path_planning/dubins_path/dubins_path.rst | 28 +++- tests/test_diff_codestyle.py | 4 +- tests/test_dubins_path_planning.py | 12 +- tests/test_utils.py | 21 +++ utils/__init__.py | 0 utils/angle.py | 77 +++++++++ 15 files changed, 235 insertions(+), 86 deletions(-) rename PathPlanning/DubinsPath/{dubins_path_planning.py => dubins_path_planner.py} (72%) create mode 100644 docs/modules/path_planning/dubins_path/RLR.jpg create mode 100644 docs/modules/path_planning/dubins_path/RSR.jpg create mode 100644 tests/test_utils.py create mode 100644 utils/__init__.py create mode 100644 utils/angle.py diff --git a/.circleci/config.yml b/.circleci/config.yml index 8df4fab9e4..275ad3bc7a 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -14,6 +14,7 @@ jobs: command: | python -m venv venv . venv/bin/activate + pip install -r requirements/requirements.txt pip install -r docs/doc_requirements.txt cd docs;make html - store_artifacts: diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planner.py similarity index 72% rename from PathPlanning/DubinsPath/dubins_path_planning.py rename to PathPlanning/DubinsPath/dubins_path_planner.py index f8e6d7b1c1..69c213ddf4 100644 --- a/PathPlanning/DubinsPath/dubins_path_planning.py +++ b/PathPlanning/DubinsPath/dubins_path_planner.py @@ -5,64 +5,82 @@ author Atsushi Sakai(@Atsushi_twi) """ -import math +import sys +import os +sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/") -import matplotlib.pyplot as plt +import math import numpy as np -from scipy.spatial.transform import Rotation as Rot +from utils.angle import angle_mod, create_2d_rotation_matrix show_animation = True -def dubins_path_planning(s_x, s_y, s_yaw, g_x, g_y, g_yaw, curvature, - step_size=0.1): +def plan_dubins_path(s_x, s_y, s_yaw, + g_x, g_y, g_yaw, + curvature, + step_size=0.1): """ - Dubins path planner - - :param s_x: x position of start point [m] - :param s_y: y position of start point [m] - :param s_yaw: yaw angle of start point [rad] - :param g_x: x position of end point [m] - :param g_y: y position of end point [m] - :param g_yaw: yaw angle of end point [rad] - :param curvature: curvature for curve [1/m] - :param step_size: (optional) step size between two path points [m] - :return: - x_list: x positions of a path - y_list: y positions of a path - yaw_list: yaw angles of a path - modes: mode list of a path - lengths: length of path segments. - """ - - g_x -= s_x - g_y -= s_y + Path dubins path + + Parameters + ---------- + s_x : float + x position of the start point [m] + s_y : float + y position of the start point [m] + s_yaw : float + yaw angle of the start point [rad] + g_x : float + x position of the goal point [m] + g_y : float + y position of the end point [m] + g_yaw : float + yaw angle of the end point [rad] + curvature : float + curvature for curve [1/m] + step_size : float (optional) + step size between two path points [m]. Default is 0.1 + + Returns + ------- + x_list: array + x positions of the path + y_list: array + y positions of the path + yaw_list: array + yaw angles of the path + modes: array + mode list of the path + lengths: array + length list of the path segments. - l_rot = Rot.from_euler('z', s_yaw).as_matrix()[0:2, 0:2] - le_xy = np.stack([g_x, g_y]).T @ l_rot - le_yaw = g_yaw - s_yaw + """ + # calculate local goal x, y, yaw + l_rot = create_2d_rotation_matrix(s_yaw) + le_xy = np.stack([g_x - s_x, g_y - s_y]).T @ l_rot + local_goal_x = le_xy[0] + local_goal_y = le_xy[1] + local_goal_yaw = g_yaw - s_yaw lp_x, lp_y, lp_yaw, modes, lengths = dubins_path_planning_from_origin( - le_xy[0], le_xy[1], le_yaw, curvature, step_size) + local_goal_x, local_goal_y, local_goal_yaw, curvature, step_size) - rot = Rot.from_euler('z', -s_yaw).as_matrix()[0:2, 0:2] + # Convert a local coordinate path to the global coordinate + rot = create_2d_rotation_matrix(-s_yaw) converted_xy = np.stack([lp_x, lp_y]).T @ rot x_list = converted_xy[:, 0] + s_x y_list = converted_xy[:, 1] + s_y - yaw_list = [pi_2_pi(i_yaw + s_yaw) for i_yaw in lp_yaw] + yaw_list = angle_mod(np.array(lp_yaw) + s_yaw) return x_list, y_list, yaw_list, modes, lengths -def mod2pi(theta): - return theta - 2.0 * math.pi * math.floor(theta / 2.0 / math.pi) - - -def pi_2_pi(angle): - return (angle + math.pi) % (2 * math.pi) - math.pi +def _mod2pi(theta): + return angle_mod(theta, zero_2_2pi=True) -def left_straight_left(alpha, beta, d): +def _LSL(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -76,9 +94,9 @@ def left_straight_left(alpha, beta, d): if p_squared < 0: return None, None, None, mode tmp1 = math.atan2((cb - ca), tmp0) - t = mod2pi(-alpha + tmp1) + t = _mod2pi(-alpha + tmp1) p = math.sqrt(p_squared) - q = mod2pi(beta - tmp1) + q = _mod2pi(beta - tmp1) return t, p, q, mode @@ -96,9 +114,9 @@ def right_straight_right(alpha, beta, d): if p_squared < 0: return None, None, None, mode tmp1 = math.atan2((ca - cb), tmp0) - t = mod2pi(alpha - tmp1) + t = angle_mod(alpha - tmp1, zero_2_2pi=True) p = math.sqrt(p_squared) - q = mod2pi(-beta + tmp1) + q = _mod2pi(-beta + tmp1) return t, p, q, mode @@ -116,8 +134,8 @@ def left_straight_right(alpha, beta, d): return None, None, None, mode p = math.sqrt(p_squared) tmp2 = math.atan2((-ca - cb), (d + sa + sb)) - math.atan2(-2.0, p) - t = mod2pi(-alpha + tmp2) - q = mod2pi(-mod2pi(beta) + tmp2) + t = _mod2pi(-alpha + tmp2) + q = _mod2pi(-_mod2pi(beta) + tmp2) return t, p, q, mode @@ -135,8 +153,8 @@ def right_straight_left(alpha, beta, d): return None, None, None, mode p = math.sqrt(p_squared) tmp2 = math.atan2((ca + cb), (d - sa - sb)) - math.atan2(2.0, p) - t = mod2pi(alpha - tmp2) - q = mod2pi(beta - tmp2) + t = _mod2pi(alpha - tmp2) + q = _mod2pi(beta - tmp2) return t, p, q, mode @@ -153,13 +171,13 @@ def right_left_right(alpha, beta, d): if abs(tmp_rlr) > 1.0: return None, None, None, mode - p = mod2pi(2 * math.pi - math.acos(tmp_rlr)) - t = mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + mod2pi(p / 2.0)) - q = mod2pi(alpha - beta - t + mod2pi(p)) + p = _mod2pi(2 * math.pi - math.acos(tmp_rlr)) + t = _mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + _mod2pi(p / 2.0)) + q = _mod2pi(alpha - beta - t + _mod2pi(p)) return t, p, q, mode -def left_right_left(alpha, beta, d): +def _LRL(alpha, beta, d): sa = math.sin(alpha) sb = math.sin(beta) ca = math.cos(alpha) @@ -170,9 +188,9 @@ def left_right_left(alpha, beta, d): tmp_lrl = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (- sa + sb)) / 8.0 if abs(tmp_lrl) > 1: return None, None, None, mode - p = mod2pi(2 * math.pi - math.acos(tmp_lrl)) - t = mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0) - q = mod2pi(mod2pi(beta) - alpha - t + mod2pi(p)) + p = _mod2pi(2 * math.pi - math.acos(tmp_lrl)) + t = _mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0) + q = _mod2pi(_mod2pi(beta) - alpha - t + _mod2pi(p)) return t, p, q, mode @@ -184,13 +202,13 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, D = math.hypot(dx, dy) d = D * curvature - theta = mod2pi(math.atan2(dy, dx)) - alpha = mod2pi(- theta) - beta = mod2pi(end_yaw - theta) + theta = _mod2pi(math.atan2(dy, dx)) + alpha = _mod2pi(- theta) + beta = _mod2pi(end_yaw - theta) - planning_funcs = [left_straight_left, right_straight_right, + planning_funcs = [_LSL, right_straight_right, left_straight_right, right_straight_left, - right_left_right, left_right_left] + right_left_right, _LRL] best_cost = float("inf") bt, bp, bq, best_mode = None, None, None, None @@ -319,6 +337,7 @@ def generate_local_course(total_length, lengths, modes, max_curvature, def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"): # pragma: no cover + import matplotlib.pyplot as plt if not isinstance(x, float): for (i_x, i_y, i_yaw) in zip(x, y, yaw): plot_arrow(i_x, i_y, i_yaw) @@ -330,6 +349,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", def main(): print("Dubins path planner sample start!!") + import matplotlib.pyplot as plt start_x = 1.0 # [m] start_y = 1.0 # [m] @@ -341,13 +361,13 @@ def main(): curvature = 1.0 - path_x, path_y, path_yaw, mode, lengths = dubins_path_planning(start_x, - start_y, - start_yaw, - end_x, - end_y, - end_yaw, - curvature) + path_x, path_y, path_yaw, mode, lengths = plan_dubins_path(start_x, + start_y, + start_yaw, + end_x, + end_y, + end_yaw, + curvature) if show_animation: plt.plot(path_x, path_y, label="final course " + "".join(mode)) diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py index b378447fcd..14b7f956b7 100644 --- a/PathPlanning/HybridAStar/car.py +++ b/PathPlanning/HybridAStar/car.py @@ -6,7 +6,7 @@ """ -from math import sqrt, cos, sin, tan, pi +from math import cos, sin, tan, pi import matplotlib.pyplot as plt import numpy as np diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py index a43362b340..f1bd03ca02 100644 --- a/PathPlanning/RRTDubins/rrt_dubins.py +++ b/PathPlanning/RRTDubins/rrt_dubins.py @@ -20,7 +20,7 @@ try: from rrt import RRT - import dubins_path_planning + import dubins_path_planner except ImportError: raise @@ -130,15 +130,15 @@ def draw_graph(self, rnd=None): # pragma: no cover plt.pause(0.01) def plot_start_goal_arrow(self): # pragma: no cover - dubins_path_planning.plot_arrow( + dubins_path_planner.plot_arrow( self.start.x, self.start.y, self.start.yaw) - dubins_path_planning.plot_arrow( + dubins_path_planner.plot_arrow( self.end.x, self.end.y, self.end.yaw) def steer(self, from_node, to_node): px, py, pyaw, mode, course_lengths = \ - dubins_path_planning.dubins_path_planning( + dubins_path_planner.plan_dubins_path( from_node.x, from_node.y, from_node.yaw, to_node.x, to_node.y, to_node.yaw, self.curvature) @@ -160,7 +160,7 @@ def steer(self, from_node, to_node): def calc_new_cost(self, from_node, to_node): - _, _, _, _, course_length = dubins_path_planning.dubins_path_planning( + _, _, _, _, course_length = dubins_path_planner.plan_dubins_path( from_node.x, from_node.y, from_node.yaw, to_node.x, to_node.y, to_node.yaw, self.curvature) diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py index 21cc9b2bc5..e2a456d75f 100644 --- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py +++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py @@ -20,7 +20,7 @@ "/../RRTStar/") try: - import dubins_path_planning + import dubins_path_planner from rrt_star import RRTStar except ImportError: raise @@ -136,15 +136,15 @@ def draw_graph(self, rnd=None): plt.pause(0.01) def plot_start_goal_arrow(self): - dubins_path_planning.plot_arrow( + dubins_path_planner.plot_arrow( self.start.x, self.start.y, self.start.yaw) - dubins_path_planning.plot_arrow( + dubins_path_planner.plot_arrow( self.end.x, self.end.y, self.end.yaw) def steer(self, from_node, to_node): px, py, pyaw, mode, course_lengths = \ - dubins_path_planning.dubins_path_planning( + dubins_path_planner.plan_dubins_path( from_node.x, from_node.y, from_node.yaw, to_node.x, to_node.y, to_node.yaw, self.curvature) @@ -166,7 +166,7 @@ def steer(self, from_node, to_node): def calc_new_cost(self, from_node, to_node): - _, _, _, _, course_lengths = dubins_path_planning.dubins_path_planning( + _, _, _, _, course_lengths = dubins_path_planner.plan_dubins_path( from_node.x, from_node.y, from_node.yaw, to_node.x, to_node.y, to_node.yaw, self.curvature) diff --git a/docs/conf.py b/docs/conf.py index de28ce4752..892e2863be 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -13,8 +13,8 @@ # documentation root, use os.path.abspath to make it absolute, like shown here. # import os -# import sys -# sys.path.insert(0, os.path.abspath('.')) +import sys +sys.path.insert(0, os.path.abspath('../')) # -- Project information ----------------------------------------------------- @@ -42,6 +42,7 @@ 'sphinx.ext.autodoc', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', 'IPython.sphinxext.ipython_console_highlighting', ] diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt index 65670d346e..b8702ee750 100644 --- a/docs/doc_requirements.txt +++ b/docs/doc_requirements.txt @@ -1,3 +1,4 @@ sphinx == 4.3.2 # For sphinx documentation sphinx_rtd_theme == 1.0.0 IPython == 7.31.1 # For sphinx documentation +sphinxcontrib-napoleon == 0.7 # For auto doc diff --git a/docs/modules/path_planning/dubins_path/RLR.jpg b/docs/modules/path_planning/dubins_path/RLR.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ff961e5e2dc4ea3d624963f01b3ff549d63a8077 GIT binary patch literal 10220 zcmb_?2UJtfw)de)lPX2&3J9S}?;s#Tf(VA*rI!#&=psb~5tJ4}5tJecy%Pu^h#(yW zqzQ!Hiy*y-z{C5$_xs=Xz4z8z-&)^3>zw(WJu`duWY3<=J~J0%7qb8@80Gm(Ld4wU`Y7C$^YM29xAR2&!IPI*5$@*ur>#HqN8_sw?uPo8smbNd26zKd zKod~A#Q$IE@88qoJpjla002?K-#ptl0Dy}Gfa?=~^LVlWfF=R}YWqEH5H|l%hWPSH 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Dubins path ~~~~~~~~~~~~ -Dubims path is a analyrical path planning algorithm for a simple car model. +Dubins path is a analytical path planning algorithm for a simple car model. It can generates a shortest path between 2D poses (x, y, yaw) with maximum curvture comstraint and tangent(yaw angle) constraint. +The path consist of 3 segments of maximum curvature curves or a straight line segment. +Each segment type can is categorized by 3 type: 'Right turn (R)' , 'Left turn (L)', and 'Straight (S).' +Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR, LRL. + +For example, one of RSR Dubins paths is: + +.. image:: dubins_path/RSR.jpg + :width: 400px + +one of RLR Dubins paths is: + +.. image:: dubins_path/RLR.jpg + :width: 200px + +Dubins path planner can output three types and distances of each course segment. + +You can generate a path from these information and the maximum curvature information. + +In the example code, a path which is minimum course length one among 6 course type is selected and then a path is constructed. + +Code +~~~~~~~~~~~~~~~~~~~~ + +.. automodule:: PathPlanning.DubinsPath.dubins_path_planner + :members: Reference @@ -20,3 +45,4 @@ Reference - `Dubins path - Wikipedia `__ - `15.3.1 Dubins Curves `__ +- `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths `__ diff --git a/tests/test_diff_codestyle.py b/tests/test_diff_codestyle.py index 3dbe11a11a..89c228e285 100644 --- a/tests/test_diff_codestyle.py +++ b/tests/test_diff_codestyle.py @@ -92,7 +92,9 @@ def find_diff(sha): def run_flake8(diff): """Run flake8 on the given diff.""" res = subprocess.run( - ['flake8', '--diff'], + ['flake8', '--diff', '--ignore', + 'E402' # top level import for sys.path.append + ], input=diff, stdout=subprocess.PIPE, encoding='utf-8', diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py index 18492ccc59..99c266bc6f 100644 --- a/tests/test_dubins_path_planning.py +++ b/tests/test_dubins_path_planning.py @@ -1,7 +1,7 @@ import numpy as np import conftest -from PathPlanning.DubinsPath import dubins_path_planning +from PathPlanning.DubinsPath import dubins_path_planner np.random.seed(12345) @@ -33,7 +33,7 @@ def test_1(): curvature = 1.0 - px, py, pyaw, mode, lengths = dubins_path_planning.dubins_path_planning( + px, py, pyaw, mode, lengths = dubins_path_planner.plan_dubins_path( start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, @@ -42,8 +42,8 @@ def test_1(): def test_2(): - dubins_path_planning.show_animation = False - dubins_path_planning.main() + dubins_path_planner.show_animation = False + dubins_path_planner.main() def test_3(): @@ -61,8 +61,8 @@ def test_3(): curvature = 1.0 / (np.random.rand() * 5.0) px, py, pyaw, mode, lengths = \ - dubins_path_planning.dubins_path_planning( - start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) + dubins_path_planner.plan_dubins_path( + start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature) check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw) diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 0000000000..b397479809 --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,21 @@ +import conftest # Add root path to sys.path +from utils import angle +from numpy.testing import assert_allclose +import numpy as np + + +def test_angle_mod(): + assert_allclose(angle.angle_mod(-4.0), 2.28318531) + assert(isinstance(angle.angle_mod(-4.0), float)) + assert_allclose(angle.angle_mod([-4.0]), [2.28318531]) + assert(isinstance(angle.angle_mod([-4.0]), np.ndarray)) + + assert_allclose(angle.angle_mod([-150.0, 190.0, 350], degree=True), + [-150., -170., -10.]) + + assert_allclose(angle.angle_mod(-60.0, zero_2_2pi=True, degree=True), + [300.]) + + +if __name__ == '__main__': + conftest.run_this_test(__file__) diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/utils/angle.py b/utils/angle.py new file mode 100644 index 0000000000..741304f00e --- /dev/null +++ b/utils/angle.py @@ -0,0 +1,77 @@ +import numpy as np +from scipy.spatial.transform import Rotation as Rot + + +def create_2d_rotation_matrix(angle): + """ + Create 2D totation matrix from an angle + + Parameters + ---------- + angle : + + Returns + ------- + + """ + return Rot.from_euler('z', angle).as_matrix()[0:2, 0:2] + + +def angle_mod(x, zero_2_2pi=False, degree=False): + """ + Angle modulo operation + Default angle modulo range is [-pi, pi) + + Parameters + ---------- + x : float or array_like + A angle or an array of angles. This array is flattened for + the calculation. When an angle is provided, a float angle is returned. + zero_2_2pi : bool, optional + Change angle modulo range to [0, 2pi) + Default is False. + degree : bool, optional + If True, then the given angles are assumed to be in degrees. + Default is False. + + Returns + ------- + ret : float or ndarray + an angle or an array of modulated angle. + + Examples + -------- + >>> angle_mod(-4.0) + 2.28318531 + + >>> angle_mod([-4.0]) + np.array(2.28318531) + + >>> angle_mod([-150.0, 190.0, 350], degree=True) + array([-150., -170., -10.]) + + >>> angle_mod(-60.0, zero_2_2pi=True, degree=True) + array([300.]) + + """ + if isinstance(x, float): + is_float = True + else: + is_float = False + + x = np.asarray(x).flatten() + if degree: + x = np.deg2rad(x) + + if zero_2_2pi: + mod_angle = x % (2 * np.pi) + else: + mod_angle = (x + np.pi) % (2 * np.pi) - np.pi + + if degree: + mod_angle = np.rad2deg(mod_angle) + + if is_float: + return mod_angle.item() + else: + return mod_angle From d74a91e062732ed9bc9e85fe708cd615bfc0bc7a Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 7 May 2022 15:21:03 +0900 Subject: [PATCH 445/909] Re-architecture document structure (#669) * Rearchitecture document structure * Rearchitecture document structure * Rearchitecture document structure * Rearchitecture document structure * Rearchitecture document structure * Rearchitecture document structure * Rearchitecture document structure --- docs/index_main.rst | 2 +- .../aerial_navigation_main.rst | 8 ++- ...=> drone_3d_trajectory_following_main.rst} | 0 ...ng.rst => rocket_powered_landing_main.rst} | 0 ...s_2.rst => Kalmanfilter_basics_2_main.rst} | 0 ...asics.rst => Kalmanfilter_basics_main.rst} | 0 docs/modules/appendix/appendix_main.rst | 7 ++- .../arm_navigation/arm_navigation_main.rst | 28 +++-------- .../n_joint_arm_to_point_control_main.rst | 13 +++++ ...obstacle_avoidance_arm_navigation_main.rst | 6 +++ ...ink_ik.rst => planar_two_link_ik_main.rst} | 0 docs/modules/bipedal/bipedal_main.rst | 6 ++- ...l_planner.rst => bipedal_planner_main.rst} | 0 docs/modules/control/control_main.rst | 7 ++- ...rst => inverted_pendulum_control_main.rst} | 2 +- ...ol.rst => move_to_a_pose_control_main.rst} | 0 ...amble_kalman_filter_localization_main.rst} | 0 ...ended_kalman_filter_localization_main.rst} | 2 +- ...=> histogram_filter_localization_main.rst} | 10 ++-- .../localization/localization_main.rst | 15 +++--- ... => particle_filter_localization_main.rst} | 2 +- ...ented_kalman_filter_localization_main.rst} | 0 ...le_fitting.rst => circle_fitting_main.rst} | 0 ...rid_map.rst => gaussian_grid_map_main.rst} | 0 ...rst => k_means_object_clustering_main.rst} | 0 ...st => lidar_to_grid_map_tutorial_main.rst} | 12 ++--- docs/modules/mapping/mapping_main.rst | 15 +++--- ..._map.rst => ray_casting_grid_map_main.rst} | 0 ...fitting.rst => rectangle_fitting_main.rst} | 0 .../{bezier_path.rst => bezier_path_main.rst} | 4 +- ...bspline_path.rst => bspline_path_main.rst} | 2 +- .../{bugplanner.rst => bugplanner_main.rst} | 0 ...othoid_path.rst => clothoid_path_main.rst} | 0 ...verage_path.rst => coverage_path_main.rst} | 0 ...cubic_spline.rst => cubic_spline_main.rst} | 6 +-- .../{dubins_path.rst => dubins_path_main.rst} | 4 +- ...h.rst => dynamic_window_approach_main.rst} | 2 + .../{eta3_spline.rst => eta3_spline_main.rst} | 0 ...me_path.rst => frenet_frame_path_main.rst} | 0 ...e_search.rst => grid_base_search_main.rst} | 0 .../{hybridastar.rst => hybridastar_main.rst} | 0 .../{lqr_path.rst => lqr_path_main.rst} | 0 ..._predictive_trajectory_generator_main.rst} | 2 +- .../path_planning/path_planning_main.rst | 46 ++++++++++-------- .../{prm_planner.rst => prm_planner_main.rst} | 0 ...t => quintic_polynomials_planner_main.rst} | 0 ...epp_path.rst => reeds_shepp_path_main.rst} | 0 .../rrt/{rrt.rst => rrt_main.rst} | 2 +- docs/modules/path_planning/rrt/rrt_star.rst | 2 +- ...ner.rst => state_lattice_planner_main.rst} | 0 ...t => visibility_road_map_planner_main.rst} | 8 +-- .../{vrm_planner.rst => vrm_planner_main.rst} | 0 .../{cgmres_nmpc.rst => cgmres_nmpc_main.rst} | 8 +-- ...> lqr_speed_and_steering_control_main.rst} | 0 ...trol.rst => lqr_steering_control_main.rst} | 0 ...ctive_speed_and_steering_control_main.rst} | 0 .../path_tracking/path_tracking_main.rst | 17 ++++--- ...ing.rst => pure_pursuit_tracking_main.rst} | 0 ...t => rear_wheel_feedback_control_main.rst} | 0 ...y_control.rst => stanley_control_main.rst} | 0 .../{FastSLAM1.rst => FastSLAM1_main.rst} | 12 ++--- .../{FastSLAM2.rst => FastSLAM2_main.rst} | 0 .../{ekf_slam.rst => ekf_slam_main.rst} | 2 +- .../slam/graph_slam/graphSLAM_SE2_example.rst | 14 +++--- .../Graph_SLAM_optimization.gif | Bin 0 -> 114966 bytes .../modules/slam/graph_slam/graphSLAM_doc.rst | 12 ++--- .../graphSLAM_doc_11_1.png | Bin 0 -> 3931 bytes .../graphSLAM_doc_11_2.png | Bin 0 -> 3931 bytes .../graphSLAM_doc_files/graphSLAM_doc_2_0.png | Bin 0 -> 7764 bytes .../graphSLAM_doc_files/graphSLAM_doc_2_2.png | Bin 0 -> 7924 bytes .../graphSLAM_doc_files/graphSLAM_doc_4_0.png | Bin 0 -> 9308 bytes .../graphSLAM_doc_files/graphSLAM_doc_9_1.png | Bin 0 -> 8521 bytes .../slam/graph_slam/graphSLAM_formulation.rst | 2 +- .../{graph_slam.rst => graph_slam_main.rst} | 6 +-- ...iterative_closest_point_matching_main.rst} | 0 docs/modules/slam/slam_main.rst | 13 +++-- 76 files changed, 167 insertions(+), 132 deletions(-) rename docs/modules/aerial_navigation/drone_3d_trajectory_following/{drone_3d_trajectory_following.rst => drone_3d_trajectory_following_main.rst} (100%) rename docs/modules/aerial_navigation/rocket_powered_landing/{rocket_powered_landing.rst => rocket_powered_landing_main.rst} (100%) rename docs/modules/appendix/{Kalmanfilter_basics_2.rst => Kalmanfilter_basics_2_main.rst} (100%) rename docs/modules/appendix/{Kalmanfilter_basics.rst => Kalmanfilter_basics_main.rst} (100%) create mode 100644 docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst create mode 100644 docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst rename docs/modules/arm_navigation/{planar_two_link_ik.rst => planar_two_link_ik_main.rst} (100%) rename docs/modules/bipedal/bipedal_planner/{bipedal_planner.rst => bipedal_planner_main.rst} (100%) rename docs/modules/control/inverted_pendulum_control/{inverted_pendulum_control.rst => inverted_pendulum_control_main.rst} (98%) rename docs/modules/control/move_to_a_pose_control/{move_to_a_pose_control.rst => move_to_a_pose_control_main.rst} (100%) rename docs/modules/localization/ensamble_kalman_filter_localization_files/{ensamble_kalman_filter_localization.rst => ensamble_kalman_filter_localization_main.rst} (100%) rename docs/modules/localization/extended_kalman_filter_localization_files/{extended_kalman_filter_localization.rst => extended_kalman_filter_localization_main.rst} (98%) rename docs/modules/localization/histogram_filter_localization/{histogram_filter_localization.rst => histogram_filter_localization_main.rst} (94%) rename docs/modules/localization/particle_filter_localization/{particle_filter_localization.rst => particle_filter_localization_main.rst} (94%) rename docs/modules/localization/unscented_kalman_filter_localization/{unscented_kalman_filter_localization.rst => unscented_kalman_filter_localization_main.rst} (100%) rename docs/modules/mapping/circle_fitting/{circle_fitting.rst => circle_fitting_main.rst} (100%) rename docs/modules/mapping/gaussian_grid_map/{gaussian_grid_map.rst => gaussian_grid_map_main.rst} (100%) rename docs/modules/mapping/k_means_object_clustering/{k_means_object_clustering.rst => k_means_object_clustering_main.rst} (100%) rename docs/modules/mapping/lidar_to_grid_map_tutorial/{lidar_to_grid_map_tutorial.rst => lidar_to_grid_map_tutorial_main.rst} (92%) rename docs/modules/mapping/ray_casting_grid_map/{ray_casting_grid_map.rst => ray_casting_grid_map_main.rst} (100%) rename docs/modules/mapping/rectangle_fitting/{rectangle_fitting.rst => rectangle_fitting_main.rst} (100%) rename docs/modules/path_planning/bezier_path/{bezier_path.rst => bezier_path_main.rst} (85%) rename docs/modules/path_planning/bspline_path/{bspline_path.rst => bspline_path_main.rst} (88%) rename docs/modules/path_planning/bugplanner/{bugplanner.rst => bugplanner_main.rst} (100%) rename docs/modules/path_planning/clothoid_path/{clothoid_path.rst => clothoid_path_main.rst} (100%) rename docs/modules/path_planning/coverage_path/{coverage_path.rst => coverage_path_main.rst} (100%) rename docs/modules/path_planning/cubic_spline/{cubic_spline.rst => cubic_spline_main.rst} (68%) rename docs/modules/path_planning/dubins_path/{dubins_path.rst => dubins_path_main.rst} (96%) rename docs/modules/path_planning/dynamic_window_approach/{dynamic_window_approach.rst => dynamic_window_approach_main.rst} (92%) rename docs/modules/path_planning/eta3_spline/{eta3_spline.rst => eta3_spline_main.rst} (100%) rename docs/modules/path_planning/frenet_frame_path/{frenet_frame_path.rst => frenet_frame_path_main.rst} (100%) rename docs/modules/path_planning/grid_base_search/{grid_base_search.rst => grid_base_search_main.rst} (100%) rename docs/modules/path_planning/hybridastar/{hybridastar.rst => hybridastar_main.rst} (100%) rename docs/modules/path_planning/lqr_path/{lqr_path.rst => lqr_path_main.rst} (100%) rename docs/modules/path_planning/model_predictive_trajectory_generator/{model_predictive_trajectory_generator.rst => model_predictive_trajectory_generator_main.rst} (90%) rename docs/modules/path_planning/prm_planner/{prm_planner.rst => prm_planner_main.rst} (100%) rename docs/modules/path_planning/quintic_polynomials_planner/{quintic_polynomials_planner.rst => quintic_polynomials_planner_main.rst} (100%) rename docs/modules/path_planning/reeds_shepp_path/{reeds_shepp_path.rst => reeds_shepp_path_main.rst} (100%) rename docs/modules/path_planning/rrt/{rrt.rst => rrt_main.rst} (99%) rename docs/modules/path_planning/state_lattice_planner/{state_lattice_planner.rst => state_lattice_planner_main.rst} (100%) rename docs/modules/path_planning/visibility_road_map_planner/{visibility_road_map_planner.rst => visibility_road_map_planner_main.rst} (91%) rename docs/modules/path_planning/vrm_planner/{vrm_planner.rst => vrm_planner_main.rst} (100%) rename docs/modules/path_tracking/cgmres_nmpc/{cgmres_nmpc.rst => cgmres_nmpc_main.rst} (92%) rename docs/modules/path_tracking/lqr_speed_and_steering_control/{lqr_speed_and_steering_control.rst => lqr_speed_and_steering_control_main.rst} (100%) rename docs/modules/path_tracking/lqr_steering_control/{lqr_steering_control.rst => lqr_steering_control_main.rst} (100%) rename docs/modules/path_tracking/model_predictive_speed_and_steering_control/{model_predictive_speed_and_steering_control.rst => model_predictive_speed_and_steering_control_main.rst} (100%) rename docs/modules/path_tracking/pure_pursuit_tracking/{pure_pursuit_tracking.rst => pure_pursuit_tracking_main.rst} (100%) rename docs/modules/path_tracking/rear_wheel_feedback_control/{rear_wheel_feedback_control.rst => rear_wheel_feedback_control_main.rst} (100%) rename docs/modules/path_tracking/stanley_control/{stanley_control.rst => stanley_control_main.rst} (100%) rename docs/modules/slam/FastSLAM1/{FastSLAM1.rst => FastSLAM1_main.rst} (98%) rename docs/modules/slam/FastSLAM2/{FastSLAM2.rst => FastSLAM2_main.rst} (100%) rename docs/modules/slam/ekf_slam/{ekf_slam.rst => ekf_slam_main.rst} (99%) create mode 100644 docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png rename docs/modules/slam/graph_slam/{graph_slam.rst => graph_slam_main.rst} (78%) rename docs/modules/slam/iterative_closest_point_matching/{iterative_closest_point_matching.rst => iterative_closest_point_matching_main.rst} (100%) diff --git a/docs/index_main.rst b/docs/index_main.rst index 79980c2bd9..151759284d 100644 --- a/docs/index_main.rst +++ b/docs/index_main.rst @@ -31,7 +31,7 @@ See this paper for more details: .. toctree:: :maxdepth: 2 - :caption: Guide + :caption: Contents getting_started modules/introduction diff --git a/docs/modules/aerial_navigation/aerial_navigation_main.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst index ea6c4c8e30..b2ccb071af 100644 --- a/docs/modules/aerial_navigation/aerial_navigation_main.rst +++ b/docs/modules/aerial_navigation/aerial_navigation_main.rst @@ -3,6 +3,10 @@ Aerial Navigation ================= -.. include:: drone_3d_trajectory_following/drone_3d_trajectory_following.rst -.. include:: rocket_powered_landing/rocket_powered_landing.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents + + drone_3d_trajectory_following/drone_3d_trajectory_following + rocket_powered_landing/rocket_powered_landing diff --git a/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst b/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst similarity index 100% rename from docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst rename to docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst diff --git a/docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst b/docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst similarity index 100% rename from docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst rename to docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst diff --git a/docs/modules/appendix/Kalmanfilter_basics_2.rst b/docs/modules/appendix/Kalmanfilter_basics_2_main.rst similarity index 100% rename from docs/modules/appendix/Kalmanfilter_basics_2.rst rename to docs/modules/appendix/Kalmanfilter_basics_2_main.rst diff --git a/docs/modules/appendix/Kalmanfilter_basics.rst b/docs/modules/appendix/Kalmanfilter_basics_main.rst similarity index 100% rename from docs/modules/appendix/Kalmanfilter_basics.rst rename to docs/modules/appendix/Kalmanfilter_basics_main.rst diff --git a/docs/modules/appendix/appendix_main.rst b/docs/modules/appendix/appendix_main.rst index 8840603bca..8a29d84676 100644 --- a/docs/modules/appendix/appendix_main.rst +++ b/docs/modules/appendix/appendix_main.rst @@ -3,7 +3,10 @@ Appendix ============== -.. include:: Kalmanfilter_basics.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents -.. include:: Kalmanfilter_basics_2.rst + Kalmanfilter_basics + Kalmanfilter_basics_2 diff --git a/docs/modules/arm_navigation/arm_navigation_main.rst b/docs/modules/arm_navigation/arm_navigation_main.rst index 4399ae13e3..bbbc872c58 100644 --- a/docs/modules/arm_navigation/arm_navigation_main.rst +++ b/docs/modules/arm_navigation/arm_navigation_main.rst @@ -3,26 +3,10 @@ Arm Navigation ============== -.. include:: planar_two_link_ik.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents - -N joint arm to point control ----------------------------- - -N joint arm to a point control simulation. - -This is a interactive simulation. - -You can set the goal position of the end effector with left-click on the -plotting area. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif - -In this simulation N = 10, however, you can change it. - -Arm navigation with obstacle avoidance --------------------------------------- - -Arm navigation with obstacle avoidance simulation. - -.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif + planar_two_link_ik + n_joint_arm_to_point_control + obstacle_avoidance_arm_navigation diff --git a/docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst b/docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst new file mode 100644 index 0000000000..cc6279f681 --- /dev/null +++ b/docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst @@ -0,0 +1,13 @@ +N joint arm to point control +---------------------------- + +N joint arm to a point control simulation. + +This is a interactive simulation. + +You can set the goal position of the end effector with left-click on the +plotting area. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif + +In this simulation N = 10, however, you can change it. diff --git a/docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst b/docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst new file mode 100644 index 0000000000..899a64a5cd --- /dev/null +++ b/docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst @@ -0,0 +1,6 @@ +Arm navigation with obstacle avoidance +-------------------------------------- + +Arm navigation with obstacle avoidance simulation. + +.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif \ No newline at end of file diff --git a/docs/modules/arm_navigation/planar_two_link_ik.rst b/docs/modules/arm_navigation/planar_two_link_ik_main.rst similarity index 100% rename from docs/modules/arm_navigation/planar_two_link_ik.rst rename to docs/modules/arm_navigation/planar_two_link_ik_main.rst diff --git a/docs/modules/bipedal/bipedal_main.rst b/docs/modules/bipedal/bipedal_main.rst index bab316b0d9..fc5b933055 100644 --- a/docs/modules/bipedal/bipedal_main.rst +++ b/docs/modules/bipedal/bipedal_main.rst @@ -3,5 +3,9 @@ Bipedal ================= -.. include:: bipedal_planner/bipedal_planner.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents + + bipedal_planner/bipedal_planner diff --git a/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst b/docs/modules/bipedal/bipedal_planner/bipedal_planner_main.rst similarity index 100% rename from docs/modules/bipedal/bipedal_planner/bipedal_planner.rst rename to docs/modules/bipedal/bipedal_planner/bipedal_planner_main.rst diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst index d6d36a726b..cee2aa9e8e 100644 --- a/docs/modules/control/control_main.rst +++ b/docs/modules/control/control_main.rst @@ -3,7 +3,10 @@ Control ================= -.. include:: inverted_pendulum_control/inverted_pendulum_control.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents -.. include:: move_to_a_pose_control/move_to_a_pose_control.rst + inverted_pendulum_control/inverted_pendulum_control + move_to_a_pose_control/move_to_a_pose_control diff --git a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control_main.rst similarity index 98% rename from docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst rename to docs/modules/control/inverted_pendulum_control/inverted_pendulum_control_main.rst index 4c1f672ba0..e41729fd61 100644 --- a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst +++ b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control_main.rst @@ -9,7 +9,7 @@ The objective of the control system is to balance the inverted pendulum by apply Modeling ~~~~~~~~~~~~ -.. image:: inverted_pendulum_control/inverted-pendulum.png +.. image:: inverted-pendulum.png :align: center - :math:`M`: mass of the cart diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control_main.rst similarity index 100% rename from docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst rename to docs/modules/control/move_to_a_pose_control/move_to_a_pose_control_main.rst diff --git a/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst b/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst similarity index 100% rename from docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst rename to docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst similarity index 98% rename from docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst rename to docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst index a9c2939678..0ec920c9df 100644 --- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst +++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst @@ -2,7 +2,7 @@ Extended Kalman Filter Localization ----------------------------------- -.. image:: extended_kalman_filter_localization_files/extended_kalman_filter_localization_1_0.png +.. image:: extended_kalman_filter_localization_1_0.png :width: 600px diff --git a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization_main.rst similarity index 94% rename from docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst rename to docs/modules/localization/histogram_filter_localization/histogram_filter_localization_main.rst index 891de94135..fafd578333 100644 --- a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst +++ b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization_main.rst @@ -53,12 +53,12 @@ the estimation error due to the movement. For example, the position probability is peaky with observations: -.. image:: histogram_filter_localization/1.png +.. image:: 1.png :width: 400px But, the probability is getting uncertain without observations: -.. image:: histogram_filter_localization/2.png +.. image:: 2.png :width: 400px @@ -88,12 +88,12 @@ The probability of each grid is updated by this formula: When the `d` is 3.0, the `h(z)` distribution is: -.. image:: histogram_filter_localization/4.png +.. image:: 4.png :width: 400px The observation probability distribution looks a circle when a RF-ID is observed: -.. image:: histogram_filter_localization/3.png +.. image:: 3.png :width: 400px Step4: Estimate position from probability @@ -110,5 +110,5 @@ There are two ways to calculate the final positions: References: ~~~~~~~~~~~ -- `PROBABILISTIC ROBOTICS`_ +- `_PROBABILISTIC ROBOTICS: `_ - `Robust Vehicle Localization in Urban Environments Using Probabilistic Maps `_ diff --git a/docs/modules/localization/localization_main.rst b/docs/modules/localization/localization_main.rst index b1769df163..db6651f6b8 100644 --- a/docs/modules/localization/localization_main.rst +++ b/docs/modules/localization/localization_main.rst @@ -3,11 +3,14 @@ Localization ============ -.. include:: extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst -.. include:: ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst -.. include:: unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst -.. include:: histogram_filter_localization/histogram_filter_localization.rst -.. include:: particle_filter_localization/particle_filter_localization.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents + + extended_kalman_filter_localization_files/extended_kalman_filter_localization + ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization + unscented_kalman_filter_localization/unscented_kalman_filter_localization + histogram_filter_localization/histogram_filter_localization + particle_filter_localization/particle_filter_localization -.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/ diff --git a/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst similarity index 94% rename from docs/modules/localization/particle_filter_localization/particle_filter_localization.rst rename to docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst index 55b44e166b..c8652ce8a7 100644 --- a/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst +++ b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst @@ -33,5 +33,5 @@ The covariance matrix :math:`\Xi` from particle information is calculated by the References: ~~~~~~~~~~~ -- `PROBABILISTIC ROBOTICS`_ +- `_PROBABILISTIC ROBOTICS: `_ - `Improving the particle filter in high dimensions using conjugate artificial process noise `_ diff --git a/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst b/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst similarity index 100% rename from docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst rename to docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst diff --git a/docs/modules/mapping/circle_fitting/circle_fitting.rst b/docs/modules/mapping/circle_fitting/circle_fitting_main.rst similarity index 100% rename from docs/modules/mapping/circle_fitting/circle_fitting.rst rename to docs/modules/mapping/circle_fitting/circle_fitting_main.rst diff --git a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map_main.rst similarity index 100% rename from docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst rename to docs/modules/mapping/gaussian_grid_map/gaussian_grid_map_main.rst diff --git a/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst b/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering_main.rst similarity index 100% rename from docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst rename to docs/modules/mapping/k_means_object_clustering/k_means_object_clustering_main.rst diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst similarity index 92% rename from docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst index 2e9470ff6c..1f62179efd 100644 --- a/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst +++ b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst @@ -16,7 +16,7 @@ a ``numpy array``, and numbers close to 1 means the cell is occupied free (*marked with green*). The grid has the ability to represent unknown (unobserved) areas, which are close to 0.5. -.. figure:: lidar_to_grid_map_tutorial/grid_map_example.png +.. figure:: grid_map_example.png In order to construct the grid map from the measurement we need to discretise the values. But, first let’s need to ``import`` some @@ -68,7 +68,7 @@ From the distances and the angles it is easy to determine the ``x`` and -.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png +.. image:: lidar_to_grid_map_tutorial_5_0.png The ``lidar_to_grid_map.py`` contains handy functions which can used to @@ -89,7 +89,7 @@ map. Let’s see how this works. -.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png +.. image:: lidar_to_grid_map_tutorial_7_0.png .. code:: ipython3 @@ -106,7 +106,7 @@ map. Let’s see how this works. -.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png +.. image:: lidar_to_grid_map_tutorial_8_0.png To fill empty areas, a queue-based algorithm can be used that can be @@ -163,7 +163,7 @@ from a center point (e.g. (10, 20)) with zeros: -.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png +.. image:: lidar_to_grid_map_tutorial_12_0.png Let’s use this flood fill on real data: @@ -194,5 +194,5 @@ Let’s use this flood fill on real data: -.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png +.. image:: lidar_to_grid_map_tutorial_14_1.png diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst index 7d2c4cfe67..0f7ec549dc 100644 --- a/docs/modules/mapping/mapping_main.rst +++ b/docs/modules/mapping/mapping_main.rst @@ -2,11 +2,14 @@ Mapping ======= +.. toctree:: + :maxdepth: 2 + :caption: Contents -.. include:: gaussian_grid_map/gaussian_grid_map.rst -.. include:: ray_casting_grid_map/ray_casting_grid_map.rst -.. include:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst -.. include:: k_means_object_clustering/k_means_object_clustering.rst -.. include:: circle_fitting/circle_fitting.rst -.. include:: rectangle_fitting/rectangle_fitting.rst + gaussian_grid_map/gaussian_grid_map + ray_casting_grid_map/ray_casting_grid_map + lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial + k_means_object_clustering/k_means_object_clustering + circle_fitting/circle_fitting + rectangle_fitting/rectangle_fitting diff --git a/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst b/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst similarity index 100% rename from docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst rename to docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst similarity index 100% rename from docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst rename to docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst diff --git a/docs/modules/path_planning/bezier_path/bezier_path.rst b/docs/modules/path_planning/bezier_path/bezier_path_main.rst similarity index 85% rename from docs/modules/path_planning/bezier_path/bezier_path.rst rename to docs/modules/path_planning/bezier_path/bezier_path_main.rst index c630d54345..fbba6a4496 100644 --- a/docs/modules/path_planning/bezier_path/bezier_path.rst +++ b/docs/modules/path_planning/bezier_path/bezier_path_main.rst @@ -5,13 +5,13 @@ A sample code of Bezier path planning. It is based on 4 control points Beizer path. -.. image:: Bezier_path/Figure_1.png +.. image:: Figure_1.png If you change the offset distance from start and end point, You can get different Beizer course: -.. image:: Bezier_path/Figure_2.png +.. image:: Figure_2.png Ref: diff --git a/docs/modules/path_planning/bspline_path/bspline_path.rst b/docs/modules/path_planning/bspline_path/bspline_path_main.rst similarity index 88% rename from docs/modules/path_planning/bspline_path/bspline_path.rst rename to docs/modules/path_planning/bspline_path/bspline_path_main.rst index a8043122fa..d3523a1918 100644 --- a/docs/modules/path_planning/bspline_path/bspline_path.rst +++ b/docs/modules/path_planning/bspline_path/bspline_path_main.rst @@ -1,7 +1,7 @@ B-Spline planning ----------------- -.. image:: bspline_path/Figure_1.png +.. image:: Figure_1.png This is a path planning with B-Spline curse. diff --git a/docs/modules/path_planning/bugplanner/bugplanner.rst b/docs/modules/path_planning/bugplanner/bugplanner_main.rst similarity index 100% rename from docs/modules/path_planning/bugplanner/bugplanner.rst rename to docs/modules/path_planning/bugplanner/bugplanner_main.rst diff --git a/docs/modules/path_planning/clothoid_path/clothoid_path.rst b/docs/modules/path_planning/clothoid_path/clothoid_path_main.rst similarity index 100% rename from docs/modules/path_planning/clothoid_path/clothoid_path.rst rename to docs/modules/path_planning/clothoid_path/clothoid_path_main.rst diff --git a/docs/modules/path_planning/coverage_path/coverage_path.rst b/docs/modules/path_planning/coverage_path/coverage_path_main.rst similarity index 100% rename from docs/modules/path_planning/coverage_path/coverage_path.rst rename to docs/modules/path_planning/coverage_path/coverage_path_main.rst diff --git a/docs/modules/path_planning/cubic_spline/cubic_spline.rst b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst similarity index 68% rename from docs/modules/path_planning/cubic_spline/cubic_spline.rst rename to docs/modules/path_planning/cubic_spline/cubic_spline_main.rst index b1973856e8..6de0297a05 100644 --- a/docs/modules/path_planning/cubic_spline/cubic_spline.rst +++ b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst @@ -8,6 +8,6 @@ with cubic spline. Heading angle of each point can be also calculated analytically. -.. image:: cubic_spline/Figure_1.png -.. image:: cubic_spline/Figure_2.png -.. image:: cubic_spline/Figure_3.png +.. image:: Figure_1.png +.. image:: Figure_2.png +.. image:: Figure_3.png diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path_main.rst similarity index 96% rename from docs/modules/path_planning/dubins_path/dubins_path.rst rename to docs/modules/path_planning/dubins_path/dubins_path_main.rst index b612a82ef2..0b8c8fb603 100644 --- a/docs/modules/path_planning/dubins_path/dubins_path.rst +++ b/docs/modules/path_planning/dubins_path/dubins_path_main.rst @@ -19,12 +19,12 @@ Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR, For example, one of RSR Dubins paths is: -.. image:: dubins_path/RSR.jpg +.. image:: RSR.jpg :width: 400px one of RLR Dubins paths is: -.. image:: dubins_path/RLR.jpg +.. image:: RLR.jpg :width: 200px Dubins path planner can output three types and distances of each course segment. diff --git a/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach_main.rst similarity index 92% rename from docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst rename to docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach_main.rst index 0ece6e0054..d645838597 100644 --- a/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst +++ b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach_main.rst @@ -1,3 +1,5 @@ +.. _dynamic_window_approach: + Dynamic Window Approach ----------------------- diff --git a/docs/modules/path_planning/eta3_spline/eta3_spline.rst b/docs/modules/path_planning/eta3_spline/eta3_spline_main.rst similarity index 100% rename from docs/modules/path_planning/eta3_spline/eta3_spline.rst rename to docs/modules/path_planning/eta3_spline/eta3_spline_main.rst diff --git a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst similarity index 100% rename from docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst rename to docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst diff --git a/docs/modules/path_planning/grid_base_search/grid_base_search.rst b/docs/modules/path_planning/grid_base_search/grid_base_search_main.rst similarity index 100% rename from docs/modules/path_planning/grid_base_search/grid_base_search.rst rename to docs/modules/path_planning/grid_base_search/grid_base_search_main.rst diff --git a/docs/modules/path_planning/hybridastar/hybridastar.rst b/docs/modules/path_planning/hybridastar/hybridastar_main.rst similarity index 100% rename from docs/modules/path_planning/hybridastar/hybridastar.rst rename to docs/modules/path_planning/hybridastar/hybridastar_main.rst diff --git a/docs/modules/path_planning/lqr_path/lqr_path.rst b/docs/modules/path_planning/lqr_path/lqr_path_main.rst similarity index 100% rename from docs/modules/path_planning/lqr_path/lqr_path.rst rename to docs/modules/path_planning/lqr_path/lqr_path_main.rst diff --git a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst similarity index 90% rename from docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst rename to docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst index 07a7ee9b59..0fc997898e 100644 --- a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst +++ b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst @@ -14,7 +14,7 @@ Path optimization sample Lookup table generation sample ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. image:: model_predictive_trajectory_generator/lookup_table.png +.. image:: lookup_table.png Ref: diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst index 5c70592afc..a3237f16f1 100644 --- a/docs/modules/path_planning/path_planning_main.rst +++ b/docs/modules/path_planning/path_planning_main.rst @@ -3,24 +3,28 @@ Path Planning ============= -.. include:: dynamic_window_approach/dynamic_window_approach.rst -.. include:: bugplanner/bugplanner.rst -.. include:: grid_base_search/grid_base_search.rst -.. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst -.. include:: state_lattice_planner/state_lattice_planner.rst -.. include:: prm_planner/prm_planner.rst -.. include:: visibility_road_map_planner/visibility_road_map_planner.rst -.. include:: vrm_planner/vrm_planner.rst -.. include:: rrt/rrt.rst -.. include:: cubic_spline/cubic_spline.rst -.. include:: bspline_path/bspline_path.rst -.. include:: clothoid_path/clothoid_path.rst -.. include:: eta3_spline/eta3_spline.rst -.. include:: bezier_path/bezier_path.rst -.. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst -.. include:: dubins_path/dubins_path.rst -.. include:: reeds_shepp_path/reeds_shepp_path.rst -.. include:: lqr_path/lqr_path.rst -.. include:: hybridastar/hybridastar.rst -.. include:: frenet_frame_path/frenet_frame_path.rst -.. include:: coverage_path/coverage_path.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents + + dynamic_window_approach/dynamic_window_approach + bugplanner/bugplanner + grid_base_search/grid_base_search + model_predictive_trajectory_generator/model_predictive_trajectory_generator + state_lattice_planner/state_lattice_planner + prm_planner/prm_planner + visibility_road_map_planner/visibility_road_map_planner + vrm_planner/vrm_planner + rrt/rrt + cubic_spline/cubic_spline + bspline_path/bspline_path + clothoid_path/clothoid_path + eta3_spline/eta3_spline + bezier_path/bezier_path + quintic_polynomials_planner/quintic_polynomials_planner + dubins_path/dubins_path + reeds_shepp_path/reeds_shepp_path + lqr_path/lqr_path + hybridastar/hybridastar + frenet_frame_path/frenet_frame_path + coverage_path/coverage_path diff --git a/docs/modules/path_planning/prm_planner/prm_planner.rst b/docs/modules/path_planning/prm_planner/prm_planner_main.rst similarity index 100% rename from docs/modules/path_planning/prm_planner/prm_planner.rst rename to docs/modules/path_planning/prm_planner/prm_planner_main.rst diff --git a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst similarity index 100% rename from docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst rename to docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst diff --git a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst similarity index 100% rename from docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst rename to docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst diff --git a/docs/modules/path_planning/rrt/rrt.rst b/docs/modules/path_planning/rrt/rrt_main.rst similarity index 99% rename from docs/modules/path_planning/rrt/rrt.rst rename to docs/modules/path_planning/rrt/rrt_main.rst index 1afa17b144..5a3a30dcff 100644 --- a/docs/modules/path_planning/rrt/rrt.rst +++ b/docs/modules/path_planning/rrt/rrt_main.rst @@ -14,7 +14,7 @@ This is a simple path planning code with Rapidly-Exploring Random Trees Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions. -.. include:: rrt/rrt_star.rst +.. include:: rrt_star.rst RRT with dubins path diff --git a/docs/modules/path_planning/rrt/rrt_star.rst b/docs/modules/path_planning/rrt/rrt_star.rst index 546e9bf6dd..d36eaac70e 100644 --- a/docs/modules/path_planning/rrt/rrt_star.rst +++ b/docs/modules/path_planning/rrt/rrt_star.rst @@ -10,7 +10,7 @@ Black circles are obstacles, green line is a searched tree, red crosses are star Simulation ^^^^^^^^^^ -.. image:: rrt/rrt_star/rrt_star_1_0.png +.. image:: rrt_star/rrt_star_1_0.png :width: 600px diff --git a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst similarity index 100% rename from docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst rename to docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst diff --git a/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst b/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst similarity index 91% rename from docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst rename to docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst index 97e083389e..3c9b7c008c 100644 --- a/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst +++ b/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst @@ -24,7 +24,7 @@ We assume this planner can be provided these information in the below figure. - 2. Goal point (Blue point) - 3. Obstacle polygons (Black lines) -.. image:: visibility_road_map_planner/step0.png +.. image:: step0.png :width: 400px @@ -33,7 +33,7 @@ Step1: Generate visibility nodes based on polygon obstacles The nodes are generated by expanded these polygons vertexes like the below figure: -.. image:: visibility_road_map_planner/step1.png +.. image:: step1.png :width: 400px Each polygon vertex is expanded outward from the vector of adjacent vertices. @@ -49,7 +49,7 @@ If the arc is collided, the graph is removed. The blue lines are generated visibility graphs in the figure: -.. image:: visibility_road_map_planner/step2.png +.. image:: step2.png :width: 400px @@ -58,7 +58,7 @@ Step3: Search the shortest path in the graphs using Dijkstra algorithm The red line is searched path in the figure: -.. image:: visibility_road_map_planner/step3.png +.. image:: step3.png :width: 400px You can find the details of Dijkstra algorithm in :ref:`dijkstra`. diff --git a/docs/modules/path_planning/vrm_planner/vrm_planner.rst b/docs/modules/path_planning/vrm_planner/vrm_planner_main.rst similarity index 100% rename from docs/modules/path_planning/vrm_planner/vrm_planner.rst rename to docs/modules/path_planning/vrm_planner/vrm_planner_main.rst diff --git a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst similarity index 92% rename from docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst index 5092fa8e2f..a1980ba17e 100644 --- a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst +++ b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst @@ -2,16 +2,16 @@ Nonlinear Model Predictive Control with C-GMRES ----------------------------------------------- -.. image:: cgmres_nmpc/cgmres_nmpc_1_0.png +.. image:: cgmres_nmpc_1_0.png :width: 600px -.. image:: cgmres_nmpc/cgmres_nmpc_2_0.png +.. image:: cgmres_nmpc_2_0.png :width: 600px -.. image:: cgmres_nmpc/cgmres_nmpc_3_0.png +.. image:: cgmres_nmpc_3_0.png :width: 600px -.. image:: cgmres_nmpc/cgmres_nmpc_4_0.png +.. image:: cgmres_nmpc_4_0.png :width: 600px .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst similarity index 100% rename from docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst rename to docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst similarity index 100% rename from docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst rename to docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst diff --git a/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst similarity index 100% rename from docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst rename to docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst index 2a572ef6df..504ba08795 100644 --- a/docs/modules/path_tracking/path_tracking_main.rst +++ b/docs/modules/path_tracking/path_tracking_main.rst @@ -3,11 +3,14 @@ Path Tracking ============= -.. include:: pure_pursuit_tracking/pure_pursuit_tracking.rst -.. include:: stanley_control/stanley_control.rst -.. include:: rear_wheel_feedback_control/rear_wheel_feedback_control.rst -.. include:: lqr_steering_control/lqr_steering_control.rst -.. include:: lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst -.. include:: model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst -.. include:: cgmres_nmpc/cgmres_nmpc.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents + pure_pursuit_tracking/pure_pursuit_tracking + stanley_control/stanley_control + rear_wheel_feedback_control/rear_wheel_feedback_control + lqr_steering_control/lqr_steering_control + lqr_speed_and_steering_control/lqr_speed_and_steering_control + model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control + cgmres_nmpc/cgmres_nmpc diff --git a/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst b/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst similarity index 100% rename from docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst rename to docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst diff --git a/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst b/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst similarity index 100% rename from docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst rename to docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst diff --git a/docs/modules/path_tracking/stanley_control/stanley_control.rst b/docs/modules/path_tracking/stanley_control/stanley_control_main.rst similarity index 100% rename from docs/modules/path_tracking/stanley_control/stanley_control.rst rename to docs/modules/path_tracking/stanley_control/stanley_control_main.rst diff --git a/docs/modules/slam/FastSLAM1/FastSLAM1.rst b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst similarity index 98% rename from docs/modules/slam/FastSLAM1/FastSLAM1.rst rename to docs/modules/slam/FastSLAM1/FastSLAM1_main.rst index f1e9dfd8aa..f09125dff0 100644 --- a/docs/modules/slam/FastSLAM1/FastSLAM1.rst +++ b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst @@ -11,7 +11,7 @@ Simulation This is a feature based SLAM example using FastSLAM 1.0. -.. image:: FastSLAM1/FastSLAM1_1_0.png +.. image:: FastSLAM1_1_0.png :width: 600px The blue line is ground truth, the black line is dead reckoning, the red @@ -46,8 +46,8 @@ an array of landmark locations I.e. Each particle maintains a deterministic pose and n-EKFs for each landmark and update it with each measurement. -Algorithm walkthrough -~~~~~~~~~~~~~~~~~~~~~ +Algorithm walk through +~~~~~~~~~~~~~~~~~~~~~~~ The particles are initially drawn from a uniform distribution the represent the initial uncertainty. At each time step we do: @@ -75,7 +75,7 @@ which are the linear and angular velocity repsectively. :math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \theta_{t+1} \end{bmatrix}= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}\begin{bmatrix} x_{t} \\ y_{t} \\ \theta_{t} \end{bmatrix}+ \begin{bmatrix} \Delta t cos(\theta) & 0\\ \Delta t sin(\theta) & 0\\ 0 & \Delta t \end{bmatrix} \begin{bmatrix} v_{t} + \sigma_v\\ w_{t} + \sigma_w\\ \end{bmatrix} \end{equation*}` -The following snippets playsback the recorded trajectory of each +The following snippets playback the recorded trajectory of each particle. To get the insight of the motion model change the value of :math:`R` and @@ -527,8 +527,8 @@ indices -.. image:: FastSLAM1/FastSLAM1_12_0.png -.. image:: FastSLAM1/FastSLAM1_12_1.png +.. image:: FastSLAM1_12_0.png +.. image:: FastSLAM1_12_1.png References diff --git a/docs/modules/slam/FastSLAM2/FastSLAM2.rst b/docs/modules/slam/FastSLAM2/FastSLAM2_main.rst similarity index 100% rename from docs/modules/slam/FastSLAM2/FastSLAM2.rst rename to docs/modules/slam/FastSLAM2/FastSLAM2_main.rst diff --git a/docs/modules/slam/ekf_slam/ekf_slam.rst b/docs/modules/slam/ekf_slam/ekf_slam_main.rst similarity index 99% rename from docs/modules/slam/ekf_slam/ekf_slam.rst rename to docs/modules/slam/ekf_slam/ekf_slam_main.rst index a79c2737ae..5a23b4c887 100644 --- a/docs/modules/slam/ekf_slam/ekf_slam.rst +++ b/docs/modules/slam/ekf_slam/ekf_slam_main.rst @@ -581,7 +581,7 @@ reckoning and control functions are passed along here as well. New LM New LM -.. image:: ekf_slam/ekf_slam_1_0.png +.. image:: ekf_slam_1_0.png References: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst index ef5d05bf22..491320512b 100644 --- a/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst +++ b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst @@ -44,7 +44,7 @@ The Dataset -.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png Each edge in this dataset is a constraint that compares the measured @@ -122,7 +122,7 @@ dataset and plot them. -.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png .. code:: ipython3 @@ -131,7 +131,7 @@ dataset and plot them. -.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png Optimization @@ -165,7 +165,7 @@ different data sources into a single optimization problem. 6 215.8405 -0.000000 -.. figure:: graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif +.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif .. code:: ipython3 @@ -173,7 +173,7 @@ different data sources into a single optimization problem. -.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png .. code:: ipython3 @@ -195,7 +195,7 @@ different data sources into a single optimization problem. -.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png +.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png .. code:: ipython3 @@ -204,5 +204,5 @@ different data sources into a single optimization problem. -.. image:: 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of the motion and the observation. -.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png +.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png .. parsed-literal:: @@ -157,7 +157,7 @@ created based on the information of the motion and the observation. -.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png +.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png In particular, the tasks are split into 2 parts, graph construction, and @@ -289,7 +289,7 @@ robot with 3DoF, namely, :math:`[x, y, \theta]^T` -.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png +.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png .. code:: ipython3 @@ -420,7 +420,7 @@ zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal -.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png +.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png Since the constraints equations derived before are non-linear, @@ -494,9 +494,9 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}` -.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png +.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png -.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png +.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png .. code:: ipython3 diff --git a/docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png b/docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png new file mode 100644 index 0000000000000000000000000000000000000000..a9859333465a59a0c4034f9d007e8dac510a9240 GIT binary patch literal 3931 zcmbtX2~<!AI4~r{fFKB>fDocaQNSP&0SQ7FB@8AqbgK|W0mBdm z1#CkYL{I`@5^w<1d?pFV6b3<20!A8*5eUH-?BDP8d;Q*DfBk>gy7yMqty`<=oU`}Y z=i2dOb}H+(tOEc*1!HgH2mo?uS$k=%g6yev%Q{i^S{-%>b84;Zy0rEjP1aWovB!r2 zfYOGQTaGspvm_fdCEK1RpA06FBRwzp0D+$5kbq!vfS=d42%igKe!)R#1PX!D-*%o% z4lzR_|M49We8CsFo(SFy0IIbZn}esKZVN|Z&%1xU$erurW=y^V@9j<6++UF%brZYx z9A;$iQysFRcIOteO`er6pHyhF4Zf7xc=V}Opr+2IusHah6RCvDW(H3K4=a*5yd9am 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z`408ZGQsIg9lOrJ1}wKMq=zMnQO1|*^rlkM8t19*`;|Ch5+v(&PDGG(FB|Q^m~(m( zwl$V9+_ZIUIr`fcYfIM1keQ9;*v78MJgChZQ^&He<>+{y3cAyyt_ELi?V-X#H$DE4_PJjM8#ym`vs_ZOL;EpsqF17?5Iy^yrD9@iucY zdNYmDu?l5((ng7W`1yzQ^orw6qqvrj`;jIu`SOdkdJbz7QORqzz~>3F4?hcVSf{u@ z9#R_ivG9;nBPRKm`$$zlh5o#p^hhP+KGX0W;6nE~y#g&zT78!NhXcsuGnex=1G2a& zuh@@@#FIiD#kX!0MqZN4x$_>E$AV$lRIa-)xfa`r)rZj3x6C{Gxav)eDo%rWxpKXL p^a7q$@b^3UzhxGG%FT}lAZ8I3v?i+?5m+XJp0+W(@`~fV{{miSrxE}F literal 0 HcmV?d00001 diff --git a/docs/modules/slam/graph_slam/graphSLAM_formulation.rst b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst index 43f007e7bf..4978596c10 100644 --- a/docs/modules/slam/graph_slam/graphSLAM_formulation.rst +++ b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst @@ -70,7 +70,7 @@ Using Bayes’ rule, we can write this probability as since :math:`p(\mathcal{Z})` is a constant (albeit, an unknown constant) and we assume that :math:`p(\mathbf{p}_1, \ldots, \mathbf{p}_N)` is -uniformly distributed `PROBABILISTIC ROBOTICS`_. Therefore, we +uniformly distributed. Therefore, we can use Eq. :eq:`infomat` and and Eq. :eq:`bayes` to simplify the Graph SLAM optimization as follows: diff --git a/docs/modules/slam/graph_slam/graph_slam.rst b/docs/modules/slam/graph_slam/graph_slam_main.rst similarity index 78% rename from docs/modules/slam/graph_slam/graph_slam.rst rename to docs/modules/slam/graph_slam/graph_slam_main.rst index a1248a01cc..987eed385c 100644 --- a/docs/modules/slam/graph_slam/graph_slam.rst +++ b/docs/modules/slam/graph_slam/graph_slam_main.rst @@ -13,9 +13,9 @@ The black stars are landmarks for graph edge generation. .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif -.. include:: graph_slam/graphSLAM_doc.rst -.. include:: graph_slam/graphSLAM_formulation.rst -.. include:: graph_slam/graphSLAM_SE2_example.rst +.. include:: graphSLAM_doc.rst +.. include:: graphSLAM_formulation.rst +.. include:: graphSLAM_SE2_example.rst References: ~~~~~~~~~~~ diff --git a/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst b/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst similarity index 100% rename from docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst rename to docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst diff --git a/docs/modules/slam/slam_main.rst b/docs/modules/slam/slam_main.rst index f4d10feb9a..86befa6e35 100644 --- a/docs/modules/slam/slam_main.rst +++ b/docs/modules/slam/slam_main.rst @@ -5,9 +5,12 @@ SLAM Simultaneous Localization and Mapping(SLAM) examples -.. include:: iterative_closest_point_matching/iterative_closest_point_matching.rst -.. include:: ekf_slam/ekf_slam.rst -.. include:: FastSLAM1/FastSLAM1.rst -.. include:: FastSLAM2/FastSLAM2.rst -.. include:: graph_slam/graph_slam.rst +.. toctree:: + :maxdepth: 2 + :caption: Contents + iterative_closest_point_matching/iterative_closest_point_matching + ekf_slam/ekf_slam + FastSLAM1/FastSLAM1 + FastSLAM2/FastSLAM2 + graph_slam/graph_slam From b9345da036bb9655c84b8081199f99d17b25afd6 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 7 May 2022 16:04:39 +0900 Subject: [PATCH 446/909] try to use python3.10 (#561) * try to use python3.10 * try python3.10 * try python3.10 --- .circleci/config.yml | 3 ++- .github/workflows/Linux_CI.yml | 3 ++- .github/workflows/MacOS_CI.yml | 4 ++-- README.md | 2 +- appveyor.yml | 2 +- docs/getting_started_main.rst | 4 ++-- requirements/environment.yml | 2 +- 7 files changed, 11 insertions(+), 9 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 275ad3bc7a..075a613be0 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -6,12 +6,13 @@ orbs: jobs: build_doc: docker: - - image: cimg/python:3.9 + - image: cimg/python:3.10 steps: - checkout - run: name: doc_build command: | + python --version python -m venv venv . venv/bin/activate pip install -r requirements/requirements.txt diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index c706a50ed6..73f8aa5c0d 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.9'] + python-version: ['3.10'] name: Python ${{ matrix.python-version }} CI @@ -26,6 +26,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | + python --version python -m pip install --upgrade pip python -m pip install -r requirements/requirements.txt - name: do all unit tests diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index 4088c4900f..5c796120a5 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -16,7 +16,7 @@ jobs: runs-on: macos-latest strategy: matrix: - python-version: [ '3.9' ] + python-version: [ '3.10' ] name: Python ${{ matrix.python-version }} CI steps: - uses: actions/checkout@v2 @@ -32,8 +32,8 @@ jobs: - name: Install dependencies run: | + python --version python -m pip install --upgrade pip - #pip install numpy # cvxpy install workaround pip install -r requirements/requirements.txt - name: do all unit tests run: bash runtests.sh \ No newline at end of file diff --git a/README.md b/README.md index 8972bae5fa..f54b880dc0 100644 --- a/README.md +++ b/README.md @@ -96,7 +96,7 @@ See this paper for more details: For running each sample code: -- [Python 3.9.x](https://www.python.org/) +- [Python 3.10.x](https://www.python.org/) - [NumPy](https://numpy.org/) diff --git a/appveyor.yml b/appveyor.yml index c35e76ae4a..4964bab3da 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -8,7 +8,7 @@ environment: CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd" matrix: - - PYTHON_DIR: C:\Python39-x64 + - PYTHON_DIR: C:\Python310-x64 branches: only: diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst index a7b9ff3607..4a19126686 100644 --- a/docs/getting_started_main.rst +++ b/docs/getting_started_main.rst @@ -26,7 +26,7 @@ See this paper for more details: Requirements ------------- -- `Python 3.9.x`_ +- `Python 3.10.x`_ - `NumPy`_ - `SciPy`_ - `Matplotlib`_ @@ -41,7 +41,7 @@ For development: - sphinx (for document generation) - pycodestyle (for code style check) -.. _`Python 3.9.x`: https://www.python.org/ +.. _`Python 3.10.x`: https://www.python.org/ .. _`NumPy`: https://numpy.org/ .. _`SciPy`: https://scipy.org/ .. _`Matplotlib`: https://matplotlib.org/ diff --git a/requirements/environment.yml b/requirements/environment.yml index 1441b79080..109fc57789 100644 --- a/requirements/environment.yml +++ b/requirements/environment.yml @@ -2,7 +2,7 @@ name: python_robotics channels: - conda-forge dependencies: - - python=3.9 + - python=3.10 - pip - scipy - numpy From e18799cbb745d3cc3ff5cad707f401ee1f218834 Mon Sep 17 00:00:00 2001 From: Drake <1024507980@qq.com> Date: Sat, 7 May 2022 16:20:20 +0800 Subject: [PATCH 447/909] modify end node's state (#656) --- PathPlanning/DStar/dstar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py index e01508b3df..f8954d776c 100644 --- a/PathPlanning/DStar/dstar.py +++ b/PathPlanning/DStar/dstar.py @@ -160,7 +160,7 @@ def run(self, start, end): rx = [] ry = [] - self.open_list.add(end) + self.insert(end, 0.0) while True: self.process_state() From a3fd04f68f3b1bada4c6393e5b9dda3704eb36bd Mon Sep 17 00:00:00 2001 From: jsbyysheng Date: Sat, 7 May 2022 17:15:01 +0800 Subject: [PATCH 448/909] fix error: 'numpy.ndarray' object has no attribute 'append' (#662) * Update grid_map_lib.py fix error: 'numpy.ndarray' object has no attribute 'append' * Update test_grid_map_lib.py * Update test_grid_map_lib.py Co-authored-by: Atsushi Sakai --- Mapping/grid_map_lib/grid_map_lib.py | 4 ++-- tests/test_grid_map_lib.py | 3 +++ 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py index 10651cc673..a93ea309d8 100644 --- a/Mapping/grid_map_lib/grid_map_lib.py +++ b/Mapping/grid_map_lib/grid_map_lib.py @@ -120,8 +120,8 @@ def set_value_from_polygon(self, pol_x, pol_y, val, inside=True): # making ring polygon if (pol_x[0] != pol_x[-1]) or (pol_y[0] != pol_y[-1]): - pol_x.append(pol_x[0]) - pol_y.append(pol_y[0]) + np.append(pol_x, pol_x[0]) + np.append(pol_y, pol_y[0]) # setting value for all grid for x_ind in range(self.width): diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py index ffdb72b1ae..ed4303d4dc 100644 --- a/tests/test_grid_map_lib.py +++ b/tests/test_grid_map_lib.py @@ -1,4 +1,5 @@ from Mapping.grid_map_lib.grid_map_lib import GridMap +import numpy as np def test_position_set(): @@ -19,6 +20,8 @@ def test_polygon_set(): grid_map = GridMap(600, 290, 0.7, 60.0, 30.5) grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False) + grid_map.set_value_from_polygon(np.array(ox), np.array(oy), + 1.0, inside=False) if __name__ == '__main__': From 3d209faefc5d0cedf9ba347d189e99caf0e5265b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sat, 7 May 2022 22:43:31 +0900 Subject: [PATCH 449/909] add gh-pages action (#671) --- .github/workflows/gh-pages.yml | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 .github/workflows/gh-pages.yml diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml new file mode 100644 index 0000000000..e51197cb37 --- /dev/null +++ b/.github/workflows/gh-pages.yml @@ -0,0 +1,29 @@ +name: Pages +on: + push: + branches: + - master +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Setup python + uses: actions/setup-python@v2 + - name: Checkout + uses: actions/checkout@master + with: + fetch-depth: 0 # otherwise, you will fail to push refs to dest repo + - name: Install dependencies + run: | + python --version + python -m pip install --upgrade pip + python -m pip install -r requirements/requirements.txt + - name: Build and Commit + uses: sphinx-notes/pages@v2 + with: + requirements_path: ./docs/doc_requirements.txt + - name: Push changes + uses: ad-m/github-push-action@master + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + branch: gh-pages \ No newline at end of file From f74f0c1da2030794cede57709df076713be04269 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 May 2022 14:11:21 +0900 Subject: [PATCH 450/909] enhance_github pages doc (#672) * enhance_github pages doc * enhance_github pages doc --- docs/_templates/layout.html | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 docs/_templates/layout.html diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html new file mode 100644 index 0000000000..7cd969e52f --- /dev/null +++ b/docs/_templates/layout.html @@ -0,0 +1,8 @@ +{% extends "!layout.html" %} + +{%- block extrahead %} +{{ super() }} + +{% endblock %} From 785e62175fe71c277d8501018347554c5edda836 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 May 2022 14:23:11 +0900 Subject: [PATCH 451/909] enhance_github pages doc (#673) --- docs/_templates/layout.html | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html index 7cd969e52f..89c38a39f9 100644 --- a/docs/_templates/layout.html +++ b/docs/_templates/layout.html @@ -6,3 +6,19 @@ src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886" crossOrigin="anonymous"> {% endblock %} + +{% block sidebarsearch %} +{{ super() }} + + + + +{% endblock %} From 20220fdfd27b277d8ff5fac22680b47ef3ce7149 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 May 2022 14:29:48 +0900 Subject: [PATCH 452/909] enhance_github pages doc (#674) --- docs/_templates/layout.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html index 89c38a39f9..6593bd00c0 100644 --- a/docs/_templates/layout.html +++ b/docs/_templates/layout.html @@ -7,7 +7,7 @@ crossOrigin="anonymous"> {% endblock %} -{% block sidebarsearch %} +{% block footer %} {{ super() }} From ec19f6373d1aeae40ace9bf6100f49da4b09a6cd Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 May 2022 14:39:01 +0900 Subject: [PATCH 453/909] enhance_github pages doc (#675) --- docs/_templates/layout.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html index 6593bd00c0..c0cedb2330 100644 --- a/docs/_templates/layout.html +++ b/docs/_templates/layout.html @@ -7,7 +7,7 @@ crossOrigin="anonymous"> {% endblock %} -{% block footer %} +{% block content %} {{ super() }} From b01128a8f65605d75016ef980009711e1f49c917 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 May 2022 14:49:20 +0900 Subject: [PATCH 454/909] enhance_github pages doc (#676) --- docs/_templates/layout.html | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html index c0cedb2330..4b5b278932 100644 --- a/docs/_templates/layout.html +++ b/docs/_templates/layout.html @@ -1,13 +1,6 @@ {% extends "!layout.html" %} -{%- block extrahead %} -{{ super() }} - -{% endblock %} - -{% block content %} +{% block sidebartitle %} {{ super() }} From f5d80ecb1cf17193b9061627215ee18348e3b6dc Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Sun, 8 May 2022 15:56:51 +0900 Subject: [PATCH 455/909] remove old doc links (#677) * remove old doc links * add github link * add github link * add github link --- .github/pull_request_template.md | 2 +- README.md | 5 ++--- docs/conf.py | 11 +++++++++++ docs/how_to_contribute_main.rst | 2 +- users_comments.md | 2 +- 5 files changed, 16 insertions(+), 6 deletions(-) diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index c541063156..c8790ad9ec 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -1,7 +1,7 @@ #### CheckList --[] Did you add an unittest for your new example or defect fix? --[] Did you add documents for your new example? --[] All CIs are green? (You can check it after submitting) +- [ ] Did you add an unittest for your new example or defect fix? +- [ ] Did you add documents for your new example? +- [ ] All CIs are green? (You can check it after submitting) From ebdc2d45003c9ce86eaebb1ebfd09d527c7e2eaa Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 11 Oct 2022 06:46:55 +0900 Subject: [PATCH 490/909] Bump matplotlib from 3.6.0 to 3.6.1 in /requirements (#736) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.0 to 3.6.1. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.0...v3.6.1) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index a85aa121e6..67637bb3e5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.23.3 scipy == 1.9.1 -matplotlib == 3.6.0 +matplotlib == 3.6.1 cvxpy == 1.2.1 pytest == 7.1.3 # For unit test pytest-xdist == 2.5.0 # For unit test From 001efc5935c4c5a3d25c2d5441db838817fa44b3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 13 Oct 2022 20:13:00 +0900 Subject: [PATCH 491/909] Bump scipy from 1.9.1 to 1.9.2 in /requirements (#737) Bumps [scipy](https://github.com/scipy/scipy) from 1.9.1 to 1.9.2. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.9.1...v1.9.2) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 67637bb3e5..e2fa5d7546 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.23.3 -scipy == 1.9.1 +scipy == 1.9.2 matplotlib == 3.6.1 cvxpy == 1.2.1 pytest == 7.1.3 # For unit test From bc88e70f7ed53226d9c200a2087ab361a2d8bf6b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 18 Oct 2022 07:02:25 +0900 Subject: [PATCH 492/909] Bump numpy from 1.23.3 to 1.23.4 in /requirements (#738) Bumps [numpy](https://github.com/numpy/numpy) from 1.23.3 to 1.23.4. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.23.3...v1.23.4) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index e2fa5d7546..160c043818 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.23.3 +numpy == 1.23.4 scipy == 1.9.2 matplotlib == 3.6.1 cvxpy == 1.2.1 From 381634821511054f50dad6743ae0700d75525c72 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 25 Oct 2022 07:59:09 +0900 Subject: [PATCH 493/909] Bump scipy from 1.9.2 to 1.9.3 in /requirements (#739) Bumps [scipy](https://github.com/scipy/scipy) from 1.9.2 to 1.9.3. - [Release notes](https://github.com/scipy/scipy/releases) - [Commits](https://github.com/scipy/scipy/compare/v1.9.2...v1.9.3) --- updated-dependencies: - dependency-name: scipy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 160c043818..1170b24088 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,5 +1,5 @@ numpy == 1.23.4 -scipy == 1.9.2 +scipy == 1.9.3 matplotlib == 3.6.1 cvxpy == 1.2.1 pytest == 7.1.3 # For unit test From 39472e66d217b6980d8ec18431302219b5fe2412 Mon Sep 17 00:00:00 2001 From: Abinash Satapathy Date: Sun, 30 Oct 2022 06:28:58 +0100 Subject: [PATCH 494/909] Update README.md (#732) * Update README.md Updated the sponsors correctly * Update README.md Remove the tag. * Update README.md Updated development references and styled the single line codes in "How to use" section * Update README.md Styled single-line comments using syntax highlighting for markdown --- README.md | 35 +++++++++++++++++++++-------------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 97a7742dca..b46195f35e 100644 --- a/README.md +++ b/README.md @@ -70,8 +70,9 @@ Python codes for robotics algorithm. * [Contribution](#contribution) * [Citing](#citing) * [Support](#support) - * [Sponsors](#Sponsors) + * [Sponsors](#sponsors) * [JetBrains](#JetBrains) + * [1Password](#1password) * [Authors](#authors) # What is this? @@ -107,15 +108,15 @@ For running each sample code: For development: -- pytest (for unit tests) +- [pytest](https://pytest.org/) (for unit tests) -- pytest-xdist (for parallel unit tests) +- [pytest-xdist](https://pypi.org/project/pytest-xdist/) (for parallel unit tests) -- mypy (for type check) +- [mypy](http://mypy-lang.org/) (for type check) -- sphinx (for document generation) +- [sphinx](https://www.sphinx-doc.org/) (for document generation) -- pycodestyle (for code style check) +- [pycodestyle](https://pypi.org/project/pycodestyle/) (for code style check) # Documentation @@ -131,18 +132,24 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation 1. Clone this repo. -> git clone https://github.com/AtsushiSakai/PythonRobotics.git + ```terminal + git clone https://github.com/AtsushiSakai/PythonRobotics.git + ``` 2. Install the required libraries. -using conda : +- using conda : -> conda env create -f requirements/environment.yml + ```terminal + conda env create -f requirements/environment.yml + ``` -using pip : +- using pip : -> pip install -r requirements/requirements.txt + ```terminal + pip install -r requirements/requirements.txt + ``` 3. Execute python script in each directory. @@ -614,7 +621,7 @@ If you use this project's code for your academic work, we encourage you to cite If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly. -# Supporting this project +# Supporting this project If you or your company would like to support this project, please consider: @@ -626,9 +633,9 @@ If you or your company would like to support this project, please consider: If you would like to support us in some other way, please contact with creating an issue. -## Sponsors +## Sponsors -### [JetBrains](https://www.jetbrains.com/) +### [JetBrains](https://www.jetbrains.com/) They are providing a free license of their IDEs for this OSS development. From 2b877369a4a9335e5b28e2b8cabfe03c8cbb7405 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 1 Nov 2022 06:15:31 +0900 Subject: [PATCH 495/909] Bump pytest-xdist from 2.5.0 to 3.0.2 in /requirements (#743) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 2.5.0 to 3.0.2. - [Release notes](https://github.com/pytest-dev/pytest-xdist/releases) - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v2.5.0...v3.0.2) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 1170b24088..82598ddd69 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.9.3 matplotlib == 3.6.1 cvxpy == 1.2.1 pytest == 7.1.3 # For unit test -pytest-xdist == 2.5.0 # For unit test +pytest-xdist == 3.0.2 # For unit test mypy == 0.982 # For unit test flake8 == 5.0.4 # For unit test From 2d0fa60fd40398244317491655f6a51462cb73c6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 2 Nov 2022 23:23:52 +0900 Subject: [PATCH 496/909] Bump pytest from 7.1.3 to 7.2.0 in /requirements (#742) Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.3 to 7.2.0. - [Release notes](https://github.com/pytest-dev/pytest/releases) - [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest/compare/7.1.3...7.2.0) --- updated-dependencies: - dependency-name: pytest dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 82598ddd69..c8660ebd16 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -2,7 +2,7 @@ numpy == 1.23.4 scipy == 1.9.3 matplotlib == 3.6.1 cvxpy == 1.2.1 -pytest == 7.1.3 # For unit test +pytest == 7.2.0 # For unit test pytest-xdist == 3.0.2 # For unit test mypy == 0.982 # For unit test flake8 == 5.0.4 # For unit test From 768df6ffabddc030fdaef3c1668c06ab30d25ba0 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Nov 2022 07:27:05 +0900 Subject: [PATCH 497/909] Bump matplotlib from 3.6.1 to 3.6.2 in /requirements (#745) Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.1 to 3.6.2. - [Release notes](https://github.com/matplotlib/matplotlib/releases) - [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.1...v3.6.2) --- updated-dependencies: - dependency-name: matplotlib dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index c8660ebd16..ffaef30d19 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,6 +1,6 @@ numpy == 1.23.4 scipy == 1.9.3 -matplotlib == 3.6.1 +matplotlib == 3.6.2 cvxpy == 1.2.1 pytest == 7.2.0 # For unit test pytest-xdist == 3.0.2 # For unit test From 8cd5b05fb43aea4f91faf2007b039667b68681a6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 8 Nov 2022 08:04:42 +0900 Subject: [PATCH 498/909] Bump cvxpy from 1.2.1 to 1.2.2 in /requirements (#746) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.1 to 1.2.2. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.1...v1.2.2) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index ffaef30d19..cc6ca162c2 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.23.4 scipy == 1.9.3 matplotlib == 3.6.2 -cvxpy == 1.2.1 +cvxpy == 1.2.2 pytest == 7.2.0 # For unit test pytest-xdist == 3.0.2 # For unit test mypy == 0.982 # For unit test From 85e00de888e8bc51ce2af0a0370e4a77d53a56b9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Thu, 10 Nov 2022 21:04:55 +0900 Subject: [PATCH 499/909] Bump mypy from 0.982 to 0.990 in /requirements (#744) Bumps [mypy](https://github.com/python/mypy) from 0.982 to 0.990. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v0.982...v0.990) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index cc6ca162c2..889d182f78 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.6.2 cvxpy == 1.2.2 pytest == 7.2.0 # For unit test pytest-xdist == 3.0.2 # For unit test -mypy == 0.982 # For unit test +mypy == 0.990 # For unit test flake8 == 5.0.4 # For unit test From b50a1180d819b0d45d9c565326ba18cbd12c3e5d Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Thu, 10 Nov 2022 21:29:11 +0900 Subject: [PATCH 500/909] fic mypy ci (#747) --- Mapping/rectangle_fitting/simulator.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py index faca2cb79e..aa32ae1b1f 100644 --- a/Mapping/rectangle_fitting/simulator.py +++ b/Mapping/rectangle_fitting/simulator.py @@ -138,7 +138,3 @@ def ray_casting_filter(theta_l, range_l, angle_resolution): ry.append(range_db[i] * np.sin(t)) return rx, ry - - -if __name__ == '__main__': - main() From 8f14488ec2f243e48222bc87923604a7d46c45af Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 15 Nov 2022 07:25:44 +0900 Subject: [PATCH 501/909] Bump mypy from 0.990 to 0.991 in /requirements (#751) Bumps [mypy](https://github.com/python/mypy) from 0.990 to 0.991. - [Release notes](https://github.com/python/mypy/releases) - [Commits](https://github.com/python/mypy/compare/v0.990...v0.991) --- updated-dependencies: - dependency-name: mypy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 889d182f78..fa20768c46 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -4,5 +4,5 @@ matplotlib == 3.6.2 cvxpy == 1.2.2 pytest == 7.2.0 # For unit test pytest-xdist == 3.0.2 # For unit test -mypy == 0.990 # For unit test +mypy == 0.991 # For unit test flake8 == 5.0.4 # For unit test From bcb2c863eb45ec8c11c1dcfdadcef98ef9e85b6b Mon Sep 17 00:00:00 2001 From: KohMat Date: Sun, 20 Nov 2022 13:19:42 +0900 Subject: [PATCH 502/909] Fix the start state to use the current accel in frent planner(#755) (#756) The robot didn't reach the target velocity when running frenet_optimal_trajectory.py. To fix this, I changed the constraints for determining polynomials that generate longitudinal trajectories. Expressly, I set the initial acceleration to the current acceleration. --- .../frenet_optimal_trajectory.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py index b7d1b8e642..331df36309 100644 --- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py +++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py @@ -116,7 +116,7 @@ def __init__(self): self.c = [] -def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): +def calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0): frenet_paths = [] # generate path to each offset goal @@ -139,7 +139,7 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0): for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S): tfp = copy.deepcopy(fp) - lon_qp = QuarticPolynomial(s0, c_speed, 0.0, tv, 0.0, Ti) + lon_qp = QuarticPolynomial(s0, c_speed, c_accel, tv, 0.0, Ti) tfp.s = [lon_qp.calc_point(t) for t in fp.t] tfp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t] @@ -225,8 +225,8 @@ def check_paths(fplist, ob): return [fplist[i] for i in ok_ind] -def frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob): - fplist = calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0) +def frenet_optimal_planning(csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob): + fplist = calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0) fplist = calc_global_paths(fplist, csp) fplist = check_paths(fplist, ob) @@ -274,6 +274,7 @@ def main(): # initial state c_speed = 10.0 / 3.6 # current speed [m/s] + c_accel = 0.0 # current acceleration [m/ss] c_d = 2.0 # current lateral position [m] c_d_d = 0.0 # current lateral speed [m/s] c_d_dd = 0.0 # current lateral acceleration [m/s] @@ -283,13 +284,14 @@ def main(): for i in range(SIM_LOOP): path = frenet_optimal_planning( - csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob) + csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob) s0 = path.s[1] c_d = path.d[1] c_d_d = path.d_d[1] c_d_dd = path.d_dd[1] c_speed = path.s_d[1] + c_accel = path.s_dd[1] if np.hypot(path.x[1] - tx[-1], path.y[1] - ty[-1]) <= 1.0: print("Goal") From 644ce8a5fda39cf8a7fcff4537fe10b3f4bad538 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 22 Nov 2022 10:46:42 +0900 Subject: [PATCH 503/909] Bump numpy from 1.23.4 to 1.23.5 in /requirements (#757) Bumps [numpy](https://github.com/numpy/numpy) from 1.23.4 to 1.23.5. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.23.4...v1.23.5) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index fa20768c46..5e3b4d8caf 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.23.4 +numpy == 1.23.5 scipy == 1.9.3 matplotlib == 3.6.2 cvxpy == 1.2.2 From 405f3e79341356c4fa3f78dbe37bcd87f12d155b Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Mon, 28 Nov 2022 21:27:22 +0900 Subject: [PATCH 504/909] Update gh-pages.yml (#758) --- .github/workflows/gh-pages.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index e51197cb37..bf46598633 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -8,7 +8,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Setup python - uses: actions/setup-python@v2 + uses: actions/setup-python@v3 - name: Checkout uses: actions/checkout@master with: @@ -26,4 +26,4 @@ jobs: uses: ad-m/github-push-action@master with: github_token: ${{ secrets.GITHUB_TOKEN }} - branch: gh-pages \ No newline at end of file + branch: gh-pages From 727f32cb8b1a144ae1b6b167f2c2d2ebee61851b Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 6 Dec 2022 07:29:38 +0900 Subject: [PATCH 505/909] Bump pytest-xdist from 3.0.2 to 3.1.0 in /requirements (#760) Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.0.2 to 3.1.0. - [Release notes](https://github.com/pytest-dev/pytest-xdist/releases) - [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst) - [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.0.2...v3.1.0) --- updated-dependencies: - dependency-name: pytest-xdist dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 5e3b4d8caf..b5e9d37b81 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -3,6 +3,6 @@ scipy == 1.9.3 matplotlib == 3.6.2 cvxpy == 1.2.2 pytest == 7.2.0 # For unit test -pytest-xdist == 3.0.2 # For unit test +pytest-xdist == 3.1.0 # For unit test mypy == 0.991 # For unit test flake8 == 5.0.4 # For unit test From 5ac66d71dd3e4cf06d7b32782af9d2e911633a07 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 20 Dec 2022 07:30:26 +0900 Subject: [PATCH 506/909] Bump numpy from 1.23.5 to 1.24.0 in /requirements (#763) Bumps [numpy](https://github.com/numpy/numpy) from 1.23.5 to 1.24.0. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.23.5...v1.24.0) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index b5e9d37b81..47d9eefab5 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.23.5 +numpy == 1.24.0 scipy == 1.9.3 matplotlib == 3.6.2 cvxpy == 1.2.2 From 6d27738e3d243cf758d6eaa16844504bb6eae2ee Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 27 Dec 2022 08:02:56 +0900 Subject: [PATCH 507/909] Bump numpy from 1.24.0 to 1.24.1 in /requirements (#765) Bumps [numpy](https://github.com/numpy/numpy) from 1.24.0 to 1.24.1. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](https://github.com/numpy/numpy/compare/v1.24.0...v1.24.1) --- updated-dependencies: - dependency-name: numpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 47d9eefab5..3f68d9ab4c 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,4 +1,4 @@ -numpy == 1.24.0 +numpy == 1.24.1 scipy == 1.9.3 matplotlib == 3.6.2 cvxpy == 1.2.2 From af39afe05cfec0ef202cc1542dfe0103c130ed3b Mon Sep 17 00:00:00 2001 From: Lingkang Zhang <36938500+zlingkang@users.noreply.github.com> Date: Fri, 30 Dec 2022 15:38:43 -0800 Subject: [PATCH 508/909] add axes3d to figure (#766) --- Bipedal/bipedal_planner/bipedal_planner.py | 1 + 1 file changed, 1 insertion(+) diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py index 9757fc42d6..8ca5ffcf78 100644 --- a/Bipedal/bipedal_planner/bipedal_planner.py +++ b/Bipedal/bipedal_planner/bipedal_planner.py @@ -51,6 +51,7 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False): if plot: fig = plt.figure() ax = Axes3D(fig) + fig.add_axes(ax) com_trajectory_for_plot = [] px, py = 0.0, 0.0 # reference footstep position From 1d683435c6d7c9eea2895b3f1441f00772f5ab32 Mon Sep 17 00:00:00 2001 From: Christian Clauss Date: Sat, 31 Dec 2022 07:51:17 +0100 Subject: [PATCH 509/909] Undefined name: import sys for line 188 (#764) --- PathPlanning/RRT/rrt_with_sobol_sampler.py | 1 + 1 file changed, 1 insertion(+) diff --git a/PathPlanning/RRT/rrt_with_sobol_sampler.py b/PathPlanning/RRT/rrt_with_sobol_sampler.py index 73488210a7..06e0c04c68 100644 --- a/PathPlanning/RRT/rrt_with_sobol_sampler.py +++ b/PathPlanning/RRT/rrt_with_sobol_sampler.py @@ -28,6 +28,7 @@ import math import random +import sys import matplotlib.pyplot as plt import numpy as np From 2916273e6239c70a12b1e46845a26bd49a727133 Mon Sep 17 00:00:00 2001 From: Andrey Bozhko Date: Sun, 1 Jan 2023 05:44:34 -0600 Subject: [PATCH 510/909] Update loop conditions to be more idiomatic; fix missing imports (#768) * missing import * use while true --- Mapping/rectangle_fitting/rectangle_fitting.py | 2 +- PathPlanning/AStar/a_star.py | 2 +- PathPlanning/BidirectionalAStar/bidirectional_a_star.py | 2 +- .../bidirectional_breadth_first_search.py | 2 +- PathPlanning/BreadthFirstSearch/breadth_first_search.py | 2 +- PathPlanning/DepthFirstSearch/depth_first_search.py | 2 +- PathPlanning/Dijkstra/dijkstra.py | 2 +- PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py | 2 +- PathPlanning/HybridAStar/dynamic_programming_heuristic.py | 2 +- tests/test_grid_map_lib.py | 1 + 10 files changed, 10 insertions(+), 9 deletions(-) diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py index 124e48fe76..f54ada1e3a 100644 --- a/Mapping/rectangle_fitting/rectangle_fitting.py +++ b/Mapping/rectangle_fitting/rectangle_fitting.py @@ -162,7 +162,7 @@ def _adoptive_range_segmentation(self, ox, oy): S.append(C) # Merge cluster - while 1: + while True: no_change = True for (c1, c2) in list(itertools.permutations(range(len(S)), 2)): if S[c1] & S[c2]: diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py index eb2672a9ce..6d20350432 100644 --- a/PathPlanning/AStar/a_star.py +++ b/PathPlanning/AStar/a_star.py @@ -71,7 +71,7 @@ def planning(self, sx, sy, gx, gy): open_set, closed_set = dict(), dict() open_set[self.calc_grid_index(start_node)] = start_node - while 1: + while True: if len(open_set) == 0: print("Open set is empty..") break diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py index ecb875f410..5387cca1ad 100644 --- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py +++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py @@ -75,7 +75,7 @@ def planning(self, sx, sy, gx, gy): current_B = goal_node meet_point_A, meet_point_B = None, None - while 1: + while True: if len(open_set_A) == 0: print("Open set A is empty..") break diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py index dd4282573e..60a8c5e170 100644 --- a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py +++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py @@ -76,7 +76,7 @@ def planning(self, sx, sy, gx, gy): meet_point_A, meet_point_B = None, None - while 1: + while True: if len(open_set_A) == 0: print("Open set A is empty..") break diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py index 406e5eb51f..ff662170e7 100644 --- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py +++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py @@ -67,7 +67,7 @@ def planning(self, sx, sy, gx, gy): open_set, closed_set = dict(), dict() open_set[self.calc_grid_index(nstart)] = nstart - while 1: + while True: if len(open_set) == 0: print("Open set is empty..") break diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py index 523225ee42..6922b8cbad 100644 --- a/PathPlanning/DepthFirstSearch/depth_first_search.py +++ b/PathPlanning/DepthFirstSearch/depth_first_search.py @@ -67,7 +67,7 @@ def planning(self, sx, sy, gx, gy): open_set, closed_set = dict(), dict() open_set[self.calc_grid_index(nstart)] = nstart - while 1: + while True: if len(open_set) == 0: print("Open set is empty..") break diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py index 9956dff326..004e49f15a 100644 --- a/PathPlanning/Dijkstra/dijkstra.py +++ b/PathPlanning/Dijkstra/dijkstra.py @@ -71,7 +71,7 @@ def planning(self, sx, sy, gx, gy): open_set, closed_set = dict(), dict() open_set[self.calc_index(start_node)] = start_node - while 1: + while True: c_id = min(open_set, key=lambda o: open_set[o].cost) current = open_set[c_id] diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py index f2416fba98..b259beb870 100644 --- a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py +++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py @@ -67,7 +67,7 @@ def planning(self, sx, sy, gx, gy): open_set, closed_set = dict(), dict() open_set[self.calc_grid_index(nstart)] = nstart - while 1: + while True: if len(open_set) == 0: print("Open set is empty..") break diff --git a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py index cc635260d6..8a78b15d5f 100644 --- a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py +++ b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py @@ -65,7 +65,7 @@ def calc_distance_heuristic(gx, gy, ox, oy, resolution, rr): open_set[calc_index(goal_node, x_w, min_x, min_y)] = goal_node priority_queue = [(0, calc_index(goal_node, x_w, min_x, min_y))] - while 1: + while True: if not priority_queue: break cost, c_id = heapq.heappop(priority_queue) diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py index 8ae142b003..92ca67e297 100644 --- a/tests/test_grid_map_lib.py +++ b/tests/test_grid_map_lib.py @@ -1,4 +1,5 @@ from Mapping.grid_map_lib.grid_map_lib import GridMap +import conftest import numpy as np From e7710546c18f8f4cd076c030939e77afc86d43c9 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Tue, 3 Jan 2023 07:52:07 +0900 Subject: [PATCH 511/909] Bump cvxpy from 1.2.2 to 1.2.3 in /requirements (#771) Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.2 to 1.2.3. - [Release notes](https://github.com/cvxpy/cvxpy/releases) - [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.2...v1.2.3) --- updated-dependencies: - dependency-name: cvxpy dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- requirements/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements/requirements.txt b/requirements/requirements.txt index 3f68d9ab4c..9a990856f1 100644 --- a/requirements/requirements.txt +++ b/requirements/requirements.txt @@ -1,7 +1,7 @@ numpy == 1.24.1 scipy == 1.9.3 matplotlib == 3.6.2 -cvxpy == 1.2.2 +cvxpy == 1.2.3 pytest == 7.2.0 # For unit test pytest-xdist == 3.1.0 # For unit test mypy == 0.991 # For unit test From 3dc96997da60116ae5bc825d2e67adefe5ff7dc8 Mon Sep 17 00:00:00 2001 From: Atsushi Sakai Date: Tue, 3 Jan 2023 22:27:55 +0900 Subject: [PATCH 512/909] Adding point cloud sampling examples (#770) --- .../point_cloud_sampling.py | 168 ++++++++++++++++++ docs/modules/mapping/mapping_main.rst | 1 + .../farthest_point_sampling.png | Bin 0 -> 253175 bytes .../point_cloud_sampling_main.rst | 70 ++++++++ .../poisson_disk_sampling.png | Bin 0 -> 265198 bytes .../voxel_point_sampling.png | Bin 0 -> 250443 bytes tests/test_point_cloud_sampling.py | 15 ++ 7 files changed, 254 insertions(+) create mode 100644 Mapping/point_cloud_sampling/point_cloud_sampling.py create mode 100644 docs/modules/mapping/point_cloud_sampling/farthest_point_sampling.png create mode 100644 docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst create mode 100644 docs/modules/mapping/point_cloud_sampling/poisson_disk_sampling.png create mode 100644 docs/modules/mapping/point_cloud_sampling/voxel_point_sampling.png create mode 100644 tests/test_point_cloud_sampling.py diff --git a/Mapping/point_cloud_sampling/point_cloud_sampling.py b/Mapping/point_cloud_sampling/point_cloud_sampling.py new file mode 100644 index 0000000000..df7cde41c0 --- /dev/null +++ b/Mapping/point_cloud_sampling/point_cloud_sampling.py @@ -0,0 +1,168 @@ +""" +Point cloud sampling example codes. This code supports +- Voxel point sampling +- Farthest point sampling +- Poisson disk sampling + +""" +import matplotlib.pyplot as plt +import numpy as np +import numpy.typing as npt +from collections import defaultdict + +do_plot = True + + +def voxel_point_sampling(original_points: npt.NDArray, voxel_size: float): + """ + Voxel Point Sampling function. + This function sample N-dimensional points with voxel grid. + Points in a same voxel grid will be merged by mean operation for sampling. + + Parameters + ---------- + original_points : (M, N) N-dimensional points for sampling. + The number of points is M. + voxel_size : voxel grid size + + Returns + ------- + sampled points (M', N) + """ + voxel_dict = defaultdict(list) + for i in range(original_points.shape[0]): + xyz = original_points[i, :] + xyz_index = tuple(xyz // voxel_size) + voxel_dict[xyz_index].append(xyz) + points = np.vstack([np.mean(v, axis=0) for v in voxel_dict.values()]) + return points + + +def farthest_point_sampling(orig_points: npt.NDArray, + n_points: int, seed: int): + """ + Farthest point sampling function + This function sample N-dimensional points with the farthest point policy. + + Parameters + ---------- + orig_points : (M, N) N-dimensional points for sampling. + The number of points is M. + n_points : number of points for sampling + seed : random seed number + + Returns + ------- + sampled points (n_points, N) + + """ + rng = np.random.default_rng(seed) + n_orig_points = orig_points.shape[0] + first_point_id = rng.choice(range(n_orig_points)) + min_distances = np.ones(n_orig_points) * float("inf") + selected_ids = [first_point_id] + while len(selected_ids) < n_points: + base_point = orig_points[selected_ids[-1], :] + distances = np.linalg.norm(orig_points[np.newaxis, :] - base_point, + axis=2).flatten() + min_distances = np.minimum(min_distances, distances) + distances_rank = np.argsort(-min_distances) # Farthest order + for i in distances_rank: # From the farthest point + if i not in selected_ids: # if not selected yes, select it + selected_ids.append(i) + break + return orig_points[selected_ids, :] + + +def poisson_disk_sampling(orig_points: npt.NDArray, n_points: int, + min_distance: float, seed: int, MAX_ITER=1000): + """ + Poisson disk sampling function + This function sample N-dimensional points randomly until the number of + points keeping minimum distance between selected points. + + Parameters + ---------- + orig_points : (M, N) N-dimensional points for sampling. + The number of points is M. + n_points : number of points for sampling + min_distance : minimum distance between selected points. + seed : random seed number + MAX_ITER : Maximum number of iteration. Default is 1000. + + Returns + ------- + sampled points (n_points or less, N) + """ + rng = np.random.default_rng(seed) + selected_id = rng.choice(range(orig_points.shape[0])) + selected_ids = [selected_id] + loop = 0 + while len(selected_ids) < n_points and loop <= MAX_ITER: + selected_id = rng.choice(range(orig_points.shape[0])) + base_point = orig_points[selected_id, :] + distances = np.linalg.norm( + orig_points[np.newaxis, selected_ids] - base_point, + axis=2).flatten() + if min(distances) >= min_distance: + selected_ids.append(selected_id) + loop += 1 + if len(selected_ids) != n_points: + print("Could not find the specified number of points...") + + return orig_points[selected_ids, :] + + +def plot_sampled_points(original_points, sampled_points, method_name): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(original_points[:, 0], original_points[:, 1], + original_points[:, 2], marker=".", label="Original points") + ax.scatter(sampled_points[:, 0], sampled_points[:, 1], + sampled_points[:, 2], marker="o", + label="Filtered points") + plt.legend() + plt.title(method_name) + plt.axis("equal") + + +def main(): + n_points = 1000 + seed = 1234 + rng = np.random.default_rng(seed) + + x = rng.normal(0.0, 10.0, n_points) + y = rng.normal(0.0, 1.0, n_points) + z = rng.normal(0.0, 10.0, n_points) + original_points = np.vstack((x, y, z)).T + print(f"{original_points.shape=}") + print("Voxel point sampling") + voxel_size = 20.0 + voxel_sampling_points = voxel_point_sampling(original_points, voxel_size) + print(f"{voxel_sampling_points.shape=}") + + print("Farthest point sampling") + n_points = 20 + farthest_sampling_points = farthest_point_sampling(original_points, + n_points, seed) + print(f"{farthest_sampling_points.shape=}") + + print("Poisson disk sampling") + n_points = 20 + min_distance = 10.0 + poisson_disk_points = poisson_disk_sampling(original_points, n_points, + min_distance, seed) + print(f"{poisson_disk_points.shape=}") + + if do_plot: + plot_sampled_points(original_points, voxel_sampling_points, + "Voxel point sampling") + plot_sampled_points(original_points, farthest_sampling_points, + "Farthest point sampling") + plot_sampled_points(original_points, poisson_disk_points, + "poisson disk sampling") + plt.show() + + +if __name__ == '__main__': + main() diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst index 0f7ec549dc..14a7cce70d 100644 --- a/docs/modules/mapping/mapping_main.rst +++ b/docs/modules/mapping/mapping_main.rst @@ -9,6 +9,7 @@ Mapping gaussian_grid_map/gaussian_grid_map ray_casting_grid_map/ray_casting_grid_map lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial + point_cloud_sampling/point_cloud_sampling k_means_object_clustering/k_means_object_clustering circle_fitting/circle_fitting rectangle_fitting/rectangle_fitting diff --git a/docs/modules/mapping/point_cloud_sampling/farthest_point_sampling.png 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