From ef3fcec685a41be16bb9aa39915a9534596cb460 Mon Sep 17 00:00:00 2001
From: Yang Wang <purduewang@gmail.com>
Date: Sat, 20 Feb 2021 07:06:16 -0500
Subject: [PATCH 001/604] Fix: cyxpy cannot be installed (#483)

Add a few lines in yml file to clarify dependencies
---
 environment.yml | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/environment.yml b/environment.yml
index a5361d4a29..1441b79080 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,9 +1,11 @@
 name: python_robotics
+channels:
+  - conda-forge
 dependencies:
-- python
-- pip
-- scipy
-- numpy
-- pandas
-- cvxpy
-- matplotlib
+  - python=3.9
+  - pip
+  - scipy
+  - numpy
+  - pandas
+  - cvxpy
+  - matplotlib

From 078c62bc3eb0ed59e6c41b10dbb52fcafa39037a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 20 Feb 2021 21:30:55 +0900
Subject: [PATCH 002/604] enable rundiffstylecheck.sh to run (#484)

---
 rundiffstylecheck.sh | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 rundiffstylecheck.sh

diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh
old mode 100644
new mode 100755

From c025299dcfe8db7383704601c28dc48f6dc262db Mon Sep 17 00:00:00 2001
From: FXCarl <fxcarlcn@gmail.com>
Date: Sun, 21 Feb 2021 14:23:13 +0800
Subject: [PATCH 003/604] Update greedy_best_first_search - calc_final_path
 method (#477)

* Update greedy_best_first_search.py

parent_index should be pind. other wise it cann't work

* add breadth first search test

* Add greedy best first search test
---
 .../greedy_best_first_search.py               |  2 +-
 tests/test_breadth_first_search.py            | 22 +++++++++----------
 tests/test_greedy_best_first_search.py        | 11 ++++++++++
 3 files changed, 23 insertions(+), 12 deletions(-)
 create mode 100644 tests/test_greedy_best_first_search.py

diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
index 7100b0e604..f2416fba98 100644
--- a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
+++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
@@ -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.parent_index]
+        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))
diff --git a/tests/test_breadth_first_search.py b/tests/test_breadth_first_search.py
index 14c1ac6988..bfc63e39d6 100644
--- a/tests/test_breadth_first_search.py
+++ b/tests/test_breadth_first_search.py
@@ -1,11 +1,11 @@
-import conftest
-from PathPlanning.BreadthFirstSearch import breadth_first_search as m
-
-
-def test_1():
-    m.show_animation = False
-    m.main()
-
-
-if __name__ == '__main__':
-    conftest.run_this_test(__file__)
+import conftest
+from PathPlanning.BreadthFirstSearch import breadth_first_search as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_greedy_best_first_search.py b/tests/test_greedy_best_first_search.py
new file mode 100644
index 0000000000..e573ecf625
--- /dev/null
+++ b/tests/test_greedy_best_first_search.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.GreedyBestFirstSearch import greedy_best_first_search as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From d391cdbb8c82f9c3e643f4c2fd95e85d03b47c9e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Feb 2021 07:27:33 +0900
Subject: [PATCH 004/604] Bump scipy from 1.6.0 to 1.6.1 (#485)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.0 to 1.6.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.0...v1.6.1)

Signed-off-by: dependabot[bot] <support@github.com>

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 8402a5206e..cb46f475da 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.1
-scipy == 1.6.0
+scipy == 1.6.1
 pandas == 1.2.2
 matplotlib == 3.3.4
 cvxpy == 1.1.10

From d53ec936bce2f01ffc99297372cba9081b92c24e Mon Sep 17 00:00:00 2001
From: zarmars <sxysgx410@126.com>
Date: Tue, 16 Mar 2021 22:13:11 +0800
Subject: [PATCH 005/604] Fix the problem that the length of candidate path is
 not correctly compared. Optimal path may be discarded. (#487)

---
 PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 3bed9e2997..7808f2c9f9 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -40,7 +40,8 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
 
 
 def mod2pi(x):
-    v = np.mod(x, 2.0 * math.pi)
+    # Be consistent with fmod in cplusplus here.
+    v = np.mod(x, np.copysign(2.0 * math.pi, x))
     if v < -math.pi:
         v += 2.0 * math.pi
     else:
@@ -76,7 +77,7 @@ def set_path(paths, lengths, ctypes):
     for tpath in paths:
         typeissame = (tpath.ctypes == path.ctypes)
         if typeissame:
-            if sum(tpath.lengths) - sum(path.lengths) <= 0.01:
+            if sum(np.abs(tpath.lengths)) - sum(np.abs(path.lengths)) <= 0.01:
                 return paths  # not insert path
 
     path.L = sum([abs(i) for i in lengths])

From b30ba3e9d022e519431993ab173e610e841f431d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 20 Mar 2021 14:32:52 +0900
Subject: [PATCH 006/604] Bump cvxpy from 1.1.10 to 1.1.11 (#488)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.10 to 1.1.11.
- [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/v1.1.10...v1.1.11)

Signed-off-by: dependabot[bot] <support@github.com>

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 cb46f475da..d2fc6db92a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.20.1
 scipy == 1.6.1
 pandas == 1.2.2
 matplotlib == 3.3.4
-cvxpy == 1.1.10
+cvxpy == 1.1.11

From dbcb8d7ea5c78cb55aae13610cf923ecf7602c33 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Mar 2021 07:24:34 +0900
Subject: [PATCH 007/604] Bump scipy from 1.6.1 to 1.6.2 (#491)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.1 to 1.6.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.1...v1.6.2)

Signed-off-by: dependabot[bot] <support@github.com>

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 d2fc6db92a..5b6b9eff2a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.1
-scipy == 1.6.1
+scipy == 1.6.2
 pandas == 1.2.2
 matplotlib == 3.3.4
 cvxpy == 1.1.11

From 5c408b5d80b53b6835d1d432f1669f06cfe7fce6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Mar 2021 07:25:17 +0900
Subject: [PATCH 008/604] Bump matplotlib from 3.3.4 to 3.4.0 (#492)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.4 to 3.4.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.4...v3.4.0)

Signed-off-by: dependabot[bot] <support@github.com>

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 5b6b9eff2a..5e8cef887e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.1
 scipy == 1.6.2
 pandas == 1.2.2
-matplotlib == 3.3.4
+matplotlib == 3.4.0
 cvxpy == 1.1.11

From f17c991c68d72cf84bb3a89393d0a12454f66583 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Mar 2021 20:23:54 +0900
Subject: [PATCH 009/604] Bump numpy from 1.20.1 to 1.20.2 (#493)

Bumps [numpy](https://github.com/numpy/numpy) from 1.20.1 to 1.20.2.
- [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/v1.20.1...v1.20.2)

Signed-off-by: dependabot[bot] <support@github.com>

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 5e8cef887e..2d7c47554b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.20.1
+numpy == 1.20.2
 scipy == 1.6.2
 pandas == 1.2.2
 matplotlib == 3.4.0

From bf2d9df83dc6b4927763f6c47786813900f14b47 Mon Sep 17 00:00:00 2001
From: Shamil <shamilgemuev@mail.ru>
Date: Fri, 2 Apr 2021 13:49:16 +0200
Subject: [PATCH 010/604] Add ICP support for 3d point clouds (#465)

* Add 3d support ICP

* icp_matching function returns R,T corresponding to 2D or 3D set of points
* update_homogeneuous_matrix - general operations for translation and rotation matrixes

* Add test for 3d point cloud (with 2d visualization)

* Separate test for 3d points to main_3d_points

* Add test for ICP 3d

* Correct style

* Add space

* Style correction

* Add more spaces

* Add 3d visualizing for ICP

* Style corrections

* Delete spaces

* Style correction

* remove space

* Separate plot drawing

* plot drawing in a separate function for both 2D and 3D versions
* figure creating before while loop

* Style correction

* Comment 3d plot drawing

Co-authored-by: Shamil GEMUEV <https://github.maf-roda.com/>
---
 .../iterative_closest_point.py                | 94 ++++++++++++++-----
 tests/test_iterative_closest_point.py         |  5 +
 2 files changed, 76 insertions(+), 23 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index db252247a8..5ed01fa383 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -1,10 +1,11 @@
 """
 Iterative Closest Point (ICP) SLAM example
-author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı
+author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı, Shamil Gemuev
 """
 
 import math
 
+# from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -19,8 +20,8 @@ def icp_matching(previous_points, current_points):
     """
     Iterative Closest Point matching
     - input
-    previous_points: 2D points in the previous frame
-    current_points: 2D points in the current frame
+    previous_points: 2D or 3D points in the previous frame
+    current_points: 2D or 3D points in the current frame
     - output
     R: Rotation matrix
     T: Translation vector
@@ -31,19 +32,16 @@ def icp_matching(previous_points, current_points):
     preError = np.inf
     count = 0
 
+    if show_animation:
+        fig = plt.figure()
+        # if previous_points.shape[0] == 3:
+        #    fig.add_subplot(111, projection='3d')
+
     while dError >= EPS:
         count += 1
 
         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")
-            plt.plot(current_points[0, :], current_points[1, :], ".b")
-            plt.plot(0.0, 0.0, "xr")
-            plt.axis("equal")
+            plot_points(previous_points, current_points, fig)
             plt.pause(0.1)
 
         indexes, error = nearest_neighbor_association(previous_points, current_points)
@@ -68,24 +66,20 @@ def icp_matching(previous_points, current_points):
             print("Not Converge...", error, dError, count)
             break
 
-    R = np.array(H[0:2, 0:2])
-    T = np.array(H[0:2, 2])
+    R = np.array(H[0:-1, 0:-1])
+    T = np.array(H[0:-1, -1])
 
     return R, T
 
 
 def update_homogeneous_matrix(Hin, R, T):
 
-    H = np.zeros((3, 3))
-
-    H[0, 0] = R[0, 0]
-    H[1, 0] = R[1, 0]
-    H[0, 1] = R[0, 1]
-    H[1, 1] = R[1, 1]
-    H[2, 2] = 1.0
+    r_size = R.shape[0]
+    H = np.zeros((r_size + 1, r_size + 1))
 
-    H[0, 2] = T[0]
-    H[1, 2] = T[1]
+    H[0:r_size, 0:r_size] = R
+    H[0:r_size, r_size] = T
+    H[r_size, r_size] = 1.0
 
     if Hin is None:
         return H
@@ -124,6 +118,28 @@ def svd_motion_estimation(previous_points, current_points):
     return R, t
 
 
+def plot_points(previous_points, current_points, figure):
+    # 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 previous_points.shape[0] == 3:
+    # plt.clf()
+    # axes = figure.add_subplot(111, projection='3d')
+    # axes.scatter(previous_points[0, :], previous_points[1, :],
+    #             previous_points[2, :], c="r", marker=".")
+    # axes.scatter(current_points[0, :], current_points[1, :],
+    #             current_points[2, :], c="b", marker=".")
+    # axes.scatter(0.0, 0.0, 0.0, c="r", marker="x")
+    # figure.canvas.draw()
+    # else:
+    plt.cla()
+    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")
+
+
 def main():
     print(__file__ + " start!!")
 
@@ -153,5 +169,37 @@ def main():
         print("T:", T)
 
 
+def main_3d_points():
+    print(__file__ + " start!!")
+
+    # simulation parameters for 3d point set
+    nPoint = 1000
+    fieldLength = 50.0
+    motion = [0.5, 2.0, -5, np.deg2rad(-10.0)]  # [x[m],y[m],z[m],roll[deg]]
+
+    nsim = 3  # number of simulation
+
+    for _ in range(nsim):
+
+        # previous points
+        px = (np.random.rand(nPoint) - 0.5) * fieldLength
+        py = (np.random.rand(nPoint) - 0.5) * fieldLength
+        pz = (np.random.rand(nPoint) - 0.5) * fieldLength
+        previous_points = np.vstack((px, py, pz))
+
+        # current points
+        cx = [math.cos(motion[3]) * x - math.sin(motion[3]) * z + motion[0]
+              for (x, z) in zip(px, pz)]
+        cy = [y + motion[1] for y in py]
+        cz = [math.sin(motion[3]) * x + math.cos(motion[3]) * z + motion[2]
+              for (x, z) in zip(px, pz)]
+        current_points = np.vstack((cx, cy, cz))
+
+        R, T = icp_matching(previous_points, current_points)
+        print("R:", R)
+        print("T:", T)
+
+
 if __name__ == '__main__':
     main()
+    main_3d_points()
diff --git a/tests/test_iterative_closest_point.py b/tests/test_iterative_closest_point.py
index 3f212f7298..3e726b5649 100644
--- a/tests/test_iterative_closest_point.py
+++ b/tests/test_iterative_closest_point.py
@@ -7,5 +7,10 @@ def test_1():
     m.main()
 
 
+def test_2():
+    m.show_animation = False
+    m.main_3d_points()
+
+
 if __name__ == '__main__':
     conftest.run_this_test(__file__)

From 3abc9c69e3074d2ead16a5ae87c529536cafb406 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 Apr 2021 20:53:26 +0900
Subject: [PATCH 011/604] enable 3d plot

---
 .../iterative_closest_point.py                | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index 5ed01fa383..2b44de2c07 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -5,7 +5,7 @@
 
 import math
 
-# from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
+from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -34,8 +34,8 @@ def icp_matching(previous_points, current_points):
 
     if show_animation:
         fig = plt.figure()
-        # if previous_points.shape[0] == 3:
-        #    fig.add_subplot(111, projection='3d')
+        if previous_points.shape[0] == 3:
+           fig.add_subplot(111, projection='3d')
 
     while dError >= EPS:
         count += 1
@@ -123,21 +123,21 @@ def plot_points(previous_points, current_points, figure):
     plt.gcf().canvas.mpl_connect(
         'key_release_event',
         lambda event: [exit(0) if event.key == 'escape' else None])
-    # if previous_points.shape[0] == 3:
-    # plt.clf()
-    # axes = figure.add_subplot(111, projection='3d')
-    # axes.scatter(previous_points[0, :], previous_points[1, :],
-    #             previous_points[2, :], c="r", marker=".")
-    # axes.scatter(current_points[0, :], current_points[1, :],
-    #             current_points[2, :], c="b", marker=".")
-    # axes.scatter(0.0, 0.0, 0.0, c="r", marker="x")
-    # figure.canvas.draw()
-    # else:
-    plt.cla()
-    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")
+    if previous_points.shape[0] == 3:
+        plt.clf()
+        axes = figure.add_subplot(111, projection='3d')
+        axes.scatter(previous_points[0, :], previous_points[1, :],
+                    previous_points[2, :], c="r", marker=".")
+        axes.scatter(current_points[0, :], current_points[1, :],
+                    current_points[2, :], c="b", marker=".")
+        axes.scatter(0.0, 0.0, 0.0, c="r", marker="x")
+        figure.canvas.draw()
+    else:
+        plt.cla()
+        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")
 
 
 def main():

From 6bd25b922159dfe6df77f03bd24d61a9ba78ae67 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 Apr 2021 21:43:29 +0900
Subject: [PATCH 012/604] fix state_lattice_planner.py coordinate conversion
 (#495)

---
 .../StateLatticePlanner/state_lattice_planner.py         | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index 51eeb000d2..b5fec697a4 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -156,8 +156,8 @@ def calc_lane_states(l_center, l_heading, l_width, v_width, d, nxy):
     :param nxy: sampling number
     :return: state list
     """
-    xc = math.cos(l_heading) * d + math.sin(l_heading) * l_center
-    yc = math.sin(l_heading) * d + math.cos(l_heading) * l_center
+    xc = d
+    yc = l_center
 
     states = []
     for i in range(nxy):
@@ -301,7 +301,7 @@ def lane_state_sampling_test1():
     k0 = 0.0
 
     l_center = 10.0
-    l_heading = np.deg2rad(90.0)
+    l_heading = np.deg2rad(0.0)
     l_width = 3.0
     v_width = 1.0
     d = 10
@@ -309,6 +309,9 @@ def lane_state_sampling_test1():
     states = calc_lane_states(l_center, l_heading, l_width, v_width, d, nxy)
     result = generate_path(states, k0)
 
+    if show_animation:
+        plt.close("all")
+
     for table in result:
         xc, yc, yawc = motion_model.generate_trajectory(
             table[3], table[4], table[5], k0)

From d4153bcaa1ac81aeedefa60fbde56fad2773b51f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 Apr 2021 21:43:52 +0900
Subject: [PATCH 013/604] add workaround for dependabot related issue (#496)

---
 .github/workflows/codeql.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 078590997d..5e6c03aa16 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -2,6 +2,8 @@ name: "Code scanning - action"
 
 on:
   push:
+    branches-ignore:
+      - 'dependabot/**'
   pull_request:
   schedule:
     - cron: '0 19 * * 0'

From 079de036f3ce80eb6245396e7afabe728bcfb280 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 2 Apr 2021 21:45:09 +0900
Subject: [PATCH 014/604] Bump pandas from 1.2.2 to 1.2.3 (#489)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.2 to 1.2.3.
- [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/v1.2.2...v1.2.3)

Signed-off-by: dependabot[bot] <support@github.com>

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 2d7c47554b..07ec77dde0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.2
-pandas == 1.2.2
+pandas == 1.2.3
 matplotlib == 3.4.0
 cvxpy == 1.1.11

From add704866aa2df4fd7699875b6ec199dfa817f56 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Apr 2021 07:07:34 +0900
Subject: [PATCH 015/604] Bump matplotlib from 3.4.0 to 3.4.1 (#497)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.0 to 3.4.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.0...v3.4.1)

Signed-off-by: dependabot[bot] <support@github.com>

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 07ec77dde0..b3610549b6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.2
 pandas == 1.2.3
-matplotlib == 3.4.0
+matplotlib == 3.4.1
 cvxpy == 1.1.11

From 52e6225e73780e13c4f79015f6efb3ebfcf38ccc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Apr 2021 07:22:10 +0900
Subject: [PATCH 016/604] Bump cvxpy from 1.1.11 to 1.1.12 (#501)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.11 to 1.1.12.
- [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/v1.1.11...v1.1.12)

Signed-off-by: dependabot[bot] <support@github.com>

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 b3610549b6..8ab982f887 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.20.2
 scipy == 1.6.2
 pandas == 1.2.3
 matplotlib == 3.4.1
-cvxpy == 1.1.11
+cvxpy == 1.1.12

From 6e98da47fd5535f8535f84ecb57964945ab0b041 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Apr 2021 07:22:32 +0900
Subject: [PATCH 017/604] Bump pandas from 1.2.3 to 1.2.4 (#502)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.3 to 1.2.4.
- [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/v1.2.3...v1.2.4)

Signed-off-by: dependabot[bot] <support@github.com>

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 8ab982f887..d2fd8f7f2b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.2
-pandas == 1.2.3
+pandas == 1.2.4
 matplotlib == 3.4.1
 cvxpy == 1.1.12

From 0705b1cb5b7c6626c54ff7868d4416240fcb8ed6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Apr 2021 07:07:54 +0900
Subject: [PATCH 018/604] Bump scipy from 1.6.2 to 1.6.3 (#504)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.2 to 1.6.3.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.2...v1.6.3)

Signed-off-by: dependabot[bot] <support@github.com>

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 d2fd8f7f2b..0e1d0dfe94 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
-scipy == 1.6.2
+scipy == 1.6.3
 pandas == 1.2.4
 matplotlib == 3.4.1
 cvxpy == 1.1.12

From 3ff6b7d1ec9ecc1865b74225d9c31d888f0fa884 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 8 May 2021 13:27:33 +0900
Subject: [PATCH 019/604] Update MacOS_CI.yml (#507)

* Update MacOS_CI.yml

* Update MacOS_CI.yml

* Update MacOS_CI.yml

* Update MacOS_CI.yml

* Update MacOS_CI.yml
---
 .github/workflows/MacOS_CI.yml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 5bd9a60828..6b3ffa2588 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -8,7 +8,7 @@ on: [push, pull_request]
 
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: macos-latest
     strategy:
       matrix:
         python-version: [ '3.9' ]
@@ -17,6 +17,9 @@ jobs:
       - uses: actions/checkout@v2
       - run: git fetch --prune --unshallow
 
+      - name: Update bash
+        run: brew install bash
+
       - name: Setup python
         uses: actions/setup-python@v2
         with:
@@ -25,6 +28,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
+          pip install numpy # cvxpy install workaround
           pip install -r requirements.txt
       - name: install coverage
         run: pip install coverage

From 02cb3ad556c999fba9f793c9b995486aa0fed830 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 May 2021 07:14:13 +0900
Subject: [PATCH 020/604] Bump matplotlib from 3.4.1 to 3.4.2 (#509)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.1 to 3.4.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.1...v3.4.2)

Signed-off-by: dependabot[bot] <support@github.com>

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 0e1d0dfe94..2c05e5ec72 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.3
 pandas == 1.2.4
-matplotlib == 3.4.1
+matplotlib == 3.4.2
 cvxpy == 1.1.12

From 0e232037c751cbeb8bb8cdac0b33bf48e4937f2a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 May 2021 07:14:31 +0900
Subject: [PATCH 021/604] Bump numpy from 1.20.2 to 1.20.3 (#510)

Bumps [numpy](https://github.com/numpy/numpy) from 1.20.2 to 1.20.3.
- [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/v1.20.2...v1.20.3)

Signed-off-by: dependabot[bot] <support@github.com>

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 2c05e5ec72..6241a11e5b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.20.2
+numpy == 1.20.3
 scipy == 1.6.3
 pandas == 1.2.4
 matplotlib == 3.4.2

From 2cf4f6f001ec7fa74dc2d3bce3818b5eff11d9eb Mon Sep 17 00:00:00 2001
From: nirnayroy <32942494+nirnayroy@users.noreply.github.com>
Date: Sat, 15 May 2021 11:31:29 +0530
Subject: [PATCH 022/604] Added D* Search to path planning folder (#490)

* changes

* updated docs and readme

* Update a_star.py

* Update a_star.py

* Create test_dstar.py

* trailing loc

* Update dstar.py

* Update dstar.py

* Update dstar.py

* Update dstar.py

* Update dstar.py

* newline

* corrected changes requested

* 13, five, 21

* corrected changes

* latest

* linted

* lint

* removed diff
---
 PathPlanning/DStar/dstar.py    | 241 +++++++++++++++++++++++++++++++++
 README.md                      |  14 ++
 docs/modules/path_planning.rst |  16 +++
 tests/test_dstar.py            |  11 ++
 4 files changed, 282 insertions(+)
 create mode 100644 PathPlanning/DStar/dstar.py
 create mode 100644 tests/test_dstar.py

diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py
new file mode 100644
index 0000000000..bc7e7c966e
--- /dev/null
+++ b/PathPlanning/DStar/dstar.py
@@ -0,0 +1,241 @@
+"""
+
+D* grid planning
+
+author: Nirnay Roy
+
+See Wikipedia article (https://en.wikipedia.org/wiki/D*)
+
+"""
+import math
+
+from sys import maxsize
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class State:
+
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        self.parent = None
+        self.state = "."
+        self.t = "new"  # tag for state
+        self.h = 0
+        self.k = 0
+
+    def cost(self, state):
+        if self.state == "#" or state.state == "#":
+            return maxsize
+
+        return math.sqrt(math.pow((self.x - state.x), 2) +
+                         math.pow((self.y - state.y), 2))
+
+    def set_state(self, state):
+        """
+        .: new
+        #: obstacle
+        e: oparent of current state
+        *: closed state
+        s: current state
+        """
+        if state not in ["s", ".", "#", "e", "*"]:
+            return
+        self.state = state
+
+
+class Map:
+
+    def __init__(self, row, col):
+        self.row = row
+        self.col = col
+        self.map = self.init_map()
+
+    def init_map(self):
+        map_list = []
+        for i in range(self.row):
+            tmp = []
+            for j in range(self.col):
+                tmp.append(State(i, j))
+            map_list.append(tmp)
+        return map_list
+
+    def get_neighbers(self, state):
+        state_list = []
+        for i in [-1, 0, 1]:
+            for j in [-1, 0, 1]:
+                if i == 0 and j == 0:
+                    continue
+                if state.x + i < 0 or state.x + i >= self.row:
+                    continue
+                if state.y + j < 0 or state.y + j >= self.col:
+                    continue
+                state_list.append(self.map[state.x + i][state.y + j])
+        return state_list
+
+    def set_obstacle(self, point_list):
+        for x, y in point_list:
+            if x < 0 or x >= self.row or y < 0 or y >= self.col:
+                continue
+
+            self.map[x][y].set_state("#")
+
+
+class Dstar:
+    def __init__(self, maps):
+        self.map = maps
+        self.open_list = set()
+
+    def process_state(self):
+        x = self.min_state()
+
+        if x is None:
+            return -1
+
+        k_old = self.get_kmin()
+        self.remove(x)
+
+        if k_old < x.h:
+            for y in self.map.get_neighbers(x):
+                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:
+            for y in self.map.get_neighbers(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):
+                    y.parent = x
+                    self.insert(y, x.h + x.cost(y))
+        else:
+            for y in self.map.get_neighbers(x):
+                if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y):
+                    y.parent = x
+                    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)
+                    else:
+                        if y.parent != x and x.h > y.h + x.cost(y) \
+                                and y.t == "close" and y.h > k_old:
+                            self.insert(y, y.h)
+        return self.get_kmin()
+
+    def min_state(self):
+        if not self.open_list:
+            return None
+        min_state = min(self.open_list, key=lambda x: x.k)
+        return min_state
+
+    def get_kmin(self):
+        if not self.open_list:
+            return -1
+        k_min = min([x.k for x in self.open_list])
+        return k_min
+
+    def insert(self, state, h_new):
+        if state.t == "new":
+            state.k = h_new
+        elif state.t == "open":
+            state.k = min(state.k, h_new)
+        elif state.t == "close":
+            state.k = min(state.h, h_new)
+        state.h = h_new
+        state.t = "open"
+        self.open_list.add(state)
+
+    def remove(self, state):
+        if state.t == "open":
+            state.t = "close"
+        self.open_list.remove(state)
+
+    def modify_cost(self, x):
+        if x.t == "close":
+            self.insert(x, x.parent.h + x.cost(x.parent))
+
+    def run(self, start, end):
+
+        rx = []
+        ry = []
+
+        self.open_list.add(end)
+
+        while True:
+            self.process_state()
+            if start.t == "close":
+                break
+
+        start.set_state("s")
+        s = start
+        s = s.parent
+        s.set_state("e")
+        tmp = start
+
+        while tmp != end:
+            tmp.set_state("*")
+            rx.append(tmp.x)
+            ry.append(tmp.y)
+            if show_animation:
+                plt.plot(rx, ry)
+                plt.pause(0.01)
+            if tmp.parent.state == "#":
+                self.modify(tmp)
+                continue
+            tmp = tmp.parent
+        tmp.set_state("e")
+
+        return rx, ry
+
+    def modify(self, state):
+        self.modify_cost(state)
+        while True:
+            k_min = self.process_state()
+            if k_min >= state.h:
+                break
+
+
+def main():
+    m = Map(100, 100)
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10)
+    for i in range(-10, 60):
+        ox.append(60)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60)
+    for i in range(-10, 61):
+        ox.append(-10)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20)
+        oy.append(i)
+    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]
+    goal = [50, 50]
+    if show_animation:
+        plt.plot(ox, oy, ".k")
+        plt.plot(start[0], start[1], "og")
+        plt.plot(goal[0], goal[1], "xb")
+
+    start = m.map[start[0]][start[1]]
+    end = m.map[goal[0]][goal[1]]
+    dstar = Dstar(m)
+    rx, ry = dstar.run(start, end)
+
+    if show_animation:
+        plt.plot(rx, ry)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/README.md b/README.md
index 804966d121..ca9e6192b9 100644
--- a/README.md
+++ b/README.md
@@ -37,6 +37,7 @@ Python codes for robotics algorithm.
       * [Grid based search](#grid-based-search)
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
+         * [D* algorithm](#d-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
       * [State Lattice Planning](#state-lattice-planning)
@@ -301,6 +302,19 @@ In the animation, cyan points are searched nodes.
 
 Its heuristic is 2D Euclid distance.
 
+### D\* algorithm
+
+This is a 2D grid based the shortest path planning with D star algorithm.
+
+![figure at master · nirnayroy/intelligentrobotics](https://github.com/nirnayroy/intelligent-robotics/blob/main/dstar.gif)
+
+The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
+
+Ref:
+
+- [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*)
+
+
 ### Potential Field algorithm
 
 This is a 2D grid based path planning with Potential Field algorithm.
diff --git a/docs/modules/path_planning.rst b/docs/modules/path_planning.rst
index f74867dcdf..a0bdd45511 100644
--- a/docs/modules/path_planning.rst
+++ b/docs/modules/path_planning.rst
@@ -39,6 +39,21 @@ In the animation, cyan points are searched nodes.
 
 Its heuristic is 2D Euclid distance.
 
+.. _a*-algorithm:
+
+D\* algorithm
+~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with D star algorithm.
+
+|dstar|
+
+The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
+
+Ref:
+
+-  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
+
 Potential Field algorithm
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -427,6 +442,7 @@ Ref:
 .. |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/nirnayroy/intelligent-robotics/blob/main/dstar.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
diff --git a/tests/test_dstar.py b/tests/test_dstar.py
new file mode 100644
index 0000000000..f8f40fecef
--- /dev/null
+++ b/tests/test_dstar.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.DStar import dstar as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 1de0ffddb1ea11396a019403bbb986c8df8e0bdd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 15 May 2021 15:21:15 +0900
Subject: [PATCH 023/604] change DStar animation

---
 PathPlanning/DStar/dstar.py    | 13 +++++++------
 README.md                      |  2 +-
 docs/modules/path_planning.rst |  2 +-
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py
index bc7e7c966e..e01508b3df 100644
--- a/PathPlanning/DStar/dstar.py
+++ b/PathPlanning/DStar/dstar.py
@@ -63,7 +63,7 @@ def init_map(self):
             map_list.append(tmp)
         return map_list
 
-    def get_neighbers(self, state):
+    def get_neighbors(self, state):
         state_list = []
         for i in [-1, 0, 1]:
             for j in [-1, 0, 1]:
@@ -99,18 +99,18 @@ def process_state(self):
         self.remove(x)
 
         if k_old < x.h:
-            for y in self.map.get_neighbers(x):
+            for y in self.map.get_neighbors(x):
                 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:
-            for y in self.map.get_neighbers(x):
+            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):
                     y.parent = x
                     self.insert(y, x.h + x.cost(y))
         else:
-            for y in self.map.get_neighbers(x):
+            for y in self.map.get_neighbors(x):
                 if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y):
                     y.parent = x
                     self.insert(y, x.h + x.cost(y))
@@ -178,7 +178,7 @@ def run(self, start, end):
             rx.append(tmp.x)
             ry.append(tmp.y)
             if show_animation:
-                plt.plot(rx, ry)
+                plt.plot(rx, ry, "-r")
                 plt.pause(0.01)
             if tmp.parent.state == "#":
                 self.modify(tmp)
@@ -226,6 +226,7 @@ def main():
         plt.plot(ox, oy, ".k")
         plt.plot(start[0], start[1], "og")
         plt.plot(goal[0], goal[1], "xb")
+        plt.axis("equal")
 
     start = m.map[start[0]][start[1]]
     end = m.map[goal[0]][goal[1]]
@@ -233,7 +234,7 @@ def main():
     rx, ry = dstar.run(start, end)
 
     if show_animation:
-        plt.plot(rx, ry)
+        plt.plot(rx, ry, "-r")
         plt.show()
 
 
diff --git a/README.md b/README.md
index ca9e6192b9..fac8b53dce 100644
--- a/README.md
+++ b/README.md
@@ -306,7 +306,7 @@ Its heuristic is 2D Euclid distance.
 
 This is a 2D grid based the shortest path planning with D star algorithm.
 
-![figure at master · nirnayroy/intelligentrobotics](https://github.com/nirnayroy/intelligent-robotics/blob/main/dstar.gif)
+![figure at master · nirnayroy/intelligentrobotics](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.
 
diff --git a/docs/modules/path_planning.rst b/docs/modules/path_planning.rst
index a0bdd45511..cfe7b6ad40 100644
--- a/docs/modules/path_planning.rst
+++ b/docs/modules/path_planning.rst
@@ -442,7 +442,7 @@ Ref:
 .. |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/nirnayroy/intelligent-robotics/blob/main/dstar.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

From 1050aea5277eb27e1dab13e71f19588b19d1763d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Jun 2021 07:54:57 +0900
Subject: [PATCH 024/604] Bump cvxpy from 1.1.12 to 1.1.13 (#512)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.12 to 1.1.13.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.12...v1.1.13)

Signed-off-by: dependabot[bot] <support@github.com>

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 6241a11e5b..fea1a9562b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.20.3
 scipy == 1.6.3
 pandas == 1.2.4
 matplotlib == 3.4.2
-cvxpy == 1.1.12
+cvxpy == 1.1.13

From 2ac1d9c8156eda2e6b87f501197a8ea42b649653 Mon Sep 17 00:00:00 2001
From: vss2sn <28676655+vss2sn@users.noreply.github.com>
Date: Mon, 7 Jun 2021 07:28:23 -0400
Subject: [PATCH 025/604] Add D* Lite. (#511)

* Add D* Lite.

* Add test. Minor changes

* Modified based on LGTM report

* Fix linter errors

* Fixes and lint.

* Update README.md
Made requested changes
Add transform between world and grid coordinates to allow negative wold coordinates
Modify to allow diagonal motion

* Added display of previous and new computed path
---
 PathPlanning/DStarLite/d_star_lite.py | 398 ++++++++++++++++++++++++++
 README.md                             |  13 +
 tests/test_d_star_lite.py             |  11 +
 3 files changed, 422 insertions(+)
 create mode 100644 PathPlanning/DStarLite/d_star_lite.py
 create mode 100644 tests/test_d_star_lite.py

diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py
new file mode 100644
index 0000000000..603c053dab
--- /dev/null
+++ b/PathPlanning/DStarLite/d_star_lite.py
@@ -0,0 +1,398 @@
+"""
+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)
+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.
+Code can be significantly optimized by using a priority queue for U, etc.
+Avoiding additional imports based on repository philosophy.
+"""
+import math
+import matplotlib.pyplot as plt
+import random
+
+show_animation = True
+pause_time = 0.001
+p_create_random_obstacle = 0
+
+
+class Node:
+    def __init__(self, x: int = 0, y: int = 0, cost: float = 0.0):
+        self.x = x
+        self.y = y
+        self.cost = cost
+
+
+def add_coordinates(node1: Node, node2: Node):
+    new_node = Node()
+    new_node.x = node1.x + node2.x
+    new_node.y = node1.y + node2.y
+    new_node.cost = node1.cost + node2.cost
+    return new_node
+
+
+def compare_coordinates(node1: Node, node2: Node):
+    return node1.x == node2.x and node1.y == node2.y
+
+
+class DStarLite:
+
+    # Please adjust the heuristic function (h) if you change the list of
+    # possible motions
+    motions = [
+        Node(1, 0, 1),
+        Node(0, 1, 1),
+        Node(-1, 0, 1),
+        Node(0, -1, 1),
+        Node(1, 1, math.sqrt(2)),
+        Node(1, -1, math.sqrt(2)),
+        Node(-1, 1, math.sqrt(2)),
+        Node(-1, -1, math.sqrt(2))
+    ]
+
+    def __init__(self, ox: list, oy: list):
+        # Ensure that within the algorithm implementation all node coordinates
+        # are indices in the grid and extend
+        # from 0 to abs(<axis>_max - <axis>_min)
+        self.x_min_world = int(min(ox))
+        self.y_min_world = int(min(oy))
+        self.x_max = int(abs(max(ox) - self.x_min_world))
+        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.start = Node(0, 0)
+        self.goal = Node(0, 0)
+        self.U = list()
+        self.km = 0.0
+        self.kold = 0.0
+        self.rhs = list()
+        self.g = list()
+        self.detected_obstacles = list()
+        if show_animation:
+            self.detected_obstacles_for_plotting_x = list()
+            self.detected_obstacles_for_plotting_y = list()
+
+    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
+
+    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])
+
+    def c(self, node1: Node, node2: Node):
+        if self.is_obstacle(node2):
+            # Attempting to move from or to an obstacle
+            return math.inf
+        new_node = Node(node1.x-node2.x, node1.y-node2.y)
+        detected_motion = list(filter(lambda motion:
+                                      compare_coordinates(motion, new_node),
+                                      self.motions))
+        return detected_motion[0].cost
+
+    def h(self, s: Node):
+        # Cannot use the 2nd euclidean norm as this might sometimes generate
+        # heuristics that overestimate the cost, making them inadmissible,
+        # due to rounding errors etc (when combined with calculate_key)
+        # To be admissible heuristic should
+        # never overestimate the cost of a move
+        # hence not using the line below
+        # return math.hypot(self.start.x - s.x, self.start.y - s.y)
+
+        # Below is the same as 1; modify if you modify the cost of each move in
+        # motion
+        # return max(abs(self.start.x - s.x), abs(self.start.y - s.y))
+        return 1
+
+    def calculate_key(self, s: Node):
+        return (min(self.g[s.x][s.y], self.rhs[s.x][s.y]) + self.h(s)
+                + self.km, min(self.g[s.x][s.y], self.rhs[s.x][s.y]))
+
+    def is_valid(self, node: Node):
+        if 0 <= node.x < self.x_max and 0 <= node.y < self.y_max:
+            return True
+        return False
+
+    def get_neighbours(self, u: Node):
+        return [add_coordinates(u, motion) for motion in self.motions
+                if self.is_valid(add_coordinates(u, motion))]
+
+    def pred(self, u: Node):
+        # Grid, so each vertex is connected to the ones around it
+        return self.get_neighbours(u)
+
+    def succ(self, u: Node):
+        # Grid, so each vertex is connected to the ones around it
+        return self.get_neighbours(u)
+
+    def initialize(self, start: Node, goal: Node):
+        self.start.x = start.x - self.x_min_world
+        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()
+
+    def update_vertex(self, u: Node):
+        if not compare_coordinates(u, self.goal):
+            self.rhs[u.x][u.y] = min([self.c(u, sprime) +
+                                      self.g[sprime.x][sprime.y]
+                                      for sprime in self.succ(u)])
+        if any([compare_coordinates(u, node) for node, key in self.U]):
+            self.U = [(node, key) for node, key in self.U
+                      if not compare_coordinates(node, u)]
+            self.U.sort(key=lambda x: x[1])
+        if self.g[u.x][u.y] != self.rhs[u.x][u.y]:
+            self.U.append((u, self.calculate_key(u)))
+            self.U.sort(key=lambda x: x[1])
+
+    def compare_keys(self, key_pair1: tuple[float, float],
+                     key_pair2: tuple[float, float]):
+        return key_pair1[0] < key_pair2[0] or \
+               (key_pair1[0] == key_pair2[0] and key_pair1[1] < key_pair2[1])
+
+    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]:
+            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]
+                for s in self.pred(u):
+                    self.update_vertex(s)
+            else:
+                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])
+
+    def detect_changes(self):
+        changed_vertices = list()
+        if len(self.spoofed_obstacles) > 0:
+            for spoofed_obstacle in self.spoofed_obstacles[0]:
+                if compare_coordinates(spoofed_obstacle, self.start) or \
+                   compare_coordinates(spoofed_obstacle, self.goal):
+                    continue
+                changed_vertices.append(spoofed_obstacle)
+                self.detected_obstacles.append(spoofed_obstacle)
+                if show_animation:
+                    self.detected_obstacles_for_plotting_x.append(
+                        spoofed_obstacle.x + self.x_min_world)
+                    self.detected_obstacles_for_plotting_y.append(
+                        spoofed_obstacle.y + self.y_min_world)
+                    plt.plot(self.detected_obstacles_for_plotting_x,
+                             self.detected_obstacles_for_plotting_y, ".k")
+                    plt.pause(pause_time)
+            self.spoofed_obstacles.pop(0)
+
+        # 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)
+            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))
+            if show_animation:
+                self.detected_obstacles_for_plotting_x.append(x +
+                                                              self.x_min_world)
+                self.detected_obstacles_for_plotting_y.append(y +
+                                                              self.y_min_world)
+                plt.plot(self.detected_obstacles_for_plotting_x,
+                         self.detected_obstacles_for_plotting_y, ".k")
+                plt.pause(pause_time)
+        return changed_vertices
+
+    def compute_current_path(self):
+        path = list()
+        current_point = Node(self.start.x, self.start.y)
+        while not compare_coordinates(current_point, self.goal):
+            path.append(current_point)
+            current_point = min(self.succ(current_point),
+                                key=lambda sprime:
+                                self.c(current_point, sprime) +
+                                self.g[sprime.x][sprime.y])
+        path.append(self.goal)
+        return path
+
+    def compare_paths(self, path1: list, path2: list):
+        if len(path1) != len(path2):
+            return False
+        for node1, node2 in zip(path1, path2):
+            if not compare_coordinates(node1, node2):
+                return False
+        return True
+
+    def display_path(self, path: list, colour: str, alpha: int = 1):
+        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)
+        plt.pause(pause_time)
+        return drawing
+
+    def main(self, start: Node, goal: Node,
+             spoofed_ox: list, spoofed_oy: list):
+        self.spoofed_obstacles = [[Node(x - self.x_min_world,
+                                        y - self.y_min_world)
+                                   for x, y in zip(rowx, rowy)]
+                                  for rowx, rowy in zip(spoofed_ox, spoofed_oy)
+                                  ]
+        pathx = []
+        pathy = []
+        self.initialize(start, goal)
+        last = self.start
+        self.compute_shortest_path()
+        pathx.append(self.start.x + self.x_min_world)
+        pathy.append(self.start.y + self.y_min_world)
+
+        if show_animation:
+            current_path = self.compute_current_path()
+            previous_path = current_path.copy()
+            previous_path_image = self.display_path(previous_path, ".c",
+                                                    alpha=0.3)
+            current_path_image = self.display_path(current_path, ".c")
+
+        while not compare_coordinates(self.goal, self.start):
+            if self.g[self.start.x][self.start.y] == math.inf:
+                print("No path possible")
+                return False, pathx, pathy
+            self.start = min(self.succ(self.start),
+                             key=lambda sprime:
+                             self.c(self.start, sprime) +
+                             self.g[sprime.x][sprime.y])
+            pathx.append(self.start.x + self.x_min_world)
+            pathy.append(self.start.y + self.y_min_world)
+            if show_animation:
+                current_path.pop(0)
+                plt.plot(pathx, pathy, "-r")
+                plt.pause(pause_time)
+            changed_vertices = self.detect_changes()
+            if len(changed_vertices) != 0:
+                print("New obstacle detected")
+                self.km += self.h(last)
+                last = self.start
+                for u in changed_vertices:
+                    if compare_coordinates(u, self.start):
+                        continue
+                    self.rhs[u.x][u.y] = math.inf
+                    self.g[u.x][u.y] = math.inf
+                    self.update_vertex(u)
+                self.compute_shortest_path()
+
+                if show_animation:
+                    new_path = self.compute_current_path()
+                    if not self.compare_paths(current_path, new_path):
+                        current_path_image[0].remove()
+                        previous_path_image[0].remove()
+                        previous_path = current_path.copy()
+                        current_path = new_path.copy()
+                        previous_path_image = self.display_path(previous_path,
+                                                                ".c",
+                                                                alpha=0.3)
+                        current_path_image = self.display_path(current_path,
+                                                               ".c")
+                        plt.pause(pause_time)
+        print("Path found")
+        return True, pathx, pathy
+
+
+def main():
+
+    # start and goal position
+    sx = 10  # [m]
+    sy = 10  # [m]
+    gx = 50  # [m]
+    gy = 50  # [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:
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+        label_column = ['Start', 'Goal', 'Path taken',
+                        'Current computed path', 'Previous computed path',
+                        'Obstacles']
+        columns = [plt.plot([], [], symbol, color=colour, alpha=alpha)[0]
+                   for symbol, colour, alpha in [['o', 'g', 1],
+                                                 ['x', 'b', 1],
+                                                 ['-', 'r', 1],
+                                                 ['.', 'c', 1],
+                                                 ['.', 'c', 0.3],
+                                                 ['.', 'k', 1]]]
+        plt.legend(columns, label_column, bbox_to_anchor=(1, 1), title="Key:",
+                   fontsize="xx-small")
+        plt.plot()
+        plt.pause(pause_time)
+
+    # Obstacles discovered at time = row
+    # time = 1, obstacles discovered at (0, 2), (9, 2), (4, 0)
+    # time = 2, obstacles discovered at (0, 1), (7, 7)
+    # ...
+    # when the spoofed obstacles are:
+    # spoofed_ox = [[0, 9, 4], [0, 7], [], [], [], [], [], [5]]
+    # spoofed_oy = [[2, 2, 0], [1, 7], [], [], [], [], [], [4]]
+
+    # Reroute
+    # spoofed_ox = [[], [], [], [], [], [], [], [40 for _ in range(10, 21)]]
+    # spoofed_oy = [[], [], [], [], [], [], [], [i for i in range(10, 21)]]
+
+    # Obstacles that demostrate large rerouting
+    spoofed_ox = [[], [], [],
+                  [i for i in range(0, 21)] + [0 for _ in range(0, 20)]]
+    spoofed_oy = [[], [], [],
+                  [20 for _ in range(0, 21)] + [i for i in range(0, 20)]]
+
+    dstarlite = DStarLite(ox, oy)
+    dstarlite.main(Node(x=sx, y=sy), Node(x=gx, y=gy),
+                   spoofed_ox=spoofed_ox, spoofed_oy=spoofed_oy)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/README.md b/README.md
index fac8b53dce..e2eafe74f0 100644
--- a/README.md
+++ b/README.md
@@ -38,6 +38,7 @@ Python codes for robotics algorithm.
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
          * [D* algorithm](#d-algorithm)
+         * [D* Lite algorithm](#dstarlite-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
       * [State Lattice Planning](#state-lattice-planning)
@@ -314,6 +315,18 @@ Ref:
 
 - [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*)
 
+### D\* Lite algorithm
+
+This algorithm finds the shortest path between two points while rerouting when obstacles are discovered. It has been implemented here for a 2D grid.
+
+![D* Lite](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif)
+
+The animation shows a robot finding its path and rerouting to avoid obstacles as they are discovered using the D* Lite search algorithm.
+
+Refs:
+
+- [D* Lite](http://idm-lab.org/bib/abstracts/papers/aaai02b.pd)
+- [Improved Fast Replanning for Robot Navigation in Unknown Terrain](http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf)
 
 ### Potential Field algorithm
 
diff --git a/tests/test_d_star_lite.py b/tests/test_d_star_lite.py
new file mode 100644
index 0000000000..b60a140a89
--- /dev/null
+++ b/tests/test_d_star_lite.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.DStarLite import d_star_lite as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From a0db9d266c65e54f61a525fb5305772e929cee08 Mon Sep 17 00:00:00 2001
From: vss2sn <28676655+vss2sn@users.noreply.github.com>
Date: Wed, 9 Jun 2021 10:02:32 -0400
Subject: [PATCH 026/604] Fix D* Lite README.md link (#513)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e2eafe74f0..c9e45d9363 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ Python codes for robotics algorithm.
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
          * [D* algorithm](#d-algorithm)
-         * [D* Lite algorithm](#dstarlite-algorithm)
+         * [D* Lite algorithm](#d-lite-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
       * [State Lattice Planning](#state-lattice-planning)

From fe4ce92c4bc5bdbc6549d2512514f5bfb7cb72e6 Mon Sep 17 00:00:00 2001
From: Haegu Lee <34788458+benthebear93@users.noreply.github.com>
Date: Mon, 21 Jun 2021 22:44:06 +0900
Subject: [PATCH 027/604] Fix No module error in GridBasedSweepCPP and
 ClosedLoopRRTStart (#516)

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart
---
 PathPlanning/ClosedLoopRRTStar/__init__.py      |  8 --------
 .../closed_loop_rrt_star_car.py                 | 17 +++++++++++++++--
 PathPlanning/GridBasedSweepCPP/__init__.py      |  7 -------
 .../grid_based_sweep_coverage_path_planner.py   | 11 ++++++++++-
 4 files changed, 25 insertions(+), 18 deletions(-)
 delete mode 100644 PathPlanning/ClosedLoopRRTStar/__init__.py
 delete mode 100644 PathPlanning/GridBasedSweepCPP/__init__.py

diff --git a/PathPlanning/ClosedLoopRRTStar/__init__.py b/PathPlanning/ClosedLoopRRTStar/__init__.py
deleted file mode 100644
index 0e49cd14d3..0000000000
--- a/PathPlanning/ClosedLoopRRTStar/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import os
-import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index 740fc5590a..c0c8c356e8 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -5,15 +5,28 @@
 author: AtsushiSakai(@Atsushi_twi)
 
 """
+import os
+import sys
 
 import matplotlib.pyplot as plt
 import numpy as np
-import reeds_shepp_path_planning
-from rrt_star_reeds_shepp import RRTStarReedsShepp
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
 import pure_pursuit
 import unicycle_model
 
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
+
+try:
+    import reeds_shepp_path_planning
+    from rrt_star_reeds_shepp import RRTStarReedsShepp
+except ImportError:
+    raise
+
 show_animation = True
 
 
diff --git a/PathPlanning/GridBasedSweepCPP/__init__.py b/PathPlanning/GridBasedSweepCPP/__init__.py
deleted file mode 100644
index f1c41ce433..0000000000
--- a/PathPlanning/GridBasedSweepCPP/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import os
-import sys
-
-GRID_MAP_LIB = os.path.dirname(os.path.abspath(__file__)) + \
-               "/../../Mapping/"
-
-sys.path.append(GRID_MAP_LIB)
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 984b0affdf..c0c8fc4319 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -5,13 +5,22 @@
 """
 
 import math
+import os
+import sys
 from enum import IntEnum
 
 import numpy as np
 from scipy.spatial.transform import Rotation as Rot
-from Mapping.grid_map_lib.grid_map_lib import GridMap
 import matplotlib.pyplot as plt
 
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../../Mapping/")
+
+try:
+    from grid_map_lib.grid_map_lib import GridMap
+except ImportError:
+    raise
+
 do_animation = True
 
 

From 9036e1527028347469a474ccc26f748a3a7bef82 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Jun 2021 07:14:54 +0900
Subject: [PATCH 028/604] Bump scipy from 1.6.3 to 1.7.0 (#517)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.3 to 1.7.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.3...v1.7.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 fea1a9562b..e0b9e76a34 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.3
-scipy == 1.6.3
+scipy == 1.7.0
 pandas == 1.2.4
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From ad366d2ec6c555bba722e1a16ed32203b16c58bd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Jun 2021 07:26:28 +0900
Subject: [PATCH 029/604] Bump numpy from 1.20.3 to 1.21.0 (#519)

Bumps [numpy](https://github.com/numpy/numpy) from 1.20.3 to 1.21.0.
- [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/v1.20.3...v1.21.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 e0b9e76a34..d98897d70d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.20.3
+numpy == 1.21.0
 scipy == 1.7.0
 pandas == 1.2.4
 matplotlib == 3.4.2

From 01874cee24e6944015a72e701140b23e5d291d47 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Jun 2021 07:26:50 +0900
Subject: [PATCH 030/604] Bump pandas from 1.2.4 to 1.2.5 (#520)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.4 to 1.2.5.
- [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/v1.2.4...v1.2.5)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 d98897d70d..70af779df9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.0
 scipy == 1.7.0
-pandas == 1.2.4
+pandas == 1.2.5
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From b0df3c7db3c100b0b3c4680305921482bf9ae2b0 Mon Sep 17 00:00:00 2001
From: mbosetti <michael.bosetti@gmail.com>
Date: Tue, 29 Jun 2021 18:37:49 -0500
Subject: [PATCH 031/604] fix dijkstra hypot check bug (#522)

---
 PathPlanning/VoronoiRoadMap/dijkstra_search.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
index aef07ebbaf..503ccc342e 100644
--- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -136,5 +136,5 @@ def is_same_node_with_xy(node_x, node_y, node_b):
     @staticmethod
     def is_same_node(node_a, node_b):
         dist = np.hypot(node_a.x - node_b.x,
-                        node_b.y - node_b.y)
+                        node_a.y - node_b.y)
         return dist <= 0.1

From 8f3337e78d0258386d220192caa97763e17a33d6 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jschwar4@andrew.cmu.edu>
Date: Thu, 1 Jul 2021 09:46:32 -0500
Subject: [PATCH 032/604] Dubins path bug fix (#521)

* Without equals sign, sometimes get points that are in the wrong direction - relative to the points before and after it- when change in x or change in y along path is 0

* Created test script for dubins path generator

* Broke == 0 into its own case in dubins planner, also Renaming files to appease CI

* Reverting some naming changes

* Turns out theres already a test for dubins.. not sure how I missed that

* Note to self: run the test cases on your own before throwing them at CI
---
 .../DubinsPath/dubins_path_planning.py        | 22 ++++++++++---------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 9c13198aff..64d29e5061 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -254,11 +254,13 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
 
     ll = 0.0
 
-    for (m, l, i) in zip(mode, lengths, range(len(mode))):
-        if l > 0.0:
-            d = step_size
+    for (m, length, i) in zip(mode, lengths, range(len(mode))):
+        if length == 0:
+            continue
+        elif length > 0.0:
+            dist = step_size
         else:
-            d = -step_size
+            dist = -step_size
 
         # set origin state
         origin_x, origin_y, origin_yaw = \
@@ -266,22 +268,22 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
 
         index -= 1
         if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
-            pd = - d - ll
+            pd = - dist - ll
         else:
-            pd = d - ll
+            pd = dist - ll
 
-        while abs(pd) <= abs(l):
+        while abs(pd) <= abs(length):
             index += 1
             path_x, path_y, path_yaw, directions = interpolate(
                 index, pd, m, max_curvature, origin_x, origin_y, origin_yaw,
                 path_x, path_y, path_yaw, directions)
-            pd += d
+            pd += dist
 
-        ll = l - pd - d  # calc remain length
+        ll = length - pd - dist  # calc remain length
 
         index += 1
         path_x, path_y, path_yaw, directions = interpolate(
-            index, l, m, max_curvature, origin_x, origin_y, origin_yaw,
+            index, length, m, max_curvature, origin_x, origin_y, origin_yaw,
             path_x, path_y, path_yaw, directions)
 
     if len(path_x) <= 1:

From 51689d62b9c0c2d245f59fd711ff36774d89a833 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Fri, 2 Jul 2021 07:53:02 -0500
Subject: [PATCH 033/604] Issue #523 fix (Reeds-Shepp planner handling length=0
 case) (#524)

* Without equals sign, sometimes get points that are in the wrong direction - relative to the points before and after it- when change in x or change in y along path is 0

* Created test script for dubins path generator

* Made len == 0 it's own case, also changed 'l' to 'len' to appease travisCI

* More variable renaming to appease CI

* Broke == 0 into its own case in dubins planner, also Renaming files to appease CI

* Reverting some naming changes

* Turns out theres already a test for dubins.. not sure how I missed that

* Note to self: run the test cases on your own before throwing them at CI

* Added handling of length=0 case in generate_local_course()

* Missed reverting 'mode' back to 'm' in one spot

* Addressing style issues (line length)
---
 .../DubinsPath/dubins_path_planning.py        |  2 +-
 .../reeds_shepp_path_planning.py              | 19 ++++++++++++-------
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 64d29e5061..a813d79b0d 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -255,7 +255,7 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
     ll = 0.0
 
     for (m, length, i) in zip(mode, lengths, range(len(mode))):
-        if length == 0:
+        if length == 0.0:
             continue
         elif length > 0.0:
             dist = step_size
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 7808f2c9f9..0b31e73793 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -285,8 +285,10 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size)
 
     ll = 0.0
 
-    for (m, l, i) in zip(mode, lengths, range(len(mode))):
-        if l > 0.0:
+    for (m, length, i) in zip(mode, lengths, range(len(mode))):
+        if length == 0.0:
+            continue
+        elif length > 0.0:
             d = step_size
         else:
             d = -step_size
@@ -300,17 +302,19 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size)
         else:
             pd = d - ll
 
-        while abs(pd) <= abs(l):
+        while abs(pd) <= abs(length):
             ind += 1
             px, py, pyaw, directions = interpolate(
-                ind, pd, m, max_curvature, 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
+        ll = length - pd - d  # calc remain length
 
         ind += 1
         px, py, pyaw, directions = interpolate(
-            ind, l, m, max_curvature, ox, oy, oyaw, px, py, pyaw, directions)
+            ind, length, m, max_curvature, ox, oy, oyaw,
+            px, py, pyaw, directions)
 
     # remove unused data
     while px[-1] == 0.0:
@@ -390,7 +394,8 @@ def main():
     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)
+        start_x, start_y, start_yaw, end_x, end_y, end_yaw,
+        curvature, step_size)
 
     if show_animation:  # pragma: no cover
         plt.cla()

From 21e748731973fef52a8e4af22d2fb0c53b53e8c1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Jul 2021 07:06:53 +0900
Subject: [PATCH 034/604] Bump pandas from 1.2.5 to 1.3.0 (#525)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.5 to 1.3.0.
- [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/v1.2.5...v1.3.0)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 70af779df9..d393dcec01 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.0
 scipy == 1.7.0
-pandas == 1.2.5
+pandas == 1.3.0
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From 6f06b535b9d614ed0c6f00dab7811d6917b8ffa1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 10 Jul 2021 07:15:10 +0900
Subject: [PATCH 035/604] fix dubins path length bug and clean up codes. (#527)

* fix dubins path length bug and clean up codes.

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error
---
 .../DubinsPath/dubins_path_planning.py        | 164 ++++++++++--------
 PathPlanning/RRTDubins/rrt_dubins.py          |   9 +-
 PathPlanning/RRTStarDubins/rrt_star_dubins.py |  15 +-
 tests/test_dubins_path_planning.py            |  30 ++--
 4 files changed, 124 insertions(+), 94 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index a813d79b0d..f8e6d7b1c1 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -14,6 +14,46 @@
 show_animation = True
 
 
+def dubins_path_planning(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
+
+    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
+
+    lp_x, lp_y, lp_yaw, modes, lengths = dubins_path_planning_from_origin(
+        le_xy[0], le_xy[1], le_yaw, curvature, step_size)
+
+    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 x_list, y_list, yaw_list, modes, lengths
+
+
 def mod2pi(theta):
     return theta - 2.0 * math.pi * math.floor(theta / 2.0 / math.pi)
 
@@ -148,14 +188,14 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
     alpha = mod2pi(- theta)
     beta = mod2pi(end_yaw - theta)
 
-    planners = [left_straight_left, right_straight_right, left_straight_right,
-                right_straight_left, right_left_right,
-                left_right_left]
+    planning_funcs = [left_straight_left, right_straight_right,
+                      left_straight_right, right_straight_left,
+                      right_left_right, left_right_left]
 
     best_cost = float("inf")
     bt, bp, bq, best_mode = None, None, None, None
 
-    for planner in planners:
+    for planner in planning_funcs:
         t, p, q, mode = planner(alpha, beta, d)
         if t is None:
             continue
@@ -166,10 +206,15 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
             best_cost = cost
     lengths = [bt, bp, bq]
 
-    x_list, y_list, yaw_list, directions = generate_local_course(
-        sum(lengths), lengths, best_mode, curvature, step_size)
+    x_list, y_list, yaw_list, directions = generate_local_course(sum(lengths),
+                                                                 lengths,
+                                                                 best_mode,
+                                                                 curvature,
+                                                                 step_size)
+
+    lengths = [length / curvature for length in lengths]
 
-    return x_list, y_list, yaw_list, best_mode, best_cost
+    return x_list, y_list, yaw_list, best_mode, lengths
 
 
 def interpolate(ind, length, mode, max_curvature, origin_x, origin_y,
@@ -203,49 +248,15 @@ def interpolate(ind, length, mode, max_curvature, origin_x, origin_y,
     return path_x, path_y, path_yaw, directions
 
 
-def dubins_path_planning(s_x, s_y, s_yaw, g_x, g_y, g_yaw, c, step_size=0.1):
-    """
-    Dubins path planner
-
-    input:
-        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]
-
-    """
-
-    g_x = g_x - s_x
-    g_y = g_y - s_y
-
-    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
-
-    lp_x, lp_y, lp_yaw, mode, lengths = dubins_path_planning_from_origin(
-        le_xy[0], le_xy[1], le_yaw, c, step_size)
-
-    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 x_list, y_list, yaw_list, mode, lengths
-
-
-def generate_local_course(total_length, lengths, mode, max_curvature,
+def generate_local_course(total_length, lengths, modes, 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)]
+    p_x = [0.0 for _ in range(n_point)]
+    p_y = [0.0 for _ in range(n_point)]
+    p_yaw = [0.0 for _ in range(n_point)]
     directions = [0.0 for _ in range(n_point)]
-    index = 1
+    ind = 1
 
     if lengths[0] > 0.0:
         directions[0] = 1
@@ -254,7 +265,7 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
 
     ll = 0.0
 
-    for (m, length, i) in zip(mode, lengths, range(len(mode))):
+    for (m, length, i) in zip(modes, lengths, range(len(modes))):
         if length == 0.0:
             continue
         elif length > 0.0:
@@ -263,54 +274,57 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
             dist = -step_size
 
         # set origin state
-        origin_x, origin_y, origin_yaw = \
-            path_x[index], path_y[index], path_yaw[index]
+        origin_x, origin_y, origin_yaw = p_x[ind], p_y[ind], p_yaw[ind]
 
-        index -= 1
+        ind -= 1
         if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
             pd = - dist - ll
         else:
             pd = dist - ll
 
         while abs(pd) <= abs(length):
-            index += 1
-            path_x, path_y, path_yaw, directions = interpolate(
-                index, pd, m, max_curvature, origin_x, origin_y, origin_yaw,
-                path_x, path_y, path_yaw, directions)
+            ind += 1
+            p_x, p_y, p_yaw, directions = interpolate(ind, pd, m,
+                                                      max_curvature,
+                                                      origin_x,
+                                                      origin_y,
+                                                      origin_yaw,
+                                                      p_x, p_y,
+                                                      p_yaw,
+                                                      directions)
             pd += dist
 
         ll = length - pd - dist  # calc remain length
 
-        index += 1
-        path_x, path_y, path_yaw, directions = interpolate(
-            index, length, m, max_curvature, origin_x, origin_y, origin_yaw,
-            path_x, path_y, path_yaw, directions)
+        ind += 1
+        p_x, p_y, p_yaw, directions = interpolate(ind, length, m,
+                                                  max_curvature,
+                                                  origin_x, origin_y,
+                                                  origin_yaw,
+                                                  p_x, p_y, p_yaw,
+                                                  directions)
 
-    if len(path_x) <= 1:
+    if len(p_x) <= 1:
         return [], [], [], []
 
     # remove unused data
-    while len(path_x) >= 1 and path_x[-1] == 0.0:
-        path_x.pop()
-        path_y.pop()
-        path_yaw.pop()
+    while len(p_x) >= 1 and p_x[-1] == 0.0:
+        p_x.pop()
+        p_y.pop()
+        p_yaw.pop()
         directions.pop()
 
-    return path_x, path_y, path_yaw, directions
+    return p_x, p_y, p_yaw, directions
 
 
 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 (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)
+        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), fc=fc,
+                  ec=ec, head_width=width, head_length=width)
         plt.plot(x, y)
 
 
@@ -327,9 +341,13 @@ def main():
 
     curvature = 1.0
 
-    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)
+    path_x, path_y, path_yaw, mode, lengths = dubins_path_planning(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/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 55cea0bde3..8ab06c7323 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -133,9 +133,10 @@ def plot_start_goal_arrow(self):  # pragma: no cover
 
     def steer(self, from_node, to_node):
 
-        px, py, pyaw, mode, course_length = dubins_path_planning.dubins_path_planning(
-            from_node.x, from_node.y, from_node.yaw,
-            to_node.x, to_node.y, to_node.yaw, self.curvature)
+        px, py, pyaw, mode, course_lengths = \
+            dubins_path_planning.dubins_path_planning(
+                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
@@ -148,7 +149,7 @@ def steer(self, from_node, to_node):
         new_node.path_x = px
         new_node.path_y = py
         new_node.path_yaw = pyaw
-        new_node.cost += course_length
+        new_node.cost += sum([abs(c) for c in course_lengths])
         new_node.parent = from_node
 
         return new_node
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index 9cfd4e692a..26367eecea 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -139,9 +139,10 @@ def plot_start_goal_arrow(self):
 
     def steer(self, from_node, to_node):
 
-        px, py, pyaw, mode, course_length = dubins_path_planning.dubins_path_planning(
-            from_node.x, from_node.y, from_node.yaw,
-            to_node.x, to_node.y, to_node.yaw, self.curvature)
+        px, py, pyaw, mode, course_lengths = \
+            dubins_path_planning.dubins_path_planning(
+                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
@@ -154,18 +155,20 @@ def steer(self, from_node, to_node):
         new_node.path_x = px
         new_node.path_y = py
         new_node.path_yaw = pyaw
-        new_node.cost += course_length
+        new_node.cost += sum([abs(c) for c in course_lengths])
         new_node.parent = from_node
 
         return new_node
 
     def calc_new_cost(self, from_node, to_node):
 
-        _, _, _, _, course_length = dubins_path_planning.dubins_path_planning(
+        _, _, _, _, course_lengths = dubins_path_planning.dubins_path_planning(
             from_node.x, from_node.y, from_node.yaw,
             to_node.x, to_node.y, to_node.yaw, self.curvature)
 
-        return from_node.cost + course_length
+        cost = sum([abs(c) for c in course_lengths])
+
+        return from_node.cost + cost
 
     def get_random_node(self):
 
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index 99fbd29100..18492ccc59 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -1,12 +1,13 @@
-import conftest
 import numpy as np
+
+import conftest
 from PathPlanning.DubinsPath import dubins_path_planning
 
 np.random.seed(12345)
 
 
-def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
-                         end_x, end_y, end_yaw):
+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)
@@ -15,6 +16,12 @@ def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
     assert (abs(pyaw[-1] - end_yaw) <= 0.01)
 
 
+def check_path_length(px, py, lengths):
+    path_len = sum(
+        [np.hypot(dx, dy) for (dx, dy) in zip(np.diff(px), np.diff(py))])
+    assert (abs(path_len - sum(lengths)) <= 0.1)
+
+
 def test_1():
     start_x = 1.0  # [m]
     start_y = 1.0  # [m]
@@ -26,12 +33,12 @@ def test_1():
 
     curvature = 1.0
 
-    px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+    px, py, pyaw, mode, lengths = dubins_path_planning.dubins_path_planning(
         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)
+    check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                         end_y, end_yaw)
+    check_path_length(px, py, lengths)
 
 
 def test_2():
@@ -53,12 +60,13 @@ def test_3():
 
         curvature = 1.0 / (np.random.rand() * 5.0)
 
-        px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+        px, py, pyaw, mode, lengths = \
+            dubins_path_planning.dubins_path_planning(
             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)
+        check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                             end_y, end_yaw)
+        check_path_length(px, py, lengths)
 
 
 if __name__ == '__main__':

From 177f04618c4bbba40bea38cd57d642f7ab9e3725 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 12 Jul 2021 21:04:27 +0900
Subject: [PATCH 036/604] fix example run issue and clean up for arm navigation
 codes. (#528)

* clean up arm navigation codes.

* add project toml

* add pylintrc

* add pylintrc

* add pylintrc
---
 ArmNavigation/n_joint_arm_3d/NLinkArm3d.py                    | 3 ++-
 .../n_joint_arm_to_point_control.py                           | 4 ++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
index 3f6c5ddd2a..0cea963168 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
@@ -63,7 +63,8 @@ def forward_kinematics(self, 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 = []
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 b7834ee98b..36093df08d 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
@@ -4,7 +4,11 @@
 Author: Daniel Ingram (daniel-s-ingram)
         Atsushi Sakai (@Atsushi_twi)
 """
+
 import numpy as np
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 
 from ArmNavigation.n_joint_arm_to_point_control.NLinkArm import NLinkArm
 

From 2c3896879b91117e8af1016acf25effd310e2def Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 17 Jul 2021 18:28:26 +0900
Subject: [PATCH 037/604] Fix reeds shepp path issue (#529)

* code clean up

* code clean up

* code clean up

* code clean up

* fix length handling issues
---
 .../reeds_shepp_path_planning.py              | 292 ++++++++----------
 tests/test_reeds_shepp_path_planning.py       |  27 +-
 2 files changed, 146 insertions(+), 173 deletions(-)

diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 0b31e73793..5bc04c8ee3 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -14,28 +14,29 @@
 
 
 class Path:
+    """
+    Path data container
+    """
 
     def __init__(self):
+        # course segment length  (negative value is backward segment)
         self.lengths = []
+        # course segment type char ("S": straight, "L": left, "R": right)
         self.ctypes = []
-        self.L = 0.0
-        self.x = []
-        self.y = []
-        self.yaw = []
-        self.directions = []
+        self.L = 0.0  # Total lengths of the path
+        self.x = []  # x positions
+        self.y = []  # y positions
+        self.yaw = []  # orientations [rad]
+        self.directions = []  # directions (1:forward, -1:backward)
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
-    """
-    Plot arrow
-    """
-
-    if not isinstance(x, float):
+    if isinstance(x, list):
         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),
-                  fc=fc, ec=ec, head_width=width, head_length=width)
+        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), fc=fc,
+                  ec=ec, head_width=width, head_length=width)
         plt.plot(x, y)
 
 
@@ -68,35 +69,35 @@ def straight_left_straight(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def set_path(paths, lengths, ctypes):
+def set_path(paths, lengths, ctypes, step_size):
     path = Path()
     path.ctypes = ctypes
     path.lengths = lengths
+    path.L = sum(np.abs(lengths))
 
     # check same path exist
-    for tpath in paths:
-        typeissame = (tpath.ctypes == path.ctypes)
-        if typeissame:
-            if sum(np.abs(tpath.lengths)) - sum(np.abs(path.lengths)) <= 0.01:
-                return paths  # not insert path
-
-    path.L = sum([abs(i) for i in lengths])
+    for i_path in paths:
+        type_is_same = (i_path.ctypes == path.ctypes)
+        length_is_close = (sum(np.abs(i_path.lengths)) - path.L) <= step_size
+        if type_is_same and length_is_close:
+            return paths  # same path found, so do not insert path
 
-    # Base.Test.@test path.L >= 0.01
-    if path.L >= 0.01:
-        paths.append(path)
+    # check path is long enough
+    if path.L <= step_size:
+        return paths  # too short, so do not insert path
 
+    paths.append(path)
     return paths
 
 
-def straight_curve_straight(x, y, phi, 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"])
+        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"])
+        paths = set_path(paths, [t, u, v], ["S", "R", "S"], step_size)
 
     return paths
 
@@ -131,22 +132,22 @@ def left_right_left(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def curve_curve_curve(x, y, phi, paths):
+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"])
+        paths = set_path(paths, [t, u, v], ["L", "R", "L"], step_size)
 
     flag, t, u, v = left_right_left(-x, y, -phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"])
+        paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"], step_size)
 
     flag, t, u, v = left_right_left(x, -y, -phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["R", "L", "R"])
+        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"])
+        paths = set_path(paths, [-t, -u, -v], ["R", "L", "R"], step_size)
 
     # backwards
     xb = x * math.cos(phi) + y * math.sin(phi)
@@ -154,55 +155,55 @@ def curve_curve_curve(x, y, phi, paths):
 
     flag, t, u, v = left_right_left(xb, yb, phi)
     if flag:
-        paths = set_path(paths, [v, u, t], ["L", "R", "L"])
+        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], ["L", "R", "L"])
+        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"])
+        paths = set_path(paths, [v, u, t], ["R", "L", "R"], step_size)
 
     flag, t, u, v = left_right_left(-xb, -yb, phi)
     if flag:
-        paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"])
+        paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"], step_size)
 
     return paths
 
 
-def curve_straight_curve(x, y, phi, paths):
+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"])
+        paths = set_path(paths, [t, u, v], ["L", "S", "L"], step_size)
 
     flag, t, u, v = left_straight_left(-x, y, -phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"])
+        paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"], step_size)
 
     flag, t, u, v = left_straight_left(x, -y, -phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["R", "S", "R"])
+        paths = set_path(paths, [t, u, v], ["R", "S", "R"], step_size)
 
     flag, t, u, v = left_straight_left(-x, -y, phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "S", "R"])
+        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"])
+        paths = set_path(paths, [t, u, v], ["L", "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"])
+        paths = set_path(paths, [-t, -u, -v], ["L", "S", "R"], step_size)
 
     flag, t, u, v = left_straight_right(x, -y, -phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["R", "S", "L"])
+        paths = set_path(paths, [t, u, v], ["R", "S", "L"], step_size)
 
     flag, t, u, v = left_straight_right(-x, -y, phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"])
+        paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"], step_size)
 
     return paths
 
@@ -222,7 +223,7 @@ def left_straight_right(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def generate_path(q0, q1, max_curvature):
+def generate_path(q0, q1, max_curvature, step_size):
     dx = q1[0] - q0[0]
     dy = q1[1] - q0[1]
     dth = q1[2] - q0[2]
@@ -232,98 +233,70 @@ def generate_path(q0, q1, max_curvature):
     y = (-s * dx + c * dy) * max_curvature
 
     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)
+    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)
 
     return paths
 
 
-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
+def calc_interpolate_dists_list(lengths, step_size):
+    interpolate_dists_list = []
+    for length in lengths:
+        d_dist = step_size if length >= 0.0 else -step_size
+        interp_dists = np.arange(0.0, length, d_dist)
+        interp_dists = np.append(interp_dists, length)
+        interpolate_dists_list.append(interp_dists)
 
-    if length > 0.0:
-        directions[ind] = 1
-    else:
-        directions[ind] = -1
+    return interpolate_dists_list
 
-    return path_x, path_y, path_yaw, directions
 
+def generate_local_course(lengths, modes, max_curvature, step_size):
+    interpolate_dists_list = calc_interpolate_dists_list(lengths, 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
+    origin_x, origin_y, origin_yaw = 0.0, 0.0, 0.0
 
-    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
+    xs, ys, yaws, directions = [], [], [], []
+    for (interp_dists, mode, length) in zip(interpolate_dists_list, modes,
+                                            lengths):
 
-    if lengths[0] > 0.0:
-        directions[0] = 1
-    else:
-        directions[0] = -1
+        for dist in interp_dists:
+            x, y, yaw, direction = interpolate(dist, length, mode,
+                                               max_curvature, origin_x,
+                                               origin_y, origin_yaw)
+            xs.append(x)
+            ys.append(y)
+            yaws.append(yaw)
+            directions.append(direction)
+        origin_x = xs[-1]
+        origin_y = ys[-1]
+        origin_yaw = yaws[-1]
 
-    ll = 0.0
+    return xs, ys, yaws, directions
 
-    for (m, length, i) in zip(mode, lengths, range(len(mode))):
-        if length == 0.0:
-            continue
-        elif length > 0.0:
-            d = step_size
-        else:
-            d = -step_size
 
-        # set origin state
-        ox, oy, oyaw = px[ind], py[ind], pyaw[ind]
-
-        ind -= 1
-        if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
-            pd = - d - ll
-        else:
-            pd = d - ll
-
-        while abs(pd) <= abs(length):
-            ind += 1
-            px, py, pyaw, directions = interpolate(
-                ind, pd, m, max_curvature, ox, oy, oyaw,
-                px, py, pyaw, directions)
-            pd += d
-
-        ll = length - pd - d  # calc remain length
-
-        ind += 1
-        px, py, pyaw, directions = interpolate(
-            ind, length, m, max_curvature, ox, oy, oyaw,
-            px, py, pyaw, directions)
-
-    # remove unused data
-    while px[-1] == 0.0:
-        px.pop()
-        py.pop()
-        pyaw.pop()
-        directions.pop()
+def interpolate(dist, length, mode, max_curvature, origin_x, origin_y,
+                origin_yaw):
+    if mode == "S":
+        x = origin_x + dist / max_curvature * math.cos(origin_yaw)
+        y = origin_y + dist / max_curvature * math.sin(origin_yaw)
+        yaw = origin_yaw
+    else:  # curve
+        ldx = math.sin(dist) / max_curvature
+        ldy = 0.0
+        yaw = None
+        if mode == "L":  # left turn
+            ldy = (1.0 - math.cos(dist)) / max_curvature
+            yaw = origin_yaw + dist
+        elif mode == "R":  # right turn
+            ldy = (1.0 - math.cos(dist)) / -max_curvature
+            yaw = origin_yaw - dist
+        gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy
+        gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy
+        x = origin_x + gdx
+        y = origin_y + gdy
 
-    return px, py, pyaw, directions
+    return x, y, yaw, 1 if length > 0.0 else -1
 
 
 def pi_2_pi(angle):
@@ -334,17 +307,18 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
     q0 = [sx, sy, syaw]
     q1 = [gx, gy, gyaw]
 
-    paths = generate_path(q0, q1, maxc)
+    paths = generate_path(q0, q1, maxc, step_size)
     for path in paths:
-        x, y, yaw, directions = generate_local_course(
-            path.L, path.lengths, path.ctypes, maxc, step_size * maxc)
+        xs, ys, yaws, directions = generate_local_course(path.lengths,
+                                                         path.ctypes, maxc,
+                                                         step_size * maxc)
 
         # convert global coordinate
-        path.x = [math.cos(-q0[2]) * ix + math.sin(-q0[2])
-                  * iy + q0[0] for (ix, iy) in zip(x, y)]
-        path.y = [-math.sin(-q0[2]) * ix + math.cos(-q0[2])
-                  * iy + q0[1] for (ix, iy) in zip(x, y)]
-        path.yaw = [pi_2_pi(iyaw + q0[2]) for iyaw in yaw]
+        path.x = [math.cos(-q0[2]) * ix + math.sin(-q0[2]) * iy + q0[0] for
+                  (ix, iy) in zip(xs, ys)]
+        path.y = [-math.sin(-q0[2]) * ix + math.cos(-q0[2]) * iy + q0[1] for
+                  (ix, iy) in zip(xs, ys)]
+        path.yaw = [pi_2_pi(yaw + q0[2]) for yaw in yaws]
         path.directions = directions
         path.lengths = [length / maxc for length in path.lengths]
         path.L = path.L / maxc
@@ -352,54 +326,42 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
     return paths
 
 
-def reeds_shepp_path_planning(sx, sy, syaw,
-                              gx, gy, gyaw, maxc, step_size=0.2):
+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:
-        return None, None, None, None, None
-
-    minL = float("Inf")
-    best_path_index = -1
-    for i, _ in enumerate(paths):
-        if paths[i].L <= minL:
-            minL = paths[i].L
-            best_path_index = i
+        return None, None, None, None, None  # could not generate any path
 
-    bpath = paths[best_path_index]
+    # search minimum cost path
+    best_path_index = paths.index(min(paths, key=lambda p: abs(p.L)))
+    b_path = paths[best_path_index]
 
-    return bpath.x, bpath.y, bpath.yaw, bpath.ctypes, bpath.lengths
+    return b_path.x, b_path.y, b_path.yaw, b_path.ctypes, b_path.lengths
 
 
 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 = 0.0  # [m]
-    start_y = 0.0  # [m]
-    start_yaw = np.deg2rad(0.0)  # [rad]
+    start_x = -1.0  # [m]
+    start_y = -4.0  # [m]
+    start_yaw = np.deg2rad(-20.0)  # [rad]
 
-    end_x = 0.0  # [m]
-    end_y = 0.0  # [m]
-    end_yaw = np.deg2rad(0.0)  # [rad]
+    end_x = 5.0  # [m]
+    end_y = 5.0  # [m]
+    end_yaw = np.deg2rad(25.0)  # [rad]
 
-    curvature = 1.0
-    step_size = 0.1
+    curvature = 0.1
+    step_size = 0.05
 
-    px, py, pyaw, mode, clen = reeds_shepp_path_planning(
-        start_x, start_y, start_yaw, end_x, end_y, end_yaw,
-        curvature, step_size)
+    xs, ys, yaws, modes, lengths = 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()
-        plt.plot(px, py, label="final course " + str(mode))
+        plt.plot(xs, ys, label="final course " + str(modes))
+        print(f"{lengths=}")
 
         # plotting
         plot_arrow(start_x, start_y, start_yaw)
@@ -410,7 +372,7 @@ def main():
         plt.axis("equal")
         plt.show()
 
-    if not px:
+    if not xs:
         assert False, "No path"
 
 
diff --git a/tests/test_reeds_shepp_path_planning.py b/tests/test_reeds_shepp_path_planning.py
index f1ce82d4c2..34ccfe7730 100644
--- a/tests/test_reeds_shepp_path_planning.py
+++ b/tests/test_reeds_shepp_path_planning.py
@@ -1,6 +1,7 @@
+import numpy as np
+
 import conftest  # Add root path to sys.path
 from PathPlanning.ReedsSheppPath import reeds_shepp_path_planning as m
-import numpy as np
 
 
 def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
@@ -8,14 +9,21 @@ def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
     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 check_path_length(px, py, lengths):
+    sum_len = sum(abs(length) for length in lengths)
+    dpx = np.diff(px)
+    dpy = np.diff(py)
+    actual_len = sum(
+        np.hypot(dx, dy) for (dx, dy) in zip(dpx, dpy))
+    diff_len = sum_len - actual_len
+    assert (diff_len <= 0.01)
+
+
 def test1():
     m.show_animation = False
     m.main()
@@ -23,6 +31,7 @@ def test1():
 
 def test2():
     N_TEST = 10
+    np.random.seed(1234)
 
     for i in range(N_TEST):
         start_x = (np.random.rand() - 0.5) * 10.0  # [m]
@@ -35,11 +44,13 @@ def test2():
 
         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)
+        px, py, pyaw, mode, lengths = m.reeds_shepp_path_planning(
+            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)
+        check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                             end_y, end_yaw)
+        check_path_length(px, py, lengths)
 
 
 if __name__ == '__main__':

From 4df7b7a6a176527e44aaee4ccc16184e02127437 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Jul 2021 07:22:44 +0900
Subject: [PATCH 038/604] Bump numpy from 1.21.0 to 1.21.1 (#531)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.0 to 1.21.1.
- [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/v1.21.0...v1.21.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 d393dcec01..e98fafb68d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.0
+numpy == 1.21.1
 scipy == 1.7.0
 pandas == 1.3.0
 matplotlib == 3.4.2

From d777bea5005eef54e461152f9207cf0fa0e4458b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Jul 2021 07:21:13 +0900
Subject: [PATCH 039/604] Bump pandas from 1.3.0 to 1.3.1 (#532)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.0 to 1.3.1.
- [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/v1.3.0...v1.3.1)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 e98fafb68d..b5f4ad9244 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.1
 scipy == 1.7.0
-pandas == 1.3.0
+pandas == 1.3.1
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From 74ca49b813dd5665c01a134dec3272a149d2e087 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Jul 2021 07:21:43 +0900
Subject: [PATCH 040/604] Bump cvxpy from 1.1.13 to 1.1.14 (#533)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.13 to 1.1.14.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.13...v1.1.14)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 b5f4ad9244..4cc16fa789 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.21.1
 scipy == 1.7.0
 pandas == 1.3.1
 matplotlib == 3.4.2
-cvxpy == 1.1.13
+cvxpy == 1.1.14

From a942f6e4ed6f7de8be7a0f0a4d1b2236ae89bae6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Aug 2021 07:20:27 +0900
Subject: [PATCH 041/604] Bump scipy from 1.7.0 to 1.7.1 (#534)

Bumps [scipy](https://github.com/scipy/scipy) from 1.7.0 to 1.7.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.7.0...v1.7.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 4cc16fa789..fc636e01ca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.1
-scipy == 1.7.0
+scipy == 1.7.1
 pandas == 1.3.1
 matplotlib == 3.4.2
 cvxpy == 1.1.14

From 6ee8985004879604d9417eef8604891bd6a4d423 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Aug 2021 07:21:08 +0900
Subject: [PATCH 042/604] Bump cvxpy from 1.1.14 to 1.1.15 (#536)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.14 to 1.1.15.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.14...v1.1.15)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 fc636e01ca..c195634a32 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.21.1
 scipy == 1.7.1
 pandas == 1.3.1
 matplotlib == 3.4.2
-cvxpy == 1.1.14
+cvxpy == 1.1.15

From d3db011ab5f9a747ffbf8763206d06456a278d39 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Aug 2021 07:21:27 +0900
Subject: [PATCH 043/604] Bump numpy from 1.21.1 to 1.21.2 (#538)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.1 to 1.21.2.
- [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/v1.21.1...v1.21.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 c195634a32..2e35c69679 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.1
+numpy == 1.21.2
 scipy == 1.7.1
 pandas == 1.3.1
 matplotlib == 3.4.2

From 3b7f88ea95e4f025e05dbe89bb3dc58066f7bdbc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Aug 2021 08:07:06 +0900
Subject: [PATCH 044/604] Bump matplotlib from 3.4.2 to 3.4.3 (#539)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.2 to 3.4.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.2...v3.4.3)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 2e35c69679..469e3e96d1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
 pandas == 1.3.1
-matplotlib == 3.4.2
+matplotlib == 3.4.3
 cvxpy == 1.1.15

From 4aae5f35c5b63498ba35fe54a3afcab21a8687ff Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 23 Aug 2021 20:42:32 +0900
Subject: [PATCH 045/604] Bump pandas from 1.3.1 to 1.3.2 (#537)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 469e3e96d1..55d3e523fd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
-pandas == 1.3.1
+pandas == 1.3.2
 matplotlib == 3.4.3
 cvxpy == 1.1.15

From 06b2450591d63901c07d43fea53d1ec5fafb0cd2 Mon Sep 17 00:00:00 2001
From: Trung Kien <kienltb.hut@gmail.com>
Date: Sun, 29 Aug 2021 11:49:15 +0700
Subject: [PATCH 046/604] Fix markdown equations syntax (#535)

---
 ...redictive_speed_and_steering_control.ipynb |  78 +-
 SLAM/EKFSLAM/ekf_slam.ipynb                   | 948 ++----------------
 2 files changed, 128 insertions(+), 898 deletions(-)

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
index 6e3fe45be3..244f63c8c1 100644
--- 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
@@ -2,16 +2,15 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "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",
-   "metadata": {},
    "source": [
     "\n",
     "\n",
@@ -26,11 +25,11 @@
     "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",
-   "metadata": {},
    "source": [
     "### MPC modeling\n",
     "\n",
@@ -46,22 +45,22 @@
     "\n",
     "a: accellation, δ: steering angle\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "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",
-   "metadata": {},
    "source": [
     "subject to:\n",
     "\n",
@@ -89,20 +88,20 @@
     "\n",
     "$$u_{min} < u_t < u_{max}$$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "This is implemented at \n",
     "\n",
     "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at f51a73f47cb922a12659f8ce2d544c347a2a8156 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/f51a73f47cb922a12659f8ce2d544c347a2a8156/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L247-L301)"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "### Vehicle model linearization\n",
     "\n",
@@ -120,21 +119,21 @@
     "\n",
     "\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "ODE is\n",
     "\n",
     "$$ \\dot{z} =\\frac{\\partial }{\\partial z} z = f(z, u) = A'z+B'u$$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "where\n",
     "\n",
@@ -168,11 +167,11 @@
     "\\end{bmatrix}\n",
     "\\end{equation*}$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "$\\begin{equation*}\n",
     "B' =\n",
@@ -196,11 +195,11 @@
     "\\end{bmatrix}\n",
     "\\end{equation*}$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "You can get a discrete-time mode with Forward Euler Discretization with sampling time dt.\n",
     "\n",
@@ -210,11 +209,11 @@
     "$$z_{k+1}=z_k+(f(\\bar{z},\\bar{u})+A'z_k+B'u_k-A'\\bar{z}-B'\\bar{u})dt$$\n",
     "\n",
     "$$z_{k+1}=(I + dtA')z_k+(dtB')u_k + (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt$$\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "So, \n",
     "\n",
@@ -222,7 +221,7 @@
     "\n",
     "where,\n",
     "\n",
-    "$\\begin{equation*}\n",
+    "$$\\begin{equation*}\n",
     "A = (I + dtA')\\\\\n",
     "=\n",
     "\\begin{bmatrix} \n",
@@ -231,14 +230,14 @@
     "0 & 0 & 1 & 0 \\\\\n",
     "0 & 0 &\\frac{tan(\\bar{\\delta})}{L}dt & 1 \\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
+    "\\end{equation*}$$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "$\\begin{equation*}\n",
+    "$$\\begin{equation*}\n",
     "B = dtB'\\\\\n",
     "=\n",
     "\\begin{bmatrix} \n",
@@ -247,14 +246,14 @@
     "dt & 0 \\\\\n",
     "0 & \\frac{\\bar{v}}{Lcos^2(\\bar{\\delta})}dt \\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
+    "\\end{equation*}$$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "$\\begin{equation*}\n",
+    "$$\\begin{equation*}\n",
     "C = (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt\\\\\n",
     "= dt(\n",
     "\\begin{bmatrix} \n",
@@ -285,28 +284,29 @@
     "0\\\\\n",
     "-\\frac{\\bar{v}\\bar{\\delta}}{Lcos^2(\\bar{\\delta})}dt\\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
+    "\\end{equation*}$$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "This equation is implemented at \n",
     "\n",
     "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at eb6d1cbe6fc90c7be9210bf153b3a04f177cc138 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/eb6d1cbe6fc90c7be9210bf153b3a04f177cc138/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L80-L102)\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "### Reference\n",
     "\n",
     "- [Vehicle Dynamics and Control \\| Rajesh Rajamani \\| Springer](http://www.springer.com/us/book/9781461414322)\n",
     "\n",
     "- [MPC Course Material \\- MPC Lab @ UC\\-Berkeley](http://www.mpc.berkeley.edu/mpc-course-material)\n"
-   ]
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {
@@ -330,4 +330,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index a64c145ad4..f4c9d61436 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -2,40 +2,39 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# EKF SLAM"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=\"animation.png\",width=600)"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
-      ]
+      ],
+      "image/png": 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"
      },
-     "execution_count": 11,
      "metadata": {
       "image/png": {
        "width": 600
       }
      },
-     "output_type": "execute_result"
+     "execution_count": 11
     }
    ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"animation.png\",width=600)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Simulation\n",
     "\n",
@@ -46,39 +45,39 @@
     "- Black line: dead reckoning \n",
     "- Blue line: ground truth\n",
     "- Red line: EKF SLAM position estimation"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "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. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "\\begin{equation}\n",
+    "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n \\end{bmatrix}\n",
-    "\\end{equation}\n",
+    "\\end{equation}$\n",
     "\n",
-    "\\begin{equation}\n",
+    "$\\begin{equation}\n",
     "P = \\begin{bmatrix} \n",
     "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n",
     "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n",
     " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
-    "\\end{equation}"
-   ]
+    "\\end{equation}$"
+   ],
+   "metadata": {}
   },
   {
    "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",
@@ -86,26 +85,26 @@
     "belief of $x$ and $y$ and is equal to $\\sigma_{yx}$. \n",
     "\n",
     "The state can be represented more concisely as follows. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "\\begin{equation}\n",
+    "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
-    "\\end{equation}\n",
-    "\\begin{equation}\n",
+    "\\end{equation}$\n",
+    "$\\begin{equation}\n",
     "P = \\begin{bmatrix} \n",
     "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation}"
-   ]
+    "\\end{equation}$"
+   ],
+   "metadata": {}
   },
   {
    "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",
@@ -113,13 +112,12 @@
     "($\\Sigma_{xm}$, $\\Sigma_{mx}$).\n",
     "\n",
     "Take care to note the difference between $X$ (state) and $x$ (pose). "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Extended Kalman Filter SLAM example\n",
@@ -148,24 +146,24 @@
     "LM_SIZE = 2  # LM state size [x,y]\n",
     "\n",
     "show_animation = True"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "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"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def ekf_slam(xEst, PEst, u, z):\n",
     "    \"\"\"\n",
@@ -187,11 +185,12 @@
     "    xEst, PEst = update(xEst, PEst, u, z, initP)\n",
     "\n",
     "    return xEst, PEst\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "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",
@@ -287,13 +286,12 @@
     "$\n",
     "\n",
     "Notice this uncertainty is only growing with respect to the pose, not the landmarks. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def predict(xEst, PEst, u):\n",
     "    \"\"\"\n",
@@ -311,13 +309,13 @@
     "    # sigma = G*sigma*G.T + Noise\n",
     "    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n",
     "    return xEst, PEst, G, Fx"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def motion_model(x, u):\n",
     "    \"\"\"\n",
@@ -337,11 +335,12 @@
     "\n",
     "    x = (F @ x) + (B @ u)\n",
     "    return x"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## 2 - Update\n",
     "In the update phase, the observations of nearby landmarks are used to correct the location estimate. \n",
@@ -392,13 +391,12 @@
     "$\n",
     "P_{t} = (I-K_tH_t)\\bar{P_t}\n",
     "$"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def update(xEst, PEst, u, z, initP):\n",
     "    \"\"\"\n",
@@ -434,13 +432,13 @@
     "    \n",
     "    xEst[2] = pi_2_pi(xEst[2])\n",
     "    return xEst, PEst\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def calc_innovation(lm, xEst, PEst, z, LMid):\n",
     "    \"\"\"\n",
@@ -492,11 +490,12 @@
     "    H = G @ F\n",
     "\n",
     "    return H"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "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",
@@ -506,13 +505,12 @@
     "\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. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def observation(xTrue, xd, u, RFID):\n",
     "    \"\"\"\n",
@@ -548,13 +546,13 @@
     "\n",
     "    xd = motion_model(xd, ud)\n",
     "    return xTrue, z, xd, ud"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def calc_n_LM(x):\n",
     "    \"\"\"\n",
@@ -590,13 +588,13 @@
     "    return G, Fx,\n",
     "\n",
     "\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def calc_LM_Pos(x, z):\n",
     "    \"\"\"\n",
@@ -665,13 +663,13 @@
     "\n",
     "def pi_2_pi(angle):\n",
     "    return (angle + math.pi) % (2 * math.pi) - math.pi"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def main():\n",
     "    print(\" start!!\")\n",
@@ -731,18 +729,22 @@
     "            plt.axis(\"equal\")\n",
     "            plt.grid(True)\n",
     "            plt.pause(0.001)"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
+   "source": [
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "main()"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       " start!!\n",
       "New LM\n",
@@ -751,810 +753,38 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\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('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",
-       "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 = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(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 (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\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",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\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",
-       "\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",
-       "\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]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
-       "    {\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.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",
-       "            /* 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",
-       "            evt.data.type = \"image/png\";\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",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\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",
-       "    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",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\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",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             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",
-       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";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.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",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['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 = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\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.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\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).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\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/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\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) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('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",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\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",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\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('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
       "text/plain": [
        "<IPython.core.display.Javascript object>"
-      ]
+      ],
+      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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('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\nmpl.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 = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(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 (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\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            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\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            evt.data.type = \"image/png\";\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            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\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 */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n\nmpl.default_extension = \"png\";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.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        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.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 = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\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.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\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).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.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/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.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 () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\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) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('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    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.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    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
+     "output_type": "display_data",
      "data": {
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\" 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\" width=\"636\">"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "New LM\n"
      ]
     }
    ],
-   "source": [
-    "%matplotlib notebook\n",
-    "%matplotlib notebook\n",
-    "main()"
-   ]
+   "metadata": {
+    "scrolled": false
+   }
   }
  ],
  "metadata": {
@@ -1578,4 +808,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file

From 4fe851cc1d1f2e5d21684e24267d78c5e0299e36 Mon Sep 17 00:00:00 2001
From: Geonhee <gunhee6392@gmail.com>
Date: Mon, 6 Sep 2021 20:46:28 +0900
Subject: [PATCH 047/604] Correct typos (#542)

* Update cubic_spline_planner.py

* Update rear_wheel_feedback.py
---
 PathPlanning/CubicSpline/cubic_spline_planner.py        | 2 +-
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py
index 7ec01d1f4e..d2ef8622ad 100644
--- a/PathPlanning/CubicSpline/cubic_spline_planner.py
+++ b/PathPlanning/CubicSpline/cubic_spline_planner.py
@@ -195,7 +195,7 @@ def main():  # pragma: no cover
     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]
-    ds = 0.1  # [m] distance of each intepolated points
+    ds = 0.1  # [m] distance of each interpolated points
 
     sp = Spline2D(x, y)
     s = np.arange(0, sp.s[-1], ds)
diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 52b4a11a0b..ff72454f34 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -12,7 +12,7 @@
 from scipy import interpolate
 from scipy import optimize
 
-Kp = 1.0  # speed propotional gain
+Kp = 1.0  # speed proportional gain
 # steering control parameter
 KTH = 1.0
 KE = 0.5

From a2fc9f914ade3ecb6cda7e687acb39b3f8fc2e46 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 16 Sep 2021 21:10:36 +0900
Subject: [PATCH 048/604] Bump pandas from 1.3.2 to 1.3.3 (#543)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.2 to 1.3.3.
- [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/v1.3.2...v1.3.3)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 55d3e523fd..7363658a5f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
-pandas == 1.3.2
+pandas == 1.3.3
 matplotlib == 3.4.3
 cvxpy == 1.1.15

From acc3604f738f2c47942f4e06f99c02b896bd3ae0 Mon Sep 17 00:00:00 2001
From: szaguldo-kamaz <86873213+szaguldo-kamaz@users.noreply.github.com>
Date: Sun, 3 Oct 2021 16:40:44 +0900
Subject: [PATCH 049/604] Limit play area in RRT (#540)

* add support for limiting the play area

* plot the play area

* Update rrt.py

* fix param list

* fix code style

* fix more code style

* fix code style even more
---
 PathPlanning/RRT/rrt.py | 46 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 43 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 834fb24e39..dfbd4b1b7f 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -32,6 +32,15 @@ def __init__(self, x, y):
             self.path_y = []
             self.parent = None
 
+    class AreaBounds:
+
+        def __init__(self, area):
+            self.xmin = float(area[0])
+            self.xmax = float(area[1])
+            self.ymin = float(area[2])
+            self.ymax = float(area[3])
+
+
     def __init__(self,
                  start,
                  goal,
@@ -40,7 +49,9 @@ def __init__(self,
                  expand_dis=3.0,
                  path_resolution=0.5,
                  goal_sample_rate=5,
-                 max_iter=500):
+                 max_iter=500,
+                 play_area=None
+                 ):
         """
         Setting Parameter
 
@@ -48,12 +59,17 @@ def __init__(self,
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
+        play_area:stay inside this area [xmin,xmax,ymin,ymax]
 
         """
         self.start = self.Node(start[0], start[1])
         self.end = self.Node(goal[0], goal[1])
         self.min_rand = rand_area[0]
         self.max_rand = rand_area[1]
+        if play_area is not None:
+            self.play_area = self.AreaBounds(play_area)
+        else:
+            self.play_area = None
         self.expand_dis = expand_dis
         self.path_resolution = path_resolution
         self.goal_sample_rate = goal_sample_rate
@@ -76,7 +92,8 @@ 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_if_outside_play_area(new_node, self.play_area) and \
+               self.check_collision(new_node, self.obstacle_list):
                 self.node_list.append(new_node)
 
             if animation and i % 5 == 0:
@@ -163,6 +180,15 @@ def draw_graph(self, rnd=None):
         for (ox, oy, size) in self.obstacle_list:
             self.plot_circle(ox, oy, size)
 
+        if self.play_area is not None:
+            plt.plot([self.play_area.xmin, self.play_area.xmax,
+                      self.play_area.xmax, self.play_area.xmin,
+                      self.play_area.xmin],
+                     [self.play_area.ymin, self.play_area.ymin,
+                      self.play_area.ymax, self.play_area.ymax,
+                      self.play_area.ymin],
+                     "-k")
+
         plt.plot(self.start.x, self.start.y, "xr")
         plt.plot(self.end.x, self.end.y, "xr")
         plt.axis("equal")
@@ -186,6 +212,18 @@ def get_nearest_node_index(node_list, rnd_node):
 
         return minind
 
+    @staticmethod
+    def check_if_outside_play_area(node, play_area):
+
+        if play_area is None:
+            return True  # no play_area was defined, every pos should be ok
+
+        if node.x < play_area.xmin or node.x > play_area.xmax or \
+           node.y < play_area.ymin or node.y > play_area.ymax:
+            return False  # outside - bad
+        else:
+            return True  # inside - ok
+
     @staticmethod
     def check_collision(node, obstacleList):
 
@@ -222,7 +260,9 @@ def main(gx=6.0, gy=10.0):
         start=[0, 0],
         goal=[gx, gy],
         rand_area=[-2, 15],
-        obstacle_list=obstacleList)
+        obstacle_list=obstacleList,
+        # play_area=[0, 10, 0, 14]
+        )
     path = rrt.planning(animation=show_animation)
 
     if path is None:

From ee729d6b6ef1809c4cd402531e8fdce91a54d155 Mon Sep 17 00:00:00 2001
From: The-Anh Vu-Le <26211510+vltanh@users.noreply.github.com>
Date: Sun, 3 Oct 2021 17:31:08 +0700
Subject: [PATCH 050/604] Modify notations (#548)

* Modify notations

I modified some of the notations so that it will be less confusing:
- It is better to specify of which the Jacobian matrix is, here it is the transition and measurement function `f` and `g` (writing it as `J_F` and `J_H` is misleading)
- It is better to use `Delta` so as not to confuse people with the `d` commonly used in derivative
- It is more correct to use `\partial` instead of `d` since it is more multivariate than it is not

* Update docs/modules/extended_kalman_filter_localization.rst

* Update docs/modules/extended_kalman_filter_localization.rst

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../extended_kalman_filter_localization.rst   | 36 +++++++++++--------
 1 file changed, 22 insertions(+), 14 deletions(-)

diff --git a/docs/modules/extended_kalman_filter_localization.rst b/docs/modules/extended_kalman_filter_localization.rst
index 565fb4ed20..5021563b5b 100644
--- a/docs/modules/extended_kalman_filter_localization.rst
+++ b/docs/modules/extended_kalman_filter_localization.rst
@@ -77,32 +77,36 @@ Motion Model
 
 The robot model is
 
-.. math::  \dot{x} = vcos(\phi)
+.. math::  \dot{x} = v \cos(\phi)
 
-.. math::  \dot{y} = vsin((\phi)
+.. math::  \dot{y} = v \sin(\phi)
 
 .. math::  \dot{\phi} = \omega
 
 So, the motion model is
 
-.. math:: \textbf{x}_{t+1} = F\textbf{x}_t+B\textbf{u}_t
+.. 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 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
 
-:math:`\begin{equation*} B= \begin{bmatrix} cos(\phi)dt & 0\\ sin(\phi)dt & 0\\ 0 & dt\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} B= \begin{bmatrix} cos(\phi) \Delta t & 0\\ sin(\phi) \Delta t & 0\\ 0 & \Delta t\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
 
-:math:`dt` is a time interval.
+:math:`\Delta t` is a time interval.
 
 This is implemented at
 `code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L53-L67>`__
 
+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*}`
+
 Its Jacobian matrix is
 
-:math:`\begin{equation*} J_F= \begin{bmatrix} \frac{dx}{dx}& \frac{dx}{dy} & \frac{dx}{d\phi} & \frac{dx}{dv}\\ \frac{dy}{dx}& \frac{dy}{dy} & \frac{dy}{d\phi} & \frac{dy}{dv}\\ \frac{d\phi}{dx}& \frac{d\phi}{dy} & \frac{d\phi}{d\phi} & \frac{d\phi}{dv}\\ \frac{dv}{dx}& \frac{dv}{dy} & \frac{dv}{d\phi} & \frac{dv}{dv}\\ \end{bmatrix} \end{equation*}`
+: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 y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{\partial v}\\ \frac{\partial \phi'}{\partial x}& \frac{\partial \phi'}{\partial y} & \frac{\partial \phi'}{\partial \phi} & \frac{\partial \phi'}{\partial v}\\ \frac{\partial v'}{\partial x}& \frac{\partial v'}{\partial y} & \frac{\partial v'}{\partial \phi} & \frac{\partial v'}{\partial v} \end{bmatrix} \end{equation*}`
 
-:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v sin(\phi)dt & cos(\phi)dt\\ 0 & 1 & v cos(\phi)dt & sin(\phi) dt\\ 0 & 0 & 1 & 0\\ 0 & 0 & 0 & 1\\ \end{bmatrix} \end{equation*}`
+: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
 ~~~~~~~~~~~~~~~~~
@@ -111,15 +115,19 @@ The robot can get x-y position infomation from GPS.
 
 So GPS Observation model is
 
-.. math:: \textbf{z}_{t} = H\textbf{x}_t
+.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t
 
 where
 
-:math:`\begin{equation*} B= \begin{bmatrix} 1 & 0 & 0& 0\\ 0 & 1 & 0& 0\\ \end{bmatrix} \end{equation*}`
+: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_H= \begin{bmatrix} \frac{dx}{dx}& \frac{dx}{dy} & \frac{dx}{d\phi} & \frac{dx}{dv}\\ \frac{dy}{dx}& \frac{dy}{dy} & \frac{dy}{d\phi} & \frac{dy}{dv}\\ \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'}{ \partialv}\\ \end{bmatrix} \end{equation*}`
 
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}`
 
@@ -132,7 +140,7 @@ Localization process using Extendted Kalman Filter:EKF is
 
 :math:`x_{Pred} = Fx_t+Bu_t`
 
-:math:`P_{Pred} = J_FP_t J_F^T + Q`
+:math:`P_{Pred} = J_f P_t J_f^T + Q`
 
 === Update ===
 
@@ -140,13 +148,13 @@ Localization process using Extendted Kalman Filter:EKF is
 
 :math:`y = z - z_{Pred}`
 
-:math:`S = J_H P_{Pred}.J_H^T + R`
+:math:`S = J_g P_{Pred}.J_g^T + R`
 
-:math:`K = P_{Pred}.J_H^T S^{-1}`
+:math:`K = P_{Pred}.J_g^T S^{-1}`
 
 :math:`x_{t+1} = x_{Pred} + Ky`
 
-:math:`P_{t+1} = ( I - K J_H) P_{Pred}`
+:math:`P_{t+1} = ( I - K J_g) P_{Pred}`
 
 Ref:
 ~~~~

From 90c6cfa27584b5d72349459a93bc655b062e7444 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Oct 2021 07:10:21 +0900
Subject: [PATCH 051/604] Bump pandas from 1.3.3 to 1.3.4 (#553)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.3 to 1.3.4.
- [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/v1.3.3...v1.3.4)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 7363658a5f..a4a84bdf76 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
-pandas == 1.3.3
+pandas == 1.3.4
 matplotlib == 3.4.3
 cvxpy == 1.1.15

From 187b6aa35f3cbdeca587c0abdb177adddefc5c2a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Oct 2021 19:36:03 +0900
Subject: [PATCH 052/604] Bump numpy from 1.21.2 to 1.21.3 (#554)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.2 to 1.21.3.
- [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/v1.21.2...v1.21.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 a4a84bdf76..48660d08a6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.2
+numpy == 1.21.3
 scipy == 1.7.1
 pandas == 1.3.4
 matplotlib == 3.4.3

From ea052a9829210d378a47d35edddaf9c99492a113 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Nov 2021 07:53:34 +0900
Subject: [PATCH 053/604] Bump numpy from 1.21.3 to 1.21.4 (#558)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.3 to 1.21.4.
- [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/v1.21.3...v1.21.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 48660d08a6..0157212e47 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.3
+numpy == 1.21.4
 scipy == 1.7.1
 pandas == 1.3.4
 matplotlib == 3.4.3

From 26ed1ab631a4dc77bbdf861b951fc3a25bd57756 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Nov 2021 07:54:03 +0900
Subject: [PATCH 054/604] Bump cvxpy from 1.1.15 to 1.1.17 (#560)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.1.15 to 1.1.17.
- [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/v1.1.15...v1.1.17)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 0157212e47..f9af4fd2ab 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.21.4
 scipy == 1.7.1
 pandas == 1.3.4
 matplotlib == 3.4.3
-cvxpy == 1.1.15
+cvxpy == 1.1.17

From 4d3fb2825cf66f6a9f7680d5ef399d89df01c8d3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Nov 2021 20:58:17 +0900
Subject: [PATCH 055/604] Bump scipy from 1.7.1 to 1.7.2 (#559)

Bumps [scipy](https://github.com/scipy/scipy) from 1.7.1 to 1.7.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.7.1...v1.7.2)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 f9af4fd2ab..77a0bea427 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.4
-scipy == 1.7.1
+scipy == 1.7.2
 pandas == 1.3.4
 matplotlib == 3.4.3
 cvxpy == 1.1.17

From 0df55e943e661162230aa96bdb352e5bb8459da9 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Fri, 12 Nov 2021 05:28:16 -0500
Subject: [PATCH 056/604] Dynamic Movement Primitives Implementation (#526)

* Without equals sign, sometimes get points that are in the wrong direction - relative to the points before and after it- when change in x or change in y along path is 0

* Created test script for dubins path generator

* Made len == 0 it's own case, also changed 'l' to 'len' to appease travisCI

* More variable renaming to appease CI

* Broke == 0 into its own case in dubins planner, also Renaming files to appease CI

* Reverting some naming changes

* Turns out theres already a test for dubins.. not sure how I missed that

* Note to self: run the test cases on your own before throwing them at CI

* Added handling of length=0 case in generate_local_course()

* Missed reverting 'mode' back to 'm' in one spot

* Addressing style issues (line length)

* Mostly works, now just need to setup linear regression to solve for weights

* Re-arranged class

* Wrote DMP program and added tests file

* Styling fixes

* More styling

* Missed one indent

* Multi-dimension path learning (e.g. in x and y instead of just x)

* Added potential field obstacle avoidance

* Potential field working much better but has issues with reaching goal state

* Path ending to short not a result of obstacles, should be fix-able

* Mostly working! end won't go to goal

* split DMP and path following

* pretty close

* Okay this is working pretty well

* looks.. okay. was using the wrong vector before

* a plan to fix this mess

* Okay seriously going to pivot to the dubins approach im done with potential field lol

* Finished obstacle circle handling (and merging circles that are closer than their radii)

* Finished circle event finder function

* Some progress in preparing for dubins curves

* Finished angle finding algo, need to test

* Okay getting back to this, going to ignore the navigation and just focus on path generation since that's what DMP is for

* Moved DMP files to path planning

* changed folder name

* Made demo path cooler

* All working and added visualization tools (will remove

* Fixed unit test and handled TODOs

* not gonna handle this one

* demo now scales with data

* CI errors

* CI errors

* Fixing code style issues

* more styling

* fixing CI errors

* formatting

* Removed dead code

* removed unused imports

* removed uneccesary initialization

* Applying PR feedback

* fixing CI errors

* added description to header and removed unused variable
---
 .../dynamic_movement_primitives.py            | 261 ++++++++++++++++++
 tests/test_dynamic_movement_primitives.py     |  49 ++++
 2 files changed, 310 insertions(+)
 create mode 100644 PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
 create mode 100644 tests/test_dynamic_movement_primitives.py

diff --git a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
new file mode 100644
index 0000000000..468d3b9f97
--- /dev/null
+++ b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
@@ -0,0 +1,261 @@
+"""
+Author: Jonathan Schwartz (github.com/SchwartzCode)
+
+This code provides a simple implementation of Dynamic Movement
+Primitives, which is an approach to learning curves by modelling
+them as a weighted sum of gaussian distributions. This approach
+can be used to dampen noise in a curve, and can also be used to
+stretch a curve by adjusting its start and end points.
+
+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
+
+"""
+
+
+from matplotlib import pyplot as plt
+import numpy as np
+
+
+class DMP(object):
+
+    def __init__(self, training_data, data_period, K=156.25, B=25):
+        """
+        Arguments:
+            training_data - input data of form [N, dim]
+            data_period   - amount of time training data covers
+            K and B       - spring and damper constants to define
+                            DMP behavior
+        """
+
+        self.K = K  # virtual spring constant
+        self.B = B  # virtual damper coefficient
+
+        self.timesteps = training_data.shape[0]
+        self.dt = data_period / self.timesteps
+
+        self.weights = None  # weights used to generate DMP trajectories
+
+        self.T_orig = data_period
+
+        self.training_data = training_data
+        self.find_basis_functions_weights(training_data, data_period)
+
+    def find_basis_functions_weights(self, training_data, data_period,
+                                     num_weights=10):
+        """
+        Arguments:
+            data [(steps x spacial dim) np array] - data to replicate with DMP
+            data_period [float] - time duration of data
+        """
+
+        if not isinstance(training_data, np.ndarray):
+            print("Warning: you should input training data as an np.ndarray")
+        elif training_data.shape[0] < training_data.shape[1]:
+            print("Warning: you probably need to transpose your training data")
+
+        dt = data_period / len(training_data)
+
+        init_state = training_data[0]
+        goal_state = training_data[-1]
+
+        # means (C) and std devs (H) of gaussian basis functions
+        C = np.linspace(0, 1, num_weights)
+        H = (0.65*(1./(num_weights-1))**2)
+
+        for dim, _ in enumerate(training_data[0]):
+
+            dimension_data = training_data[:, dim]
+
+            q0 = init_state[dim]
+            g = goal_state[dim]
+
+            q = q0
+            qd_last = 0
+
+            phi_vals = []
+            f_vals = []
+
+            for i, _ in enumerate(dimension_data):
+                if i + 1 == len(dimension_data):
+                    qd = 0
+                else:
+                    qd = (dimension_data[i+1] - dimension_data[i]) / dt
+
+                phi = [np.exp(-0.5 * ((i * dt / data_period) - c)**2 / H)
+                       for c in C]
+                phi = phi/np.sum(phi)
+
+                qdd = (qd - qd_last)/dt
+
+                f = (qdd * data_period**2 - self.K * (g - q) + self.B * qd
+                     * data_period) / (g - q0)
+
+                phi_vals.append(phi)
+                f_vals.append(f)
+
+                qd_last = qd
+                q += qd * dt
+
+            phi_vals = np.asarray(phi_vals)
+            f_vals = np.asarray(f_vals)
+
+            w = np.linalg.lstsq(phi_vals, f_vals, rcond=None)
+
+            if self.weights is None:
+                self.weights = np.asarray(w[0])
+            else:
+                self.weights = np.vstack([self.weights, w[0]])
+
+    def recreate_trajectory(self, init_state, goal_state, T):
+        """
+        init_state - initial state/position
+        goal_state - goal state/position
+        T  - amount of time to travel q0 -> g
+        """
+
+        nrBasis = len(self.weights[0])  # number of gaussian basis functions
+
+        # means (C) and std devs (H) of gaussian basis functions
+        C = np.linspace(0, 1, nrBasis)
+        H = (0.65*(1./(nrBasis-1))**2)
+
+        # initialize virtual system
+        time = 0
+
+        q = init_state
+        dimensions = self.weights.shape[0]
+        qd = np.zeros(dimensions)
+
+        positions = np.array([])
+        for k in range(self.timesteps):
+            time = time + self.dt
+
+            qdd = np.zeros(dimensions)
+
+            for dim in range(dimensions):
+
+                if time <= T:
+                    phi = [np.exp(-0.5 * ((time / T) - c)**2 / H) for c in C]
+                    phi = phi / np.sum(phi)
+                    f = np.dot(phi, self.weights[dim])
+                else:
+                    f = 0
+
+                # simulate dynamics
+                qdd[dim] = (self.K*(goal_state[dim] - q[dim])/T**2
+                            - self.B*qd[dim]/T
+                            + (goal_state[dim] - init_state[dim])*f/T**2)
+
+            qd = qd + qdd * self.dt
+            q = q + qd * self.dt
+
+            if positions.size == 0:
+                positions = q
+            else:
+                positions = np.vstack([positions, q])
+
+        t = np.arange(0, self.timesteps * self.dt, self.dt)
+        return t, positions
+
+    @staticmethod
+    def dist_between(p1, p2):
+        return np.linalg.norm(p1 - p2)
+
+    def view_trajectory(self, path, title=None, demo=False):
+
+        path = np.asarray(path)
+
+        plt.cla()
+        plt.plot(self.training_data[:, 0], self.training_data[:, 1],
+                 label="Training Data")
+        plt.plot(path[:, 0], path[:, 1],
+                 linewidth=2, label="DMP Approximation")
+
+        plt.xlabel("X Position")
+        plt.ylabel("Y Position")
+        plt.legend()
+
+        if title is not None:
+            plt.title(title)
+
+        if demo:
+            plt.xlim([-0.5, 5])
+            plt.ylim([-2, 2])
+            plt.draw()
+            plt.pause(0.02)
+        else:
+            plt.show()
+
+    def show_DMP_purpose(self):
+        """
+        This function conveys the purpose of DMPs:
+            to capture a trajectory and be able to stretch
+            and squeeze it in terms of start and stop position
+            or time
+        """
+
+        q0_orig = self.training_data[0]
+        g_orig = self.training_data[-1]
+        T_orig = self.T_orig
+
+        data_range = (np.amax(self.training_data[:, 0])
+                      - np.amin(self.training_data[:, 0])) / 4
+
+        q0_right = q0_orig + np.array([data_range, 0])
+        q0_up = q0_orig + np.array([0, data_range/2])
+        g_left = g_orig - np.array([data_range, 0])
+        g_down = g_orig - np.array([0, data_range/2])
+
+        q0_vals = np.vstack([np.linspace(q0_orig, q0_right, 20),
+                             np.linspace(q0_orig, q0_up, 20)])
+        g_vals = np.vstack([np.linspace(g_orig, g_left, 20),
+                            np.linspace(g_orig, g_down, 20)])
+        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))
+
+            _, 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))
+
+            _, 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)
+
+            _, path = self.recreate_trajectory(q0_orig, g_orig, new_T_value)
+            self.view_trajectory(path, title=plot_title, demo=True)
+
+
+def example_DMP():
+    """
+    Creates a noisy trajectory, fits weights to it, and then adjusts the
+    trajectory by moving its start position, goal position, or period
+    """
+    t = np.arange(0, 3*np.pi/2, 0.01)
+    t1 = np.arange(3*np.pi/2, 2*np.pi, 0.01)[:-1]
+    t2 = np.arange(0, np.pi/2, 0.01)[:-1]
+    t3 = np.arange(np.pi, 3*np.pi/2, 0.01)
+    data_x = t + 0.02*np.random.rand(t.shape[0])
+    data_y = np.concatenate([np.cos(t1) + 0.1*np.random.rand(t1.shape[0]),
+                             np.cos(t2) + 0.1*np.random.rand(t2.shape[0]),
+                             np.sin(t3) + 0.1*np.random.rand(t3.shape[0])])
+    training_data = np.vstack([data_x, data_y]).T
+
+    period = 3*np.pi/2
+    DMP_controller = DMP(training_data, period)
+
+    DMP_controller.show_DMP_purpose()
+
+
+if __name__ == '__main__':
+
+    example_DMP()
diff --git a/tests/test_dynamic_movement_primitives.py b/tests/test_dynamic_movement_primitives.py
new file mode 100644
index 0000000000..3ab1c8a32c
--- /dev/null
+++ b/tests/test_dynamic_movement_primitives.py
@@ -0,0 +1,49 @@
+import conftest
+import numpy as np
+from PathPlanning.DynamicMovementPrimitives import \
+            dynamic_movement_primitives
+
+
+def test_1():
+    # test that trajectory can be learned from user-passed data
+    T = 5
+    t = np.arange(0, T, 0.01)
+    sin_t = np.sin(t)
+    train_data = np.array([t, sin_t]).T
+
+    DMP_controller = dynamic_movement_primitives.DMP(train_data, T)
+    DMP_controller.recreate_trajectory(train_data[0], train_data[-1], 4)
+
+
+def test_2():
+    # test that length of trajectory is equal to desired number of timesteps
+    T = 5
+    t = np.arange(0, T, 0.01)
+    sin_t = np.sin(t)
+    train_data = np.array([t, sin_t]).T
+
+    DMP_controller = dynamic_movement_primitives.DMP(train_data, T)
+    t, path = DMP_controller.recreate_trajectory(train_data[0],
+                                                 train_data[-1], 4)
+
+    assert(path.shape[0] == DMP_controller.timesteps)
+
+
+def test_3():
+    # check that learned trajectory is close to initial
+    T = 3*np.pi/2
+    A_noise = 0.02
+    t = np.arange(0, T, 0.01)
+    noisy_sin_t = np.sin(t) + A_noise*np.random.rand(len(t))
+    train_data = np.array([t, noisy_sin_t]).T
+
+    DMP_controller = dynamic_movement_primitives.DMP(train_data, T)
+    t, pos = DMP_controller.recreate_trajectory(train_data[0],
+                                                train_data[-1], T)
+
+    diff = abs(pos[:, 1] - noisy_sin_t)
+    assert(max(diff) < 5*A_noise)
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 17920dcbf489dff0f804ac541973c1c7b8ed77f0 Mon Sep 17 00:00:00 2001
From: Kajal <kajalpuri95@gmail.com>
Date: Fri, 12 Nov 2021 11:29:36 +0100
Subject: [PATCH 057/604] Changes in Slam/Ekf_slam.ipynb file, added a
 dtype=object statement to jF=np.array so it can render the output without
 numerous depreciation warnings (#552)

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 1163 ++++++++++++++++++++++++++++++++---
 1 file changed, 1068 insertions(+), 95 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index f4c9d61436..7634bfde9e 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -2,39 +2,40 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# EKF SLAM"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"animation.png\",width=600)"
-   ],
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "image/png": "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        "<IPython.core.display.Image object>"
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"
+      ]
      },
+     "execution_count": 1,
      "metadata": {
       "image/png": {
        "width": 600
       }
      },
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=\"animation.png\",width=600)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Simulation\n",
     "\n",
@@ -45,21 +46,21 @@
     "- Black line: dead reckoning \n",
     "- Blue line: ground truth\n",
     "- Red line: EKF SLAM position estimation"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "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. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n \\end{bmatrix}\n",
@@ -73,11 +74,11 @@
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
     "\\end{equation}$"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "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",
@@ -85,11 +86,11 @@
     "belief of $x$ and $y$ and is equal to $\\sigma_{yx}$. \n",
     "\n",
     "The state can be represented more concisely as follows. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
@@ -100,11 +101,11 @@
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
     "\\end{equation}$"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "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",
@@ -112,12 +113,13 @@
     "($\\Sigma_{xm}$, $\\Sigma_{mx}$).\n",
     "\n",
     "Take care to note the difference between $X$ (state) and $x$ (pose). "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Extended Kalman Filter SLAM example\n",
@@ -146,24 +148,24 @@
     "LM_SIZE = 2  # LM state size [x,y]\n",
     "\n",
     "show_animation = True"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "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"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def ekf_slam(xEst, PEst, u, z):\n",
     "    \"\"\"\n",
@@ -185,12 +187,11 @@
     "    xEst, PEst = update(xEst, PEst, u, z, initP)\n",
     "\n",
     "    return xEst, PEst\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "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",
@@ -286,12 +287,13 @@
     "$\n",
     "\n",
     "Notice this uncertainty is only growing with respect to the pose, not the landmarks. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def predict(xEst, PEst, u):\n",
     "    \"\"\"\n",
@@ -309,13 +311,13 @@
     "    # sigma = G*sigma*G.T + Noise\n",
     "    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n",
     "    return xEst, PEst, G, Fx"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def motion_model(x, u):\n",
     "    \"\"\"\n",
@@ -335,12 +337,11 @@
     "\n",
     "    x = (F @ x) + (B @ u)\n",
     "    return x"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 2 - Update\n",
     "In the update phase, the observations of nearby landmarks are used to correct the location estimate. \n",
@@ -391,12 +392,13 @@
     "$\n",
     "P_{t} = (I-K_tH_t)\\bar{P_t}\n",
     "$"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def update(xEst, PEst, u, z, initP):\n",
     "    \"\"\"\n",
@@ -432,13 +434,13 @@
     "    \n",
     "    xEst[2] = pi_2_pi(xEst[2])\n",
     "    return xEst, PEst\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def calc_innovation(lm, xEst, PEst, z, LMid):\n",
     "    \"\"\"\n",
@@ -490,12 +492,11 @@
     "    H = G @ F\n",
     "\n",
     "    return H"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "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",
@@ -505,12 +506,13 @@
     "\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. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def observation(xTrue, xd, u, RFID):\n",
     "    \"\"\"\n",
@@ -546,13 +548,13 @@
     "\n",
     "    xd = motion_model(xd, ud)\n",
     "    return xTrue, z, xd, ud"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def calc_n_LM(x):\n",
     "    \"\"\"\n",
@@ -580,7 +582,7 @@
     "\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]])\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",
@@ -588,13 +590,13 @@
     "    return G, Fx,\n",
     "\n",
     "\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def calc_LM_Pos(x, z):\n",
     "    \"\"\"\n",
@@ -663,13 +665,13 @@
     "\n",
     "def pi_2_pi(angle):\n",
     "    return (angle + math.pi) % (2 * math.pi) - math.pi"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def main():\n",
     "    print(\" start!!\")\n",
@@ -729,22 +731,18 @@
     "            plt.axis(\"equal\")\n",
     "            plt.grid(True)\n",
     "            plt.pause(0.001)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "%matplotlib notebook\n",
-    "%matplotlib notebook\n",
-    "main()"
-   ],
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       " start!!\n",
       "New LM\n",
@@ -753,38 +751,1013 @@
      ]
     },
     {
-     "output_type": "display_data",
      "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(\"<div id='\" + id + \"'></div>\");\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",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 = '<i class=\"fa ' + image + ' fa-lg\"></i>';\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 = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\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": [
        "<IPython.core.display.Javascript object>"
-      ],
-      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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('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\nmpl.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 = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(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 (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\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            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\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\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.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.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\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            evt.data.type = \"image/png\";\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            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.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    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\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 */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\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\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.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\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n\nmpl.default_extension = \"png\";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.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        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.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 = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\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.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\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).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.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/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.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 () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\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) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('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    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.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    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.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.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "text/html": [
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\" width=\"636\">"
       ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "New LM\n"
      ]
     }
    ],
-   "metadata": {
-    "scrolled": false
-   }
+   "source": [
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "main()"
+   ]
   }
  ],
  "metadata": {
@@ -803,9 +1776,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}

From e7c990a72992a31366ef4bc1118fa664ce25ad7e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 16 Nov 2021 22:27:18 +0900
Subject: [PATCH 058/604] clean up doc dir (#563)

---
 Introduction/introduction.ipynb               |  44 --------
 docs/README.md                                |  11 --
 docs/index.rst                                |  16 +--
 docs/jupyternotebook2rst.py                   |  93 -----------------
 .../aerial_navigation.rst                     |   0
 .../rocket_powered_landing.rst                |   0
 .../{ => appendix}/Kalmanfilter_basics.rst    |   0
 .../{ => appendix}/Kalmanfilter_basics_2.rst  |   0
 .../Kalmanfilter_basics_2_5_0.png             | Bin
 .../Kalmanfilter_basics_14_1.png              | Bin
 .../Kalmanfilter_basics_16_0.png              | Bin
 .../Kalmanfilter_basics_19_1.png              | Bin
 .../Kalmanfilter_basics_21_1.png              | Bin
 .../Kalmanfilter_basics_22_0.png              | Bin
 .../Kalmanfilter_basics_28_1.png              | Bin
 docs/modules/{ => appendix}/appendix.rst      |   0
 .../Planar_Two_Link_IK.rst                    |   0
 .../Planar_Two_Link_IK_12_0.png               | Bin
 .../Planar_Two_Link_IK_5_0.png                | Bin
 .../Planar_Two_Link_IK_7_0.png                | Bin
 .../Planar_Two_Link_IK_9_0.png                | Bin
 .../{ => arm_navigation}/arm_navigation.rst   |   0
 .../extended_kalman_filter_localization.rst   |   0
 ...xtended_kalman_filter_localization_1_0.png | Bin
 .../{ => localization}/localization.rst       |   0
 docs/modules/{ => mapping}/mapping.rst        |   0
 .../{ => path_planning}/path_planning.rst     |   0
 docs/modules/{ => path_planning}/rrt_star.rst |   0
 .../rrt_star_files/rrt_star_1_0.png           | Bin
 ..._predictive_speed_and_steering_control.rst |   0
 .../{ => path_tracking}/cgmres_nmpc.rst       |   0
 .../{ => path_tracking}/path_tracking.rst     |   0
 docs/modules/{ => slam}/FastSLAM1.rst         |   0
 .../FastSLAM1_files/FastSLAM1_12_0.png        | Bin
 .../FastSLAM1_files/FastSLAM1_12_1.png        | Bin
 .../FastSLAM1_files/FastSLAM1_1_0.png         | Bin
 docs/modules/{ => slam}/slam.rst              |   0
 docs/notebook_template.ipynb                  |  96 ------------------
 38 files changed, 8 insertions(+), 252 deletions(-)
 delete mode 100644 Introduction/introduction.ipynb
 delete mode 100644 docs/jupyternotebook2rst.py
 rename docs/modules/{ => aerial_navigation}/aerial_navigation.rst (100%)
 rename docs/modules/{ => aerial_navigation}/rocket_powered_landing.rst (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics.rst (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_2.rst (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_2_files/Kalmanfilter_basics_2_5_0.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_14_1.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_16_0.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_19_1.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_21_1.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_22_0.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_28_1.png (100%)
 rename docs/modules/{ => appendix}/appendix.rst (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK.rst (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_12_0.png (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_5_0.png (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_7_0.png (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_9_0.png (100%)
 rename docs/modules/{ => arm_navigation}/arm_navigation.rst (100%)
 rename docs/modules/{ => localization}/extended_kalman_filter_localization.rst (100%)
 rename docs/modules/{ => localization}/extended_kalman_filter_localization_files/extended_kalman_filter_localization_1_0.png (100%)
 rename docs/modules/{ => localization}/localization.rst (100%)
 rename docs/modules/{ => mapping}/mapping.rst (100%)
 rename docs/modules/{ => path_planning}/path_planning.rst (100%)
 rename docs/modules/{ => path_planning}/rrt_star.rst (100%)
 rename docs/modules/{ => path_planning}/rrt_star_files/rrt_star_1_0.png (100%)
 rename docs/modules/{ => path_tracking}/Model_predictive_speed_and_steering_control.rst (100%)
 rename docs/modules/{ => path_tracking}/cgmres_nmpc.rst (100%)
 rename docs/modules/{ => path_tracking}/path_tracking.rst (100%)
 rename docs/modules/{ => slam}/FastSLAM1.rst (100%)
 rename docs/modules/{ => slam}/FastSLAM1_files/FastSLAM1_12_0.png (100%)
 rename docs/modules/{ => slam}/FastSLAM1_files/FastSLAM1_12_1.png (100%)
 rename docs/modules/{ => slam}/FastSLAM1_files/FastSLAM1_1_0.png (100%)
 rename docs/modules/{ => slam}/slam.rst (100%)
 delete mode 100644 docs/notebook_template.ipynb

diff --git a/Introduction/introduction.ipynb b/Introduction/introduction.ipynb
deleted file mode 100644
index c2fcc709a5..0000000000
--- a/Introduction/introduction.ipynb
+++ /dev/null
@@ -1,44 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "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": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/README.md b/docs/README.md
index 1c6eea4a65..728a81290e 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -24,14 +24,3 @@ if you want to building each time a file is changed:
 sphinx-autobuild . _build/html
 ```
 
-### Jupyter notebook integration
-
-When you want to generate rst files from each jupyter notebooks,
-
-you can use
-
-```
-cd path/to/docs
-python jupyternotebook2rst.py
-```
-
diff --git a/docs/index.rst b/docs/index.rst
index 3798a1a1a6..978c216f11 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -35,14 +35,14 @@ See this paper for more details:
 
    getting_started
    modules/introduction
-   modules/localization
-   modules/mapping
-   modules/slam
-   modules/path_planning
-   modules/path_tracking
-   modules/arm_navigation
-   modules/aerial_navigation
-   modules/appendix
+   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/appendix/appendix
 
 
 Indices and tables
diff --git a/docs/jupyternotebook2rst.py b/docs/jupyternotebook2rst.py
deleted file mode 100644
index 3db67244b6..0000000000
--- a/docs/jupyternotebook2rst.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""
-
-Jupyter notebook converter to rst file
-
-author: Atsushi Sakai
-
-"""
-import subprocess
-import os.path
-import os
-import glob
-
-
-NOTEBOOK_DIR = "../"
-
-
-def get_notebook_path_list(ndir):
-    path = glob.glob(ndir + "**/*.ipynb", recursive=True)
-    return path
-
-
-def convert_rst(rstpath):
-
-    with open(rstpath, "r") as bfile:
-        filedata = bfile.read()
-
-        # convert from code directive to code-block
-        # because showing code in Sphinx
-        before = ".. code:: ipython3"
-        after = ".. code-block:: ipython3"
-        filedata = filedata.replace(before, after)
-
-    with open(rstpath, "w") as afile:
-        afile.write(filedata)
-
-
-def generate_rst(npath):
-    print("====Start generating rst======")
-
-    # generate dir
-    dirpath = os.path.dirname(npath)
-    # print(dirpath)
-
-    rstpath = os.path.abspath("./modules/" + npath[3:-5] + "rst")
-    # print(rstpath)
-
-    basename = os.path.basename(rstpath)
-
-    cmd = "jupyter nbconvert --to rst "
-    cmd += npath
-    print(cmd)
-    subprocess.call(cmd, shell=True)
-
-    rstpath = dirpath + "/" + basename
-    convert_rst(rstpath)
-
-    # clean up old files
-    cmd = "rm -rf "
-    cmd += "./modules/"
-    cmd += basename[:-4]
-    cmd += "*"
-    # print(cmd)
-    subprocess.call(cmd, shell=True)
-
-    # move files to module dir
-    cmd = "mv "
-    cmd += dirpath
-    cmd += "/*.rst ./modules/"
-    print(cmd)
-    subprocess.call(cmd, shell=True)
-
-    cmd = "mv "
-    cmd += dirpath
-    cmd += "/*_files ./modules/"
-    print(cmd)
-    subprocess.call(cmd, shell=True)
-
-
-def main():
-    print("start!!")
-
-    notebook_path_list = get_notebook_path_list(NOTEBOOK_DIR)
-    # print(notebook_path_list)
-
-    for npath in notebook_path_list:
-        if "template" not in npath:
-            generate_rst(npath)
-
-    print("done!!")
-
-
-if __name__ == '__main__':
-    main()
diff --git a/docs/modules/aerial_navigation.rst b/docs/modules/aerial_navigation/aerial_navigation.rst
similarity index 100%
rename from docs/modules/aerial_navigation.rst
rename to docs/modules/aerial_navigation/aerial_navigation.rst
diff --git a/docs/modules/rocket_powered_landing.rst b/docs/modules/aerial_navigation/rocket_powered_landing.rst
similarity index 100%
rename from docs/modules/rocket_powered_landing.rst
rename to docs/modules/aerial_navigation/rocket_powered_landing.rst
diff --git a/docs/modules/Kalmanfilter_basics.rst b/docs/modules/appendix/Kalmanfilter_basics.rst
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diff --git a/docs/modules/appendix.rst b/docs/modules/appendix/appendix.rst
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diff --git a/docs/modules/Planar_Two_Link_IK.rst b/docs/modules/arm_navigation/Planar_Two_Link_IK.rst
similarity index 100%
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rename to docs/modules/arm_navigation/Planar_Two_Link_IK.rst
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diff --git a/docs/modules/Planar_Two_Link_IK_files/Planar_Two_Link_IK_5_0.png b/docs/modules/arm_navigation/Planar_Two_Link_IK_files/Planar_Two_Link_IK_5_0.png
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diff --git a/docs/modules/Planar_Two_Link_IK_files/Planar_Two_Link_IK_7_0.png b/docs/modules/arm_navigation/Planar_Two_Link_IK_files/Planar_Two_Link_IK_7_0.png
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diff --git a/docs/modules/Planar_Two_Link_IK_files/Planar_Two_Link_IK_9_0.png b/docs/modules/arm_navigation/Planar_Two_Link_IK_files/Planar_Two_Link_IK_9_0.png
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diff --git a/docs/modules/arm_navigation.rst b/docs/modules/arm_navigation/arm_navigation.rst
similarity index 100%
rename from docs/modules/arm_navigation.rst
rename to docs/modules/arm_navigation/arm_navigation.rst
diff --git a/docs/modules/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization.rst
similarity index 100%
rename from docs/modules/extended_kalman_filter_localization.rst
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diff --git a/docs/modules/localization.rst b/docs/modules/localization/localization.rst
similarity index 100%
rename from docs/modules/localization.rst
rename to docs/modules/localization/localization.rst
diff --git a/docs/modules/mapping.rst b/docs/modules/mapping/mapping.rst
similarity index 100%
rename from docs/modules/mapping.rst
rename to docs/modules/mapping/mapping.rst
diff --git a/docs/modules/path_planning.rst b/docs/modules/path_planning/path_planning.rst
similarity index 100%
rename from docs/modules/path_planning.rst
rename to docs/modules/path_planning/path_planning.rst
diff --git a/docs/modules/rrt_star.rst b/docs/modules/path_planning/rrt_star.rst
similarity index 100%
rename from docs/modules/rrt_star.rst
rename to docs/modules/path_planning/rrt_star.rst
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diff --git a/docs/modules/Model_predictive_speed_and_steering_control.rst b/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
similarity index 100%
rename from docs/modules/Model_predictive_speed_and_steering_control.rst
rename to docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
diff --git a/docs/modules/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc.rst
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rename from docs/modules/cgmres_nmpc.rst
rename to docs/modules/path_tracking/cgmres_nmpc.rst
diff --git a/docs/modules/path_tracking.rst b/docs/modules/path_tracking/path_tracking.rst
similarity index 100%
rename from docs/modules/path_tracking.rst
rename to docs/modules/path_tracking/path_tracking.rst
diff --git a/docs/modules/FastSLAM1.rst b/docs/modules/slam/FastSLAM1.rst
similarity index 100%
rename from docs/modules/FastSLAM1.rst
rename to docs/modules/slam/FastSLAM1.rst
diff --git a/docs/modules/FastSLAM1_files/FastSLAM1_12_0.png b/docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png
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rename to docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png
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similarity index 100%
rename from docs/modules/FastSLAM1_files/FastSLAM1_12_1.png
rename to docs/modules/slam/FastSLAM1_files/FastSLAM1_12_1.png
diff --git a/docs/modules/FastSLAM1_files/FastSLAM1_1_0.png b/docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png
similarity index 100%
rename from docs/modules/FastSLAM1_files/FastSLAM1_1_0.png
rename to docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png
diff --git a/docs/modules/slam.rst b/docs/modules/slam/slam.rst
similarity index 100%
rename from docs/modules/slam.rst
rename to docs/modules/slam/slam.rst
diff --git a/docs/notebook_template.ipynb b/docs/notebook_template.ipynb
deleted file mode 100644
index 40943b09a2..0000000000
--- a/docs/notebook_template.ipynb
+++ /dev/null
@@ -1,96 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Title"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'hoge.png'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-d7c1437408c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 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 <asakai.amsl+github@gmail.com>
Date: Tue, 16 Nov 2021 23:06:25 +0900
Subject: [PATCH 059/604] 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 <asakai.amsl+github@gmail.com>
Date: Tue, 16 Nov 2021 23:14:26 +0900
Subject: [PATCH 060/604] 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 <asakai.amsl+github@gmail.com>
Date: Wed, 17 Nov 2021 19:42:14 +0900
Subject: [PATCH 061/604] 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 <asakai.amsl+github@gmail.com>
Date: Wed, 17 Nov 2021 20:04:09 +0900
Subject: [PATCH 062/604] 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 <asakai.amsl+github@gmail.com>
Date: Wed, 17 Nov 2021 22:58:23 +0900
Subject: [PATCH 063/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 18 Nov 2021 22:35:10 +0900
Subject: [PATCH 064/604] 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",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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": [
-       "<matplotlib.contour.QuadContourSet at 0x139196438>"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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",
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-    "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": {
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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "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",
-    "<u>**Prediction**</u>\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",
-    "<u>**Correction**</u>\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"
-   ]
-  }
- ],
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diff --git a/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb b/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb
deleted file mode 100644
index 073fd06a16..0000000000
--- a/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb
+++ /dev/null
@@ -1,264 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extended Kalman Filter Localization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "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 <asakai.amsl+github@gmail.com>
Date: Fri, 19 Nov 2021 15:54:52 +0900
Subject: [PATCH 065/604] 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 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png

diff --git a/Mapping/lidar_to_grid_map/animation.gif b/Mapping/lidar_to_grid_map/animation.gif
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diff --git a/README.md b/README.md
index 5a06f09ca8..024c600cb1 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,8 @@
 # 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)
+[![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest)
 [![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)
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial.rst b/docs/modules/mapping/lidar_to_grid_map_tutorial.rst
new file mode 100644
index 0000000000..64dba26d4f
--- /dev/null
+++ b/docs/modules/mapping/lidar_to_grid_map_tutorial.rst
@@ -0,0 +1,195 @@
+
+This simple tutorial shows how to read LIDAR (range) measurements from a
+file and convert it to occupancy grid.
+
+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.
+
+.. figure:: lidar_to_grid_map_tutorial_files/grid_map_example.png
+   :alt: Example
+
+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.
+
+.. code:: ipython3
+
+    import math
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from math import cos, sin, radians, pi
+
+The measurement file contains the distances and the corresponding angles
+in a ``csv`` (comma separated values) format. Let’s write the
+``file_read`` method:
+
+.. code:: ipython3
+
+    def file_read(f):
+        """
+        Reading LIDAR laser beams (angles and corresponding distance data)
+        """
+        measures = [line.split(",") for line in open(f)]
+        angles = []
+        distances = []
+        for measure in measures:
+            angles.append(float(measure[0]))
+            distances.append(float(measure[1]))
+        angles = np.array(angles)
+        distances = np.array(distances)
+        return angles, distances
+
+From the distances and the angles it is easy to determine the ``x`` and
+``y`` coordinates with ``sin`` and ``cos``. In order to display it
+``matplotlib.pyplot`` (``plt``) is used.
+
+.. code:: ipython3
+
+    ang, dist = file_read("lidar01.csv")
+    ox = np.sin(ang) * dist
+    oy = np.cos(ang) * dist
+    plt.figure(figsize=(6,10))
+    plt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], "ro-") # lines from 0,0 to the 
+    plt.axis("equal")
+    bottom, top = plt.ylim()  # return the current ylim
+    plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation
+    plt.grid(True)
+    plt.show()
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png
+
+
+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.
+
+.. code:: ipython3
+
+    import lidar_to_grid_map as lg
+    map1 = np.ones((50, 50)) * 0.5
+    line = lg.bresenham((2, 2), (40, 30))
+    for l in line:
+        map1[l[0]][l[1]] = 1
+    plt.imshow(map1)
+    plt.colorbar()
+    plt.show()
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png
+
+
+.. code:: ipython3
+
+    line = lg.bresenham((2, 30), (40, 30))
+    for l in line:
+        map1[l[0]][l[1]] = 1
+    line = lg.bresenham((2, 30), (2, 2))
+    for l in line:
+        map1[l[0]][l[1]] = 1
+    plt.imshow(map1)
+    plt.colorbar()
+    plt.show()
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png
+
+
+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.
+
+.. code:: ipython3
+
+    from collections import deque
+    def flood_fill(cpoint, pmap):
+        """
+        cpoint: starting point (x,y) of fill
+        pmap: occupancy map generated from Bresenham ray-tracing
+        """
+        # Fill empty areas with queue method
+        sx, sy = pmap.shape
+        fringe = deque()
+        fringe.appendleft(cpoint)
+        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
+                    fringe.appendleft((nx - 1, ny))
+            # East
+            if nx < sx - 1:
+                if pmap[nx + 1, ny] == 0.5:
+                    pmap[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
+                    fringe.appendleft((nx, ny - 1))
+            # South
+            if ny < sy - 1:
+                if pmap[nx, ny + 1] == 0.5:
+                    pmap[nx, ny + 1] = 0.0
+                    fringe.appendleft((nx, ny + 1))
+
+This algotihm will fill the area bounded by the yellow lines starting
+from a center point (e.g. (10, 20)) 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
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diff --git a/docs/modules/mapping/mapping.rst b/docs/modules/mapping/mapping.rst
index 7f30abcead..ccdd3198b1 100644
--- a/docs/modules/mapping/mapping.rst
+++ b/docs/modules/mapping/mapping.rst
@@ -17,6 +17,13 @@ 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
 -------------------------
 
@@ -41,3 +48,4 @@ The red circle is the estimated object shape using circle fitting.
 .. |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

From d34f3ca7fa84f42673f3556565b1776d3009e6a6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 21 Nov 2021 22:39:18 +0900
Subject: [PATCH 066/604] 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",
-      "text/plain": [
-       "<Figure size 432x720 with 1 Axes>"
-      ]
-     },
-     "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": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "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": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "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": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "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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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 2 Axes>"
-      ]
-     },
-     "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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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "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(\"<div id='\" + id + \"'></div>\");\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",
-       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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",
-       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\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 = '<i class=\"fa ' + image + ' fa-lg\"></i>';\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 = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\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": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img 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\" width=\"639.9999861283738\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "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": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "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",
-      "text/plain": [
-       "<Figure size 432x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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Vdf6465AkdSZyjqCqvgl8cxLLHqGN5jDWCLju/dXn9Z+adU9VTboGSdIE2cSEJPWcQbCOkmyS5IdJvt76d0xyVms240vtRPhUmmPdj0lyaZLV7bXrpGsclSRrkpzb1nNlG7Z1ktOSXNzet5p0naMwz7ofkeTKgW3/zEnXOSpJtkzy5SQ/TnJhkt2nZdsbBOvuUODCgf53AO+tqocAvwBePpGqxmP2ugO8tqp2ba/VkyhqjJ7S1nPm0sHDgRVVtROwovVPq9nrDt3f/cy2n7Zzf4PeD5xcVQ8DHkX3b2Aqtr1BsA6S3B94FvCJ1h/gqcCX2yjHAs+ZTHWjNXvdBXRNpBzbuqd22/dZkvsAewCfBKiq31fVjUzJtjcI1s37gNcBt7f+bYAbq+rW1j/NzWbMXvcZ/5DkR0nem+RuE6hrXAo4Ncmqdvc7wHZVdVXrvhrYbjKljdxc6w7w6rbtj95YD40MYUfgOuBT7bDoJ5JswZRse4NgLSXZB7i2qlZNupZxW2Dd3wA8DHgcsDXw+nHXNkZPrqrH0LWe+6okewx+WN1leNN6Kd5c6/4R4MHArsBVwLsnWN8obQo8BvhIVT0auJlZh4E25m1vEKy9JwF/nWQNXcupT6U7drhlkpn7MuZsNmMK/Mm6J/lsVV1Vnd8Bn6JrYXYqVdWV7f1a4AS6db0myfYA7f3ayVU4OnOte1VdU1W3VdXtwMeZ3m1/BXBFVZ3V+r9MFwxTse0NgrVUVW+oqvtX1TK65jH+tar2B74FPL+N9hLgaxMqcWTmWfcDBv4hhO4Y6XkTLHNkkmyR5F4z3cDedOt6It02hynd9vOt+8y2b57LlG77qroauDzJzm3QXsAFTMm2/7NtfXQj9Hrgi0neDvyQdlKpJz6XZAkQYDVw8ITrGZXtgBO6vGNT4PNVdXKSs4HjkrwcuAzYb4I1jsp86/6ZdrlwAWuAV0yuxJE7hO5v/a7AJcBL6X5Mb/Tb3juLJannPDQkST1nEEhSzxkEktRzBoEk9ZxBIEk9ZxD0VJLbWmuR5yU5Psk91nL6wwanSfLNJFsuMP4RSf52PWu+X5IvLz7mH01zYJIPte6Dk7x4fWpYZDn3W8tpTk8yFc+8ndFaon3+4mPeMf6yJFN578HGxCDor1taa5GPAH7PWlz7n2QT4DDgjiCoqme2RrhGpqp+VlVD/yczx/QfrapPb8iaBhwIrFUQbCgDd7RL68QgEMCZwEMAkvy/1qjY+YMNiyX5dZJ3JzkH+Du6//S+leRb7fM1SbZt3S9ujZCdk+QzsxeW5MFJTm7LOTPJw9rwfdseyjlJzphjujt+PbZf4F9t87k4yTsHxntpkp8k+T5dsxgzw+/YK0nykCT/0pb1gyQPbsNfm+TsVv//bsO2SPKNNu55SV4wq67nA8vpbjZaneTuSfZqjZOd2xpjm68hvhcN7JntNrC8o5N8v83j2XN8F3u27+5EujtcSXJAm2Z1ko+le27EJu1X+nmtlv/Zxv3vbT3PSfKVmb27Nu5Hkvx7kkvaco5O1/7+MbP+Ht7b/k5WpLuhcHaNj03y7badT8mdd6A/ti33HOBV83wvGqeq8tXDF/Dr9r4p3W3xr2z9W7f3u9M1F7BN6y9gv4Hp1wDbzu4HHg78ZOazgfkdAfxt614B7NS6H0/XVAXAucAOrXvLOWpeBpzXug+ku7vzPsDmdHd1PgDYHvgPYAlwV+DfgA/NUcNZwHNb9+Z0ezd70z2HNnQ/kr5O1/Tw84CPD9RxnzlqOx1YPjC/y4GHtv5PA4fNM83HW/ceA+v2j8ABM99D+z63mDXtnnQNn+3Y+v8COAnYrPV/GHgx8FjgtIHptmzv2wwMeztwSOs+hq4dqdA1sXwT8J/a97EK2HXg72H/1v3mge/4GLqmVjYDvgssacNfABzdun8E7NG63zWz3r4m93KXsr/unmTmATJncmeTGK9J8tzW/QBgJ+AG4DbgK0PM96nA8VV1PUBV/XzwwyT3BJ4IHJ+uuQKAmV/L/wYck+Q44KtDLGtFVf2yzfcC4IF0YXR6VV3Xhn8JeOisGu5FFzgntBp/24bvTRcGP2yj3rOt/5nAu5O8A/h6VZ25SF07A5dW1U9a/7F0v3zfN8e4X2g1nJHk3unOs+xN17jfzDmVzYGl/OnDgL5fVZe27r3o/tM/u32vd6drAO0k4EFJPgh8Azi1jf+IdM2hbNnW85SB+Z5UVZXkXOCaqjq3fT/n04XxarpmyL/Uxv8sf7q9dgYeAZzW6tkEuKqt35ZVNbPH9xm61kw1QQZBf91SVX/0SMkkewJPA3avqt8kOZ3uPyGA31bVbRtguXehe3bDnzzOsqoOTvJ4ugffrEry2Kq6YYF5/W6g+zbW/+85wD9V1cf+5IPkMcAzgbcnWVFVb13PZc2Y3cZLtTqeV1UXLTLtzYMlAsdW1Rtmj5TkUcDT6c4D7Qe8jO6X+3Oq6pwkB9LtYcyY+V5v54+/49uZ/zuevR4Bzq+q3WfVMu8FBZoczxFo0H2AX7QQeBjwhAXG/RVwrzmG/yuwb5JtoHue7+CHVXUTcGmSfdvnaf9RkeTBVXVWVb2Z7iEgD1iHdTgL+M9JtkmyGbDv7BGq6lfAFUme05Z7t3aM/BTgZW2vhSQ7JLlvuquBflNVn6U7lPGYOZY7+H1cBCxL8pDW/yLg2/PU+4K2rCcDv2x7OKcAh6T9lE7y6CHWewXw/CT3bdNsneSB6c7b3KWqvgK8aaD2e9H9Qt8M2H+I+c92F+5sbfdvgO/M+vwiYEmS3Vs9myV5eHUXFNzY1pd1XLY2MPcINOhk4OAkF9L9Q/73BcY9Cjg5yc+q6ikzA6vq/CT/AHw7yW10h1kOnDXt/sBHkryJ7ljyF4FzgHcl2Ynu1+SKNmytVNVVSY4AvgfcSHcYYy4vAj6W5K3AH4B9q+rUJH8BfK/9H/xr4AC6E+nvSnJ7G/eVc8zvGOCjSW4BdqdrmfL4dFf0nA18dJ46fpvkh3Tfw8vasLfRHUb6UZK7AJcC+yyy3he07/PUNs0f6A5H3UL3VK2ZH30zewx/Txea17X3uUJ9ITcDu7VlXksLtIF6fp/uJPoH0j3mcdO2TufTfTdHJynuPFSlCbL1UUlrLcmvq+qek65DG4aHhiSp59wjkKSec49AknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ77/xD8j+K3VrtHAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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",
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diff --git a/SLAM/GraphBasedSLAM/LaTeX/.gitignore b/SLAM/GraphBasedSLAM/LaTeX/.gitignore
deleted file mode 100644
index b0b343402c..0000000000
--- a/SLAM/GraphBasedSLAM/LaTeX/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# Ignore LaTeX generated files
-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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anx3L270xnj/PzOXPZWV87eZUtQ7gzgAg6uc26OOBfjKXJO5wiLjT+P0Vg74800+TTzGo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RpstvfdV/V9PP00ricqbH1fJyPiOuzxcKkiC4aLDrx8uh4uDpJJeniy8JHG8+35PCRVD2fMJiaa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\naDSaNoI2+BqNRtNG0AZfo9Fo2gj/D1KYU/J75gHUAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f58b45e2438>"
-      ]
-     },
-     "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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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587b80bc50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g_odom.plot(title=\"Odometry edges\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Jzn1WrGAcfP/+FC+rDMEgj9mtG7NgS0tFjjqKnHXffaw3q89br56lpVNdVFSI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\nFTs2RGNhsDdQwXa9ICp46ZiyZb7fH++ePWdmZ1MQUK/5nme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ILDMMDI1YvWlrqUU4hRsj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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587b80b668>"
-      ]
-     },
-     "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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\nBL6Dg4NDK+H/A7aRY5b1tCw8AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587c25a0b8>"
-      ]
-     },
-     "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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\nRqPRVAj/D0Fj/txvoohWAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587c00dba8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g_odom.plot(title=\"Odometry edges\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kWku1JLBlLdUyAkd69hQ5q0dUPqFPXl1oqalpu0pZZQbtHWkLbO97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\nXvgUCWjQIb10hNcDHOIvddM6Jvn0CsVauqSGn8Gbb5K/qjT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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587c7e3cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g_scan.plot(title=\"Scan-matching edges\")"
-   ]
-  }
- ],
- "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": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf b/SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf
deleted file mode 100644
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diff --git a/docs/_static/custom.css b/docs/_static/custom.css
new file mode 100644
index 0000000000..51c98d21ce
--- /dev/null
+++ b/docs/_static/custom.css
@@ -0,0 +1,23 @@
+/*
+ * Necessary parts from
+ * Sphinx stylesheet -- basic theme
+ * absent from sphinx_rtd_theme
+ * (see https://github.com/readthedocs/sphinx_rtd_theme/issues/301)
+ * Ref https://github.com/PyAbel/PyAbel/commit/7e4dee81eac3f0a6955a85a4a42cf04a4e0d995c
+ */
+
+/* -- math display ---------------------------------------------------------- */
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+    visibility: hidden;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
diff --git a/docs/conf.py b/docs/conf.py
index 64b32d5047..de28ce4752 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -42,7 +42,6 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
-    'sphinx.ext.viewcode',
     'IPython.sphinxext.ipython_console_highlighting',
 ]
 
@@ -53,7 +52,7 @@
 # You can specify multiple suffix as a list of string:
 #
 # source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+source_suffix = '_main.rst'
 
 # The master toctree document.
 master_doc = 'index'
@@ -100,6 +99,8 @@
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
 
+html_css_files = ['custom.css']
+
 # Custom sidebar templates, must be a dictionary that maps document names
 # to template names.
 #
diff --git a/docs/getting_started.rst b/docs/getting_started_main.rst
similarity index 100%
rename from docs/getting_started.rst
rename to docs/getting_started_main.rst
diff --git a/docs/index.rst b/docs/index_main.rst
similarity index 100%
rename from docs/index.rst
rename to docs/index_main.rst
diff --git a/docs/modules/aerial_navigation/aerial_navigation.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst
similarity index 100%
rename from docs/modules/aerial_navigation/aerial_navigation.rst
rename to docs/modules/aerial_navigation/aerial_navigation_main.rst
diff --git a/docs/modules/appendix/appendix.rst b/docs/modules/appendix/appendix_main.rst
similarity index 100%
rename from docs/modules/appendix/appendix.rst
rename to docs/modules/appendix/appendix_main.rst
diff --git a/docs/modules/arm_navigation/arm_navigation.rst b/docs/modules/arm_navigation/arm_navigation_main.rst
similarity index 100%
rename from docs/modules/arm_navigation/arm_navigation.rst
rename to docs/modules/arm_navigation/arm_navigation_main.rst
diff --git a/docs/modules/introduction.rst b/docs/modules/introduction_main.rst
similarity index 100%
rename from docs/modules/introduction.rst
rename to docs/modules/introduction_main.rst
diff --git a/docs/modules/localization/localization.rst b/docs/modules/localization/localization_main.rst
similarity index 100%
rename from docs/modules/localization/localization.rst
rename to docs/modules/localization/localization_main.rst
diff --git a/docs/modules/mapping/mapping.rst b/docs/modules/mapping/mapping_main.rst
similarity index 100%
rename from docs/modules/mapping/mapping.rst
rename to docs/modules/mapping/mapping_main.rst
diff --git a/docs/modules/path_planning/path_planning.rst b/docs/modules/path_planning/path_planning_main.rst
similarity index 100%
rename from docs/modules/path_planning/path_planning.rst
rename to docs/modules/path_planning/path_planning_main.rst
diff --git a/docs/modules/path_tracking/path_tracking.rst b/docs/modules/path_tracking/path_tracking_main.rst
similarity index 100%
rename from docs/modules/path_tracking/path_tracking.rst
rename to docs/modules/path_tracking/path_tracking_main.rst
diff --git a/docs/modules/slam/FastSLAM1.rst b/docs/modules/slam/FastSLAM1.rst
index 052f767489..6e26aef72f 100644
--- a/docs/modules/slam/FastSLAM1.rst
+++ b/docs/modules/slam/FastSLAM1.rst
@@ -2,14 +2,6 @@
 FastSLAM1.0
 -----------
 
-.. code-block:: ipython3
-
-    from IPython.display import Image
-    Image(filename="animation.png",width=600)
-
-
-
-
 .. image:: FastSLAM1_files/FastSLAM1_1_0.png
    :width: 600px
 
@@ -31,8 +23,6 @@ positions by FastSLAM.
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
    :alt: gif
 
-   gif
-
 Introduction
 ~~~~~~~~~~~~
 
@@ -547,6 +537,9 @@ indices
 References
 ~~~~~~~~~~
 
-http://www.probabilistic-robotics.org/
+-  `PROBABILISTIC ROBOTICS`_
+
+-  `FastSLAM Lecture`_
 
-http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
+.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
+.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
diff --git a/docs/modules/slam/ekf_slam.rst b/docs/modules/slam/ekf_slam.rst
new file mode 100644
index 0000000000..df520fbdaf
--- /dev/null
+++ b/docs/modules/slam/ekf_slam.rst
@@ -0,0 +1,593 @@
+EKF SLAM
+--------
+
+This is an Extended Kalman Filter based SLAM example.
+
+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|
+
+Simulation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This is a simulation of EKF SLAM.
+
+-  Black stars: landmarks
+-  Green crosses: estimates of landmark positions
+-  Black line: dead reckoning
+-  Blue line: ground truth
+-  Red line: EKF SLAM position estimation
+
+Introduction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+EKF SLAM models the SLAM problem in a single EKF where the modeled state
+is both the pose :math:`(x, y, \theta)` and an array of landmarks
+:math:`[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]` for :math:`n`
+landmarks. The covariance between each of the positions and landmarks
+are also tracked.
+
+:math:`\begin{equation} X = \begin{bmatrix} x \\ y \\ \theta \\ x_1 \\ y_1 \\ x_2 \\ y_2 \\ \dots \\ x_n \\ y_n \end{bmatrix} \end{equation}`
+
+:math:`\begin{equation} P = \begin{bmatrix} \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} \\ \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} \\  & & & & \vdots & & & & & \\ \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} \end{bmatrix} \end{equation}`
+
+A single estimate of the pose is tracked over time, while the confidence
+in the pose is tracked by the covariance matrix :math:`P`. :math:`P` is
+a symmetric square matrix which each element in the matrix corresponding
+to the covariance between two parts of the system. For example,
+:math:`\sigma_{xy}` represents the covariance between the belief of
+:math:`x` and :math:`y` and is equal to :math:`\sigma_{yx}`.
+
+The state can be represented more concisely as follows.
+
+:math:`\begin{equation} X = \begin{bmatrix} x \\ m \end{bmatrix} \end{equation}`
+:math:`\begin{equation} P = \begin{bmatrix} \Sigma_{xx} & \Sigma_{xm}\\ \Sigma_{mx} & \Sigma_{mm}\\ \end{bmatrix} \end{equation}`
+
+Here the state simplifies to a combination of pose (:math:`x`) and map
+(:math:`m`). The covariance matrix becomes easier to understand and
+simply reads as the uncertainty of the robots pose
+(:math:`\Sigma_{xx}`), the uncertainty of the map (:math:`\Sigma_{mm}`),
+and the uncertainty of the robots pose with respect to the map and vice
+versa (:math:`\Sigma_{xm}`, :math:`\Sigma_{mx}`).
+
+Take care to note the difference between :math:`X` (state) and :math:`x`
+(pose).
+
+.. code:: ipython3
+
+    """
+    Extended Kalman Filter SLAM example
+    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
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+At each time step, the following is done. - predict the new state using
+the control functions - update the belief in landmark positions based on
+the estimated state and measurements
+
+.. code:: ipython3
+
+    def ekf_slam(xEst, PEst, u, z):
+        """
+        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 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)
+        initP = np.eye(2)
+    
+        # Update
+        xEst, PEst = update(xEst, PEst, u, z, initP)
+    
+        return xEst, PEst
+
+
+1- Predict
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+**Predict State update:** The following equations describe the predicted
+motion model of the robot in case we provide only the control
+:math:`(v,w)`, 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*}`
+
+:math:`\begin{equation*} B= \begin{bmatrix} \Delta t cos(\theta) & 0\\ \Delta t sin(\theta) & 0\\ 0 & \Delta t \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} U= \begin{bmatrix} v_t\\ w_t\\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} X = FX + BU \end{equation*}`
+
+: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*}`
+
+Notice that while :math:`U` is only defined by :math:`v_t` and
+:math:`w_t`, in the actual calculations, a :math:`+\sigma_v` and
+:math:`+\sigma_w` appear. These values represent the error between the
+given control inputs and the actual control inputs.
+
+As a result, the simulation is set up as the following. :math:`R`
+represents the process noise which is added to the control inputs to
+simulate noise experienced in the real world. A set of truth values are
+computed from the raw control values while the values dead reckoning
+values incorporate the error into the estimation.
+
+:math:`\begin{equation*} R= \begin{bmatrix} \sigma_v\\ \sigma_w\\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} X_{true} = FX + B(U) \end{equation*}`
+
+:math:`\begin{equation*} X_{DR} = FX + B(U + R) \end{equation*}`
+
+The implementation of the motion model prediciton code is shown in
+``motion_model``. The ``observation`` function shows how the simulation
+uses (or doesn’t use) the process noise ``Rsim`` to the find the ground
+truth and dead reckoning estimates of the pose.
+
+**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, :math:`Cx`.
+
+:math:`P = G^TPG + Cx`
+
+Notice this uncertainty is only growing with respect to the pose, not
+the landmarks.
+
+.. code:: ipython3
+
+    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)
+        # 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
+
+.. 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
+        """
+        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
+
+2 - Update
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In the update phase, the observations of nearby landmarks are used to
+correct the location estimate.
+
+For every landmark observed, it is associated to a particular landmark
+in the known map. If no landmark exists in the position surrounding the
+landmark, it is taken as a NEW landmark. The distance threshold for how
+far a landmark must be from the next known landmark before its
+considered to be a new landmark is set by ``M_DIST_TH``.
+
+With an observation associated to the appropriate landmark, the
+**innovation** can be calculated. Innovation (:math:`y`) is the
+difference between the observation and the observation that *should*
+have been made if the observation were made from the pose predicted in
+the predict stage.
+
+:math:`y = z_t - h(X)`
+
+With the innovation calculated, the question becomes which to trust more
+- the observations or the predictions? To determine this, we calculate
+the Kalman Gain - a percent of how much of the innovation to add to the
+prediction based on the uncertainty in the predict step and the update
+step.
+
+:math:`K = \bar{P_t}H_t^T(H_t\bar{P_t}H_t^T + Q_t)^{-1}`
+In these equations, :math:`H` is the jacobian of the
+measurement function. The multiplications by :math:`H^T` and :math:`H`
+represent the application of the delta to the measurement covariance.
+Intuitively, this equation is applying the following from the single
+variate Kalman equation but in the multivariate form, i.e. finding the
+ratio of the uncertainty of the process compared the measurement.
+
+K = :math:`\frac{\bar{P_t}}{\bar{P_t} + Q_t}`
+
+If :math:`Q_t << \bar{P_t}`, (i.e. the measurement covariance is low
+relative to the current estimate), then the Kalman gain will be
+:math:`~1`. This results in adding all of the innovation to the estimate
+– and therefore completely believing the measurement.
+
+However, if :math:`Q_t >> \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 <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/>`__.
+
+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
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
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diff --git a/docs/modules/slam/graphSLAM_doc.rst b/docs/modules/slam/graphSLAM_doc.rst
new file mode 100644
index 0000000000..b17c3b18a2
--- /dev/null
+++ 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 067/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Thu, 25 Nov 2021 23:05:25 +0900
Subject: [PATCH 068/604] 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
@@ -24,3 +24,6 @@ if you want to building each time a file is changed:
 sphinx-autobuild . _build/html
 ```
 
+#### Check the generated doc
+
+Open the index.html file under docs/_build/
diff --git a/docs/_static/img/doc_ci.png b/docs/_static/img/doc_ci.png
new file mode 100644
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diff --git 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 <asakai.amsl+github@gmail.com>
Date: Fri, 26 Nov 2021 21:46:14 +0900
Subject: [PATCH 069/604] 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 @@
 <!-- 
 Thanks for contributing a pull request! 
-Please ensure that your PR satisfies the checklist before submitting:
-- [] This project only accept codes for python 3.9 or higher.
-- [] If you add a new algorithm sample code, please add a unit test file under `test` dir.
-     This sample test code might help you : https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_a_star.py
-- [] If you fix a bug of existing code please add a test function in the test code to show the issue was solved.
-- [] Please fix all issues on CI (All CI should be green), before code review.
+Please check this document before submitting:
+- [How to contribute](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#adding-a-new-algorithm-example)
 
 Note that this is my hobby project; I appreciate your
 patience during the review process.
@@ -21,3 +17,8 @@ Again, thanks for contributing!
 
 #### Additional information
 <!--Any additional information you think is important.-->
+
+#### 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 <asakai.amsl+github@gmail.com>
Date: Fri, 26 Nov 2021 23:14:23 +0900
Subject: [PATCH 070/604] 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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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": "3.7.5"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/PathPlanning/RRTStar/Figure_1.png b/PathPlanning/RRTStar/Figure_1.png
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diff --git a/PathPlanning/RRTStar/rrt_star.ipynb b/PathPlanning/RRTStar/rrt_star.ipynb
deleted file mode 100644
index 20efa46878..0000000000
--- a/PathPlanning/RRTStar/rrt_star.ipynb
+++ /dev/null
@@ -1,74 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "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 <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
 
+
+
+
 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 <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>`__
 
 RRT with dubins path
 ~~~~~~~~~~~~~~~~~~~~
@@ -367,20 +351,9 @@ Ref:
    Autonomous
    Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
 
-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 <http://ieeexplore.ieee.org/document/637936/>`__
 
 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 <http://ieeexplore.ieee.org/document/637936/>`__
+
+
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 <https://arxiv.org/pdf/1105.1186.pdf>`__
+-  `Incremental Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/abs/1005.0416>`__
 
--  `Sampling-based Algorithms for Optimal Motion
-   Planning <https://arxiv.org/pdf/1105.1186.pdf>`__

From 50ba79a3f53cb96c34002d54375be022a9bec5c9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Nov 2021 20:10:45 +0900
Subject: [PATCH 071/604] 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
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diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb
deleted file mode 100644
index 308512bfd7..0000000000
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb
+++ /dev/null
@@ -1,482 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    div#notebook-container    { width: 95%; }\n",
-       "    div#menubar-container     { width: 65%; }\n",
-       "    div#maintoolbar-container { width: 99%; }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "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",
-    "<style>\n",
-    "    div#notebook-container    { width: 95%; }\n",
-    "    div#menubar-container     { width: 65%; }\n",
-    "    div#maintoolbar-container { width: 99%; }\n",
-    "</style>\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 <i>forward_kinematics</i> - forward kinematics specifies the end-effector position <i>given</i> 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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 <i>shoulder</i>, <i>elbow</i>, and <i>wrist</i>. 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 <i>$x$</i> and <i>$y$</i>. 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": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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, $r$<sup>2</sup> 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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Fmphoh3cmTIBu3QoeY0EYY7Y48rCoXrkrlY0vv/ySX3/9FaBgiT2vlbTcPCwDNqFNmpS5/fBhO3yRX7162SdJwX7N69VyfgwbZod6fstjpaucYv3sM/sL7tFHnRenq+lDTEpl4dixY3Tv3p3Q0FC+LWjB87xW0vJAcgc7W+T++2H58vTt48fbWSz5GY6YM8c5seVFlSr2hulff+Xt5mdOsYrYewJ5KWDmaXrlrlQWBg4cSGxsLDNmzCh4Z3mtpHX6tJ2f5+SKkI6YMCH9050AZ8+mLxXsCx591I6PO0uXLvaXny/R5K5UBkuXLmXx4sW8+uqr1HZGCYC8VNKC1AeYsntU0oXq1Ml66GTGDMdK4yrvocldqTTOnj1L//79qV+/PsOGDSt4h3mppJXCw0XDRo2yM1PSunjRVmJUvkOTu1JpBAcH061bN+bMmeOcio95qaSV4tQpj863u+oqGDIkc/uCBbYAl/INmtyVSqN48eJMnDiRJk2aOKfDvFTSSuEF5X6HDbO1zrNq99DsaZVHmtyVAi5cuED79u3ZsGGDczvOSyWtFF6Q3EuXtsMzGa1bl7lUgfJOuSZ3Y8w8Y8zfxpgd2Ww3xphpxph9xpjtxhgXFrlWyjXGjBnDqlWriIuLc27HeamklcILkjvYFY6yWkEwr2UJlGc4cuX+AdA2h+3tgFrJrz7AewUPSyn3iYyMZNKkSTzxxBPcddddzus4P5W0YmPtywuSe1CQnRqZ0Y4d8MUX7o9H5Y0ja6huAE7nsMsDwEdi/QyUNcZc5awAlXKlxMREevXqRbly5XjL2Ssf56WSVoozZ+xXL0juAA8+aMsSpAgJsbVZOnb0XEzKMc4Yc68MHE7z/khym1Jeb+HChURERDBt2jTKO3uGSl4qaaVwc9Gw3KSUJbj8cltNcfdu6No184NOyvu49WFaY0wf7NAN1RxduFApF+rSpQtlypThvrSLdzpLjx6pZQ4dlVJ6wItKD952m60xk1IzXfkGZ/z+PQpUTfO+SnJbJiISJiKhIhLq8tVslMqBiHDq1CkCAgLo0KEDxl1Pg+ZWSctDdWVyo4nd9zgjuS8HHk+eNXMLcFZE/nJCv0q5zEcffUStWrXYvXu3ew88Zw4cOWInix85krlalZcmd+V7ch2WMcZ8AtwBVDDGHAFeA4IARGQmsBJoD+wDzgNPuipYpZzh+PHjPPfcc9StW5frvG3NNE3uyklyTe4i0iWX7QL0d1pESjng4sWLBDtaQjeDQYMGERMTw5w5cwjwtjuDp0/bOYg+Ng4SE+NzIfs9L/uXrVRmSUlJlCpViqlTp/Lss89SsWJFGuZlemEaK1as4NNPP+WVV17h+uuvd3KkTuDBipD5ERUF7dvb5eu0LIF38aHS86qw2r9/PzExMUyaNIkHH3yQBQsW5Puqfd26ddx444288MILTo7SSTxcNMxRhw/Dq6/Chx+mJvWvvgJXTDpS+aPJXXm9qKgoAJ566inGjh1boL6mTJnCP//8k+9fDi7nJaUHciJiV2zati19+4svQtu2vrVakT/TYRnl9bZv306JEiUYOXJkvvvYsmULO3fuBKBMmTLOCs35fCC5GwOvvZa5/ddf7ZW88g6a3JXXi4qK4o477qB4Ppedu3DhAt26daNjx44kenvFKx9I7gAPPADNm2duf/VVe3NVeZ4md+X1oqKiaNQo/8VGx48fz+7du5k2bRqBgYFOjMwFfCS5GwOTJ2du//NPmDrV/fGozDS5K68WGxvLvn37aJCXyoppREVF8cYbb9C9e3fuueceJ0fnZHFx9rLXB5I72IJiWRUQmzgR/v7b/fGo9DS5K6+2c+dOkpKS8pXcExMT6d27N2XLlmXKlCkuiM7JvLCuTG7eeAMy/jF07hyMGeOZeFQqTe7Kq0VFRVGyZElq1KiR588mJibSqlUrpk+fToUKFVwQnZP54NOp110Hfftmbp85E/btc388KpURDz15EBoaKhERER45tlJe6Ycf7EpN334Lbdp4OhqHHT9uqxr/+2/69ocfhkWLPBOTPzPGbBGR0Nz20yt35XdEhL59+7JmzRpPh5I3Trpy37RpEy1btqREiRJce+21LHJxhq1UyS69l9Fnn8GmTS49tMqBJnfld8LDwwkLC2PXrl2eDiVvnJDc161bR+vWrbn99tv56quvaNGiBd27d+ePP/5wUpBZe/55uPLKzO3DhmlZAk/R5K78yokTJxg8eDC33nor/fr183Q4eVPA5B4TE0PXrl0ZPnw4Y8eOpVWrVsyaNYugoCCWLl3qxEAzK1kSRo/O3P7DD7BihUsPrbKhyV35lcGDB/PPP/8we/Zs75/TntGpU/bZ/dKl8/Xx2bNnEx8fz9ChQy+1FS1alIoVK3Lw4EFnRZmtJ5+EOnUytw8fDgkJLj+8ykCTu/IbP/zwAwsWLGDkyJHUq1fP0+HkXQErQoaHh9OxY0eCgoJISEi49IqJiSEoKMjJwWZWpIid457Rrl3w/vsuP7zKQJO78hvNmzfno48+Yvjw4Z4OJX8K8HRqdHQ0W7ZsISwsjKCgoHSvEydOuG3N4vvug9tvz9yuZQncT5O78gsxMTEEBATQvXt3ihYt6ulw8qcAyT0yMhIRYdmyZWzevPnS65133gG4VP++X79+VK5c2WVrxmZVlqBsWRg6VKtFupsmd+XzNm7cSLVq1fjpp588HUrBFCC5Hz58GIDWrVsTGhp66XXs2DFKly5N06ZNAejSpQtbt251WshZuflmO8c9ONgm9d9/hyFDwFd/5/oqh5K7MaatMWaPMWafMSbT37zGmGrGmHXGmF+MMduNMe2dH6pSmcXFxdGrVy9KlizJDTfc4OlwCqYAyT0h+Y5lkTSXxyLCwoUL6dy586W/Zlq0aEGlSpUKHmsu3noL9uyxV/E+9MCtX8k1uRtjAoF3gHZAXaCLMaZuht1eBhaJyE3Ao8C7zg5Uqay88cYb7Nq1i1mzZlE6n7NMvEYBkntISAgAe/bsudQ2b948Dh8+zIgRI5wSXl5UrQrVq7v9sCoNR67cmwL7RGS/iFwEFgIPZNhHgJQVEC4D/nReiEplbefOnYwfP57HHnuMdu3aeTqcgrl40VbcymfRsObNm1O5cmX69+/PmjVrePPNNxkwYADTp0/PV10e5fscSe6VgcNp3h9JbktrFNDNGHMEWAkMdEp0SuVg6dKllClThqn+UED8zBn7NZ9X7sHBwSxevJhz585x7733Mn/+fMLDw+nVq5cTg1S+xFn3r7sAH4jIW8aYW4GPjTE3iEhS2p2MMX2APoDbpmYp/zVy5Eh69epFxYoVPR1KwTmh9MAtt9xCZGSkkwJSvs6RK/ejQNU076skt6XVE1gEICI/AcWATDVWRSRMREJFJNQv/odUHnH48OFL66G64+agW7ip3G+vXr2oUqUKAFWqVPHIlf2RI/DUU7BsmdsPXag4cuW+GahljLkGm9QfBR7LsM8h4E7gA2NMHWxyP+HMQJWC1IqPP//8M4cOHaJUqVKeDsk53JTc58yZ49L+c3L2LEyYYJfhu3ABNm6Ee+/V+e+ukutpFZEEY8wAYDUQCMwTkZ3GmNFAhIgsB4YAs40xz2FvrvYQTxWKV37tk08+YdWqVUydOtV/Ejv45EIdeXHoEDRqZMvnpNizB+bOzXqxD1VwuliH8hknT56kTp061KhRgx9//NH3CoPlZMoU+6TPmTP2kU4/IwKtW8P69enbK1WyKzb50+9pV9PFOpTfee6554iOjmbOnDn+ldjBXrkHBsJll3k6EpcwBiZNytx+/Lh94Ek5nyZ35RNEhKpVq/LKK6/4/pOoWTl9Gi6/PN8VIX1BkybwyCOZ2ydPhpMn3R+Pv9NbGconGGMYP368p8NwnQI8neoLRGD1ati2LX17vXq2THA+n91SOdArd+X1JkyYwLfffuvpMFxq+e7dfJqUxA8//MCRI0fwp/kIW7fCXXdBu3b2JmqKXr0gMtLOmPHjP1g8Rq/clVfbtGkTL730Ev369eOuu+7ydDgu8/revWw9fx5atACgdOnSNGzYkFtvvZVWrVrRsmVLihcv7uEo8+bAARg5EhYsyHr7N99AfLy91aCcT2fLKK918eJFGjduTHR0NDt37qRMmTK5f8hHnQgJ4Xj9+hwdMID9+/ezc+dOtmzZwpYtW4iPj6d48eK0bduWrl270qFDB4KDgz0dcrZOn4Zx42DGDFsyByKAF4DRwG3p9l2yBDp2dH+MvszR2TJ65a681sSJE9mxYwcrVqzw68QOUDE6moo1anDDPfekaz9//jw//PADK1asYMmSJXzxxRdcccUV9O3blwEDBnDFFVd4KOLMLlyA6dNh/HiIjk5pfQZ4D/vA+t+X9r3lFnsj9bbbMvejnEREPPJq3LixKJWdffv2SXBwsDz66KOeDsX1Ll4UAZHXX89xt4SEBFm5cqV06NBBjDFSvHhxGTZsmJw6dcpNgWYtMVHkww9Fqla1PwacEkhM/v7/BEYKnBUQqVlTZPFikaQkj4bs07APj+aaYzW5K6+UmJgoYWFhcvz4cU+H4np//23/V5w+3eGP7N69W7p37y7GGClXrpzMnDlTEhMTXRhk1lavFmnQICWpnxeYIHCZQHhym31VrCgyY4b9PaYKxtHkrrNllNeJj48nICCA3r17e9Wwg8vko/RA7dq1+eijj9i2bRv169fn6aef5o477mD//v0uCjK9X36Bu++Ge+6ByMhE4EPgOmA4cDtg12wtUQJeftk+hdq/PwQFuSU8hU6FVF7m8OHD1KxZk6+//trTobhPAerK1K9fn7Vr1zJv3jwiIyNp2LAhn332mZMDTC8xETp1gtTZqQ8DPYCrgHXACgIC6tK7N+zdC2PGgJ/fMvFKmtyV1xARnnnmGU6ePEnt2rU9HY77FLBomDGGJ598ku3bt3PDDTfQuXNnhg8fTlJSUu4fzofAQOjZ8xcgJrmlN3aBtk3AHXToAFFREBYGV1/tkhCUAzS5K6+xaNEivvzyS8aOHcs111zj6XDcJ6VUYgGfUA0JCWH9+vX07duXiRMn8thjj3HRzkV0moMHD9KtWzdGjmzE1VfPSG5tBzxCkyaG9eth+XKom3GVZeV+jgzMu+KlN1RVWidPnpSKFStKkyZNJCEhwdPhuNfbb9u7jqdPO6W7pKQkmThxogBy3333yYULF/LRR/r3p06dkiFDhkhwcLAUK1ZMRowYIcuWRQuIXHutyKef6gwYd0FnyyhfEhYWJkWKFJFt27Z5OhT3e+UVEWPsnEIneu+99wSQjh075ukX5nffiYSGiqT9T5Ey/fLJJ5+Uw4cPX2pfskQkLs6ZUavcaHJXPmfv3r2eDsEz+vcXKVfOJV1PnTpVAHnmmWdy3TcyUqRt25Tpi4ly440fy5EjR0REJCoqSrZv3+6SGFXeOJrcdcxdeVRMTAw7duwAoGbNmh6OxkNcWBFy0KBBDB06lHfffZdZs2Zluc/hw9CjBzRsCHaS0ndAKFFR3XnpJbss3w033MCNN97okhiVa2hyVx712muv0bhxYw4fPuzpUDzHxeV+J0yYQNu2bXn22WfZunXrpfboaBg+HK67Dj78EEQigXuAu4AzQDjbt7+CiybdKBdzKLkbY9oaY/YYY/YZY4Zns09nY8yvxpidxphs6sAplWrz5s28/fbbPPnkk1StWtXT4XiOi5N7YGAg8+fPp0KFCnTt2pWzZy8wdSrUqGFrqV+4kLLnFGBz8tfdwGMEBgbw99/Z9ay8Wm7jNthFsX8HrgWCgUigboZ9agG/AJcnv78it351zL1wu3jxotSvX1+uvvpqiY6O9nQ4nnXttSJdu7r8MCtXfi2AlC07Onlc/bTAMIEtye+PJ7eJXHONyCefOP0er3ICHBxzd6QqZFNgn4jsBzDGLAQeAH5Ns09v4B0ROZP8C0N/16scTZ48me3bt7N06VIu89N1Qx3mhlWY1q2DV165B+hMdPQ4IBaYCUQDVwKNgCsoVw5eeQX69YOiRV0aknIxR4ZlKgNpB0SPJLeldR1wnTHmR2PMz8aYts4KUPmnhIQEunTpwgMPPODpUDwrMdEOfrsoue/YYVc6at0atmwBuBO4CLyBvW77BXieYsXs+PunQFKvAAAgAElEQVTvv8PgwZrY/YGz6rkXwQ7N3AFUATYYY24Ukei0Oxlj+gB9AKpVq+akQytf9Oqrr6YM6RVuKYXPnZzcT56EF1+EDz4g+YaoAAb4E6gG9AeGYQw88QSMHg2F+baHP3Lkyv0okPY/e5XktrSOAMtFJF5E/gB+wyb7dEQkTERCRSS0YsWK+Y1Z+bCFCxfyzTffALYmSqFXwLoy2QkMhC++gKSkKKA9sCx5ywhgPzCMdu3sgtXvv6+J3R85ktw3A7WMMdcYY4KBR4HlGfZZir1qxxhTATtM457ao8pnHD16lL59+zJp0iS9ak/houT+77+HqVnzSaAB8BPwT/KWojRqFMB338HKlVC/vlMPq7xIrsldRBKAAcBqYBewSER2GmNGG2PuT95tNXDKGPMrtubnMBE55aqgle8RsRUf4+PjmTVrll61p3BS0bC0pkyZwnXXXUdk5ALKlBmCnez2OCEhEB4OmzfDnXc67XDKSzk05i4iK4GVGdpeTfO9AM8nv5TKZMmSJSxfvpzJkydTo0YNT4fjPVKu3MuXz/NHDx2ClFtXKdUfg4ODqVChAp06dWLs2LH8978hDBxoF8zo319vlBYm+oSqcrno6GgGDBhA48aNGTx4sKfD8S75GJbZuRM6dIDateHgwSQ+/fRT6tSpw7vvvgvA448/zscff0xISAhdusD+/fD885rYCxtnzZZRKltlypTh9ddf5+abb6ZIEf0nl05Kci9bNtdd//wTXnsN5s1LmQGznsaNh3HqVAT169fPsvZLQIBDXSs/pP+nKZcSEQICAujbt6+nQ/FOp0/b7BsYmO0u//wDkyfDW29BbGxK64vAJE6dqsrYsR8yfHhXAnPoQxU+OiyjXOb8+fM0adKExYsXezoU75XD06kXL8KMGVCzJowdC7GxR4GUeQr3AROBPfz3v49rYleZaHJXLjNq1Ci2bNmCPtOQgyySuwgsXgz16sHAgXDixFlgJPbRkTHJe90OvEDVqsV57DH7GaXS0mEZ5RJbt27lrbfeonfv3rRs2dLT4XivU6fSzZT54Qd44QX4+WewZQJmAaOBk8BjwCDAjuSMHAkDBkCxYm6PWvkATe7K6eLj4+nZsydXXHEFkyZN8nQ43u30aahRg127YMQIWLYs7cYhwAygFTAZaExwsL2af+kll9caUz5Ok7tyuq+//ppt27axZMkSyupUjRz9dTKIUZHPMOeGlBkwG7BVGq8DBmNLB7QFDF272rH36tU9Fq7yIZrcldN16NCBiIgIGjdu7OlQvNrgwd8zLXozEl0SW0F7OLACeBKYB9QAatC6tZ0t06iRB4NVPkdvqCqnERH27t0L4N7EvnAhBAfb6SU+Ijw8nFkz+yAcxxZKvRH4HluK9x0AbrwRVq2C775zbWL3ttMnYtdz/fDDzNs2bYKWLaFECbj2Wli0yLE+BwyAnj2dG6e30+SunGbu3LnUrVuXLbZwuPtERkLdujZD+YiRI0ZwIe43oCfwATAQWwNmOFWqFOeDD+CXX6BtW3B1GR5vO32LFtlbEY89lr593Tpbl/722+Grr6BFC+jeHf74I/c+hw61dXX27XNNzN5Ik7tyij///JOhQ4dy22230cjd4weRkdCggXuPmV/x8fDOOxy6tCD4VuBnYCoQBAznt99sjXV3TV33ttM3bZpN2kFBqW0xMdC1q11QZOxYaNUKZs2y+yxdmnuf1avDbbfBe++5LGyvo8ldOcXAgQOJi4sjLCzM/RUft23zruyUlbST1wcMoNqlQi//YK/c3wZqEBKykOLF3RtaXk5fYiK8/bYtFVysGFx1lU26KfPsz5+3UzmrVYNSpezVdWRk+j6+/tom2rJloUwZCA2Fb7+12/btg40boVOn9J+ZPdv+Xhw6NLWtaFGoWBEOHnQs9oceslfv9sa1/9Pkrgrs888/5/PPP2fUqFHUqpVpjRbXOnEC/vrLDtJ6q//+F5o1g4cftmMfX33FuLlzKVGiRPIO04HnKVEilnHjxrk1tLycvqQkm3RHj4Zu3eDLL2H8eJt0jbGlEVq1guXLYcIE+PxzOzZ+991w5oztY/16+M9/4K677PYFC6BNG0gpObRmDZQsmfmXTXg4dOxor9QTElJfMTHpr/Bz0qwZHD8OUVEOnx7f5sgq2q54NW7c2DlLgSuPe+utt6Rp06Zy8eJF9x/8229FQOTkSfcfOze7dok88ICN7+qrRebOFUlIuLR5/vz5EhISIsYYCQkJkfnz57s9xLycvjffFClZUuTXX7PePmyYyJVXipw4kdp28qSIMSLh4fZ9r14iHTtmf4zevUVCQ9O3nTlj+7B/H2R+TZuWe+wiIvHxIoGBImFhju3vrYAIcSDH6pW7KrDnn3+eH3/8kSBHL6Gcads2qFw59SnPHTvs1JJateD+++HcOffHdOwYPP003HADrF0L48bB3r3w1FPpBtK7du3KgQMHSEpK4sCBA3Tt2tXtoWY8ff362fcZR9aSkmDiRDvrpE6dzP1cvAhhYbZmfIUKqe3ly9uhl6PJC3MWLw6rV8PUqfY0ZXTsWPrPgx3WEbEPeG3enPp6x04quvRXR3axpyhSxA4FZXVcf6TJXeXbjz/+yOrVqwHyXMo3KiqK9u3bU758ecqXL8+DDz7I33//nfcgIiPTjyk8/bS947Z3L1x/PbjzCdlz52DUKFvpa+5ceOYZ+P13+zjppSEY75Lx9HXpAlu3Zt5v+3Y7hPPgg9n3c/asHW5JKybGtl95pX0/Zgz06GGHdipXhnvusfXpU1y4kLnufMq959at7fh8yuvYMShdGpo2zTn2tIoWtccoDDS5q3yJjY2lR48e9O/fn/j4+Dx99ujRo7Rq1QpjDOHh4bz33nts2LCBt99+O++BpJ3qcfy4nRfXvr1937MnLFmS9z7zKj7eTsOoWRNefx3uvRd27bLTPry8aFrGmTItWkClSpn3++sv+zUlSWd08mTW29evt19vu81+vewyW+nyxAk7h3/v3vTzz8uVg+jo9H0kJNivaa8fROz8/M6dU38ZZBd7WtHRhadsg0PJ3RjT1hizxxizzxgzPIf9HjLGiDEm1HkhKm/0+uuvs2/fPsLCwvI8HDNt2jTKlCnD559/Ttu2bencuTNNmjThXF6HUOLiYPfu1Ox05AhUqZK6vVq11Ms+VxCBL76wwy/PPGOXRvr5Z/j0U5vovVzG05eTlKS9e3fW26tWtV/3709tS0iwV+ht28I116TfPzDQ3mi95x47AydF7dqZ562HhNive/akts2bZ//TjhiRe+wpTpyws3muu87xz/i03AblgUDs0xXXAsFAJFA3i/1KYwtj/AyE5tav3lD1XVu3bpXAwEB56qmn8vX5evXqyfPPP3/p/enTp6VkyZLywQcf5DUQe0dtzx77fvNmkaZNU7efPy9SqlS+YszVjz+KNGtmj1+njsjy5SJJSa45lotkPH1p2ZWRU8XHi9SrJ3LNNSIffyyyZo3I9OkiL7xgtycmitx0k8iNN4osWyayYoVI69b2PvKhQ3afPn1E+vcX+ewzkbVrRV5/XSQoSOT991OPs3q1Pfbff6e2xcWJVK4s0ry5yHffiUyeLFKsmMjs2Vn/XBljT7Fypb0x64333vMCB2+oOpLcbwVWp3k/AhiRxX5TgXuB9Zrc/Vd8fLw0atRIKlWqJKdOncrz5//9918JCAiQTz75RP755x/ZsGGDNG3aVGrXri2xsbF56+z990VKlLCZRUTkr79sNkmxe7fI9dfnOcYc7dljp3uAnRoSFmYznw/KePrSyipB7t9vf/QKFeznGjQQ+eST1O0HDojcd5/IZZeJlC8v0rWryJEjqdvfekukSRO7vXRpkVtuEVm6NP0x4uJEypUT+eij9O0//SRSv75I0aL2uEuWZP9zZZfcn31W5I47sv+cr3Bmcu8EzEnzvjswI8M+jYAlyd9nm9yxRTQigIhq1aq550wop0pMTJSwsDD54osv8vX5jRs3CiC7d++Wyy+/XAApWrSo/PTTT84JsFkzka++st8PGyby0kvO6ffYMZF+/exculKlREaPFvn3X+f07YWyS5Du8OyzIu3b5//zWcWekGCv/j/+OP/9egu3JXfsuP16oLrkktzTvvTKvXB69913pVSpUnLx4kVZs2aNzJw5U66//nopV66c/PXXXwU/QGSkSMOGIjVritx7r0h0dMH6O3fOjh+UKiVSpIgdVzh+vOBxeqmePW0SBPu1Z0/3x3D4sEjx4lkPF+Ukp9g/+cT+k/DRP7LScduwDHAZdpmYA8mvC8CfuSV4Te6+JTExUWrVqiVvvPFGgfrp3bu3NG/ePF3bb7/9JoAsXLiwQH07VXy8yKxZdugFRB56KO/ZRuXbJ5+IrF/vvP4WLBD5/nvn9edJjiZ3RyYnbwZqGWOuAY4Cj2LX+0q5IXsWuPTYgTFmPTBURCIc6Fv5iEmTJrF3715eeeUVAgICGDJkSL4WZd62bRs333xzurZiyevEVcptHps7iNjn54cPt1NDmje3z8nfequnIytUHn3Uuf116eLc/nxBrlMhRSQBGACsBnYBi0RkpzFmtDHmflcHqDzv2LFjTJw4kZtvvpkOHTrw4osv0rp1aw7ncZphYmIiO3bs4IorrkjXHh4eTrly5bgtZTK0p/z8s50s/Z//2CS/dKld1FQTu/JFjlzeu+KlwzK+4+GHH5aiRYvK7t27JSkpST788EMpVaqUlCtXTlauXOlwPzt27BBASpUqJe+8846sXbtWRo8eLUWLFpV58+a58CfIxW+/iXTqZIdfKlUSmTnTPwZnlV/CWWPurnppcvcNERERAsi4cePStf/222/SoEEDMcbI6NGjJcmBOd7z58+XoKAg6d69u5QuXVrKlCkjN998syxevNhV4efs+HGRAQPsjdKSJUVGjbI3UJXyYprcldOsW7cuy4qPMTEx0q1bNwHkkUceyXWe+tChQ6V+/fquCtNx//4rMmaMnQETGCjy9NN2jrxSPsDR5K61ZVS2/kouKHLHHXdkWWKgRIkSfPTRR0yYMIFPP/2UNm3acCalcHcWfvnlF2666SaXxZurhASYM8dWjHzlFVvlaudOWxcmu6IpSvkoTe4qSxs2bCAkJIRVq1bluJ8xhhdffJFPP/2UzZs306JFi0u/FDKKjIykoScW1RCxK0s0aAC9e9s11/77XzsLpnZt98ejlBsYe5XvfqGhoRIRobMlvdGFCxdo0KAB8fHxREVFUbJkSYc+t3btWu6//36uvvpq1q5dS5W0Rbw85X//s+u+ff+9vWKfMMHWrXX3UoBKOYkxZouI5FqcUa/cVSZjxozht99+IywszOHEDtC6dWu++eYbjh07RqtWrbK9gneL33+HRx6Bm2+GX3+1Kzvs3GnXatPErgoBTe4qncjISCZNmkSPHj1o06ZNnj/frFkzvv76a/766y/uuusuTp8+7YIoc3DyJAwaZJcL+vJLO7b++++2JK8nVopSykM0uat0Nm/eTKVKlXjzzTfz3UezZs1YsWIFe/fupUOHDsTGxjoxwmycPw9vvAE1atjVIJ58EvbtswXFS5d2/fGV8jKa3FU6vXr14rfffqN8yqKa+dSqVSvCw8PZuHEjPXr0ICkpyUkRZpCYCO+/b1dgeOkluOMOu47qrFlw1VWuOaZSPkCTuwJg//79fP3114Cd4ugMnTp1YtKkSSxatIgxY8Y4pc9LRGDlSrsA6FNP2QU5v//erqKc1QrOShUymtwVIkLfvn3p3Lmz08fIhw4dyuOPP86oUaP48ssvndNpRATceaddqzQ2Fj77LLUujFIK0OSugI8++ojvvvuOCRMmUM7JqwcbY5g5cyY33XQT3bt35+DBg/nv7I8/4LHHoEkTiIqC6dPtTJhOnXQGjFIZaHIv5I4fP85zzz1H8+bNefrpp11yjOLFi/PZZ5+RmJhI165dSUhZzt5Rp07Bc8/ZB46WLoWRI+0MmAEDIDjYJTEr5es0uRdygwcPJiYmhtmzZxMQ4Lp/DjVq1OC9997jxx9/ZNKkSY59KDYWJk60M2CmTYMnnoC9e2HsWChTxmWxKuUPHFmsQ/kpEeHOO++kSZMm1HHDTcjHHnuM5cuXM2rUKDp06MCNN96Y9Y6JiTB/Prz8Mhw5AvfdZ58srVfP5TEq5S+0/IByq5MnT1KnTh1q1KjBxo0b0/+1IAKrV8OLL8L27RAaCpMn2+mNSilAyw+oXIwYMYLZs2e7/bgVKlRgypQpbNq0iXnz5qVu2LrVVmls1w7OnYOFC2HTJk3sSuWTQ8ndGNPWGLPHGLPPGDM8i+3PG2N+NcZsN8asMcaEOD9U5Sw//vgjEydOZOfOnR45frdu3WjevDkjR47k7Pbt0K0bNG4M27bB1Kmwa5etC+PCewBK+btch2WMMYHAb8BdwBHsgtldROTXNPu0AjaJyHljTD/gDhF5JKd+dVjGM+Li4mjYsCGxsbHs2LGDUqVKeSSOLWvX0uTOO3k+IIA3g4PtbJgXX4TLLvNIPEr5CkeHZRy5odoU2Cci+5M7Xgg8AFxK7iKyLs3+PwPd8haucpdx48axe/duVq1a5ZnEfuECzJhB43Hj6AX8W6sW8u23mKpV3R+LUn7Mkb97KwNpl7k/ktyWnZ5Azis8KI84evQoEyZMoFu3brRt29a9B09Kgo8/tnPVhw2DW29l5rZtzNy9WxO7Ui7g1KmQxphuQCjQMpvtfYA+ANWqVXPmoZUDKleuzJdffkmjRo3ce+Bvv7ULZmzbBo0a2UJfrVvr3XylXMiR/7+OAmkvraokt6VjjGkDjATuF5G4rDoSkTARCRWR0IoVK+YnXpVPZ8+eBeDuu++mQoUK7jnotm1wzz1w990QHQ0LFsDmzdC6tXuOr1Qh5khy3wzUMsZcY4wJBh4FlqfdwRhzEzALm9j/dn6YqiAOHDhA9erVmT9/vnsOeOiQfZq0USObzKdMgd27oUsXnQGjlJvkOiwjIgnGmAHAaiAQmCciO40xo4EIEVkOTAZKAZ8ZW8DpkIjc78K4lYNEhKeffpqEhARauLpq4pkzdsGMadPs+2HDYPhwuPxy1x5XKZWJQ2PuIrISWJmh7dU03+d9PTblFuHh4axevZrp06e77j5HXJxdo3TsWDv88vjjdgUkva+ilMfo38h+7MSJEwwePJhbb72Vfv36Of8ASUl2HP3662HIEGjaFH75BT74QBO7Uh6myd2PbdiwgQsXLjB79mwCAwOd2/maNbaueteuULYsfPMNfP01NGjg3OMopfJFk7sfe+ihhzh48CD1nFlNcft2W/+lTRs4edLOXd+yxdaFUUp5DU3ufujcuXN88803AAVe6PqSw4fhySftmqU//wxvvgl79ti6MPmYAbNp0yZatmxJiRIluPbaa1m0aJFz4lRKAZrc/dLIkSNp27Yte/fuLXBfccePw4gRcN11dnx9yBC7CtKQIVCsWL76XLduHa1bt+b222/nq6++okWLFnTv3p0//vijwPEqpZKJiEdejRs3FuV8GzduFGOMDBw4sGAdXbgg3w8YIFcYIztApFs3kT/+KHB8//77r1x11VUyevToNIe6ICVLlpQpU6YUuH+l/B12CnquOVav3P1IXFwcvXr1okqVKowbNy5/nSQl2VrqdepQb8YMLgQE8EqrVnZsvXr1Asc4e/Zs4uPjGTp06KW2okWLUrFixYItnq2USkeTux+ZMGECv/76K7NmzaJ06dJ572D9erj5ZvskaenSlP/6a54bOZIv1q1jx44dTokxPDycjh07EhQUREJCwqVXTEwMQUFBTjmGUkqTu18JCQmhX79+tGvXLm8f3LHDrlPaqhUcPw4ffmhXRrrnHgY++yzFixdn6tSpBY4vOjqaLVu2EBYWRlBQULrXiRMntJicUk6kyd2P9OjRg3fffdfxDxw9Cj172rnp//0vTJxoZ8A8/jgkz4svX7483bp1Y8GCBZeKj+VXZGQkIsKyZcvYvHnzpdc777wDQMOGDQHYsWMHjRo1olatWtx///2cO3euQMdVqjDS5O4H5s6dy6xZsxBHFzs/exZGjoRatWD+fBg0yM6AeeEFKF480+69evUiNjaWzz77rEBxHj5slwVo3bo1oaGhl17Hjh2jdOnSNG3aFICnn36asWPHsnfvXq6//nomTZpUoOMqVRhpcvdxhw4dYvDgwXzxxRe573zxIkyfDjVrwvjx8OCDtlrjlCmQw3z4Jk2aULNmzQLPRU9ISACgSJHUkkYiwsKFC+ncuTNFixbl+PHj/PHHH7Rv3x6Anj17smTJkgIdV6nCSJO7DxMR+vXrh4gwc+ZMkityZrUjfPYZ1K0Lzz4LN94IEREQHg7XXJPrcYwxPPjgg6xbt45//vkn3/GGhNh10/fs2XOpbd68eRw+fJgRI0YAcOTIEapUqXJpe7Vq1S5d8SulHKfJ3Yd98sknrFy5knHjxlE9u2mKGzbALbdA5852yGXlSlsXpnHjPB2rXbt2JCQk8P333+c73ubNm1O5cmX69+/PmjVrePPNNxkwYADTp0+nRo0aAI4PLSmlcqTJ3UfFxMQwePBgmjZtyoABAzLv8OuvcP/90LKlvXE6b55dGaldO8juCj8Ht956K8HBwfzwww/5jjk4OJjFixdz7tw57r33XubPn094eDi9evW6tE+VKlU4cuTIpfeHDh1KdyWvlHKMU9dQVe5TsmRJ5s+fz9VXX52+4uOff8KoUTB3LpQqZRfPePZZKFGiQMcrVqwYDRo0ICIiokD93HLLLURGRma7/corr6R69eqsXLmS9u3bM3fuXDp27FigYypVGGly90EXLlygWLFi3H333amN//wDkyfbm6Px8TBwILz8MjhxvdQGDRo4duO2gN577z2eeOIJBg0aRO3atQkPD3f5MZXyN5rcfcy///7LTTfdxKBBg+xwTHw8hIXB66/DiRPwyCMwbhwkj2E7U+3atTl16hRnzpzhchcunVe/fn1++eUXl/WvVGHg0Ji7MaatMWaPMWafMWZ4FtuLGmM+Td6+yRhT3dmBKuvll19m37593NSwISxZAvXqwYABdibM//5n68K4ILFD6mwXnb2ilPfLNbkbYwKBd4B2QF2gizGmbobdegJnRKQm8DYw0dmBFmbh4eFUr14dYwz/93//R5vQUJoPGwadOkFQEKxYAevW2ZWRXKhSpUqAXb5PKeXdHLlybwrsE5H9InIRWAg8kGGfB4APk79fDNxpsp10rfIiPDycPn36pKuY+GNEBOG7dsGcORAZaevCuOF0pxQjK8hcd6WUezgy5l4ZSPt3+BHg5uz2EZEEY8xZoDxw0hlBFmYjR47k/Pnz6dpigZ4XLjD744/pHBfHM888w/nz5y891ZlWjx496NGjBydPnqRTp06Ztvfr149HHnmEw4cP071790zbhwwZQocOHdizZw99+vS5FNP//d//AXaYqE2bNmzbto3Bgwdn+vz48eNp1qwZGzdu5KWXXsq0ferUqTRs2JDvvvuOsWPHZto+a9YsateuzYoVK3jrrbcuta9fvz7TvkqpVG6d526M6WOMiTDGROif9o45dOhQlu1xcXFujsSWDahQoQLBwcFuP7ZSKm9Mbk8EGmNuBUaJyD3J70cAiMgbafZZnbzPT8aYIsAxoKLk0HloaKgUdM50YVC9evUsF7EICQnhwIED7g9IKeVRxpgtIhKa236OXLlvBmoZY64xxgQDjwLLM+yzHHgi+ftOwNqcErty3Lhx4yiR4QGkEiVK5H+lJaVUoZBrcheRBGAAsBrYBSwSkZ3GmNHGmPuTd5sLlDfG7AOeBzJNl1T507VrV8LCwggJCcEYQ0hICGFhYXTt2tXToSmlvFiuwzKuosMySimVd84cllFKKeVjNLkrpZQf0uSulFJ+SJO7Ukr5IU3uSinlhzw2W8YYcwLI/HROZhXQMgYp9Fyk0nORSs9FqsJwLkJEpGJuO3ksuTvKGBPhyLSfwkDPRSo9F6n0XKTSc5FKh2WUUsoPaXJXSik/5AvJPczTAXgRPRep9Fyk0nORSs9FMq8fc1dKKZV3vnDlrpRSKo+8LrkbY8oZY741xuxN/np5NvslGmO2Jb8yliD2abogeSoHzkUPY8yJNP8WenkiTlczxswzxvxtjNmRzXZjjJmWfJ62G2MauTtGd3HgXNxhjDmb5t/Eq+6O0Rt4XXLHlgteIyK1gDVkXz44VkQaJr/uz2Yfn6MLkqdy8FwAfJrm38IctwbpPh8AbXPY3g6olfzqA7znhpg85QNyPhcAP6T5NzHaDTF5HW9M7mkX2/4Q+I8HY/EEXZA8lSPnolAQkQ3A6Rx2eQD4SKyfgbLGmKvcE517OXAuFN6Z3CuJyF/J3x8DKmWzX7Hk9Vh/Nsb40y+ArBYkr5zdPsmLqaQsSO5vHDkXAA8lD0UsNsZUdU9oXsfRc1VY3GqMiTTGrDLG1PN0MJ5QxBMHNcZ8B1yZxaaRad+IiBhjspvOEyIiR40x1wJrjTFRIvK7s2NVXm8F8ImIxBlj+mL/omnt4ZiUZ23F5od/jTHtgaXY4apCxSPJXUTaZLfNGHPcGHOViPyV/Gfl39n0cTT5635jzHrgJsAfkvtRIO3VZ5Xktqz2OZK8IPllwCn3hOdWuZ4LEUn7c88BJrkhLm/kyL+bQkFE/knz/UpjzLvGmAoi4u81Z9LxxmGZtIttPwEsy7iDMeZyY0zR5O8rAM2BX90WoWvpguSpcj0XGcaV78eu81sYLQceT541cwtwNs3wZqFijLky5R6UMaYpNs/548VPjjxy5Z6LCcAiY0xPbNXIzgDGmFDgaRHpBdQBZhljkrD/4SaIiF8kdxFJMMakLEgeCMxLWZAciBCR5dgFyT9OXpD8NDbp+R0Hz8WzyQu1J2DPRQ+PBexCxphPgDuACsaYI8BrQBCAiMwEVgLtgX3AeeBJz9U/gDEAAABVSURBVETqeg6ci05AP2NMAhALPOqnFz850idUlVLKD3njsIxSSqkC0uSulFJ+SJO7Ukr5IU3uSinlhzS5K6WUH9LkrpRSfkiTu1JK+SFN7kop5Yf+H0Pm5tsi8hAoAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "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 <i>atan2</i> 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",
-    "$\\theta_0 = atan2(y, x)-atan2(l_1\\sin(\\theta_1), l_0+l_1\\cos(\\theta_1))$"
-   ]
-  }
- ],
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diff --git a/PathTracking/cgmres_nmpc/Figure_1.png b/PathTracking/cgmres_nmpc/Figure_1.png
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diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb b/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb
deleted file mode 100644
index 04fa940a8f..0000000000
--- a/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb
+++ /dev/null
@@ -1,208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Nonlinear Model Predictive Control with C-GMRES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"Figure_4.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"Figure_1.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"Figure_2.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "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",
-    "$$|u_{t+1}-u_{t}|<du_{max}$$\n",
-    "\n",
-    "- Maximum steering angle\n",
-    "\n",
-    "$$|u_{t}|<u_{max}$$\n",
-    "\n",
-    "- Initial state\n",
-    "\n",
-    "$$z_0 = z_{0,ob}$$\n",
-    "\n",
-    "- Maximum and minimum speed\n",
-    "\n",
-    "$$v_{min} < v_t < v_{max}$$\n",
-    "\n",
-    "- Maximum and minimum input\n",
-    "\n",
-    "$$u_{min} < u_t < u_{max}$$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "This is implemented at \n",
-    "\n",
-    "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at f51a73f47cb922a12659f8ce2d544c347a2a8156 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/f51a73f47cb922a12659f8ce2d544c347a2a8156/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L247-L301)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Vehicle model linearization\n",
-    "\n",
-    "\n",
-    "\n",
-    "Vehicle model is \n",
-    "\n",
-    "$$ \\dot{x} = vcos(\\phi)$$\n",
-    "\n",
-    "$$ \\dot{y} = vsin((\\phi)$$\n",
-    "\n",
-    "$$ \\dot{v} = a$$\n",
-    "\n",
-    "$$ \\dot{\\phi} = \\frac{vtan(\\delta)}{L}$$\n",
-    "\n",
-    "\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "ODE is\n",
-    "\n",
-    "$$ \\dot{z} =\\frac{\\partial }{\\partial z} z = f(z, u) = A'z+B'u$$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "where\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "A' =\n",
-    "\\begin{bmatrix}\n",
-    "\\frac{\\partial }{\\partial x}vcos(\\phi) & \n",
-    "\\frac{\\partial }{\\partial y}vcos(\\phi) & \n",
-    "\\frac{\\partial }{\\partial v}vcos(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\phi}vcos(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial x}vsin(\\phi) & \n",
-    "\\frac{\\partial }{\\partial y}vsin(\\phi) & \n",
-    "\\frac{\\partial }{\\partial v}vsin(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\phi}vsin(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial x}a& \n",
-    "\\frac{\\partial }{\\partial y}a& \n",
-    "\\frac{\\partial }{\\partial v}a&\n",
-    "\\frac{\\partial }{\\partial \\phi}a\\\\\n",
-    "\\frac{\\partial }{\\partial x}\\frac{vtan(\\delta)}{L}& \n",
-    "\\frac{\\partial }{\\partial y}\\frac{vtan(\\delta)}{L}& \n",
-    "\\frac{\\partial }{\\partial v}\\frac{vtan(\\delta)}{L}&\n",
-    "\\frac{\\partial }{\\partial \\phi}\\frac{vtan(\\delta)}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\\\\n",
-    "　=\n",
-    "\\begin{bmatrix}\n",
-    "0 & 0 & cos(\\bar{\\phi}) & -\\bar{v}sin(\\bar{\\phi})\\\\\n",
-    "0 & 0 & sin(\\bar{\\phi}) & \\bar{v}cos(\\bar{\\phi}) \\\\\n",
-    "0 & 0 & 0 & 0 \\\\\n",
-    "0 & 0 &\\frac{tan(\\bar{\\delta})}{L} & 0 \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "$\\begin{equation*}\n",
-    "B' =\n",
-    "\\begin{bmatrix}\n",
-    "\\frac{\\partial }{\\partial a}vcos(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\delta}vcos(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial a}vsin(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\delta}vsin(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial a}a &\n",
-    "\\frac{\\partial }{\\partial \\delta}a\\\\\n",
-    "\\frac{\\partial }{\\partial a}\\frac{vtan(\\delta)}{L} &\n",
-    "\\frac{\\partial }{\\partial \\delta}\\frac{vtan(\\delta)}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\\\\n",
-    "　=\n",
-    "\\begin{bmatrix}\n",
-    "0 & 0 \\\\\n",
-    "0 & 0 \\\\\n",
-    "1 & 0 \\\\\n",
-    "0 & \\frac{\\bar{v}}{Lcos^2(\\bar{\\delta})} \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "You can get a discrete-time mode with Forward Euler Discretization with sampling time dt.\n",
-    "\n",
-    "$$z_{k+1}=z_k+f(z_k,u_k)dt$$\n",
-    "\n",
-    "Using first degree Tayer expantion around zbar and ubar\n",
-    "$$z_{k+1}=z_k+(f(\\bar{z},\\bar{u})+A'z_k+B'u_k-A'\\bar{z}-B'\\bar{u})dt$$\n",
-    "\n",
-    "$$z_{k+1}=(I + dtA')z_k+(dtB')u_k + (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt$$\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "So, \n",
-    "\n",
-    "$$z_{k+1}=Az_k+Bu_k +C$$\n",
-    "\n",
-    "where,\n",
-    "\n",
-    "$$\\begin{equation*}\n",
-    "A = (I + dtA')\\\\\n",
-    "=\n",
-    "\\begin{bmatrix} \n",
-    "1 & 0 & cos(\\bar{\\phi})dt & -\\bar{v}sin(\\bar{\\phi})dt\\\\\n",
-    "0 & 1 & sin(\\bar{\\phi})dt & \\bar{v}cos(\\bar{\\phi})dt \\\\\n",
-    "0 & 0 & 1 & 0 \\\\\n",
-    "0 & 0 &\\frac{tan(\\bar{\\delta})}{L}dt & 1 \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$$"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "$$\\begin{equation*}\n",
-    "B = dtB'\\\\\n",
-    "=\n",
-    "\\begin{bmatrix} \n",
-    "0 & 0 \\\\\n",
-    "0 & 0 \\\\\n",
-    "dt & 0 \\\\\n",
-    "0 & \\frac{\\bar{v}}{Lcos^2(\\bar{\\delta})}dt \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$$"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "$$\\begin{equation*}\n",
-    "C = (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt\\\\\n",
-    "= dt(\n",
-    "\\begin{bmatrix} \n",
-    "\\bar{v}cos(\\bar{\\phi})\\\\\n",
-    "\\bar{v}sin(\\bar{\\phi}) \\\\\n",
-    "\\bar{a}\\\\\n",
-    "\\frac{\\bar{v}tan(\\bar{\\delta})}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "-\n",
-    "\\begin{bmatrix} \n",
-    "\\bar{v}cos(\\bar{\\phi})-\\bar{v}sin(\\bar{\\phi})\\bar{\\phi}\\\\\n",
-    "\\bar{v}sin(\\bar{\\phi})+\\bar{v}cos(\\bar{\\phi})\\bar{\\phi}\\\\\n",
-    "0\\\\\n",
-    "\\frac{\\bar{v}tan(\\bar{\\delta})}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "-\n",
-    "\\begin{bmatrix} \n",
-    "0\\\\\n",
-    "0 \\\\\n",
-    "\\bar{a}\\\\\n",
-    "\\frac{\\bar{v}\\bar{\\delta}}{Lcos^2(\\bar{\\delta})}\\\\\n",
-    "\\end{bmatrix}\n",
-    ")\\\\\n",
-    "=\n",
-    "\\begin{bmatrix} \n",
-    "\\bar{v}sin(\\bar{\\phi})\\bar{\\phi}dt\\\\\n",
-    "-\\bar{v}cos(\\bar{\\phi})\\bar{\\phi}dt\\\\\n",
-    "0\\\\\n",
-    "-\\frac{\\bar{v}\\bar{\\delta}}{Lcos^2(\\bar{\\delta})}dt\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$$"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "This equation is implemented at \n",
-    "\n",
-    "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at eb6d1cbe6fc90c7be9210bf153b3a04f177cc138 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/eb6d1cbe6fc90c7be9210bf153b3a04f177cc138/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L80-L102)\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Reference\n",
-    "\n",
-    "- [Vehicle Dynamics and Control \\| Rajesh Rajamani \\| Springer](http://www.springer.com/us/book/9781461414322)\n",
-    "\n",
-    "- [MPC Course Material \\- MPC Lab @ UC\\-Berkeley](http://www.mpc.berkeley.edu/mpc-course-material)\n"
-   ],
-   "metadata": {}
-  }
- ],
- "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
-}
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diff --git a/PathTracking/rear_wheel_feedback/Figure_2.png b/PathTracking/rear_wheel_feedback/Figure_2.png
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diff --git a/docs/modules/aerial_navigation/aerial_navigation_main.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst
index e6a8881616..e072707acc 100644
--- a/docs/modules/aerial_navigation/aerial_navigation_main.rst
+++ b/docs/modules/aerial_navigation/aerial_navigation_main.rst
@@ -8,11 +8,7 @@ Drone 3d trajectory following
 
 This is a 3d trajectory following simulation for a quadrotor.
 
-|3|
-
-rocket powered landing
------------------------------
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif
 
 .. include:: rocket_powered_landing.rst
 
-.. |3| 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.rst
index da7546e8f0..6c90d2b44e 100644
--- a/docs/modules/aerial_navigation/rocket_powered_landing.rst
+++ b/docs/modules/aerial_navigation/rocket_powered_landing.rst
@@ -1,3 +1,5 @@
+Rocket powered landing
+-----------------------------
 
 Simulation
 ~~~~~~~~~~
@@ -32,8 +34,6 @@ Simulation
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif
    :alt: gif
 
-   gif
-
 Equation generation
 ~~~~~~~~~~~~~~~~~~~
 
@@ -145,8 +145,8 @@ Equation generation
 
 
 
-Ref
-~~~
+References
+~~~~~~~~~~
 
 -  Python implementation of ‘Successive Convexification for 6-DoF Mars
    Rocket Powered Landing with Free-Final-Time’ paper by Michael Szmuk
diff --git a/docs/modules/arm_navigation/arm_navigation_main.rst b/docs/modules/arm_navigation/arm_navigation_main.rst
index 81dea01b0e..4399ae13e3 100644
--- a/docs/modules/arm_navigation/arm_navigation_main.rst
+++ b/docs/modules/arm_navigation/arm_navigation_main.rst
@@ -3,7 +3,7 @@
 Arm Navigation
 ==============
 
-.. include:: Planar_Two_Link_IK.rst
+.. include:: planar_two_link_ik.rst
 
 
 N joint arm to point control
@@ -14,9 +14,9 @@ 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
-ploting area.
+plotting area.
 
-|n_joints_arm|
+.. 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.
 
@@ -25,7 +25,4 @@ Arm navigation with obstacle avoidance
 
 Arm navigation with obstacle avoidance simulation.
 
-|obstacle|
-
-.. |n_joints_arm| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
-.. |obstacle| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
diff --git a/docs/modules/arm_navigation/Planar_Two_Link_IK.rst b/docs/modules/arm_navigation/planar_two_link_ik.rst
similarity index 99%
rename from docs/modules/arm_navigation/Planar_Two_Link_IK.rst
rename to docs/modules/arm_navigation/planar_two_link_ik.rst
index c5643ced6d..2da2b0dc30 100644
--- a/docs/modules/arm_navigation/Planar_Two_Link_IK.rst
+++ b/docs/modules/arm_navigation/planar_two_link_ik.rst
@@ -5,14 +5,11 @@ Two joint arm to point control
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/two_joint_arm_to_point_control/animation.gif
    :alt: TwoJointArmToPointControl
 
-   TwoJointArmToPointControl
-
 This is two 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
-ploting area.
+You can set the goal position of the end effector with left-click on the plotting area.
 
 Inverse Kinematics for a Planar Two-Link Robotic Arm
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/PathPlanning/BezierPath/Figure_1.png b/docs/modules/path_planning/Bezier_path_planning/Figure_1.png
similarity index 100%
rename from PathPlanning/BezierPath/Figure_1.png
rename to docs/modules/path_planning/Bezier_path_planning/Figure_1.png
diff --git a/PathPlanning/BezierPath/Figure_2.png b/docs/modules/path_planning/Bezier_path_planning/Figure_2.png
similarity index 100%
rename from PathPlanning/BezierPath/Figure_2.png
rename to docs/modules/path_planning/Bezier_path_planning/Figure_2.png
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index 50ed9bdbcf..d284c4e06d 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -337,13 +337,13 @@ A sample code of Bezier path planning.
 
 It is based on 4 control points Beier path.
 
-|Bezier1|
+.. 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:
 
-|Bezier2|
+.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_2.png?raw=True
 
 Ref:
 
@@ -439,7 +439,6 @@ Ref:
 .. |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
-.. |Bezier2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_2.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
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.rst
index fdb6573d08..59a7166674 100644
--- a/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
+++ b/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
@@ -15,7 +15,7 @@ AtsushiSakai/PythonRobotics <https://github.com/AtsushiSakai/PythonRobotics/blob
 This is a path tracking simulation using model predictive control (MPC).
 
 The MPC controller controls vehicle speed and steering base on
-linealized model.
+linearized model.
 
 This code uses cvxpy as an optimization modeling tool.
 
diff --git a/docs/modules/path_tracking/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc.rst
index a6959e7262..6f6a102618 100644
--- a/docs/modules/path_tracking/cgmres_nmpc.rst
+++ b/docs/modules/path_tracking/cgmres_nmpc.rst
@@ -2,60 +2,21 @@
 Nonlinear Model Predictive Control with C-GMRES
 -----------------------------------------------
 
-.. code-block:: ipython3
-
-    from IPython.display import Image
-    Image(filename="Figure_4.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_1_0.png
    :width: 600px
 
-
-
-.. code-block:: ipython3
-
-    Image(filename="Figure_1.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_2_0.png
    :width: 600px
 
-
-
-.. code-block:: ipython3
-
-    Image(filename="Figure_2.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_3_0.png
    :width: 600px
 
-
-
-.. code-block:: ipython3
-
-    Image(filename="Figure_3.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_4_0.png
    :width: 600px
 
-
-
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif
    :alt: gif
 
-   gif
-
 Mathematical Formulation
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst
index 7788866ad3..3f86da0b07 100644
--- a/docs/modules/path_tracking/path_tracking_main.rst
+++ b/docs/modules/path_tracking/path_tracking_main.rst
@@ -8,7 +8,7 @@ move to a pose control
 
 This is a simulation of moving to a pose control
 
-|2|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
 
 Ref:
 
@@ -21,7 +21,7 @@ Pure pursuit tracking
 Path tracking simulation with pure pursuit steering control and PID
 speed control.
 
-|3|
+.. 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.
@@ -37,7 +37,7 @@ Stanley control
 Path tracking simulation with Stanley steering control and PID speed
 control.
 
-|4|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
 
 Ref:
 
@@ -53,7 +53,7 @@ Rear wheel feedback control
 Path tracking simulation with rear wheel feedback steering control and
 PID speed control.
 
-|5|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
 
 Ref:
 
@@ -68,7 +68,7 @@ Linear–quadratic regulator (LQR) steering control
 Path tracking simulation with LQR steering control and PID speed
 control.
 
-|6|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
 
 Ref:
 
@@ -82,7 +82,7 @@ Linear–quadratic regulator (LQR) speed and steering control
 
 Path tracking simulation with LQR speed and steering control.
 
-|7|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
 
 Ref:
 
@@ -92,16 +92,5 @@ Ref:
 
 .. include:: Model_predictive_speed_and_steering_control.rst
 
-Ref:
-
--  `notebook <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.ipynb>`__
-
-
 .. include:: cgmres_nmpc.rst
 
-.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
-.. |3| image:: 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 <asakai.amsl+github@gmail.com>
Date: Sat, 27 Nov 2021 22:13:55 +0900
Subject: [PATCH 072/604] 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": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "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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": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAD4CAYAAAD2FnFTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAeUklEQVR4nO3de3RU9b338fc3mYHIxRsiUrlbRIFELpF6QxC80DbldliI4kNwaV2K+LSlpaJ26bE9dWFt0XWqtsvaLuF4Q6lQyql1ISCgnkcIMVoCCpiCCXqQi4AJRDLJ9/ljhjSETTJhhsnt81orK7P3/s2e7y8/mM/s65i7IyIiUltaYxcgIiJNkwJCREQCKSBERCSQAkJERAIpIEREJFCosQs4Geecc4736tWrscsQEWlWNmzYsMfdO8fbvlkGRK9evcjLy2vsMkREmhUz29GQ9trFJCIigRQQIiISSAEhIiKBmuUxCBFpWioqKigpKaG8vLyxSxEgIyODbt26EQ6HE1qPAkJEElZSUkLHjh3p1asXZtbY5bRq7s7evXspKSmhd+/eCa1Lu5hEJGHl5eV06tRJ4dAEmBmdOnVKytacAkJEkkLh0HQkayy0i0lEUmrH3jL+sLaIJe9/RtnXEdq3DTF+8Df4/vA+9OzUvrHLkxq0BSEiKbPq4y8Y88RaXl5XTOnXERwo/TrCy+uKGfPEWlZ9/MVJr3vXrl3cfPPN9OnTh6FDh3L55ZezePHi5BUfp169erFnz57j5j/yyCMntb4lS5awadOm6umRI0em7EJhBYSIpMSOvWXMeD6fwxWVRKqO/aKySJVzuKKSGc/ns2NvWYPX7e6MHz+eq6++mqKiIjZs2MDLL79MSUnJcW0jkchJ9yERJwoId6eqquqEz6sdEKmkgBCRlPjD2iIqKk/8RghQUVnFs2v/2eB1r1y5kjZt2nDnnXdWz+vZsyf33HMPAM899xxjx45l1KhRjB49Gndn9uzZDBw4kMzMTBYuXAjAW2+9RU5OTvU6Zs6cyXPPPQdEtwweeughhgwZQmZmJh999BEAe/fu5frrr2fAgAHcfvvtBH1L55w5czh8+DCDBg1i6tSpbN++nX79+jFt2jQGDhxIcXExHTp0qG6/aNEipk+fzrvvvsvSpUuZPXs2gwYN4pNPPgHg1VdfZdiwYVx44YWsXbu2wX+veCkgRCQllrz/2XFbDrVFqpzF7+9s8LoLCwsZMmRInW3y8/NZtGgRq1ev5rXXXqOgoIAPPviAN998k9mzZ/P555/X+zrnnHMO+fn53HXXXfz6178G4OGHH+aqq66isLCQCRMm8Omnnx73vLlz53LaaadRUFDACy+8AMDWrVuZMWMGhYWF9OzZM/D1rrjiCsaOHctjjz1GQUEBF1xwARDdClq3bh1PPPEEDz/8cL11nywFhIikRNnX8e3aKTuS+C6gu+++m0suuYRLL720et51113H2WefDcDbb7/NTTfdRHp6Ol26dGHEiBGsX7++3vVOnDgRgKFDh7J9+3YA1qxZwy233ALAd7/7Xc4666y4auzZsyeXXXZZQ7pVZx2nggJCRFKifdv4Tpps36bhJ1cOGDCA/Pz86umnnnqKFStWsHv37n+tt339Z0iFQqFjjgfUvpagbdu2AKSnpyd8LKN2PTVPTa3vGoZk1lEXBYSIpMT4wd8glFb3+fmhNGPC4PMbvO5Ro0ZRXl7O7373u+p5hw4dOmH74cOHs3DhQiorK9m9ezdr1qxh2LBh9OzZk02bNvH111+zf/9+VqxYUe9rX3311bz44osAvP7663z55ZeB7cLhMBUVFSdcT5cuXdi8eTNVVVXHnH3VsWNHvvrqq3rrOBUUECKSEt8f3odwet1vOeH0NG4f3vDbQ5gZS5YsYfXq1fTu3Zthw4aRm5vLo48+Gth+woQJZGVlcckllzBq1Ch+9atfcd5559G9e3cmT57MwIEDmTx5MoMHD673tR966CHWrFnDgAEDeO211+jRo0dguzvuuIOsrCymTp0auHzu3Lnk5ORwxRVX0LVr1+r5U6ZM4bHHHmPw4MHVB6lTxYKOuDd12dnZri8MEmk6Nm/ezMUXX1xvu1Uff8GM5/OpqKw65oB1KM0Ip6fx9C1DuKbfuaey1FYjaEzMbIO7Z8e7Dm1BiEjKXNPvXP7+w+HcNKwHHdqGMIMObUPcNKwHf//hcIVDE6NbbYhISvXs1J5fjB/IL8YPbOxSpB7aghARkUAKCBERCaSAEBGRQDoGISKpta8I3n0SPnwFjpRCmw6QNRmumAln92ns6qQGbUGISOpsXQ6/uxLyF8CRrwCP/s5fEJ2/dflJrzo9PZ1BgwZV/8ydO/eEbWvfIfXBBx/kzTffPOnXPmr//v08/fTT1dOfffYZkyZNSni9jUXXQYhIwuK6DmJfUTQEKk58hTPhdnDXOye1JdGhQwdKS0vjajt9+nRycnKS/ua9fft2cnJy2LhxY1LXezKazHUQZjbGzD42s21mNidgeVszWxhb/p6Z9aq1vIeZlZrZT5JRj4g0Qe8+CZUnvtUEEF3+P08l9WXnzJlD//79ycrK4ic/+UngLbSnT5/OokWLgOhtve+77z4GDRpEdnY2+fn53HDDDVxwwQX8/ve/B6C0tJTRo0dX3/r7L3/5S/VrffLJJwwaNIjZs2ezfft2Bg6Mns5bXl7OrbfeSmZmJoMHD2bVqlVA9FbkEydOZMyYMfTt25ef/vSnSe1/IhI+BmFm6cBTwHVACbDezJa6e81vuLgN+NLdv2lmU4BHgRtrLJ8HvJ5oLSLShH34ClTVExBVFfDhQvjubxq8+qPft3DUfffdx7XXXsvixYv56KOPMDP279/PmWeeydixY+vcgujRowcFBQX86Ec/Yvr06bzzzjuUl5czcOBA7rzzTjIyMli8eDGnn346e/bs4bLLLmPs2LHMnTuXjRs3UlBQAHDMnVafeuopzIx//OMffPTRR1x//fVs2bIFgIKCAt5//33atm1Lv379uOeee+jevXuD/wbJloyD1MOAbe5eBGBmLwPjgJoBMQ7499jjRcCTZmbu7mY2Hvgn0PCvkRKR5uNIfLt/4m5Xy9HvW6gpEomQkZHBbbfdRk5OzjFfBlSXsWPHApCZmUlpaSkdO3akY8eOtG3blv3799O+fXvuv/9+1qxZQ1paGjt37mTXrl11rvPtt9+u/gKjiy66iJ49e1YHxOjRoznjjDMA6N+/Pzt27GgSAZGMXUznA8U1pkti8wLbuHsEOAB0MrMOwL1Avd94YWZ3mFmemeXVvIWviDQTbTrU36Yh7eIQCoVYt24dkyZNYtmyZYwZMyau5x29nXZaWlr146PTkUiEF154gd27d7NhwwYKCgro0qVLvbfojuf14NTfwrshGvsspn8HHnf3ej8yuPsz7p7t7tmdO3c+9ZWJSHJlTYa0cN1t0sKQdWPdbRqgtLSUAwcO8J3vfIfHH3+cDz74AEj8FtoHDhzg3HPPJRwOs2rVKnbs2FHveocPH179bXJbtmzh008/pV+/fiddQyokIyB2AjW3hbrF5gW2MbMQcAawF/gW8Csz2w78ELjfzGYmoSYRaWqumAnp9QREehguv/ukVn/0GMTRnzlz5vDVV1+Rk5NDVlYWV111FfPmzQMSv4X21KlTycvLIzMzkwULFnDRRRcB0KlTJ6688koGDhzI7Nmzj3nOjBkzqKqqIjMzkxtvvJHnnnvumC2Hpijh01xjb/hbgNFEg2A9cLO7F9ZoczeQ6e53xg5ST3T3ybXW8+9Aqbv/ur7X1GmuIk1LvLf7ZutyeGVa9Gylmges08LRcJi8APped+oKbUWaxGmusWMKM4E3gM3AK+5eaGY/N7OxsWZ/JHrMYRswCzjuVFgRaQX6Xhe9zmFoLrTtCGbR30Nzo/MVDk2KLpQTkYTFvQUhKdMktiBERKRlUkCIiEggBYSIiATS7b5FJKWKDxYzf9N8lhUt41DFIdqF25HTJ4fc/rl0P73xrx6Wf9EWhIikzNqStUz860T+vOXPlFWU4ThlFWX8ecufmfjXiawtWXvS6y4pKWHcuHH07duXCy64gB/84AccOXLkuHYjR45EJ7nERwEhIilRfLCYWatnUR4pJ+LH3koi4hHKI+XMWj2L4oPFJ1jDibk7EydOZPz48WzdupUtW7ZQWlrKAw88kKzyWyUFhIikxPxN84lU1n2PoUhlhAWbFjR43StXriQjI4Nbb70ViN7P6PHHH+dPf/oTZWVlTJkyhYsvvpgJEyZw+PDh6ue99NJLZGZmMnDgQO69997q+R06dGD27NkMGDCAa6+9lnXr1jFy5Ej69OnD0qVLG1xfc6WAEJGUWFa07Lgth9oiHmFZ0bIGr7uwsJChQ4ceM+/000+nR48e/OY3v6Fdu3Zs3ryZhx9+mA0bNgDRb3u79957WblyJQUFBaxfv54lS5YAUFZWxqhRoygsLKRjx4787Gc/Y/ny5SxevJgHH3ywwfU1VwoIEUmJQ3V9k1wNZRXJvfP/W2+9xS233AJAVlYWWVlZAKxfv56RI0fSuXNnQqEQU6dOZc2aNQC0adOm+s6vmZmZjBgxgnA4TGZm5jHf8dDSKSBEJCXahdvF1a59uH2D192/f//qLYOjDh48yKeffkoo1PCTNcPhMGYGHHvL76O3+24tFBAikhI5fXIIWd1v1iELkdMnvi/1qWn06NEcOnSIBQuixy8qKyv58Y9/zPTp0xkzZgwvvvgiABs3buTDDz8EYNiwYaxevZo9e/ZQWVnJSy+9xIgRIxr82i2ZAkJEUiK3fy6h9HoCIj3EtP7TGrxuM2Px4sW8+uqr9O3blwsvvJCMjAweeeQR7rrrLkpLS7n44ot58MEHq49VdO3alblz53LNNddwySWXMHToUMaNG3dSfWupdLM+EUlYvDfrW1uyllmrZxGpjBxzwDpkIULpIeaNmMfwbsNPZamthm7WJyLNyvBuw3nte68x6cJJdAh3wDA6hDsw6cJJvPa91xQOTYxutSEiKdX99O48cNkDPHCZLmJr6rQFISJJ0Rx3V7dUyRoLBYSIJCwjI4O9e/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"
-     },
-     "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": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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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": "<Figure size 432x288 with 1 Axes>",
-      "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": "<Figure size 432x288 with 2 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 2 Axes>",
-      "image/png": "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\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 <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
 
-|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 <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
 
 
-
-
 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 <https://www.youtube.com/watch?v=Cj6tAQe7UCY>`__
 
-.. |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 <asakai.amsl+github@gmail.com>
Date: Sun, 28 Nov 2021 08:13:28 +0900
Subject: [PATCH 073/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 28 Nov 2021 16:00:02 +0900
Subject: [PATCH 074/604] 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 <https://arxiv.org/pdf/1801.07000.pdf>`_
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 <https://www.researchgate.net/publication/267963417_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 <asakai.amsl+github@gmail.com>
Date: Mon, 29 Nov 2021 00:01:06 +0900
Subject: [PATCH 075/604] 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 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
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 <http://ieeexplore.ieee.org/document/5940562/>`__
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 <https://github.com/ApolloAuto/apollo>`_
+
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 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
-
-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 <https://arxiv.org/abs/1604.07446>`__
-
-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 <http://robots.stanford.edu/papers/thrun.stanley05.pdf>`__
-
--  `Automatic Steering Methods for Autonomous Automobile Path
-   Tracking <https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf>`__
-
-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 <https://arxiv.org/abs/1604.07446>`__
-
-.. _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 <https://github.com/ApolloAuto/apollo>`__
-
-.. _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 <http://ieeexplore.ieee.org/document/5940562/>`__
-
-.. 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 <https://arxiv.org/abs/1604.07446>`_
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 <https://arxiv.org/abs/1604.07446>`__
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 <http://robots.stanford.edu/papers/thrun.stanley05.pdf>`_
+
+-  `Automatic Steering Methods for 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/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 <http://www.probabilistic-robotics.org/>`_
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
-.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
+-  `FastSLAM Lecture <http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf>`_
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 <http://www.probabilistic-robotics.org/>`_
+
+- `SLAM simulations by Tim Bailey <http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm>`_
+
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 <http://www.probabilistic-robotics.org/>`_
 
-.. _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
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diff --git a/docs/modules/slam/graphSLAM_SE2_example.rst 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://robots.stanford.edu/papers/thrun.graphslam.pdf>`_
 
--  http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf
+-  `Introduction to Mobile Robotics Graph-Based SLAM <http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf>`_
 
--  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>`_
 
 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 <http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf>`_
+
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 <https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf>`_
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 076/604] 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] <support@github.com>

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 077/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Wed, 1 Dec 2021 23:00:39 +0900
Subject: [PATCH 078/604] 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 <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
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 <https://en.wikipedia.org/wiki/B-spline>`__
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 <https://sites.google.com/site/ece452bugalgorithms/>`_
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 <https://ieeexplore.ieee.org/abstract/document/1013479>`_
+
+
+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 <http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
+
+
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 <https://en.wikipedia.org/wiki/Dubins_path>`__
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 <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
+
+.. 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 <https://ieeexplore.ieee.org/document/4339545/>`__
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 <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>`__
+
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 <https://en.wikipedia.org/wiki/D*>`__
+
+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 <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
+
+
+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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
+
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 <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
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 <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
-
-.. 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 <https://en.wikipedia.org/wiki/D*>`__
-
-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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
-
-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 <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
-
-
-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 <http://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>`__
-
-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 <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
-
-　　
-
-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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
-
-.. _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 <https://arxiv.org/pdf/1404.2334.pdf>`__
-
-.. _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 <https://arxiv.org/abs/1405.5848>`__
-
-.. _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 <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
-
--  `Real-time Motion Planning with Applications to Autonomous Urban
-   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
-
--  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
-   Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
-
-.. _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 <http://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>`__
-
-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 <https://en.wikipedia.org/wiki/B-spline>`__
-
-.. _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 <https://ieeexplore.ieee.org/document/4339545/>`__
-
-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 <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
-
-
-.. 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 <https://en.wikipedia.org/wiki/Dubins_path>`__
-
-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 <http://planning.cs.uiuc.edu/node822.html>`__
-
--  `optimal paths for a car that goes both forwards and
-   backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
-
--  `ghliu/pyReedsShepp: Implementation of Reeds Shepp
-   curve. <https://github.com/ghliu/pyReedsShepp>`__
-
-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 <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>`__
-
+.. 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 <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
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 <http://planning.cs.uiuc.edu/node822.html>`__
+
+-  `optimal paths for a car that goes both forwards and
+   backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
+
+-  `ghliu/pyReedsShepp: Implementation of Reeds Shepp
+   curve. <https://github.com/ghliu/pyReedsShepp>`__
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 <https://arxiv.org/pdf/1404.2334.pdf>`__
+
+.. _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 <https://arxiv.org/abs/1405.5848>`__
+
+.. _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 <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
+
+-  `Real-time Motion Planning with Applications to Autonomous Urban
+   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
+
+-  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
+   Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
+
+.. _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 <http://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>`__
\ 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 <http://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>`__
+
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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
+

From c70add32a15ad7e662185fa816ba152d244be002 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 4 Dec 2021 20:56:54 +0900
Subject: [PATCH 079/604] 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
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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.rst
new file mode 100644
index 0000000000..97e083389e
--- /dev/null
+++ b/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst
@@ -0,0 +1,71 @@
+Visibility Road-Map planner
+---------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VisibilityRoadMap/animation.gif
+
+`[Code] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/VisibilityRoadMap/visibility_road_map.py>`_
+
+This visibility road-map planner uses Dijkstra method for graph search.
+
+In the animation, the black lines are polygon obstacles,
+
+red crosses are visibility nodes, and blue lines area collision free visibility graphs.
+
+The red line is the final path searched by dijkstra algorithm frm the visibility graphs.
+
+Algorithms
+~~~~~~~~~~
+
+In this chapter, how does the visibility road map planner search a path.
+
+We assume this planner can be provided these information in the below figure.
+
+- 1. 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 <https://en.wikipedia.org/wiki/Visibility_graph>`_
+
+

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 080/604] 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] <support@github.com>

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 081/604] 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 082/604] 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] <support@github.com>

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 083/604] 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] <support@github.com>

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 084/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Sat, 25 Dec 2021 13:53:27 +0900
Subject: [PATCH 085/604] 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 086/604] 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] <support@github.com>

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 087/604] 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 <asakai.amsl+github@gmail.com>

* 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 <asakai.amsl+github@gmail.com>
---
 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: <float> time increment
+
+See Also 
+--------
+- PathFinderController class
+
+
+Ref:
+----
 -  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
    p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
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 <asakai.amsl+github@gmail.com>
Date: Sat, 25 Dec 2021 23:06:37 +0900
Subject: [PATCH 088/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 26 Dec 2021 10:55:21 +0900
Subject: [PATCH 089/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 26 Dec 2021 11:10:33 +0900
Subject: [PATCH 090/604] 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 091/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Tue, 28 Dec 2021 08:29:45 +0900
Subject: [PATCH 092/604] 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 093/604] 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] <support@github.com>

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 094/604] 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] <support@github.com>

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 095/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Thu, 6 Jan 2022 20:26:24 +0900
Subject: [PATCH 096/604] 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 <letrungkien.k53.hut@gmail.com>
Date: Sat, 8 Jan 2022 06:44:42 +0700
Subject: [PATCH 097/604] 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 <letrungkien.k53.hut@gmail.com>

* 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 <letrungkien.k53.hut@gmail.com>
---
 .../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 <asakai.amsl+github@gmail.com>
Date: Sat, 8 Jan 2022 17:06:29 +0900
Subject: [PATCH 098/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 9 Jan 2022 00:01:29 +0900
Subject: [PATCH 099/604] 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 100/604] 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] <support@github.com>

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 101/604] 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] <support@github.com>

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 102/604] 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] <support@github.com>

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 103/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Tue, 18 Jan 2022 21:28:07 +0900
Subject: [PATCH 104/604] 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 <asakai.amsl+github@gmail.com>
Date: Tue, 18 Jan 2022 21:31:10 +0900
Subject: [PATCH 105/604] 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 <asakai.amsl+github@gmail.com>
Date: Tue, 18 Jan 2022 21:39:47 +0900
Subject: [PATCH 106/604] 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 107/604] 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] <support@github.com>

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 108/604] 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 <asakai.amsl+github@gmail.com>

* Update docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>

* Changed Eqs 1 and 2 color to black

* Added cross-reference labels for Equations 1 and 2

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../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: <float> time increment
+
+- | **dt** : <float> 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 109/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Sat, 22 Jan 2022 22:18:22 +0900
Subject: [PATCH 110/604] 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** : <float> time increment
 
-See Also 
---------
-- PathFinderController class
 
 
-Ref:
-----
+References
+~~~~~~~~~~~~
+- PathFinderController class
 -  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
    p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
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 <letrungkien.k53.hut@gmail.com>
Date: Sun, 23 Jan 2022 17:35:54 +0700
Subject: [PATCH 111/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 23 Jan 2022 20:07:34 +0900
Subject: [PATCH 112/604] 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 113/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Thu, 27 Jan 2022 21:37:19 +0900
Subject: [PATCH 114/604] 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 b/docs/modules/localization/histogram_filter_localization/1.png
new file mode 100644
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diff --git a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
index afe689b9a5..891de94135 100644
--- a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
+++ 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 <https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html>`_
+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 <http://driving.stanford.edu/papers/ICRA2010.pdf>`_
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 <letrungkien.k53.hut@gmail.com>
Date: Sat, 29 Jan 2022 14:16:34 +0700
Subject: [PATCH 115/604] 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
+.. include:: inverted_pendulum_control/inverted_pendulum_control.rst
+
 .. include:: move_to_a_pose_control/move_to_a_pose_control.rst
 
diff --git a/docs/modules/control/inverted_pendulum_control/inverted-pendulum.png b/docs/modules/control/inverted_pendulum_control/inverted-pendulum.png
new file mode 100644
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diff --git a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
new file mode 100644
index 0000000000..5a447f7cd4
--- /dev/null
+++ b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
@@ -0,0 +1,97 @@
+Inverted Pendulum Control
+-----------------------------
+
+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.
+
+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 <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__ 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 <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__  (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 <ingramds92@gmail.com>
Date: Sun, 6 Feb 2022 08:18:54 -0500
Subject: [PATCH 116/604] 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 117/604] 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] <support@github.com>

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 118/604] 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] <support@github.com>

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 119/604] 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] <support@github.com>

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 120/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Mon, 21 Feb 2022 17:19:22 +0900
Subject: [PATCH 121/604] 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 <aki.y.ishikawa@gmail.com>
Date: Tue, 1 Mar 2022 21:33:31 +0900
Subject: [PATCH 122/604] 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 <hruhnb@gmail.com>
Date: Sat, 12 Mar 2022 04:38:25 -0500
Subject: [PATCH 123/604] 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 124/604] 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] <support@github.com>

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 125/604] 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] <support@github.com>

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 126/604] 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] <support@github.com>

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 127/604] 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] <support@github.com>

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 128/604] 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] <support@github.com>

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 129/604] 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] <support@github.com>

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 130/604] 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] <support@github.com>

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 131/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Sun, 10 Apr 2022 19:30:31 +0900
Subject: [PATCH 132/604] 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 <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__  (DARE):
+
+and :math:`P` is the unique positive definite solution to the discrete time
+`algebraic Riccati equation <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__  (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 <https://www.researchgate.net/publication/237062806>`__
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 <asakai.amsl+github@gmail.com>
Date: Mon, 11 Apr 2022 23:06:13 +0900
Subject: [PATCH 133/604] 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 <cmower@users.noreply.github.com>
Date: Tue, 19 Apr 2022 04:53:24 +0100
Subject: [PATCH 134/604] 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 <asakai.amsl+github@gmail.com>

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../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 135/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Mon, 2 May 2022 21:01:46 +0900
Subject: [PATCH 136/604] 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 <asakai.amsl+github@gmail.com>
Date: Mon, 2 May 2022 21:06:33 +0900
Subject: [PATCH 137/604] 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 <https://en.wikipedia.org/wiki/Dubins_path>`__
+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 <https://en.wikipedia.org/wiki/Dubins_path>`__
+-  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__

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 138/604] 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] <support@github.com>

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 <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 13:19:30 +0900
Subject: [PATCH 139/604] 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
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diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path.rst
index 6765a84617..b612a82ef2 100644
--- a/docs/modules/path_planning/dubins_path/dubins_path.rst
+++ b/docs/modules/path_planning/dubins_path/dubins_path.rst
@@ -7,12 +7,37 @@ A sample code for Dubins path planning.
 
 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 <https://en.wikipedia.org/wiki/Dubins_path>`__
 -  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__
+-  `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths <https://gieseanw.wordpress.com/2012/10/21/a-comprehensive-step-by-step-tutorial-to-computing-dubins-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 <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 15:21:03 +0900
Subject: [PATCH 140/604] 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: <http://www.probabilistic-robotics.org>`_
 - `Robust Vehicle Localization in Urban Environments Using Probabilistic Maps <http://driving.stanford.edu/papers/ICRA2010.pdf>`_
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: <http://www.probabilistic-robotics.org>`_
 - `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000.pdf>`_
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:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
 
diff --git a/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
new file mode 100644
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diff --git a/docs/modules/slam/graph_slam/graphSLAM_doc.rst b/docs/modules/slam/graph_slam/graphSLAM_doc.rst
index 4367f770c3..5297604809 100644
--- a/docs/modules/slam/graph_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:: 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
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z@+8nlngESj67_|gh}af;BYS?SJLUosMt?E3@%?~lvAhx#wLqRD+k^IaGoMUnjdmZ_
z7h;B~_b+?50<->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 <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 16:04:39 +0900
Subject: [PATCH 141/604] 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 142/604] 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 <jsbyysheng@gmail.com>
Date: Sat, 7 May 2022 17:15:01 +0800
Subject: [PATCH 143/604] 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 <asakai.amsl+github@gmail.com>
---
 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 <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 22:43:31 +0900
Subject: [PATCH 144/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:11:21 +0900
Subject: [PATCH 145/604] 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() }}
+<script async
+        src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
+        crossOrigin="anonymous"></script>
+{% endblock %}

From 785e62175fe71c277d8501018347554c5edda836 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:23:11 +0900
Subject: [PATCH 146/604] 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"></script>
 {% endblock %}
+
+{% block sidebarsearch %}
+{{ super() }}
+<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
+     crossorigin="anonymous"></script>
+<!-- PythonRoboticsDoc -->
+<ins class="adsbygoogle"
+     style="display:block"
+     data-ad-client="ca-pub-9612347954373886"
+     data-ad-slot="1579532132"
+     data-ad-format="auto"
+     data-full-width-responsive="true"></ins>
+<script>
+     (adsbygoogle = window.adsbygoogle || []).push({});
+</script>
+{% endblock %}

From 20220fdfd27b277d8ff5fac22680b47ef3ce7149 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:29:48 +0900
Subject: [PATCH 147/604] 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"></script>
 {% endblock %}
 
-{% block sidebarsearch %}
+{% block footer %}
 {{ super() }}
 <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
      crossorigin="anonymous"></script>

From ec19f6373d1aeae40ace9bf6100f49da4b09a6cd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:39:01 +0900
Subject: [PATCH 148/604] 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"></script>
 {% endblock %}
 
-{% block footer %}
+{% block content %}
 {{ super() }}
 <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
      crossorigin="anonymous"></script>

From b01128a8f65605d75016ef980009711e1f49c917 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:49:20 +0900
Subject: [PATCH 149/604] 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() }}
-<script async
-        src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
-        crossOrigin="anonymous"></script>
-{% endblock %}
-
-{% block content %}
+{% block sidebartitle %}
 {{ super() }}
 <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
      crossorigin="anonymous"></script>

From f5d80ecb1cf17193b9061627215ee18348e3b6dc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 15:56:51 +0900
Subject: [PATCH 150/604] 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 @@
 <!-- 
 Thanks for contributing a pull request! 
 Please check this document before submitting:
-- [How to contribute](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#adding-a-new-algorithm-example)
+- [How to contribute](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html#adding-a-new-algorithm-example)
 
 Note that this is my hobby project; I appreciate your
 patience during the review process.
diff --git a/README.md b/README.md
index f54b880dc0..2d0ee8d653 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,6 @@
 ![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://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
-[![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest)
 [![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)
@@ -126,7 +125,7 @@ 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: [https://pythonrobotics.readthedocs.io/](https://pythonrobotics.readthedocs.io/)
+You can check the full documentation 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)
 
@@ -609,7 +608,7 @@ 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](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#)
+Please check this document:[How To Contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html)
 
 # Citing
 
diff --git a/docs/conf.py b/docs/conf.py
index 892e2863be..899f961359 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -95,6 +95,17 @@
     'display_version': False,
 }
 
+# replace "view page source" with "edit on github" in Read The Docs theme
+#  * https://github.com/readthedocs/sphinx_rtd_theme/issues/529
+html_context = {
+    'display_github': True,
+    'github_user': 'AtsushiSakai',
+    'github_repo': 'PythonRobotics',
+    'github_version': 'master',
+    "conf_py_path": "/docs/",
+    "source_suffix": source_suffix,
+}
+
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index 4b39089e7b..8a2f73e9cd 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -147,7 +147,7 @@ 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/
+.. _`Python Robotics Docs`: https://atsushisakai.github.io/PythonRobotics
 .. _`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
diff --git a/users_comments.md b/users_comments.md
index 43ffc7cc36..66e8b4022e 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -6,7 +6,7 @@ This is an electric wheelchair control demo by [Katsushun89](https://github.com/
 
 [WHILL Model CR](https://whill.jp/model-cr) is the control target, [M5Stack](https://m5stack.com/) is used for the controller, and [toio](https://toio.io/) is used for the control input device.
 
-[move-to-a-pose-control](https://pythonrobotics.readthedocs.io/en/latest/modules/path_tracking.html#move-to-a-pose-control) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
+[Move to a Pose Control — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/control/move_to_a_pose_control/move_to_a_pose_control.html) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
 
 ![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)
 

From 6961d84bbb896ee330f0d86d00e7efe2ee8ba26d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 May 2022 07:23:38 +0900
Subject: [PATCH 151/604] Bump matplotlib from 3.5.1 to 3.5.2 in /requirements
 (#678)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.1 to 3.5.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.1...v3.5.2)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 46f56702ea..33c43404ec 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.22.3
 scipy == 1.8.0
 pandas == 1.4.2
-matplotlib == 3.5.1
+matplotlib == 3.5.2
 cvxpy == 1.2.0
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 2c44c736090b49798db7474d1b6bdd6e277521dc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 May 2022 07:15:43 +0900
Subject: [PATCH 152/604] Bump cvxpy from 1.2.0 to 1.2.1 in /requirements
 (#680)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.0 to 1.2.1.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.0...v1.2.1)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 33c43404ec..d404da757e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.22.3
 scipy == 1.8.0
 pandas == 1.4.2
 matplotlib == 3.5.2
-cvxpy == 1.2.0
+cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.950 # For unit test

From 31178c8e377a7a4939a9ca91d903a18863214198 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 17 May 2022 21:57:07 +0900
Subject: [PATCH 153/604] Enhance dubins path docs (#679)

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs
---
 .../DubinsPath/dubins_path_planner.py         | 374 +++++++-----------
 docs/conf.py                                  |   1 +
 .../path_planning/dubins_path/dubins_path.jpg | Bin 0 -> 22927 bytes
 .../dubins_path/dubins_path_main.rst          |  50 ++-
 tests/test_dubins_path_planning.py            |  19 +
 tests/test_utils.py                           |   6 +
 utils/angle.py                                |  10 +-
 utils/plot.py                                 |  49 +++
 8 files changed, 275 insertions(+), 234 deletions(-)
 create mode 100644 docs/modules/path_planning/dubins_path/dubins_path.jpg
 create mode 100644 utils/plot.py

diff --git a/PathPlanning/DubinsPath/dubins_path_planner.py b/PathPlanning/DubinsPath/dubins_path_planner.py
index 69c213ddf4..4d4ee07291 100644
--- a/PathPlanning/DubinsPath/dubins_path_planner.py
+++ b/PathPlanning/DubinsPath/dubins_path_planner.py
@@ -7,21 +7,20 @@
 """
 import sys
 import os
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
-import math
+from math import sin, cos, atan2, sqrt, acos, pi, hypot
 import numpy as np
-from utils.angle import angle_mod, create_2d_rotation_matrix
+from utils.angle import angle_mod, rot_mat_2d
 
 show_animation = True
 
 
-def plan_dubins_path(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, selected_types=None):
     """
-    Path dubins path
+    Plan dubins path
 
     Parameters
     ----------
@@ -41,6 +40,11 @@ def plan_dubins_path(s_x, s_y, s_yaw,
         curvature for curve [1/m]
     step_size : float (optional)
         step size between two path points [m]. Default is 0.1
+    selected_types : a list of string or None
+        selected path planning types. If None, all types are used for
+        path planning, and minimum path length result is returned.
+        You can select used path plannings types by a string list.
+        e.g.: ["RSL", "RSR"]
 
     Returns
     -------
@@ -53,21 +57,49 @@ def plan_dubins_path(s_x, s_y, s_yaw,
     modes: array
         mode list of the path
     lengths: array
-        length list of the path segments.
-
+        arrow_length list of the path segments.
+
+    Examples
+    --------
+    You can generate a dubins path.
+
+    >>> start_x = 1.0  # [m]
+    >>> start_y = 1.0  # [m]
+    >>> start_yaw = np.deg2rad(45.0)  # [rad]
+    >>> end_x = -3.0  # [m]
+    >>> end_y = -3.0  # [m]
+    >>> end_yaw = np.deg2rad(-45.0)  # [rad]
+    >>> curvature = 1.0
+    >>> path_x, path_y, path_yaw, mode, _ = plan_dubins_path(
+                start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+    >>> plt.plot(path_x, path_y, label="final course " + "".join(mode))
+    >>> 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.show()
+
+    .. image:: dubins_path.jpg
     """
+    if selected_types is None:
+        planning_funcs = _PATH_TYPE_MAP.values()
+    else:
+        planning_funcs = [_PATH_TYPE_MAP[ptype] for ptype in selected_types]
+
     # calculate local goal x, y, yaw
-    l_rot = create_2d_rotation_matrix(s_yaw)
+    l_rot = rot_mat_2d(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(
-        local_goal_x, local_goal_y, local_goal_yaw, curvature, step_size)
+    lp_x, lp_y, lp_yaw, modes, lengths = _dubins_path_planning_from_origin(
+        local_goal_x, local_goal_y, local_goal_yaw, curvature, step_size,
+        planning_funcs)
 
     # Convert a local coordinate path to the global coordinate
-    rot = create_2d_rotation_matrix(-s_yaw)
+    rot = rot_mat_2d(-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
@@ -80,276 +112,179 @@ def _mod2pi(theta):
     return angle_mod(theta, zero_2_2pi=True)
 
 
-def _LSL(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
+def _calc_trig_funcs(alpha, beta):
+    sin_a = sin(alpha)
+    sin_b = sin(beta)
+    cos_a = cos(alpha)
+    cos_b = cos(beta)
+    cos_ab = cos(alpha - beta)
+    return sin_a, sin_b, cos_a, cos_b, cos_ab
 
-    tmp0 = d + sa - sb
 
+def _LSL(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["L", "S", "L"]
-    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sa - sb))
-    if p_squared < 0:
+    p_squared = 2 + d ** 2 - (2 * cos_ab) + (2 * d * (sin_a - sin_b))
+    if p_squared < 0:  # invalid configuration
         return None, None, None, mode
-    tmp1 = math.atan2((cb - ca), tmp0)
-    t = _mod2pi(-alpha + tmp1)
-    p = math.sqrt(p_squared)
-    q = _mod2pi(beta - tmp1)
-
-    return t, p, q, mode
+    tmp = atan2((cos_b - cos_a), d + sin_a - sin_b)
+    d1 = _mod2pi(-alpha + tmp)
+    d2 = sqrt(p_squared)
+    d3 = _mod2pi(beta - tmp)
+    return d1, d2, d3, mode
 
 
-def right_straight_right(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    tmp0 = d - sa + sb
+def _RSR(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["R", "S", "R"]
-    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sb - sa))
+    p_squared = 2 + d ** 2 - (2 * cos_ab) + (2 * d * (sin_b - sin_a))
     if p_squared < 0:
         return None, None, None, mode
-    tmp1 = math.atan2((ca - cb), tmp0)
-    t = angle_mod(alpha - tmp1, zero_2_2pi=True)
-    p = math.sqrt(p_squared)
-    q = _mod2pi(-beta + tmp1)
-
-    return t, p, q, mode
+    tmp = atan2((cos_a - cos_b), d - sin_a + sin_b)
+    d1 = _mod2pi(alpha - tmp)
+    d2 = sqrt(p_squared)
+    d3 = _mod2pi(-beta + tmp)
+    return d1, d2, d3, mode
 
 
-def left_straight_right(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    p_squared = -2 + (d * d) + (2 * c_ab) + (2 * d * (sa + sb))
+def _LSR(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
+    p_squared = -2 + d ** 2 + (2 * cos_ab) + (2 * d * (sin_a + sin_b))
     mode = ["L", "S", "R"]
     if p_squared < 0:
         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)
-
-    return t, p, q, mode
+    d1 = sqrt(p_squared)
+    tmp = atan2((-cos_a - cos_b), (d + sin_a + sin_b)) - atan2(-2.0, d1)
+    d2 = _mod2pi(-alpha + tmp)
+    d3 = _mod2pi(-_mod2pi(beta) + tmp)
+    return d2, d1, d3, mode
 
 
-def right_straight_left(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    p_squared = (d * d) - 2 + (2 * c_ab) - (2 * d * (sa + sb))
+def _RSL(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
+    p_squared = d ** 2 - 2 + (2 * cos_ab) - (2 * d * (sin_a + sin_b))
     mode = ["R", "S", "L"]
     if p_squared < 0:
         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)
-
-    return t, p, q, mode
+    d1 = sqrt(p_squared)
+    tmp = atan2((cos_a + cos_b), (d - sin_a - sin_b)) - atan2(2.0, d1)
+    d2 = _mod2pi(alpha - tmp)
+    d3 = _mod2pi(beta - tmp)
+    return d2, d1, d3, mode
 
 
-def right_left_right(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
+def _RLR(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["R", "L", "R"]
-    tmp_rlr = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (sa - sb)) / 8.0
-    if abs(tmp_rlr) > 1.0:
+    tmp = (6.0 - d ** 2 + 2.0 * cos_ab + 2.0 * d * (sin_a - sin_b)) / 8.0
+    if abs(tmp) > 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))
-    return t, p, q, mode
+    d2 = _mod2pi(2 * pi - acos(tmp))
+    d1 = _mod2pi(alpha - atan2(cos_a - cos_b, d - sin_a + sin_b) + d2 / 2.0)
+    d3 = _mod2pi(alpha - beta - d1 + d2)
+    return d1, d2, d3, mode
 
 
 def _LRL(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["L", "R", "L"]
-    tmp_lrl = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (- sa + sb)) / 8.0
-    if abs(tmp_lrl) > 1:
+    tmp = (6.0 - d ** 2 + 2.0 * cos_ab + 2.0 * d * (- sin_a + sin_b)) / 8.0
+    if abs(tmp) > 1.0:
         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))
+    d2 = _mod2pi(2 * pi - acos(tmp))
+    d1 = _mod2pi(-alpha - atan2(cos_a - cos_b, d + sin_a - sin_b) + d2 / 2.0)
+    d3 = _mod2pi(_mod2pi(beta) - alpha - d1 + _mod2pi(d2))
+    return d1, d2, d3, mode
+
 
-    return t, p, q, mode
+_PATH_TYPE_MAP = {"LSL": _LSL, "RSR": _RSR, "LSR": _LSR, "RSL": _RSL,
+                  "RLR": _RLR, "LRL": _LRL, }
 
 
-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, planning_funcs):
     dx = end_x
     dy = end_y
-    D = math.hypot(dx, dy)
-    d = D * curvature
+    d = hypot(dx, dy) * curvature
 
-    theta = _mod2pi(math.atan2(dy, dx))
-    alpha = _mod2pi(- theta)
+    theta = _mod2pi(atan2(dy, dx))
+    alpha = _mod2pi(-theta)
     beta = _mod2pi(end_yaw - theta)
 
-    planning_funcs = [_LSL, right_straight_right,
-                      left_straight_right, right_straight_left,
-                      right_left_right, _LRL]
-
     best_cost = float("inf")
-    bt, bp, bq, best_mode = None, None, None, None
+    b_d1, b_d2, b_d3, b_mode = None, None, None, None
 
     for planner in planning_funcs:
-        t, p, q, mode = planner(alpha, beta, d)
-        if t is None:
+        d1, d2, d3, mode = planner(alpha, beta, d)
+        if d1 is None:
             continue
 
-        cost = (abs(t) + abs(p) + abs(q))
-        if best_cost > cost:
-            bt, bp, bq, best_mode = t, p, q, mode
-            best_cost = cost
-    lengths = [bt, bp, bq]
+        cost = (abs(d1) + abs(d2) + abs(d3))
+        if best_cost > cost:  # Select minimum length one.
+            b_d1, b_d2, b_d3, b_mode, best_cost = d1, d2, d3, mode, cost
 
-    x_list, y_list, yaw_list, directions = generate_local_course(sum(lengths),
-                                                                 lengths,
-                                                                 best_mode,
-                                                                 curvature,
-                                                                 step_size)
+    lengths = [b_d1, b_d2, b_d3]
+    x_list, y_list, yaw_list = _generate_local_course(lengths, b_mode,
+                                                      curvature, step_size)
 
     lengths = [length / curvature for length in lengths]
 
-    return x_list, y_list, yaw_list, best_mode, lengths
+    return x_list, y_list, yaw_list, b_mode, lengths
 
 
-def interpolate(ind, length, mode, max_curvature, origin_x, origin_y,
-                origin_yaw, path_x, path_y, path_yaw, directions):
+def _interpolate(length, mode, max_curvature, origin_x, origin_y,
+                 origin_yaw, path_x, path_y, path_yaw):
     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
+        path_x.append(origin_x + length / max_curvature * cos(origin_yaw))
+        path_y.append(origin_y + length / max_curvature * sin(origin_yaw))
+        path_yaw.append(origin_yaw)
     else:  # curve
-        ldx = math.sin(length) / max_curvature
+        ldx = sin(length) / max_curvature
         ldy = 0.0
         if mode == "L":  # left turn
-            ldy = (1.0 - math.cos(length)) / max_curvature
+            ldy = (1.0 - 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
+            ldy = (1.0 - cos(length)) / -max_curvature
+        gdx = cos(-origin_yaw) * ldx + sin(-origin_yaw) * ldy
+        gdy = -sin(-origin_yaw) * ldx + cos(-origin_yaw) * ldy
+        path_x.append(origin_x + gdx)
+        path_y.append(origin_y + gdy)
 
-    return path_x, path_y, path_yaw, directions
-
-
-def generate_local_course(total_length, lengths, modes, max_curvature,
-                          step_size):
-    n_point = math.trunc(total_length / step_size) + len(lengths) + 4
+        if mode == "L":  # left turn
+            path_yaw.append(origin_yaw + length)
+        elif mode == "R":  # right turn
+            path_yaw.append(origin_yaw - length)
 
-    p_x = [0.0 for _ in range(n_point)]
-    p_y = [0.0 for _ in range(n_point)]
-    p_yaw = [0.0 for _ in range(n_point)]
-    directions = [0.0 for _ in range(n_point)]
-    ind = 1
+    return path_x, path_y, path_yaw
 
-    if lengths[0] > 0.0:
-        directions[0] = 1
-    else:
-        directions[0] = -1
 
-    ll = 0.0
+def _generate_local_course(lengths, modes, max_curvature, step_size):
+    p_x, p_y, p_yaw = [0.0], [0.0], [0.0]
 
-    for (m, length, i) in zip(modes, lengths, range(len(modes))):
+    for (mode, length) in zip(modes, lengths):
         if length == 0.0:
             continue
-        elif length > 0.0:
-            dist = step_size
-        else:
-            dist = -step_size
 
         # set origin state
-        origin_x, origin_y, origin_yaw = p_x[ind], p_y[ind], p_yaw[ind]
-
-        ind -= 1
-        if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
-            pd = - dist - ll
-        else:
-            pd = dist - ll
-
-        while abs(pd) <= abs(length):
-            ind += 1
-            p_x, p_y, p_yaw, directions = interpolate(ind, pd, m,
-                                                      max_curvature,
-                                                      origin_x,
-                                                      origin_y,
-                                                      origin_yaw,
-                                                      p_x, p_y,
-                                                      p_yaw,
-                                                      directions)
-            pd += dist
-
-        ll = length - pd - dist  # calc remain length
-
-        ind += 1
-        p_x, p_y, p_yaw, directions = interpolate(ind, length, m,
-                                                  max_curvature,
-                                                  origin_x, origin_y,
-                                                  origin_yaw,
-                                                  p_x, p_y, p_yaw,
-                                                  directions)
-
-    if len(p_x) <= 1:
-        return [], [], [], []
-
-    # remove unused data
-    while len(p_x) >= 1 and p_x[-1] == 0.0:
-        p_x.pop()
-        p_y.pop()
-        p_yaw.pop()
-        directions.pop()
-
-    return p_x, p_y, p_yaw, directions
-
-
-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)
-    else:
-        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), fc=fc,
-                  ec=ec, head_width=width, head_length=width)
-        plt.plot(x, y)
+        origin_x, origin_y, origin_yaw = p_x[-1], p_y[-1], p_yaw[-1]
+
+        current_length = step_size
+        while abs(current_length + step_size) <= abs(length):
+            p_x, p_y, p_yaw = _interpolate(current_length, mode, max_curvature,
+                                           origin_x, origin_y, origin_yaw,
+                                           p_x, p_y, p_yaw)
+            current_length += step_size
+
+        p_x, p_y, p_yaw = _interpolate(length, mode, max_curvature, origin_x,
+                                       origin_y, origin_yaw, p_x, p_y, p_yaw)
+
+    return p_x, p_y, p_yaw
 
 
 def main():
     print("Dubins path planner sample start!!")
     import matplotlib.pyplot as plt
+    from utils.plot import plot_arrow
 
     start_x = 1.0  # [m]
     start_y = 1.0  # [m]
@@ -370,12 +305,9 @@ def main():
                                                                curvature)
 
     if show_animation:
-        plt.plot(path_x, path_y, label="final course " + "".join(mode))
-
-        # plotting
+        plt.plot(path_x, path_y, label="".join(mode))
         plot_arrow(start_x, start_y, start_yaw)
         plot_arrow(end_x, end_y, end_yaw)
-
         plt.legend()
         plt.grid(True)
         plt.axis("equal")
diff --git a/docs/conf.py b/docs/conf.py
index 899f961359..8a3ba1ac2d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -39,6 +39,7 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
+    'matplotlib.sphinxext.plot_directive',
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
diff --git a/docs/modules/path_planning/dubins_path/dubins_path.jpg b/docs/modules/path_planning/dubins_path/dubins_path.jpg
new file mode 100644
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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 0b8c8fb603..516638d83d 100644
--- a/docs/modules/path_planning/dubins_path/dubins_path_main.rst
+++ b/docs/modules/path_planning/dubins_path/dubins_path_main.rst
@@ -9,40 +9,68 @@ Dubins path
 ~~~~~~~~~~~~
 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.
+It can generates a shortest path between two 2D poses (x, y, yaw) with maximum curvature constraint and tangent(yaw angle) constraint.
 
-The path consist of 3 segments of maximum curvature curves or a straight line segment.
+Generated paths 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. 
+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:
+Dubins path planner can output each segment type and distance of each course segment.
+
+For example, a RSR Dubins path is:
 
 .. image:: RSR.jpg
    :width: 400px
 
-one of RLR Dubins paths is:
+Each segment distance can be calculated by:
+
+:math:`\alpha = mod(-\theta)`
+
+:math:`\beta = mod(x_{e, yaw} - \theta)`
+
+:math:`p^2 = 2 + d ^ 2 - 2\cos(\alpha-\beta) + 2d(\sin\alpha - \sin\beta)`
+
+:math:`t = atan2(\cos\beta - \cos\alpha, d + \sin\alpha - \sin\beta)`
+
+:math:`d_1 = mod(-\alpha + t)`
+
+:math:`d_2 = p`
+
+:math:`d_3 = mod(\beta - t)`
+
+where :math:`\theta` is tangent and d is distance from :math:`x_s` to :math:`x_e`
+
+A RLR Dubins path is:
 
 .. image:: RLR.jpg
    :width: 200px
 
-Dubins path planner can output three types and distances of each course segment.
+Each segment distance can be calculated by:
+
+:math:`t = (6.0 - d^2 + 2\cos(\alpha-\beta) + 2d(\sin\alpha - \sin\beta)) / 8.0`
+
+:math:`d_2 = mod(2\pi - acos(t))`
+
+:math:`d_1 = mod(\alpha - atan2(\cos\beta - \cos\alpha, d + \sin\alpha - \sin\beta) + d_2 / 2.0)`
+
+:math:`d_3 = mod(\alpha - \beta - d_1 + d_2)`
 
 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.
+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.
 
-Code
+API
 ~~~~~~~~~~~~~~~~~~~~
 
-.. automodule:: PathPlanning.DubinsPath.dubins_path_planner
-    :members:
+.. autofunction:: PathPlanning.DubinsPath.dubins_path_planner.plan_dubins_path
 
 
 Reference
 ~~~~~~~~~~~~~~~~~~~~
-
+-  `On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents <https://www.jstor.org/stable/2372560?origin=crossref>`__
 -  `Dubins path - Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
 -  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__
 -  `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths <https://gieseanw.wordpress.com/2012/10/21/a-comprehensive-step-by-step-tutorial-to-computing-dubins-paths/>`__
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index 99c266bc6f..aceb0b752a 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -69,5 +69,24 @@ def test_3():
         check_path_length(px, py, lengths)
 
 
+def test_path_plannings_types():
+    dubins_path_planner.show_animation = False
+    start_x = 1.0  # [m]
+    start_y = 1.0  # [m]
+    start_yaw = np.deg2rad(45.0)  # [rad]
+
+    end_x = -3.0  # [m]
+    end_y = -3.0  # [m]
+    end_yaw = np.deg2rad(-45.0)  # [rad]
+
+    curvature = 1.0
+
+    _, _, _, mode, _ = dubins_path_planner.plan_dubins_path(
+        start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature,
+        selected_types=["RSL"])
+
+    assert mode == ["R", "S", "L"]
+
+
 if __name__ == '__main__':
     conftest.run_this_test(__file__)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index b397479809..36861c52b4 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -4,6 +4,12 @@
 import numpy as np
 
 
+def test_rot_mat_2d():
+    assert_allclose(angle.rot_mat_2d(0.0),
+                    np.array([[1., 0.],
+                              [0., 1.]]))
+
+
 def test_angle_mod():
     assert_allclose(angle.angle_mod(-4.0), 2.28318531)
     assert(isinstance(angle.angle_mod(-4.0), float))
diff --git a/utils/angle.py b/utils/angle.py
index 741304f00e..c9e02c5f2e 100644
--- a/utils/angle.py
+++ b/utils/angle.py
@@ -2,9 +2,9 @@
 from scipy.spatial.transform import Rotation as Rot
 
 
-def create_2d_rotation_matrix(angle):
+def rot_mat_2d(angle):
     """
-    Create 2D totation matrix from an angle
+    Create 2D rotation matrix from an angle
 
     Parameters
     ----------
@@ -12,6 +12,12 @@ def create_2d_rotation_matrix(angle):
 
     Returns
     -------
+    A 2D rotation matrix
+
+    Examples
+    --------
+    >>> angle_mod(-4.0)
+
 
     """
     return Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
diff --git a/utils/plot.py b/utils/plot.py
new file mode 100644
index 0000000000..2c70a60bbe
--- /dev/null
+++ b/utils/plot.py
@@ -0,0 +1,49 @@
+"""
+Matplotlib based plotting utilities
+"""
+import math
+import matplotlib.pyplot as plt
+
+
+def plot_arrow(x, y, yaw, arrow_length=1.0,
+               origin_point_plot_style="xr",
+               head_width=0.1, fc="r", ec="k", **kwargs):
+    """
+    Plot an arrow or arrows based on 2D state (x, y, yaw)
+
+    All optional settings of matplotlib.pyplot.arrow can be used.
+    - matplotlib.pyplot.arrow:
+    https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.arrow.html
+
+    Parameters
+    ----------
+    x : a float or array_like
+        a value or a list of arrow origin x position.
+    y : a float or array_like
+        a value or a list of arrow origin y position.
+    yaw : a float or array_like
+        a value or a list of arrow yaw angle (orientation).
+    arrow_length : a float (optional)
+        arrow length. default is 1.0
+    origin_point_plot_style : str (optional)
+        origin point plot style. If None, not plotting.
+    head_width : a float (optional)
+        arrow head width. default is 0.1
+    fc : string (optional)
+        face color
+    ec : string (optional)
+        edge color
+    """
+    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, head_width=head_width,
+                       fc=fc, ec=ec, **kwargs)
+    else:
+        plt.arrow(x, y,
+                  arrow_length * math.cos(yaw),
+                  arrow_length * math.sin(yaw),
+                  head_width=head_width,
+                  fc=fc, ec=ec,
+                  **kwargs)
+        if origin_point_plot_style is not None:
+            plt.plot(x, y, origin_point_plot_style)

From dc879da7b245cb70f229ca1f2bd1239dfb7bf831 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 22 May 2022 16:53:14 +0900
Subject: [PATCH 154/604] Using utility rot_mat_2d (#683)

---
 .../ensemble_kalman_filter.py                     | 11 +++++++----
 .../extended_kalman_filter.py                     | 11 +++++++----
 Localization/particle_filter/particle_filter.py   | 13 ++++++++-----
 .../unscented_kalman_filter.py                    | 10 +++++++---
 Mapping/rectangle_fitting/rectangle_fitting.py    | 11 ++++++++---
 Mapping/rectangle_fitting/simulator.py            | 15 ++++++---------
 .../grid_based_sweep_coverage_path_planner.py     | 15 ++++++++-------
 PathPlanning/HybridAStar/car.py                   |  9 +++++++--
 PathPlanning/InformedRRTStar/informed_rrt_star.py |  9 ++++++---
 SLAM/GraphBasedSLAM/graph_based_slam.py           |  9 ++++++---
 10 files changed, 70 insertions(+), 43 deletions(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 8ef2089b01..1e8d1fc6d6 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -10,11 +10,15 @@
 
 """
 
-import math
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
+import math
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 #  Simulation parameter
 Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2
@@ -168,8 +172,7 @@ 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[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
+    fx = np.stack([x, y]).T @ rot_mat_2d(angle)
 
     px = np.array(fx[:, 0] + xEst[0, 0]).flatten()
     py = np.array(fx[:, 1] + xEst[1, 0]).flatten()
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 51e8f22f4d..a5dea19fd8 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -6,11 +6,15 @@
 
 """
 
-import math
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
+import math
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 # Covariance for EKF simulation
 Q = np.diag([
@@ -149,8 +153,7 @@ 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(eigvec[1, bigind], eigvec[0, bigind])
-    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
-    fx = rot @ (np.array([x, y]))
+    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")
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 002718f028..1c45687ed1 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -5,12 +5,16 @@
 author: Atsushi Sakai (@Atsushi_twi)
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import math
 
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 # Estimation parameter of PF
 Q = np.diag([0.2]) ** 2  # range error
@@ -189,10 +193,9 @@ 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[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()
-    py = np.array(fx[1, :] + x_est[1, 0]).flatten()
+    fx = rot_mat_2d(angle) @ 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")
 
 
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 7f80bccf51..4f42cfd5d1 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -6,13 +6,18 @@
 
 """
 
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
 import math
 
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
 import scipy.linalg
 
+from utils.angle import rot_mat_2d
+
 # Covariance for UKF simulation
 Q = np.diag([
     0.1,  # variance of location on x-axis
@@ -182,8 +187,7 @@ 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(eigvec[1, bigind], eigvec[0, bigind])
-    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
-    fx = rot @ np.array([x, y])
+    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")
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 078916f3b8..6d3e63f0f3 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -12,11 +12,17 @@
 
 """
 
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
+
 import matplotlib.pyplot as plt
 import numpy as np
 import itertools
 from enum import Enum
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 from Mapping.rectangle_fitting.simulator \
     import VehicleSimulator, LidarSimulator
@@ -104,8 +110,7 @@ def _rectangle_search(self, x, y):
         min_cost = (-float('inf'), None)
         for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta):
 
-            rot = Rot.from_euler('z', theta).as_matrix()[0:2, 0:2]
-            c = X @ rot
+            c = X @ rot_mat_2d(theta)
             c1 = c[:, 0]
             c2 = c[:, 1]
 
diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py
index c7c3141fa9..e75738c3b1 100644
--- a/Mapping/rectangle_fitting/simulator.py
+++ b/Mapping/rectangle_fitting/simulator.py
@@ -5,12 +5,16 @@
 author: Atsushi Sakai
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import numpy as np
 import matplotlib.pyplot as plt
 import math
 import random
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 
 class VehicleSimulator:
@@ -41,8 +45,7 @@ def plot(self):
         plt.plot(gx, gy, "--b")
 
     def calc_global_contour(self):
-        rot = Rot.from_euler('z', self.yaw).as_matrix()[0:2, 0:2]
-        gxy = np.stack([self.vc_x, self.vc_y]).T @ rot
+        gxy = np.stack([self.vc_x, self.vc_y]).T @ rot_mat_2d(self.yaw)
         gx = gxy[:, 0] + self.x
         gy = gxy[:, 1] + self.y
 
@@ -137,11 +140,5 @@ def ray_casting_filter(theta_l, range_l, angle_resolution):
         return rx, ry
 
 
-def main():
-    print("start!!")
-
-    print("done!!")
-
-
 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 c0c8fc4319..2f4d32b187 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -4,15 +4,18 @@
 author: Atsushi Sakai
 """
 
-import math
-import os
 import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
+import math
 from enum import IntEnum
 
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
 import matplotlib.pyplot as plt
 
+from utils.angle import rot_mat_2d
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../../Mapping/")
 
@@ -148,16 +151,14 @@ 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
+    converted_xy = np.stack([tx, ty]).T @ rot_mat_2d(th)
 
     return converted_xy[:, 0], converted_xy[:, 1]
 
 
 def convert_global_coordinate(x, y, sweep_vec, sweep_start_position):
     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([x, y]).T @ rot
+    converted_xy = np.stack([x, y]).T @ rot_mat_2d(-th)
     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
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 14b7f956b7..870dd18535 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -6,11 +6,16 @@
 
 """
 
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
 from math import cos, sin, tan, pi
 
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 WB = 3.0  # rear to front wheel
 W = 2.0  # width of car
@@ -45,7 +50,7 @@ def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree):
 
 def rectangle_check(x, y, yaw, ox, oy):
     # transform obstacles to base link frame
-    rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2]
+    rot = rot_mat_2d(yaw)
     for iox, ioy in zip(ox, oy):
         tx = iox - x
         ty = ioy - y
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index baf7dcce91..10af3a81bd 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -9,15 +9,19 @@
 https://arxiv.org/pdf/1404.2334.pdf
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import copy
 import math
 import random
 
 import matplotlib.pyplot as plt
-from scipy.spatial.transform import Rotation as Rot
 import numpy as np
 
+from utils.angle import rot_mat_2d
+
 show_animation = True
 
 
@@ -308,8 +312,7 @@ def plot_ellipse(xCenter, cBest, cMin, e_theta):  # pragma: no cover
         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]
-        rot = Rot.from_euler('z', -angle).as_matrix()[0:2, 0:2]
-        fx = rot @ np.array([x, y])
+        fx = rot_mat_2d(-angle) @ 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/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index 9335dbe6a1..0827732cd8 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -10,6 +10,9 @@
 (http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf)
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import copy
 import itertools
@@ -63,7 +66,7 @@ def cal_observation_sigma():
     return sigma
 
 
-def calc_rotational_matrix(angle):
+def calc_3d_rotational_matrix(angle):
     return Rot.from_euler('z', angle).as_matrix()
 
 
@@ -82,8 +85,8 @@ def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
     edge.e[1, 0] = y2 - y1 - tmp3 + tmp4
     edge.e[2, 0] = 0
 
-    Rt1 = calc_rotational_matrix(tangle1)
-    Rt2 = calc_rotational_matrix(tangle2)
+    Rt1 = calc_3d_rotational_matrix(tangle1)
+    Rt2 = calc_3d_rotational_matrix(tangle2)
 
     sig1 = cal_observation_sigma()
     sig2 = cal_observation_sigma()

From a1782fdea2787cb25105b04e9b6772c2b615c34b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 May 2022 07:34:32 +0900
Subject: [PATCH 155/604] Bump numpy from 1.22.3 to 1.22.4 in /requirements
 (#685)

Bumps [numpy](https://github.com/numpy/numpy) from 1.22.3 to 1.22.4.
- [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/v1.22.3...v1.22.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 d404da757e..7041485cef 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.22.3
+numpy == 1.22.4
 scipy == 1.8.0
 pandas == 1.4.2
 matplotlib == 3.5.2

From ddc396f402044dad00d57da022d675ace544c482 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 May 2022 08:10:20 +0900
Subject: [PATCH 156/604] Bump scipy from 1.8.0 to 1.8.1 in /requirements
 (#686)

Bumps [scipy](https://github.com/scipy/scipy) from 1.8.0 to 1.8.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.8.0...v1.8.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 7041485cef..75d9fc4249 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.22.4
-scipy == 1.8.0
+scipy == 1.8.1
 pandas == 1.4.2
 matplotlib == 3.5.2
 cvxpy == 1.2.1

From 4fffd61e70e2a18bcdb1f597fe4f533cc0928191 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 31 May 2022 07:26:05 +0900
Subject: [PATCH 157/604] Bump mypy from 0.950 to 0.960 in /requirements (#689)

Bumps [mypy](https://github.com/python/mypy) from 0.950 to 0.960.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.950...v0.960)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 75d9fc4249..d07012f977 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.950 # For unit test
+mypy == 0.960 # For unit test
 flake8 == 4.0.1 # For unit test

From 71de5d038f348d347d7b5dc00c914d523cd59f92 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Jun 2022 07:35:05 +0900
Subject: [PATCH 158/604] Bump mypy from 0.960 to 0.961 in /requirements (#691)

Bumps [mypy](https://github.com/python/mypy) from 0.960 to 0.961.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.960...v0.961)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 d07012f977..ac5f3be2d3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.960 # For unit test
+mypy == 0.961 # For unit test
 flake8 == 4.0.1 # For unit test

From 8a0aab89a966ce411034da199ab0ac7042414d97 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jun 2022 07:22:03 +0900
Subject: [PATCH 159/604] Bump numpy from 1.22.4 to 1.23.0 in /requirements
 (#692)

Bumps [numpy](https://github.com/numpy/numpy) from 1.22.4 to 1.23.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.22.4...v1.23.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 ac5f3be2d3..9d3ca6847e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.22.4
+numpy == 1.23.0
 scipy == 1.8.1
 pandas == 1.4.2
 matplotlib == 3.5.2

From 8a8f4f7434bf786a5f190139cc3cef19c4756cdd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jun 2022 20:41:25 +0900
Subject: [PATCH 160/604] Bump pandas from 1.4.2 to 1.4.3 in /requirements
 (#693)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.2 to 1.4.3.
- [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/v1.4.2...v1.4.3)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 9d3ca6847e..87e09cd9c0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.0
 scipy == 1.8.1
-pandas == 1.4.2
+pandas == 1.4.3
 matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test

From 6d97a8f7f0bfe70e312fadbe593739c6ecfe2f3c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Jul 2022 07:31:57 +0900
Subject: [PATCH 161/604] Bump numpy from 1.23.0 to 1.23.1 in /requirements
 (#694)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.0 to 1.23.1.
- [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/v1.23.0...v1.23.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 87e09cd9c0..9130a07b6a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.0
+numpy == 1.23.1
 scipy == 1.8.1
 pandas == 1.4.3
 matplotlib == 3.5.2

From c6bdd48715adcbe17c4146b7cae3b0fc569f7bde Mon Sep 17 00:00:00 2001
From: Tim Gates <tim.gates@iress.com>
Date: Tue, 19 Jul 2022 08:22:09 +1000
Subject: [PATCH 162/604] docs: Fix a few typos (#695)

There are small typos in:
- ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
- ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
- docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
- docs/modules/slam/ekf_slam/ekf_slam_main.rst

Fixes:
- Should read `configuration` rather than `configuation`.
- Should read `trajectory` rather than `tracjectory`.
- Should read `prediction` rather than `prediciton`.

Signed-off-by: Tim Gates <tim.gates@iress.com>
---
 .../arm_obstacle_navigation/arm_obstacle_navigation.py          | 2 +-
 .../arm_obstacle_navigation/arm_obstacle_navigation_2.py        | 2 +-
 docs/modules/slam/FastSLAM1/FastSLAM1_main.rst                  | 2 +-
 docs/modules/slam/ekf_slam/ekf_slam_main.rst                    | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
index f3bd25577c..75b1f9d2c4 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
@@ -105,7 +105,7 @@ def astar_torus(grid, start_node, goal_node):
 
     Args:
         grid: An occupancy grid (ndarray)
-        start_node: Initial joint configuation (tuple)
+        start_node: Initial joint configuration (tuple)
         goal_node: Goal joint configuration (tuple)
 
     Returns:
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index 429cd4d211..40e6746bfc 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -136,7 +136,7 @@ def astar_torus(grid, start_node, goal_node):
 
     Args:
         grid: An occupancy grid (ndarray)
-        start_node: Initial joint configuation (tuple)
+        start_node: Initial joint configuration (tuple)
         goal_node: Goal joint configuration (tuple)
 
     Returns:
diff --git a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
index f09125dff0..f19527c453 100644
--- a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
+++ b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
@@ -39,7 +39,7 @@ an array of landmark locations
 -  The blue line is the true trajectory
 -  The red line is the estimated trajectory
 -  The red dots represent the distribution of particles
--  The black line represent dead reckoning tracjectory
+-  The black line represent dead reckoning trajectory
 -  The blue x is the observed and estimated landmarks
 -  The black x is the true landmark
 
diff --git a/docs/modules/slam/ekf_slam/ekf_slam_main.rst b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
index 5a23b4c887..70a7d131ae 100644
--- a/docs/modules/slam/ekf_slam/ekf_slam_main.rst
+++ b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
@@ -151,7 +151,7 @@ values incorporate the error into the estimation.
 
 :math:`\begin{equation*} X_{DR} = FX + B(U + R) \end{equation*}`
 
-The implementation of the motion model prediciton code is shown in
+The implementation of the motion model prediction code is shown in
 ``motion_model``. The ``observation`` function shows how the simulation
 uses (or doesn’t use) the process noise ``Rsim`` to the find the ground
 truth and dead reckoning estimates of the pose.

From def289b723e9216830c2a7b2577cb31b55710167 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 24 Jul 2022 14:58:14 +0900
Subject: [PATCH 163/604] enhance cubic spline path doc (#698)

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc
---
 .../CubicSpline/cubic_spline_planner.py       | 276 ++++++++++++++----
 PathPlanning/CubicSpline/spline_continuity.py |  55 ++++
 .../frenet_optimal_trajectory.py              |   2 +-
 docs/conf.py                                  |   3 +-
 .../path_planning/cubic_spline/Figure_1.png   | Bin 26172 -> 0 bytes
 .../cubic_spline/cubic_spline_1d.png          | Bin 0 -> 25173 bytes
 ...re_3.png => cubic_spline_2d_curvature.png} | Bin
 .../cubic_spline/cubic_spline_2d_path.png     | Bin 0 -> 28480 bytes
 .../{Figure_2.png => cubic_spline_2d_yaw.png} | Bin
 .../cubic_spline/cubic_spline_main.rst        | 214 +++++++++++++-
 .../path_planning/cubic_spline/spline.png     | Bin 0 -> 22116 bytes
 .../cubic_spline/spline_continuity.png        | Bin 0 -> 48278 bytes
 12 files changed, 478 insertions(+), 72 deletions(-)
 create mode 100644 PathPlanning/CubicSpline/spline_continuity.py
 delete mode 100644 docs/modules/path_planning/cubic_spline/Figure_1.png
 create mode 100644 docs/modules/path_planning/cubic_spline/cubic_spline_1d.png
 rename docs/modules/path_planning/cubic_spline/{Figure_3.png => cubic_spline_2d_curvature.png} (100%)
 create mode 100644 docs/modules/path_planning/cubic_spline/cubic_spline_2d_path.png
 rename docs/modules/path_planning/cubic_spline/{Figure_2.png => cubic_spline_2d_yaw.png} (100%)
 create mode 100644 docs/modules/path_planning/cubic_spline/spline.png
 create mode 100644 docs/modules/path_planning/cubic_spline/spline_continuity.png

diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py
index d2ef8622ad..7cc7bedc79 100644
--- a/PathPlanning/CubicSpline/cubic_spline_planner.py
+++ b/PathPlanning/CubicSpline/cubic_spline_planner.py
@@ -9,87 +9,133 @@
 import bisect
 
 
-class Spline:
+class CubicSpline1D:
     """
-    Cubic Spline class
+    1D Cubic Spline class
+
+    Parameters
+    ----------
+    x : list
+        x coordinates for data points. This x coordinates must be
+        sorted
+        in ascending order.
+    y : list
+        y coordinates for data points
+
+    Examples
+    --------
+    You can interpolate 1D data points.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> x = np.arange(5)
+    >>> y = [1.7, -6, 5, 6.5, 0.0]
+    >>> sp = CubicSpline1D(x, y)
+    >>> xi = np.linspace(0.0, 5.0)
+    >>> yi = [sp.calc_position(x) for x in xi]
+    >>> plt.plot(x, y, "xb", label="Data points")
+    >>> plt.plot(xi, yi , "r", label="Cubic spline interpolation")
+    >>> plt.grid(True)
+    >>> plt.legend()
+    >>> plt.show()
+
+    .. image:: cubic_spline_1d.png
+
     """
 
     def __init__(self, x, y):
-        self.b, self.c, self.d, self.w = [], [], [], []
 
+        h = np.diff(x)
+        if np.any(h < 0):
+            raise ValueError("x coordinates must be sorted in ascending order")
+
+        self.a, self.b, self.c, self.d = [], [], [], []
         self.x = x
         self.y = y
-
         self.nx = len(x)  # dimension of x
-        h = np.diff(x)
 
-        # calc coefficient c
+        # calc coefficient a
         self.a = [iy for iy in y]
 
         # calc coefficient c
         A = self.__calc_A(h)
-        B = self.__calc_B(h)
+        B = self.__calc_B(h, self.a)
         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)
+            d = (self.c[i + 1] - self.c[i]) / (3.0 * h[i])
+            b = 1.0 / h[i] * (self.a[i + 1] - self.a[i]) \
+                - h[i] / 3.0 * (2.0 * self.c[i] + self.c[i + 1])
+            self.d.append(d)
+            self.b.append(b)
 
-    def calc(self, t):
+    def calc_position(self, x):
         """
-        Calc position
+        Calc `y` position for given `x`.
 
-        if t is outside of the input x, return None
+        if `x` is outside the data point's `x` range, return None.
 
+        Returns
+        -------
+        y : float
+            y position for given x.
         """
-
-        if t < self.x[0]:
+        if x < self.x[0]:
             return None
-        elif t > self.x[-1]:
+        elif x > self.x[-1]:
             return None
 
-        i = self.__search_index(t)
-        dx = t - self.x[i]
-        result = self.a[i] + self.b[i] * dx + \
+        i = self.__search_index(x)
+        dx = x - self.x[i]
+        position = self.a[i] + self.b[i] * dx + \
             self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0
 
-        return result
+        return position
 
-    def calcd(self, t):
+    def calc_first_derivative(self, x):
         """
-        Calc first derivative
+        Calc first derivative at given x.
+
+        if x is outside the input x, return None
 
-        if t is outside of the input x, return None
+        Returns
+        -------
+        dy : float
+            first derivative for given x.
         """
 
-        if t < self.x[0]:
+        if x < self.x[0]:
             return None
-        elif t > self.x[-1]:
+        elif x > 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
+        i = self.__search_index(x)
+        dx = x - self.x[i]
+        dy = self.b[i] + 2.0 * self.c[i] * dx + 3.0 * self.d[i] * dx ** 2.0
+        return dy
 
-    def calcdd(self, t):
+    def calc_second_derivative(self, x):
         """
-        Calc second derivative
+        Calc second derivative at given x.
+
+        if x is outside the input x, return None
+
+        Returns
+        -------
+        ddy : float
+            second derivative for given x.
         """
 
-        if t < self.x[0]:
+        if x < self.x[0]:
             return None
-        elif t > self.x[-1]:
+        elif x > 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
+        i = self.__search_index(x)
+        dx = x - self.x[i]
+        ddy = 2.0 * self.c[i] + 6.0 * self.d[i] * dx
+        return ddy
 
     def __search_index(self, x):
         """
@@ -112,30 +158,82 @@ def __calc_A(self, h):
         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):
+    def __calc_B(self, h, a):
         """
         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]
+            B[i + 1] = 3.0 * (a[i + 2] - a[i + 1]) / h[i + 1]\
+                - 3.0 * (a[i + 1] - a[i]) / h[i]
         return B
 
 
-class Spline2D:
+class CubicSpline2D:
     """
-    2D Cubic Spline class
-
+    Cubic CubicSpline2D class
+
+    Parameters
+    ----------
+    x : list
+        x coordinates for data points.
+    y : list
+        y coordinates for data points.
+
+    Examples
+    --------
+    You can interpolate a 2D data points.
+
+    >>> 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]
+    >>> ds = 0.1  # [m] distance of each interpolated points
+    >>> sp = CubicSpline2D(x, y)
+    >>> s = 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))
+    >>> plt.subplots(1)
+    >>> plt.plot(x, y, "xb", label="Data points")
+    >>> plt.plot(rx, ry, "-r", label="Cubic spline path")
+    >>> plt.grid(True)
+    >>> plt.axis("equal")
+    >>> plt.xlabel("x[m]")
+    >>> plt.ylabel("y[m]")
+    >>> plt.legend()
+    >>> plt.show()
+
+    .. image:: cubic_spline_2d_path.png
+
+    >>> 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]")
+
+    .. image:: cubic_spline_2d_yaw.png
+
+    >>> 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]")
+
+    .. image:: cubic_spline_2d_curvature.png
     """
 
     def __init__(self, x, y):
         self.s = self.__calc_s(x, y)
-        self.sx = Spline(self.s, x)
-        self.sy = Spline(self.s, y)
+        self.sx = CubicSpline1D(self.s, x)
+        self.sy = CubicSpline1D(self.s, y)
 
     def __calc_s(self, x, y):
         dx = np.diff(x)
@@ -148,35 +246,70 @@ def __calc_s(self, x, y):
     def calc_position(self, s):
         """
         calc position
+
+        Parameters
+        ----------
+        s : float
+            distance from the start point. if `s` is outside the data point's
+            range, return None.
+
+        Returns
+        -------
+        x : float
+            x position for given s.
+        y : float
+            y position for given s.
         """
-        x = self.sx.calc(s)
-        y = self.sy.calc(s)
+        x = self.sx.calc_position(s)
+        y = self.sy.calc_position(s)
 
         return x, y
 
     def calc_curvature(self, s):
         """
         calc curvature
+
+        Parameters
+        ----------
+        s : float
+            distance from the start point. if `s` is outside the data point's
+            range, return None.
+
+        Returns
+        -------
+        k : float
+            curvature for given s.
         """
-        dx = self.sx.calcd(s)
-        ddx = self.sx.calcdd(s)
-        dy = self.sy.calcd(s)
-        ddy = self.sy.calcdd(s)
+        dx = self.sx.calc_first_derivative(s)
+        ddx = self.sx.calc_second_derivative(s)
+        dy = self.sy.calc_first_derivative(s)
+        ddy = self.sy.calc_second_derivative(s)
         k = (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2))
         return k
 
     def calc_yaw(self, s):
         """
         calc yaw
+
+        Parameters
+        ----------
+        s : float
+            distance from the start point. if `s` is outside the data point's
+            range, return None.
+
+        Returns
+        -------
+        yaw : float
+            yaw angle (tangent vector) for given s.
         """
-        dx = self.sx.calcd(s)
-        dy = self.sy.calcd(s)
+        dx = self.sx.calc_first_derivative(s)
+        dy = self.sy.calc_first_derivative(s)
         yaw = math.atan2(dy, dx)
         return yaw
 
 
 def calc_spline_course(x, y, ds=0.1):
-    sp = Spline2D(x, y)
+    sp = CubicSpline2D(x, y)
     s = list(np.arange(0, sp.s[-1], ds))
 
     rx, ry, ryaw, rk = [], [], [], []
@@ -190,14 +323,30 @@ def calc_spline_course(x, y, ds=0.1):
     return rx, ry, ryaw, rk, s
 
 
-def main():  # pragma: no cover
-    print("Spline 2D test")
+def main_1d():
+    print("CubicSpline1D test")
+    import matplotlib.pyplot as plt
+    x = np.arange(5)
+    y = [1.7, -6, 5, 6.5, 0.0]
+    sp = CubicSpline1D(x, y)
+    xi = np.linspace(0.0, 5.0)
+
+    plt.plot(x, y, "xb", label="Data points")
+    plt.plot(xi, [sp.calc_position(x) for x in xi], "r",
+             label="Cubic spline interpolation")
+    plt.grid(True)
+    plt.legend()
+    plt.show()
+
+
+def main_2d():  # pragma: no cover
+    print("CubicSpline1D 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]
     ds = 0.1  # [m] distance of each interpolated points
 
-    sp = Spline2D(x, y)
+    sp = CubicSpline2D(x, y)
     s = np.arange(0, sp.s[-1], ds)
 
     rx, ry, ryaw, rk = [], [], [], []
@@ -209,8 +358,8 @@ def main():  # pragma: no cover
         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.plot(x, y, "xb", label="Data points")
+    plt.plot(rx, ry, "-r", label="Cubic spline path")
     plt.grid(True)
     plt.axis("equal")
     plt.xlabel("x[m]")
@@ -235,4 +384,5 @@ def main():  # pragma: no cover
 
 
 if __name__ == '__main__':
-    main()
+    # main_1d()
+    main_2d()
diff --git a/PathPlanning/CubicSpline/spline_continuity.py b/PathPlanning/CubicSpline/spline_continuity.py
new file mode 100644
index 0000000000..ea85b37f7c
--- /dev/null
+++ b/PathPlanning/CubicSpline/spline_continuity.py
@@ -0,0 +1,55 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import interpolate
+
+
+class Spline2D:
+
+    def __init__(self, x, y, kind="cubic"):
+        self.s = self.__calc_s(x, y)
+        self.sx = interpolate.interp1d(self.s, x, kind=kind)
+        self.sy = interpolate.interp1d(self.s, y, kind=kind)
+
+    def __calc_s(self, x, y):
+        self.ds = np.hypot(np.diff(x), np.diff(y))
+        s = [0.0]
+        s.extend(np.cumsum(self.ds))
+        return s
+
+    def calc_position(self, s):
+        x = self.sx(s)
+        y = self.sy(s)
+        return x, y
+
+
+def main():
+    x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]
+    y = [0.7, -6, -5, -3.5, 0.0, 5.0, -2.0]
+    ds = 0.1  # [m] distance of each interpolated points
+
+    plt.subplots(1)
+    plt.plot(x, y, "xb", label="Data points")
+
+    for (kind, label) in [("linear", "C0 (Linear spline)"),
+                          ("quadratic", "C0 & C1 (Quadratic spline)"),
+                          ("cubic", "C0 & C1 & C2 (Cubic spline)")]:
+        rx, ry = [], []
+        sp = Spline2D(x, y, kind=kind)
+        s = np.arange(0, sp.s[-1], ds)
+        for i_s in s:
+            ix, iy = sp.calc_position(i_s)
+            rx.append(ix)
+            ry.append(iy)
+        plt.plot(rx, ry, "-", label=label)
+
+    plt.grid(True)
+    plt.axis("equal")
+    plt.xlabel("x[m]")
+    plt.ylabel("y[m]")
+    plt.legend()
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index ad3da20835..ef20af22f0 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -248,7 +248,7 @@ def frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob):
 
 
 def generate_target_course(x, y):
-    csp = cubic_spline_planner.Spline2D(x, y)
+    csp = cubic_spline_planner.CubicSpline2D(x, y)
     s = np.arange(0, csp.s[-1], 0.1)
 
     rx, ry, ryaw, rk = [], [], [], []
diff --git a/docs/conf.py b/docs/conf.py
index 8a3ba1ac2d..24b5518b6f 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -44,6 +44,7 @@
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
     'sphinx.ext.napoleon',
+    'sphinx.ext.imgconverter',
     'IPython.sphinxext.ipython_console_highlighting',
 ]
 
@@ -64,7 +65,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
diff --git a/docs/modules/path_planning/cubic_spline/Figure_1.png b/docs/modules/path_planning/cubic_spline/Figure_1.png
deleted file mode 100644
index 13eea2122d8f0281c25ee8ed33d463e98fa9d853..0000000000000000000000000000000000000000
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diff --git a/docs/modules/path_planning/cubic_spline/Figure_2.png b/docs/modules/path_planning/cubic_spline/cubic_spline_2d_yaw.png
similarity index 100%
rename from docs/modules/path_planning/cubic_spline/Figure_2.png
rename to docs/modules/path_planning/cubic_spline/cubic_spline_2d_yaw.png
diff --git a/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
index 6de0297a05..33471f7c85 100644
--- a/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
+++ b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
@@ -1,13 +1,213 @@
 Cubic spline planning
 ---------------------
 
-A sample code for cubic path planning.
+Spline curve continuity
+~~~~~~~~~~~~~~~~~~~~~~~~
 
-This code generates a curvature continuous path based on x-y waypoints
-with cubic spline.
+Spline curve smoothness is depending on the which kind of spline model is used.
+
+The smoothness of the spline curve is expressed as :math:`C_0, C_1`, and so on.
+
+This representation represents continuity of the curve.
+For example, for a spline curve in two-dimensional space:
+
+- :math:`C_0` is position continuous
+- :math:`C_1` is tangent vector continuous
+- :math:`C_2` is curvature vector continuous
+
+as shown in the following figure:
+
+.. image:: spline_continuity.png
+
+Cubic spline can generate a curve with :math:`C_0, C_1, C_2`.
+
+1D cubic spline
+~~~~~~~~~~~~~~~~~~~
+
+Cubic spline interpolation is a method of smoothly
+interpolating between multiple data points when given
+multiple data points, as shown in the figure below.
+
+.. image:: spline.png
+
+It separates between each interval between data points.
+
+The each interval part is approximated by each cubic polynomial.
+
+The cubic spline uses the cubic polynomial equation for interpolation:
+
+:math:`S_j(x)=a_j+b_j(x-x_j)+c_j(x-x_j)^2+d_j(x-x_j)^3`
+
+where :math:`x_j < x < x_{j+1}`, :math:`x_j` is the j-th node of the spline,
+:math:`a_j`, :math:`b_j`, :math:`c_j`, :math:`d_j` are the coefficients
+of the spline.
+
+As the above equation, there are 4 unknown parameters :math:`(a,b,c,d)` for
+one interval, so if the number of data points is :math:`N`, the
+interpolation has :math:`4N` unknown parameters.
+
+The following five conditions are used to determine the :math:`4N`
+unknown parameters:
+
+Constraint 1: Terminal constraints
+===================================
+
+:math:`S_j(x_j)=y_j`
+
+This constraint is the terminal constraint of each interval.
+
+The polynomial of each interval will pass through the x,y coordinates of
+the data points.
+
+Constraint 2: Point continuous constraints
+============================================
+
+:math:`S_j(x_{j+1})=S_{j+1}(x_{j+1})=y_{j+1}`
+
+This constraint is a continuity condition for the boundary of each interval.
+
+This constraint ensures that the boundary of each interval is continuous.
+
+Constraint 3: Tangent vector continuous constraints
+====================================================
+
+:math:`S'_j(x_{j+1})=S'_{j+1}(x_{j+1})`
+
+This constraint is a continuity condition for the first derivative of
+the boundary of each interval.
+
+This constraint makes the vectors of the boundaries of each
+interval continuous.
+
+
+Constraint 4: Curvature continuous constraints
+==============================================
+
+:math:`S''_j(x_{j+1})=S''_{j+1}(x_{j+1})`
+
+This constraint is the continuity condition for the second derivative of
+the boundary of each interval.
+
+This constraint makes the curvature of the boundaries of each
+interval continuous.
+
+
+Constraint 5: Terminal curvature constraints
+========================================================
+
+:math:`S''_0(0)=S''_{n+1}(x_{n})=0`.
+
+The constraint is a boundary condition for the second derivative of the starting and ending points.
+
+Our sample code assumes these terminal curvatures are 0, which is well known as Natural Cubic Spline.
+
+How to calculate the unknown parameters :math:`a_j, b_j, c_j, d_j`
+===================================================================
+
+Step1: calculate :math:`a_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`a_j` can be calculated by y positions of the data points:
+
+:math:`a_j = y_i`.
+
+Step2: calculate :math:`c_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`c_j` can be calculated by solving the linear equation:
+
+:math:`Ac_j = B`.
+
+The matrix :math:`A` and :math:`B` are defined as follows:
+
+.. math::
+	A=\left[\begin{array}{cccccc}
+	1 & 0 & 0 & 0 & \cdots & 0 \\
+	h_{0} & 2\left(h_{0}+h_{1}\right) & h_{1} & 0 & \cdots & 0 \\
+	0 & h_{1} & 2\left(h_{1}+h_{2}\right) & h_{2} & \cdots & 0 \\
+	0 & 0 & h_{2} & 2\left(h_{2}+h_{3}\right) & \cdots & 0 \\
+	0 & 0 & 0 & h_{3} & \ddots & \\
+	\vdots & \vdots & & & & \\
+	0 & 0 & 0 & \cdots & 0 & 1
+	\end{array}\right]
+
+.. math::
+	B=\left[\begin{array}{c}
+	0 \\
+	\frac{3}{h_{1}}\left(a_{2}-a_{1}\right)-\frac{3}{h_{0}}\left(a_{1}-a_{0}\right) \\
+	\vdots \\
+	\frac{3}{h_{n-1}}\left(a_{n}-a_{n-1}\right)-\frac{3}{h_{n-2}}\left(a_{n-1}-a_{n-2}\right) \\
+	0
+	\end{array}\right]
+
+where :math:`h_{i}` is the x position distance between the i-th and (i+1)-th data points.
+
+Step3: calculate :math:`d_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`d_j` can be calculated by the following equation:
+
+:math:`d_{j}=\frac{c_{j+1}-c_{j}}{3 h_{j}}`
+
+Step4: calculate :math:`b_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`b_j` can be calculated by the following equation:
+
+:math:`b_{i}=\frac{1}{h_i}(a_{i+1}-a_{i})-\frac{h_i}{3}(2c_{i}+c_{i+1})`
+
+How to calculate the first and second derivatives of the spline curve
+======================================================================
+
+After we can get the coefficients of the spline curve, we can calculate
+
+the first derivative by:
+
+:math:`y^{\prime}(x)=b+2cx+3dx^2`
+
+the second derivative by:
+
+:math:`y^{\prime \prime}(x)=2c+6dx`
+
+These equations can be calculated by differentiating the cubic polynomial.
+
+API
+===
+
+This is the 1D cubic spline class API:
+
+.. autoclass:: PathPlanning.CubicSpline.cubic_spline_planner.CubicSpline1D
+	:members:
+
+2D cubic spline
+~~~~~~~~~~~~~~~~~~~
+
+Data x positions needs to be mono-increasing for 1D cubic spline.
+
+So, it cannot be used for 2D path planning.
+
+2D cubic spline uses two 1D cubic splines based on path distance from
+the start point for each dimension x and y.
+
+This can generate a curvature continuous path based on x-y waypoints.
+
+Heading angle of each point can be calculated analytically by:
+
+:math:`\theta=\tan ^{-1} \frac{y’}{x’}`
+
+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
+===
+
+.. autoclass:: PathPlanning.CubicSpline.cubic_spline_planner.CubicSpline2D
+	:members:
+
+References
+~~~~~~~~~~
+-  `Cubic Splines James Keesling <https://people.clas.ufl.edu/kees/files/CubicSplines.pdf>`__
+-  `Curves and Splines <http://www.cs.cmu.edu/afs/cs/academic/class/15462-s10/www/lec-slides/lec06.pdf>`__
 
-Heading angle of each point can be also calculated analytically.
 
-.. image:: Figure_1.png
-.. image:: Figure_2.png
-.. image:: Figure_3.png
diff --git a/docs/modules/path_planning/cubic_spline/spline.png b/docs/modules/path_planning/cubic_spline/spline.png
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QknroM)^W`;b(_%t0uq{|egFUf

literal 0
HcmV?d00001


From bccf816cca09afd075aca4613f2984f0ba28f07e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Jul 2022 07:35:53 +0900
Subject: [PATCH 164/604] Bump mypy from 0.961 to 0.971 in /requirements (#699)

Bumps [mypy](https://github.com/python/mypy) from 0.961 to 0.971.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.961...v0.971)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 9130a07b6a..ee0f1a4090 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.961 # For unit test
+mypy == 0.971 # For unit test
 flake8 == 4.0.1 # For unit test

From 76d06e0b06e6e2344a73fdcc8f4388635cf06593 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Aug 2022 07:17:28 +0900
Subject: [PATCH 165/604] Bump scipy from 1.8.1 to 1.9.0 in /requirements
 (#702)

Bumps [scipy](https://github.com/scipy/scipy) from 1.8.1 to 1.9.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.8.1...v1.9.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

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 ee0f1a4090..49f028f7d0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.23.1
-scipy == 1.8.1
+scipy == 1.9.0
 pandas == 1.4.3
 matplotlib == 3.5.2
 cvxpy == 1.2.1

From 48a31af8e2a7d947683f4f14c3b5b853fbe1a69c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Aug 2022 07:17:47 +0900
Subject: [PATCH 166/604] Bump flake8 from 4.0.1 to 5.0.2 in /requirements
 (#703)

Bumps [flake8](https://github.com/pycqa/flake8) from 4.0.1 to 5.0.2.
- [Release notes](https://github.com/pycqa/flake8/releases)
- [Commits](https://github.com/pycqa/flake8/compare/4.0.1...5.0.2)

---
updated-dependencies:
- dependency-name: flake8
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

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 49f028f7d0..abf9dc31fb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.971 # For unit test
-flake8 == 4.0.1 # For unit test
+flake8 == 5.0.2 # For unit test

From 7716bdd2485dd0e39a388f5f5d417e70e0f3cefe Mon Sep 17 00:00:00 2001
From: Jeff Irion <JeffLIrion@users.noreply.github.com>
Date: Wed, 3 Aug 2022 05:21:58 -0700
Subject: [PATCH 167/604] Fix `upper_triangular_matrix_to_full_matrix` (#704)

---
 SLAM/GraphBasedSLAM/graphslam/util.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/SLAM/GraphBasedSLAM/graphslam/util.py b/SLAM/GraphBasedSLAM/graphslam/util.py
index 8f7c8e174b..619aff20af 100644
--- a/SLAM/GraphBasedSLAM/graphslam/util.py
+++ b/SLAM/GraphBasedSLAM/graphslam/util.py
@@ -68,11 +68,10 @@ def upper_triangular_matrix_to_full_matrix(arr, n):
 
     """
     triu0 = np.triu_indices(n, 0)
-    triu1 = np.triu_indices(n, 1)
     tril1 = np.tril_indices(n, -1)
 
     mat = np.zeros((n, n), dtype=float)
     mat[triu0] = arr
-    mat[tril1] = mat[triu1]
+    mat[tril1] = mat.T[tril1]
 
     return mat

From f40b750ccab627d66570086fa2fd3a86c91b90d7 Mon Sep 17 00:00:00 2001
From: jsbyysheng <jsbyysheng@gmail.com>
Date: Sun, 7 Aug 2022 13:19:47 +0800
Subject: [PATCH 168/604] fix bugs in check a position inside the polygon
 (#701)

* fix bugs in check a position inside the polygon

It is possible that the position `(iox, ioy)` to be checked with `iox == min_x`. This will cause a incorrect inside check.

* add a boundary condition for gridmap
---
 Mapping/grid_map_lib/grid_map_lib.py | 6 +++---
 tests/test_grid_map_lib.py           | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index a93ea309d8..85dd76e71d 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -194,7 +194,7 @@ def check_inside_polygon(iox, ioy, x, y):
                 min_x, max_x = x[i2], x[i1]
             else:
                 min_x, max_x = x[i1], x[i2]
-            if not min_x < iox < max_x:
+            if not min_x <= iox < max_x:
                 continue
 
             tmp1 = (y[i2] - y[i1]) / (x[i2] - x[i1])
@@ -226,8 +226,8 @@ def plot_grid_map(self, ax=None):
 
 
 def test_polygon_set():
-    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
-    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
+    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)
 
diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
index ed4303d4dc..8ae142b003 100644
--- a/tests/test_grid_map_lib.py
+++ b/tests/test_grid_map_lib.py
@@ -14,8 +14,8 @@ def test_position_set():
 
 
 def test_polygon_set():
-    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
-    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
+    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)
 

From 51e5a66b4d0c9e1529af1737e546becb80179c8c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Aug 2022 08:12:04 +0900
Subject: [PATCH 169/604] Bump flake8 from 5.0.2 to 5.0.4 in /requirements
 (#706)

Bumps [flake8](https://github.com/pycqa/flake8) from 5.0.2 to 5.0.4.
- [Release notes](https://github.com/pycqa/flake8/releases)
- [Commits](https://github.com/pycqa/flake8/compare/5.0.2...5.0.4)

---
updated-dependencies:
- dependency-name: flake8
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

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 abf9dc31fb..2268e0472b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.971 # For unit test
-flake8 == 5.0.2 # For unit test
+flake8 == 5.0.4 # For unit test

From cdf5637a8dd40603f78ff0791fb555947a216c77 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Aug 2022 07:15:49 +0900
Subject: [PATCH 170/604] Bump numpy from 1.23.1 to 1.23.2 in /requirements
 (#708)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.1 to 1.23.2.
- [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/v1.23.1...v1.23.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 2268e0472b..85ff9f8d73 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.1
+numpy == 1.23.2
 scipy == 1.9.0
 pandas == 1.4.3
 matplotlib == 3.5.2

From ba0a5106d9c35600504bc338cfc4746247485956 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Aug 2022 07:16:16 +0900
Subject: [PATCH 171/604] Bump matplotlib from 3.5.2 to 3.5.3 in /requirements
 (#707)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.2 to 3.5.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.2...v3.5.3)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 85ff9f8d73..d401546e6c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.23.2
 scipy == 1.9.0
 pandas == 1.4.3
-matplotlib == 3.5.2
+matplotlib == 3.5.3
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 46b6981f34dd783132ea5a23b46af69dc6888024 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Aug 2022 07:31:42 +0900
Subject: [PATCH 172/604] Bump scipy from 1.9.0 to 1.9.1 in /requirements
 (#714)

Bumps [scipy](https://github.com/scipy/scipy) from 1.9.0 to 1.9.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.9.0...v1.9.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 d401546e6c..6aee1a3d9e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.23.2
-scipy == 1.9.0
+scipy == 1.9.1
 pandas == 1.4.3
 matplotlib == 3.5.3
 cvxpy == 1.2.1

From def001ea209388e6dd106c7fdf329f9a32e37188 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Sep 2022 07:16:50 +0900
Subject: [PATCH 173/604] Bump pytest from 7.1.2 to 7.1.3 in /requirements
 (#717)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.2 to 7.1.3.
- [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/7.1.2...7.1.3)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 6aee1a3d9e..2d5ecbc470 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.9.1
 pandas == 1.4.3
 matplotlib == 3.5.3
 cvxpy == 1.2.1
-pytest == 7.1.2 # For unit test
+pytest == 7.1.3 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.971 # For unit test
 flake8 == 5.0.4 # For unit test

From 1ff3095c14817835450d6788b9adc09b4dc4cfa2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Sep 2022 07:17:15 +0900
Subject: [PATCH 174/604] Bump pandas from 1.4.3 to 1.4.4 in /requirements
 (#718)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.3 to 1.4.4.
- [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/v1.4.3...v1.4.4)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 2d5ecbc470..615377611b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.2
 scipy == 1.9.1
-pandas == 1.4.3
+pandas == 1.4.4
 matplotlib == 3.5.3
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test

From 76b0d04a3ce61f6a1935e496ea43c809fb247097 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 11 Sep 2022 07:21:37 +0900
Subject: [PATCH 175/604] using pathlib based local module import (#722)

* using pathlib based local module import

* remove unused import
---
 .../drone_3d_trajectory_following/__init__.py |  5 ++--
 ArmNavigation/n_joint_arm_3d/__init__.py      |  4 +--
 .../n_joint_arm_to_point_control.py           |  2 +-
 .../rrt_star_seven_joint_arm_control.py       | 13 ++++-----
 .../ensemble_kalman_filter.py                 |  5 ++--
 .../extended_kalman_filter.py                 |  5 ++--
 .../particle_filter/particle_filter.py        |  4 +--
 .../unscented_kalman_filter.py                |  4 +--
 Mapping/rectangle_fitting/__init_.py          |  5 ++--
 .../rectangle_fitting/rectangle_fitting.py    |  5 ++--
 Mapping/rectangle_fitting/simulator.py        |  4 +--
 .../closed_loop_rrt_star_car.py               | 24 +++++----------
 .../ClosedLoopRRTStar/pure_pursuit.py         |  6 +++-
 .../DubinsPath/dubins_path_planner.py         |  5 ++--
 .../eta3_spline_trajectory.py                 | 12 +++-----
 .../frenet_optimal_trajectory.py              | 18 ++++--------
 .../grid_based_sweep_coverage_path_planner.py | 17 +++--------
 PathPlanning/HybridAStar/car.py               |  7 +++--
 PathPlanning/HybridAStar/hybrid_a_star.py     | 18 ++++--------
 .../InformedRRTStar/informed_rrt_star.py      |  4 +--
 .../{LQRplanner.py => lqr_planner.py}         |  0
 PathPlanning/LQRRRTStar/lqr_rrt_star.py       | 16 ++++------
 ...y_generator.py => trajectory_generator.py} |  7 +++--
 PathPlanning/RRT/rrt_with_pathsmoothing.py    | 13 +++------
 PathPlanning/RRT/rrt_with_sobol_sampler.py    |  5 ++--
 PathPlanning/RRTDubins/rrt_dubins.py          | 29 +++++++------------
 PathPlanning/RRTStar/rrt_star.py              | 12 +++-----
 PathPlanning/RRTStarDubins/rrt_star_dubins.py | 27 ++++++-----------
 .../RRTStarReedsShepp/rrt_star_reeds_shepp.py | 16 +++-------
 .../state_lattice_planner.py                  | 23 +++++++--------
 .../VisibilityRoadMap/visibility_road_map.py  | 10 +++----
 PathPlanning/VoronoiRoadMap/__init__.py       |  4 ---
 .../VoronoiRoadMap/voronoi_road_map.py        |  6 +++-
 .../lqr_speed_steer_control.py                | 12 ++------
 .../lqr_steer_control/lqr_steer_control.py    | 14 +++------
 ...odel_predictive_speed_and_steer_control.py | 12 ++------
 .../stanley_controller/stanley_controller.py  | 12 ++------
 SLAM/GraphBasedSLAM/graph_based_slam.py       |  4 +--
 tests/test_LQR_planner.py                     |  2 +-
 39 files changed, 144 insertions(+), 247 deletions(-)
 rename PathPlanning/LQRPlanner/{LQRplanner.py => lqr_planner.py} (100%)
 rename PathPlanning/ModelPredictiveTrajectoryGenerator/{model_predictive_trajectory_generator.py => trajectory_generator.py} (95%)

diff --git a/AerialNavigation/drone_3d_trajectory_following/__init__.py b/AerialNavigation/drone_3d_trajectory_following/__init__.py
index 087dab646e..2194d4c303 100644
--- a/AerialNavigation/drone_3d_trajectory_following/__init__.py
+++ b/AerialNavigation/drone_3d_trajectory_following/__init__.py
@@ -1,4 +1,3 @@
-import os
 import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
diff --git a/ArmNavigation/n_joint_arm_3d/__init__.py b/ArmNavigation/n_joint_arm_3d/__init__.py
index ba5dac65f6..2194d4c303 100644
--- a/ArmNavigation/n_joint_arm_3d/__init__.py
+++ b/ArmNavigation/n_joint_arm_3d/__init__.py
@@ -1,3 +1,3 @@
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
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 36093df08d..498ef41c1f 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
@@ -5,11 +5,11 @@
         Atsushi Sakai (@Atsushi_twi)
 """
 
-import numpy as np
 import sys
 from pathlib import Path
 sys.path.append(str(Path(__file__).parent.parent.parent))
 
+import numpy as np
 from ArmNavigation.n_joint_arm_to_point_control.NLinkArm import NLinkArm
 
 # Simulation parameters
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 0de4c7edb8..fca55184da 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
@@ -3,18 +3,15 @@
 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
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from n_joint_arm_3d.NLinkArm3d import NLinkArm
 
 show_animation = True
 verbose = False
diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 1e8d1fc6d6..fc8280e7bf 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -9,10 +9,9 @@
 (https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135)
 
 """
-
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 import matplotlib.pyplot as plt
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index a5dea19fd8..c2fe8e3a87 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -5,10 +5,9 @@
 author: Atsushi Sakai (@Atsushi_twi)
 
 """
-
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 import matplotlib.pyplot as plt
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 1c45687ed1..17051623ca 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -6,8 +6,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 4f42cfd5d1..dbcdeef015 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -7,8 +7,8 @@
 """
 
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 
diff --git a/Mapping/rectangle_fitting/__init_.py b/Mapping/rectangle_fitting/__init_.py
index 087dab646e..2194d4c303 100644
--- a/Mapping/rectangle_fitting/__init_.py
+++ b/Mapping/rectangle_fitting/__init_.py
@@ -1,4 +1,3 @@
-import os
 import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 6d3e63f0f3..124e48fe76 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -13,9 +13,8 @@
 """
 
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
-
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import matplotlib.pyplot as plt
 import numpy as np
diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py
index e75738c3b1..faca2cb79e 100644
--- a/Mapping/rectangle_fitting/simulator.py
+++ b/Mapping/rectangle_fitting/simulator.py
@@ -6,8 +6,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import numpy as np
 import matplotlib.pyplot as plt
diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index dc9a575694..01ab8349a9 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -5,27 +5,17 @@
 author: AtsushiSakai(@Atsushi_twi)
 
 """
-import os
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-import pure_pursuit
-import unicycle_model
-
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-try:
-    import reeds_shepp_path_planning
-    from rrt_star_reeds_shepp import RRTStarReedsShepp
-except ImportError:
-    raise
+from ClosedLoopRRTStar import pure_pursuit
+from ClosedLoopRRTStar import unicycle_model
+from ReedsSheppPath import reeds_shepp_path_planning
+from RRTStarReedsShepp.rrt_star_reeds_shepp import RRTStarReedsShepp
 
 show_animation = True
 
diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index ca763f0d4f..982ebeca06 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -10,7 +10,11 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-import unicycle_model
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from ClosedLoopRRTStar import unicycle_model
 
 Kp = 2.0  # speed propotional gain
 Lf = 0.5  # look-ahead distance
diff --git a/PathPlanning/DubinsPath/dubins_path_planner.py b/PathPlanning/DubinsPath/dubins_path_planner.py
index 4d4ee07291..a7e8a100cc 100644
--- a/PathPlanning/DubinsPath/dubins_path_planner.py
+++ b/PathPlanning/DubinsPath/dubins_path_planner.py
@@ -6,9 +6,8 @@
 
 """
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from math import sin, cos, atan2, sqrt, acos, pi, hypot
 import numpy as np
diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
index bd211046c7..d034cb8a32 100644
--- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
+++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
@@ -16,14 +16,10 @@
 import matplotlib.pyplot as plt
 from matplotlib.collections import LineCollection
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../Eta3SplinePath")
-
-try:
-    from eta3_spline_path import Eta3Path, Eta3PathSegment
-except ImportError:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from Eta3SplinePath.eta3_spline_path import Eta3Path, Eta3PathSegment
 
 show_animation = True
 
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index ef20af22f0..b7d1b8e642 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -19,18 +19,12 @@
 import copy
 import math
 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
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from QuinticPolynomialsPlanner.quintic_polynomials_planner import \
+    QuinticPolynomial
+from CubicSpline import cubic_spline_planner
 
 SIM_LOOP = 500
 
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 2f4d32b187..31dc917566 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -4,25 +4,16 @@
 author: Atsushi Sakai
 """
 
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
-
 import math
 from enum import IntEnum
-
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from utils.angle import rot_mat_2d
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../Mapping/")
-
-try:
-    from grid_map_lib.grid_map_lib import GridMap
-except ImportError:
-    raise
+from Mapping.grid_map_lib.grid_map_lib import GridMap
 
 do_animation = True
 
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 870dd18535..0367ab2775 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -7,8 +7,9 @@
 """
 
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+root_dir = pathlib.Path(__file__).parent.parent.parent
+sys.path.append(str(root_dir))
 
 from math import cos, sin, tan, pi
 
@@ -76,7 +77,7 @@ 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_mat_2d(-yaw)
     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 4ef0c1dd21..b87fe706c2 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -8,22 +8,16 @@
 
 import heapq
 import math
-import os
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.spatial import cKDTree
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../ReedsSheppPath")
-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,\
-        BUBBLE_R
-except Exception:
-    raise
+from dynamic_programming_heuristic import calc_distance_heuristic
+from ReedsSheppPath import reeds_shepp_path_planning as rs
+from car import move, check_car_collision, MAX_STEER, WB, plot_car, BUBBLE_R
 
 XY_GRID_RESOLUTION = 2.0  # [m]
 YAW_GRID_RESOLUTION = np.deg2rad(15.0)  # [rad]
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 10af3a81bd..25c50a0cd2 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -10,8 +10,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import copy
 import math
diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/lqr_planner.py
similarity index 100%
rename from PathPlanning/LQRPlanner/LQRplanner.py
rename to PathPlanning/LQRPlanner/lqr_planner.py
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index 28d332aa00..e3f18a722a 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -7,21 +7,15 @@
 """
 import copy
 import math
-import os
 import random
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../LQRPlanner/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRTStar/")
-
-try:
-    from LQRplanner import LQRPlanner
-    from rrt_star import RRTStar
-except ImportError:
-    raise
+from LQRPlanner.lqr_planner import LQRPlanner
+from RRTStar.rrt_star import RRTStar
 
 show_animation = True
 
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
similarity index 95%
rename from PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
rename to PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
index 3ba1dd9fc2..a44e1ac956 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
@@ -7,11 +7,14 @@
 """
 
 import math
-
 import matplotlib.pyplot as plt
 import numpy as np
+import sys
+import pathlib
+path_planning_dir = pathlib.Path(__file__).parent.parent
+sys.path.append(str(path_planning_dir))
 
-import motion_model
+import ModelPredictiveTrajectoryGenerator.motion_model as motion_model
 
 # optimization parameter
 max_iter = 100
diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 9826dba072..616c73a697 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -7,18 +7,13 @@
 """
 
 import math
-import os
 import random
-import sys
-
 import matplotlib.pyplot as plt
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-try:
-    from rrt import RRT
-except ImportError:
-    raise
+from rrt import RRT
 
 show_animation = True
 
diff --git a/PathPlanning/RRT/rrt_with_sobol_sampler.py b/PathPlanning/RRT/rrt_with_sobol_sampler.py
index 8045d8f764..73488210a7 100644
--- a/PathPlanning/RRT/rrt_with_sobol_sampler.py
+++ b/PathPlanning/RRT/rrt_with_sobol_sampler.py
@@ -28,12 +28,11 @@
 
 import math
 import random
-from sobol import sobol_quasirand
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
 
+from sobol import sobol_quasirand
+
 show_animation = True
 
 
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index f1bd03ca02..f938419f35 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -6,23 +6,17 @@
 """
 import copy
 import math
-import os
 import random
-import sys
-
-import matplotlib.pyplot as plt
 import numpy as np
+import matplotlib.pyplot as plt
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))  # root dir
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../DubinsPath/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../RRT/")
-
-try:
-    from rrt import RRT
-    import dubins_path_planner
-except ImportError:
-    raise
+from RRT.rrt import RRT
+from DubinsPath import dubins_path_planner
+from utils.plot import plot_arrow
 
 show_animation = True
 
@@ -130,10 +124,8 @@ def draw_graph(self, rnd=None):  # pragma: no cover
         plt.pause(0.01)
 
     def plot_start_goal_arrow(self):  # pragma: no cover
-        dubins_path_planner.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planner.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
+        plot_arrow(self.start.x, self.start.y, self.start.yaw)
+        plot_arrow(self.end.x, self.end.y, self.end.yaw)
 
     def steer(self, from_node, to_node):
 
@@ -214,6 +206,7 @@ def generate_final_course(self, goal_index):
 
 
 def main():
+
     print("Start " + __file__)
     # ====Search Path with RRT====
     obstacleList = [
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index e2c05456de..a7aad49d60 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -7,17 +7,12 @@
 """
 
 import math
-import os
 import sys
-
 import matplotlib.pyplot as plt
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRT/")
-
-try:
-    from rrt import RRT
-except ImportError:
-    raise
+from RRT.rrt import RRT
 
 show_animation = True
 
@@ -59,6 +54,7 @@ def __init__(self,
         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
+        self.node_list = []
 
     def planning(self, animation=True):
         """
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index e2a456d75f..7c52879b7c 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -4,26 +4,19 @@
 author: AtsushiSakai(@Atsushi_twi)
 
 """
-
 import copy
 import math
-import os
 import random
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))  # root dir
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../DubinsPath/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../RRTStar/")
-
-try:
-    import dubins_path_planner
-    from rrt_star import RRTStar
-except ImportError:
-    raise
+from DubinsPath import dubins_path_planner
+from RRTStar.rrt_star import RRTStar
+from utils.plot import plot_arrow
 
 show_animation = True
 
@@ -136,10 +129,8 @@ def draw_graph(self, rnd=None):
         plt.pause(0.01)
 
     def plot_start_goal_arrow(self):
-        dubins_path_planner.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planner.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
+        plot_arrow(self.start.x, self.start.y, self.start.yaw)
+        plot_arrow(self.end.x, self.end.y, self.end.yaw)
 
     def steer(self, from_node, to_node):
 
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index 323bc76be2..66185017a0 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -7,23 +7,15 @@
 """
 import copy
 import math
-import os
 import random
 import sys
-
+import pathlib
 import matplotlib.pyplot as plt
 import numpy as np
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../ReedsSheppPath/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../RRTStar/")
-
-try:
-    import reeds_shepp_path_planning
-    from rrt_star import RRTStar
-except ImportError:
-    raise
+from ReedsSheppPath import reeds_shepp_path_planning
+from RRTStar.rrt_star import RRTStar
 
 show_animation = True
 
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index b5fec697a4..fb80e1f582 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -4,11 +4,16 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-- lookuptable.csv is generated with this script: https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
+- lookuptable.csv is generated with this script:
+https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning
+/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
 
 Ref:
 
-- 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
+- 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
 
 """
 import sys
@@ -17,17 +22,11 @@
 import numpy as np
 import math
 import pandas as pd
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../ModelPredictiveTrajectoryGenerator/")
-
-
-try:
-    import model_predictive_trajectory_generator as planner
-    import motion_model
-except ImportError:
-    raise
-
+import ModelPredictiveTrajectoryGenerator.trajectory_generator as planner
+import ModelPredictiveTrajectoryGenerator.motion_model as motion_model
 
 table_path = os.path.dirname(os.path.abspath(__file__)) + "/lookuptable.csv"
 
diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
index 6458cd882c..e9f8733c83 100644
--- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py
+++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
@@ -6,17 +6,15 @@
 
 """
 
-import os
 import sys
 import math
 import numpy as np
 import matplotlib.pyplot as plt
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-from PathPlanning.VisibilityRoadMap.geometry import Geometry
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../VoronoiRoadMap/")
-from dijkstra_search import DijkstraSearch
+from VisibilityRoadMap.geometry import Geometry
+from VoronoiRoadMap.dijkstra_search import DijkstraSearch
 
 show_animation = True
 
diff --git a/PathPlanning/VoronoiRoadMap/__init__.py b/PathPlanning/VoronoiRoadMap/__init__.py
index 087dab646e..e69de29bb2 100644
--- a/PathPlanning/VoronoiRoadMap/__init__.py
+++ b/PathPlanning/VoronoiRoadMap/__init__.py
@@ -1,4 +0,0 @@
-import os
-import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index d184230fd5..0a1f6f5526 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -9,8 +9,12 @@
 import math
 import numpy as np
 import matplotlib.pyplot as plt
-from dijkstra_search import DijkstraSearch
 from scipy.spatial import cKDTree, Voronoi
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from VoronoiRoadMap.dijkstra_search import DijkstraSearch
 
 show_animation = True
 
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 db5a755008..7e57abb80e 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -7,19 +7,13 @@
 """
 import math
 import sys
-import os
-
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.linalg as la
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except ImportError:
-    raise
+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 5596ea0fdf..95a1b92ce5 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -10,16 +10,10 @@
 import math
 import numpy as np
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+from PathPlanning.CubicSpline import cubic_spline_planner
 
 Kp = 1.0  # speed proportional gain
 
@@ -29,7 +23,7 @@
 
 # parameters
 dt = 0.1  # time tick[s]
-L = 0.5  # Wheel base of the vehicle [m]
+L = 0.5  # Wheelbase of the vehicle [m]
 max_steer = np.deg2rad(45.0)  # maximum steering angle[rad]
 
 show_animation = True
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 ac8b883bd9..59e69aa999 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
@@ -10,16 +10,10 @@
 import math
 import numpy as np
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+from PathPlanning.CubicSpline import cubic_spline_planner
 
 NX = 4  # x = x, y, v, yaw
 NU = 2  # a = [accel, steer]
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index 3a67d6268e..9c475cbaf2 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -12,16 +12,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+from PathPlanning.CubicSpline import cubic_spline_planner
 
 k = 0.5  # control gain
 Kp = 1.0  # speed proportional gain
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index 0827732cd8..5510a41110 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -11,8 +11,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import copy
 import itertools
diff --git a/tests/test_LQR_planner.py b/tests/test_LQR_planner.py
index b535b99baf..be12195eac 100644
--- a/tests/test_LQR_planner.py
+++ b/tests/test_LQR_planner.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from PathPlanning.LQRPlanner import LQRplanner as m
+from PathPlanning.LQRPlanner import lqr_planner as m
 
 
 def test_1():

From cd5a50b2aa2bd80fd74fceb654d54d168c9f59da Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Sep 2022 06:45:54 +0900
Subject: [PATCH 176/604] Bump numpy from 1.23.2 to 1.23.3 in /requirements
 (#723)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.2 to 1.23.3.
- [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/v1.23.2...v1.23.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 615377611b..61a73fd2c6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.2
+numpy == 1.23.3
 scipy == 1.9.1
 pandas == 1.4.4
 matplotlib == 3.5.3

From e40b4d9dec729b02a78dd98062e736f7477d6ad1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 27 Sep 2022 21:49:53 +0900
Subject: [PATCH 177/604] Enhance bspline code and doc clean up (#720)

* Start bspline code and doc cluean up

* code clean up

* code clean up

* code clean up

* code clean up

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve codes.

* improve codes.

* improve codes.
---
 .github/workflows/Linux_CI.yml                |   2 +-
 .github/workflows/MacOS_CI.yml                |   2 +-
 PathPlanning/BSplinePath/bspline_path.py      | 150 +++++++++++++-----
 docs/index_main.rst                           |   1 +
 .../path_planning/bspline_path/Figure_1.png   | Bin 22882 -> 0 bytes
 .../bspline_path/approx_and_curvature.png     | Bin 0 -> 43661 bytes
 .../bspline_path/approximation1.png           | Bin 0 -> 28603 bytes
 .../bspline_path/basis_functions.png          | Bin 0 -> 70364 bytes
 .../bspline_path/bspline_path_main.rst        | 140 +++++++++++++++-
 .../bspline_path/bspline_path_planning.png    | Bin 0 -> 34312 bytes
 .../bspline_path/interp_and_curvature.png     | Bin 0 -> 37021 bytes
 .../bspline_path/interpolation1.png           | Bin 0 -> 27476 bytes
 docs/modules/utils/plot/curvature_plot.png    | Bin 0 -> 33206 bytes
 docs/modules/utils/plot/plot_main.rst         |  16 ++
 docs/modules/utils/utils_main.rst             |  12 ++
 tests/test_bspline_path.py                    |  74 +++++++++
 utils/plot.py                                 |  37 +++++
 17 files changed, 387 insertions(+), 47 deletions(-)
 delete mode 100644 docs/modules/path_planning/bspline_path/Figure_1.png
 create mode 100644 docs/modules/path_planning/bspline_path/approx_and_curvature.png
 create mode 100644 docs/modules/path_planning/bspline_path/approximation1.png
 create mode 100644 docs/modules/path_planning/bspline_path/basis_functions.png
 create mode 100644 docs/modules/path_planning/bspline_path/bspline_path_planning.png
 create mode 100644 docs/modules/path_planning/bspline_path/interp_and_curvature.png
 create mode 100644 docs/modules/path_planning/bspline_path/interpolation1.png
 create mode 100644 docs/modules/utils/plot/curvature_plot.png
 create mode 100644 docs/modules/utils/plot/plot_main.rst
 create mode 100644 docs/modules/utils/utils_main.rst
 create mode 100644 tests/test_bspline_path.py

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 73f8aa5c0d..83c55b17f1 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']
+        python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627
 
     name: Python ${{ matrix.python-version }} CI
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 5c796120a5..a3459f2560 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' ]
+        python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v2
diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py
index d4b24c7645..a2a396efaa 100644
--- a/PathPlanning/BSplinePath/bspline_path.py
+++ b/PathPlanning/BSplinePath/bspline_path.py
@@ -5,57 +5,114 @@
 author: Atsushi Sakai (@Atsushi_twi)
 
 """
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import numpy as np
 import matplotlib.pyplot as plt
-import scipy.interpolate as scipy_interpolate
+import scipy.interpolate as interpolate
 
+from utils.plot import plot_curvature
 
-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
+def approximate_b_spline_path(x: list,
+                              y: list,
+                              n_path_points: int,
+                              degree: int = 3,
+                              s=None,
+                              ) -> tuple:
     """
-    t = range(len(x))
-    x_tup = scipy_interpolate.splrep(t, x, k=degree)
-    y_tup = scipy_interpolate.splrep(t, y, k=degree)
+    Approximate points with a B-Spline path
+
+    Parameters
+    ----------
+    x : array_like
+        x position list of approximated points
+    y : array_like
+        y position list of approximated points
+    n_path_points : int
+        number of path points
+    degree : int, optional
+        B Spline curve degree. Must be 2<= k <= 5. Default: 3.
+    s : int, optional
+        smoothing parameter. If this value is bigger, the path will be
+        smoother, but it will be less accurate. If this value is smaller,
+        the path will be more accurate, but it will be less smooth.
+        When `s` is 0, it is equivalent to the interpolation. Default is None,
+        in this case `s` will be `len(x)`.
+
+    Returns
+    -------
+    x : array
+        x positions of the result path
+    y : array
+        y positions of the result path
+    heading : array
+        heading of the result path
+    curvature : array
+        curvature of the result path
 
-    x_list = list(x_tup)
-    x_list[1] = x + [0.0, 0.0, 0.0, 0.0]
-
-    y_list = list(y_tup)
-    y_list[1] = y + [0.0, 0.0, 0.0, 0.0]
+    """
+    distances = _calc_distance_vector(x, y)
 
-    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)
+    spl_i_x = interpolate.UnivariateSpline(distances, x, k=degree, s=s)
+    spl_i_y = interpolate.UnivariateSpline(distances, y, k=degree, s=s)
 
-    return rx, ry
+    sampled = np.linspace(0.0, distances[-1], n_path_points)
+    return _evaluate_spline(sampled, spl_i_x, spl_i_y)
 
 
-def interpolate_b_spline_path(x: list, y: list, n_path_points: int,
+def interpolate_b_spline_path(x, y,
+                              n_path_points: int,
                               degree: int = 3) -> tuple:
     """
-    interpolate points with a B-Spline path
+    Interpolate x-y points with a B-Spline path
+
+    Parameters
+    ----------
+    x : array_like
+        x positions of interpolated points
+    y : array_like
+        y positions of interpolated points
+    n_path_points : int
+        number of path points
+    degree : int, optional
+        B-Spline degree. Must be 2<= k <= 5. Default: 3
+
+    Returns
+    -------
+    x : array
+        x positions of the result path
+    y : array
+        y positions of the result path
+    heading : array
+        heading of the result path
+    curvature : array
+        curvature of the result 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)
+    return approximate_b_spline_path(x, y, n_path_points, degree, s=0.0)
 
-    travel = np.linspace(0.0, len(x) - 1, n_path_points)
-    return spl_i_x(travel), spl_i_y(travel)
+
+def _calc_distance_vector(x, y):
+    dx, dy = np.diff(x), np.diff(y)
+    distances = np.cumsum([np.hypot(idx, idy) for idx, idy in zip(dx, dy)])
+    distances = np.concatenate(([0.0], distances))
+    distances /= distances[-1]
+    return distances
+
+
+def _evaluate_spline(sampled, spl_i_x, spl_i_y):
+    x = spl_i_x(sampled)
+    y = spl_i_y(sampled)
+    dx = spl_i_x.derivative(1)(sampled)
+    dy = spl_i_y.derivative(1)(sampled)
+    heading = np.arctan2(dy, dx)
+    ddx = spl_i_x.derivative(2)(sampled)
+    ddy = spl_i_y.derivative(2)(sampled)
+    curvature = (ddy * dx - ddx * dy) / np.power(dx * dx + dy * dy, 2.0 / 3.0)
+    return np.array(x), y, heading, curvature,
 
 
 def main():
@@ -63,17 +120,28 @@ def main():
     # way points
     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
+    n_course_point = 50  # sampling number
 
-    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)
+    plt.subplots()
+    rax, ray, heading, curvature = approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5)
+    plt.plot(rax, ray, '-r', label="Approximated B-Spline path")
+    plot_curvature(rax, ray, heading, curvature)
 
-    # show results
+    plt.title("B-Spline approximation")
     plt.plot(way_point_x, way_point_y, '-og', label="way points")
-    plt.plot(rax, ray, '-r', label="Approximated B-Spline path")
+    plt.grid(True)
+    plt.legend()
+    plt.axis("equal")
+
+    plt.subplots()
+    rix, riy, heading, curvature = interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point)
     plt.plot(rix, riy, '-b', label="Interpolated B-Spline path")
+    plot_curvature(rix, riy, heading, curvature)
+
+    plt.title("B-Spline interpolation")
+    plt.plot(way_point_x, way_point_y, '-og', label="way points")
     plt.grid(True)
     plt.legend()
     plt.axis("equal")
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 151759284d..67bd9889f2 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -44,6 +44,7 @@ See this paper for more details:
    modules/aerial_navigation/aerial_navigation
    modules/bipedal/bipedal
    modules/control/control
+   modules/utils/utils
    modules/appendix/appendix
    how_to_contribute
 
diff --git a/docs/modules/path_planning/bspline_path/Figure_1.png b/docs/modules/path_planning/bspline_path/Figure_1.png
deleted file mode 100644
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diff --git a/docs/modules/path_planning/bspline_path/bspline_path_main.rst b/docs/modules/path_planning/bspline_path/bspline_path_main.rst
index d3523a1918..e734352e34 100644
--- a/docs/modules/path_planning/bspline_path/bspline_path_main.rst
+++ b/docs/modules/path_planning/bspline_path/bspline_path_main.rst
@@ -1,14 +1,146 @@
 B-Spline planning
 -----------------
 
-.. image:: Figure_1.png
+.. image:: bspline_path_planning.png
 
-This is a path planning with B-Spline curse.
+This is a B-Spline path planning routines.
 
 If you input waypoints, it generates a smooth path with B-Spline curve.
 
-The final course should be on the first and last waypoints.
+This codes provide two types of B-Spline curve generations:
 
-Ref:
+1. Interpolation: generate a curve that passes through all waypoints.
+
+2. Approximation: generate a curve that approximates the waypoints. (Not passing through all waypoints)
+
+Bspline basics
+~~~~~~~~~~~~~~
+
+BSpline (Basis-Spline) is a piecewise polynomial spline curve.
+
+It is expressed by the following equation.
+
+:math:`\mathbf{S}(x)=\sum_{i=k-p}^k \mathbf{c}_i B_{i, p}(x)`
+
+here:
+
+* :math:`S(x)` is the curve point on the spline at x.
+* :math:`c_i` is the representative point generating the spline, called the control point.
+* :math:`p+1` is the dimension of the BSpline.
+* :math:`k` is the number of knots.
+* :math:`B_{i,p}(x)` is a function called Basis Function.
+
+The the basis function can be calculated by the following `De Boor recursion formula <https://en.wikipedia.org/wiki/De_Boor%27s_algorithm>`_:
+
+:math:`B_{i, 0}(x):= \begin{cases}1 & \text { if } \quad t_i \leq x<t_{i+1} \\ 0 & \text { otherwise }\end{cases}`
+
+:math:`B_{i, p}(x):=\frac{x-t_i}{t_{i+p}-t_i} B_{i, p-1}(x)+\frac{t_{i+p+1}-x}{t_{i+p+1}-t_{i+1}} B_{i+1, p-1}(x)`
+
+here:
+
+* :math:`t_i` is each element of the knot vector.
+
+This figure shows the BSpline basis functions for each of :math:`i`:
+
+.. image:: basis_functions.png
+
+Note that when all the basis functions are added together, summation is 1.0 for any x value.
+
+This means that the result curve is smooth when each control point is weighted addition by this basis function,
+
+This code is for generating the upper basis function graph using `scipy <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BSpline.html>`_.
+
+.. code-block:: python
+
+	from scipy.interpolate import BSpline
+
+	def B_orig(x, k, i, t):
+		if k == 0:
+			return 1.0 if t[i] <= x < t[i + 1] else 0.0
+		if t[i + k] == t[i]:
+			c1 = 0.0
+		else:
+			c1 = (x - t[i]) / (t[i + k] - t[i]) * B(x, k - 1, i, t)
+
+		if t[i + k + 1] == t[i + 1]:
+			c2 = 0.0
+		else:
+			c2 = (t[i + k + 1] - x) / (t[i + k + 1] - t[i + 1]) * B(x, k - 1, i + 1, t)
+		return c1 + c2
+
+
+	def B(x, k, i, t):
+		c = np.zeros_like(t)
+		c[i] = 1
+		return BSpline(t, c, k)(x)
+
+
+	def main():
+		k = 3  # degree of the spline
+		t = [0, 1, 2, 3, 4, 5]  # knots vector
+
+		x = np.linspace(0, 5, 1000, endpoint=False)
+		t = np.r_[[np.min(t)]*k, t, [np.max(t)]*k]
+
+		n = len(t) - k - 1
+		for i in range(n):
+			y = np.array([B(ix, k, i, t) for ix in x])
+			plt.plot(x, y, label=f'i = {i}')
+
+		plt.title(f'Basis functions (k = {k}, knots = {t})')
+		plt.show()
+
+Bspline interpolation planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+:meth:`PathPlanning.BSplinePath.bspline_path.interpolate_b_spline_path` generates a curve that passes through all waypoints.
+
+This is a simple example of the interpolation planning:
+
+.. image:: interpolation1.png
+
+This figure also shows curvatures of each path point using :ref:`utils.plot.plot_curvature <plot_curvature>`.
+
+The default spline degree is 3, so curvature changes smoothly.
+
+.. image:: interp_and_curvature.png
+
+API
+++++
+
+.. autofunction:: PathPlanning.BSplinePath.bspline_path.interpolate_b_spline_path
+
+
+Bspline approximation planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+:meth:`PathPlanning.BSplinePath.bspline_path.approximate_b_spline_path`
+generates a curve that approximates the waypoints, which means that
+the curve might not pass through waypoints.
+
+Users can adjust path smoothness by the smoothing parameter `s`. If this
+value is bigger, the path will be smoother, but it will be less accurate.
+If this value is smaller, the path will be more accurate, but it will be
+less smooth.
+
+This is a simple example of the approximation planning:
+
+.. image:: approximation1.png
+
+This figure also shows curvatures of each path point using :ref:`utils.plot.plot_curvature <plot_curvature>`.
+
+The default spline degree is 3, so curvature changes smoothly.
+
+.. image:: approx_and_curvature.png
+
+API
+++++
+
+.. autofunction:: PathPlanning.BSplinePath.bspline_path.approximate_b_spline_path
+
+
+References
+~~~~~~~~~~
 
 -  `B-spline - Wikipedia <https://en.wikipedia.org/wiki/B-spline>`__
+-  `scipy.interpolate.UnivariateSpline <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.UnivariateSpline.html>`__
\ No newline at end of file
diff --git a/docs/modules/path_planning/bspline_path/bspline_path_planning.png b/docs/modules/path_planning/bspline_path/bspline_path_planning.png
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HcmV?d00001

diff --git a/docs/modules/utils/plot/plot_main.rst b/docs/modules/utils/plot/plot_main.rst
new file mode 100644
index 0000000000..b5fef0c4a1
--- /dev/null
+++ b/docs/modules/utils/plot/plot_main.rst
@@ -0,0 +1,16 @@
+.. _plot_utils:
+
+Plotting Utilities
+-------------------
+
+This module contains a number of utility functions for plotting data.
+
+.. _plot_curvature:
+
+plot_curvature
+~~~~~~~~~~~~~~~
+
+.. autofunction:: utils.plot.plot_curvature
+
+.. image:: curvature_plot.png
+
diff --git a/docs/modules/utils/utils_main.rst b/docs/modules/utils/utils_main.rst
new file mode 100644
index 0000000000..ff79a26205
--- /dev/null
+++ b/docs/modules/utils/utils_main.rst
@@ -0,0 +1,12 @@
+.. _utils:
+
+Utilities
+==========
+
+Common utilities for PythonRobotics.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   plot/plot
diff --git a/tests/test_bspline_path.py b/tests/test_bspline_path.py
new file mode 100644
index 0000000000..4918c9810f
--- /dev/null
+++ b/tests/test_bspline_path.py
@@ -0,0 +1,74 @@
+import conftest
+import numpy as np
+import pytest
+from PathPlanning.BSplinePath import bspline_path
+
+
+def test_list_input():
+    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 = 50  # sampling number
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+
+def test_array_input():
+    way_point_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0])
+    way_point_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0])
+    n_course_point = 50  # sampling number
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+
+def test_degree_change():
+    way_point_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0])
+    way_point_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0])
+    n_course_point = 50  # sampling number
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5, degree=4)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, degree=4)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5, degree=2)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, degree=2)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+    with pytest.raises(ValueError):
+        bspline_path.approximate_b_spline_path(
+            way_point_x, way_point_y, n_course_point, s=0.5, degree=1)
+
+    with pytest.raises(ValueError):
+        bspline_path.interpolate_b_spline_path(
+            way_point_x, way_point_y, n_course_point, degree=1)
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/utils/plot.py b/utils/plot.py
index 2c70a60bbe..3499694821 100644
--- a/utils/plot.py
+++ b/utils/plot.py
@@ -3,6 +3,7 @@
 """
 import math
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 def plot_arrow(x, y, yaw, arrow_length=1.0,
@@ -47,3 +48,39 @@ def plot_arrow(x, y, yaw, arrow_length=1.0,
                   **kwargs)
         if origin_point_plot_style is not None:
             plt.plot(x, y, origin_point_plot_style)
+
+
+def plot_curvature(x_list, y_list, heading_list, curvature,
+                   k=0.01, c="-c", label="Curvature"):
+    """
+    Plot curvature on 2D path. This plot is a line from the original path,
+    the lateral distance from the original path shows curvature magnitude.
+    Left turning shows right side plot, right turning shows left side plot.
+    For straight path, the curvature plot will be on the path, because
+    curvature is 0 on the straight path.
+
+    Parameters
+    ----------
+    x_list : array_like
+        x position list of the path
+    y_list : array_like
+        y position list of the path
+    heading_list : array_like
+        heading list of the path
+    curvature : array_like
+        curvature list of the path
+    k : float
+        curvature scale factor to calculate distance from the original path
+    c : string
+        color of the plot
+    label : string
+        label of the plot
+    """
+    cx = [x + d * k * np.cos(yaw - np.pi / 2.0) for x, y, yaw, d in
+          zip(x_list, y_list, heading_list, curvature)]
+    cy = [y + d * k * np.sin(yaw - np.pi / 2.0) for x, y, yaw, d in
+          zip(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)

From 955ad60ee3cab7911fd15e7db6318233674b60f7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 30 Sep 2022 09:14:27 +0900
Subject: [PATCH 178/604] Remove pandas dependency (#725)

* Remove pandas dependency

* Remove pandas dependency
---
 .../lookup_table_generator.py                 | 110 +++++++++++++++++
 .../lookuptable_generator.py                  | 114 ------------------
 .../StateLatticePlanner/lookup_table.csv      |  82 +++++++++++++
 .../StateLatticePlanner/lookuptable.csv       |  25 ----
 .../state_lattice_planner.py                  |  62 +++++-----
 README.md                                     |   2 -
 docs/getting_started_main.rst                 |   2 -
 requirements/environment.yml                  |   1 -
 requirements/requirements.txt                 |   1 -
 9 files changed, 226 insertions(+), 173 deletions(-)
 create mode 100644 PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
 delete mode 100644 PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
 create mode 100644 PathPlanning/StateLatticePlanner/lookup_table.csv
 delete mode 100644 PathPlanning/StateLatticePlanner/lookuptable.csv

diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
new file mode 100644
index 0000000000..6cb226adef
--- /dev/null
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
@@ -0,0 +1,110 @@
+"""
+
+Lookup Table generation for model predictive trajectory generator
+
+author: Atsushi Sakai
+
+"""
+import sys
+import pathlib
+path_planning_dir = pathlib.Path(__file__).parent.parent
+sys.path.append(str(path_planning_dir))
+
+from matplotlib import pyplot as plt
+import numpy as np
+import math
+
+from ModelPredictiveTrajectoryGenerator import trajectory_generator,\
+    motion_model
+
+
+def calc_states_list(max_yaw=np.deg2rad(-30.0)):
+
+    x = np.arange(10.0, 30.0, 5.0)
+    y = np.arange(0.0, 20.0, 2.0)
+    yaw = np.arange(-max_yaw, max_yaw, max_yaw)
+
+    states = []
+    for iyaw in yaw:
+        for iy in y:
+            for ix in x:
+                states.append([ix, iy, iyaw])
+    print("n_state:", len(states))
+
+    return states
+
+
+def search_nearest_one_from_lookup_table(tx, ty, tyaw, lookup_table):
+    mind = float("inf")
+    minid = -1
+
+    for (i, table) in enumerate(lookup_table):
+
+        dx = tx - table[0]
+        dy = ty - table[1]
+        dyaw = tyaw - table[2]
+        d = math.sqrt(dx ** 2 + dy ** 2 + dyaw ** 2)
+        if d <= mind:
+            minid = i
+            mind = d
+
+    # print(minid)
+
+    return lookup_table[minid]
+
+
+def save_lookup_table(file_name, table):
+    np.savetxt(file_name, np.array(table),
+               fmt='%s', delimiter=",", header="x,y,yaw,s,km,kf", comments="")
+
+    print("lookup table file is saved as " + file_name)
+
+
+def generate_lookup_table():
+    states = calc_states_list(max_yaw=np.deg2rad(-30.0))
+    k0 = 0.0
+
+    # x, y, yaw, s, km, kf
+    lookup_table = [[1.0, 0.0, 0.0, 1.0, 0.0, 0.0]]
+
+    for state in states:
+        best_p = search_nearest_one_from_lookup_table(
+            state[0], state[1], state[2], lookup_table)
+
+        target = motion_model.State(x=state[0], y=state[1], yaw=state[2])
+        init_p = np.array(
+            [np.hypot(state[0], state[1]), best_p[4], best_p[5]]).reshape(3, 1)
+
+        x, y, yaw, p = trajectory_generator.optimize_trajectory(target,
+                                                                k0, init_p)
+
+        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])])
+
+    print("finish lookup table generation")
+
+    save_lookup_table("lookup_table.csv", lookup_table)
+
+    for table in lookup_table:
+        x_c, y_c, yaw_c = motion_model.generate_trajectory(
+            table[3], table[4], table[5], k0)
+        plt.plot(x_c, y_c, "-r")
+        x_c, y_c, yaw_c = motion_model.generate_trajectory(
+            table[3], -table[4], -table[5], k0)
+        plt.plot(x_c, y_c, "-r")
+
+    plt.grid(True)
+    plt.axis("equal")
+    plt.show()
+
+    print("Done")
+
+
+def main():
+    generate_lookup_table()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
deleted file mode 100644
index c57a05da57..0000000000
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
+++ /dev/null
@@ -1,114 +0,0 @@
-"""
-
-Lookup Table generation for model predictive trajectory generator
-
-author: Atsushi Sakai
-
-"""
-from matplotlib import pyplot as plt
-import numpy as np
-import math
-import model_predictive_trajectory_generator as planner
-import motion_model
-import pandas as pd
-
-
-def calc_states_list():
-    maxyaw = np.deg2rad(-30.0)
-
-    x = np.arange(10.0, 30.0, 5.0)
-    y = np.arange(0.0, 20.0, 2.0)
-    yaw = np.arange(-maxyaw, maxyaw, maxyaw)
-
-    states = []
-    for iyaw in yaw:
-        for iy in y:
-            for ix in x:
-                states.append([ix, iy, iyaw])
-    print("nstate:", len(states))
-
-    return states
-
-
-def search_nearest_one_from_lookuptable(tx, ty, tyaw, lookuptable):
-    mind = float("inf")
-    minid = -1
-
-    for (i, table) in enumerate(lookuptable):
-
-        dx = tx - table[0]
-        dy = ty - table[1]
-        dyaw = tyaw - table[2]
-        d = math.sqrt(dx ** 2 + dy ** 2 + dyaw ** 2)
-        if d <= mind:
-            minid = i
-            mind = d
-
-    # print(minid)
-
-    return lookuptable[minid]
-
-
-def save_lookup_table(fname, table):
-    mt = np.array(table)
-    print(mt)
-    # save csv
-    df = pd.DataFrame()
-    df["x"] = mt[:, 0]
-    df["y"] = mt[:, 1]
-    df["yaw"] = mt[:, 2]
-    df["s"] = mt[:, 3]
-    df["km"] = mt[:, 4]
-    df["kf"] = mt[:, 5]
-    df.to_csv(fname, index=None)
-
-    print("lookup table file is saved as " + fname)
-
-
-def generate_lookup_table():
-    states = calc_states_list()
-    k0 = 0.0
-
-    # x, y, yaw, s, km, kf
-    lookuptable = [[1.0, 0.0, 0.0, 1.0, 0.0, 0.0]]
-
-    for state in states:
-        bestp = search_nearest_one_from_lookuptable(
-            state[0], state[1], state[2], lookuptable)
-
-        target = motion_model.State(x=state[0], y=state[1], yaw=state[2])
-        init_p = np.array(
-            [math.sqrt(state[0] ** 2 + state[1] ** 2), bestp[4], bestp[5]]).reshape(3, 1)
-
-        x, y, yaw, p = planner.optimize_trajectory(target, k0, init_p)
-
-        if x is not None:
-            print("find good path")
-            lookuptable.append(
-                [x[-1], y[-1], yaw[-1], float(p[0]), float(p[1]), float(p[2])])
-
-    print("finish lookup table generation")
-
-    save_lookup_table("lookuptable.csv", lookuptable)
-
-    for table in lookuptable:
-        xc, yc, yawc = motion_model.generate_trajectory(
-            table[3], table[4], table[5], k0)
-        plt.plot(xc, yc, "-r")
-        xc, yc, yawc = motion_model.generate_trajectory(
-            table[3], -table[4], -table[5], k0)
-        plt.plot(xc, yc, "-r")
-
-    plt.grid(True)
-    plt.axis("equal")
-    plt.show()
-
-    print("Done")
-
-
-def main():
-    generate_lookup_table()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/PathPlanning/StateLatticePlanner/lookup_table.csv b/PathPlanning/StateLatticePlanner/lookup_table.csv
new file mode 100644
index 0000000000..2c48a11a18
--- /dev/null
+++ b/PathPlanning/StateLatticePlanner/lookup_table.csv
@@ -0,0 +1,82 @@
+x,y,yaw,s,km,kf
+1.0,0.0,0.0,1.0,0.0,0.0
+9.975352133559392,0.0482183513545736,0.5660837104214496,10.00000000000002,0.0015736624607596847,0.31729782170754367
+15.021899857204536,0.023109001221800096,0.541061424167431,15.128443053611093,0.0006480950273134919,0.20847475849103875
+20.062147834064536,0.0406648159648112,0.5374967866814861,20.205553097986094,0.000452860235044122,0.15502921850050788
+24.924468605496358,-0.04047324014767662,0.5146575311501209,25.16775431470035,6.940620839146646e-05,0.12259452810198132
+9.971782095506931,1.9821448683146543,0.5206511572266477,10.287478424823748,0.05861430948618236,0.07036494964262185
+15.00170010872385,2.0003864283110473,0.5236741185892617,15.245993376540827,0.02657730439557895,0.09933479864250763
+19.991716537639487,1.9940629519465154,0.5226444441451559,20.277923997037238,0.015108540596275507,0.09478988637993524
+24.946447973869596,2.0815930190993206,0.5306354765686239,25.20115925294013,0.010036251429787917,0.08505936469481987
+10.033694822745312,3.9724800521928056,0.5349578864544497,11.087694168363686,0.10279429366023954,-0.12501011715795404
+15.010712586144749,4.0046776414868095,0.5234972445048012,15.729582155835587,0.05010930398580602,-0.0008557723200034717
+19.9175798257299,4.053680042954521,0.5265397234296523,20.52466275843717,0.029584390559990882,0.035276591227371874
+24.98769016158626,3.991699950598091,0.5229000018897194,25.543297770996556,0.018800715817231053,0.04750751098144048
+10.018665105170687,6.004814533505462,0.5183921334245007,12.249438857228967,0.13207408248643182,-0.2742892277307502
+14.988626131330372,5.991226410357179,0.5248160422552801,16.53593823576699,0.06924423592936522,-0.08634227857486092
+20.014420271646646,6.006767110727591,0.5233060851224174,21.23271362632659,0.041402041787912916,-0.01770377839603589
+24.998338724667267,5.997352722087869,0.5235621854299422,26.009046544833613,0.027285850882345728,0.011507045054418165
+10.040118020822444,8.017131894913126,0.5076867575242261,13.8061261785273,0.14561700178565884,-0.3527538468214878
+14.96397914886416,7.974972375534203,0.5303378183744862,17.667338175004062,0.08318912494381935,-0.15372672981944802
+20.045725182938817,8.023486945646207,0.5201839069343577,22.126364299043573,0.05173969669894265,-0.06557547083017647
+25.004687466358227,8.00036398460779,0.5234938146870878,26.740089158520917,0.034867425244601645,-0.02199309906456302
+10.065138949829214,10.03244363616002,0.49375882493214895,15.701940360254637,0.14847998727328912,-0.39355037236614626
+15.05373212031198,10.026401491912143,0.5111826366036252,19.105461052226858,0.09205576730549936,-0.20458802229704312
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+15.032725028551983,17.978015286760307,0.0020752804670070013,27.089888269358894,0.09590219542773218,-0.3801465515462427
+20.03544756240551,17.98685790169768,-0.005300968094156033,29.75070206477736,0.07340450527104486,-0.29182757725382324
+24.960019173190652,17.98909417109214,0.011594018486178026,33.0995680641525,0.05634561447882407,-0.22402297280749597
diff --git a/PathPlanning/StateLatticePlanner/lookuptable.csv b/PathPlanning/StateLatticePlanner/lookuptable.csv
deleted file mode 100644
index 85eff9b932..0000000000
--- a/PathPlanning/StateLatticePlanner/lookuptable.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-x,y,yaw,s,km,kf
-1.0,0.0,0.0,1.0,0.0,0.0
-0.9734888894493215,-0.009758406565994977,0.5358080146312756,0.9922329557399788,-0.10222538550473198,3.0262632253145982
-10.980728996433243,-0.0003093605787364978,0.522622783944529,11.000391678142623,0.00010296091030877934,0.2731556687244648
-16.020309241920156,0.0001292339008200291,0.5243399938698222,16.100019813021202,0.00013263212395994706,0.18999138959173634
-20.963495745193626,-0.00033031017429944326,0.5226120033275024,21.10082901143343,0.00011687467551566884,0.14550546012583987
-6.032553475650599,2.008504211720188,0.5050517859971599,6.400329805864408,0.1520002249689879,-0.13105940607691127
-10.977487445230075,2.0078696810700034,0.5263634407901872,11.201040572298973,0.04895863722280565,0.08356555007223682
-15.994057699325753,2.025659106131227,0.5303858891065698,16.200300421483128,0.0235708657178127,0.10002225103921249
-20.977228843605943,2.0281289825388513,0.5300376140865567,21.20043308669372,0.013795675421657671,0.09331700188063087
-25.95453914157977,1.9926432818499131,0.5226203078411618,26.200880299840527,0.00888830054451281,0.0830622000626594
-0.9999999999999999,0.0,0.0,1.0,0.0,0.0
-5.999999999670752,5.231312388722274e-05,1.4636120911014667e-05,5.996117185283419,4.483756968024291e-06,-3.4422519205046243e-06
-10.999749470720566,-0.011978787043239986,0.022694802592583763,10.99783855994015,-0.00024209503125174496,0.01370491008661795
-15.999885224357776,-0.018937230084507616,0.011565580878015763,15.99839381389597,-0.0002086399372890716,0.005267247862667496
-20.999882688881286,-0.030304200126461317,0.009117836885732596,20.99783120184498,-0.00020013159571184735,0.0034529188783636866
-25.999911270030413,-0.025754431694664327,0.0074809531598503615,25.99674782258235,-0.0001111138115390646,0.0021946603965658368
-10.952178818975062,1.993067260835455,0.0045572480669707136,11.17961498195845,0.04836813285436623,-0.19328716251760758
-16.028306009258,2.0086286208315407,0.009306594796759554,16.122411866381054,0.02330689045417979,-0.08877002085985948
-20.971603115419715,1.9864158336174966,0.007016819403167119,21.093006725076872,0.013439123130720928,-0.05238318300611623
-25.997019676818372,1.9818581321430093,0.007020172975955249,26.074021794586585,0.00876496148602802,-0.03362579291686346
-16.017903482982604,4.009490840390534,-5.293114796312698e-05,16.600937712976638,0.044543450568614244,-0.17444651314466567
-20.98845988414163,3.956600199823583,-0.01050744134070728,21.40149119463485,0.02622674388276059,-0.10625681152519345
-25.979448381017914,3.9968223055054977,-0.00012819253386682928,26.30504721211744,0.017467093413146118,-0.06914750106424669
-25.96268055563514,5.9821266846168,4.931311239565056e-05,26.801388563459287,0.025426008913226557,-0.10047663078001536
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index fb80e1f582..e26fb8288c 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -4,9 +4,9 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-- lookuptable.csv is generated with this script:
+- plookuptable.csv is generated with this script:
 https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning
-/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
+/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
 
 Ref:
 
@@ -21,27 +21,25 @@
 from matplotlib import pyplot as plt
 import numpy as np
 import math
-import pandas as pd
 import pathlib
 sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
 import ModelPredictiveTrajectoryGenerator.trajectory_generator as planner
 import ModelPredictiveTrajectoryGenerator.motion_model as motion_model
 
-table_path = os.path.dirname(os.path.abspath(__file__)) + "/lookuptable.csv"
+TABLE_PATH = os.path.dirname(os.path.abspath(__file__)) + "/lookup_table.csv"
 
 show_animation = True
 
 
-def search_nearest_one_from_lookuptable(tx, ty, tyaw, lookup_table):
+def search_nearest_one_from_lookup_table(t_x, t_y, t_yaw, lookup_table):
     mind = float("inf")
     minid = -1
 
     for (i, table) in enumerate(lookup_table):
-
-        dx = tx - table[0]
-        dy = ty - table[1]
-        dyaw = tyaw - table[2]
+        dx = t_x - table[0]
+        dy = t_y - table[1]
+        dyaw = t_yaw - table[2]
         d = math.sqrt(dx ** 2 + dy ** 2 + dyaw ** 2)
         if d <= mind:
             minid = i
@@ -50,24 +48,22 @@ def search_nearest_one_from_lookuptable(tx, ty, tyaw, lookup_table):
     return lookup_table[minid]
 
 
-def get_lookup_table():
-    data = pd.read_csv(table_path)
-
-    return np.array(data)
+def get_lookup_table(table_path):
+    return np.loadtxt(table_path, delimiter=',', skiprows=1)
 
 
 def generate_path(target_states, k0):
     # x, y, yaw, s, km, kf
-    lookup_table = get_lookup_table()
+    lookup_table = get_lookup_table(TABLE_PATH)
     result = []
 
     for state in target_states:
-        bestp = search_nearest_one_from_lookuptable(
+        bestp = search_nearest_one_from_lookup_table(
             state[0], state[1], state[2], lookup_table)
 
         target = motion_model.State(x=state[0], y=state[1], yaw=state[2])
         init_p = np.array(
-            [math.sqrt(state[0] ** 2 + state[1] ** 2), bestp[4], bestp[5]]).reshape(3, 1)
+            [np.hypot(state[0], state[1]), bestp[4], bestp[5]]).reshape(3, 1)
 
         x, y, yaw, p = planner.optimize_trajectory(target, k0, init_p)
 
@@ -82,18 +78,28 @@ def generate_path(target_states, k0):
 
 def calc_uniform_polar_states(nxy, nh, d, a_min, a_max, p_min, p_max):
     """
-    calc uniform state
 
-    :param nxy: number of position sampling
-    :param nh: number of heading sampleing
-    :param d: distance of terminal state
-    :param a_min: position sampling min angle
-    :param a_max: position sampling max angle
-    :param p_min: heading sampling min angle
-    :param p_max: heading sampling max angle
-    :return: states list
-    """
+    Parameters
+    ----------
+    nxy :
+        number of position sampling
+    nh :
+        number of heading sampleing
+    d :
+        distance of terminal state
+    a_min :
+        position sampling min angle
+    a_max :
+        position sampling max angle
+    p_min :
+        heading sampling min angle
+    p_max :
+        heading sampling max angle
+
+    Returns
+    -------
 
+    """
     angle_samples = [i / (nxy - 1) for i in range(nxy)]
     states = sample_states(angle_samples, a_min, a_max, d, p_max, p_min, nh)
 
@@ -312,11 +318,11 @@ def lane_state_sampling_test1():
         plt.close("all")
 
     for table in result:
-        xc, yc, yawc = motion_model.generate_trajectory(
+        x_c, y_c, yaw_c = motion_model.generate_trajectory(
             table[3], table[4], table[5], k0)
 
         if show_animation:
-            plt.plot(xc, yc, "-r")
+            plt.plot(x_c, y_c, "-r")
 
     if show_animation:
         plt.grid(True)
diff --git a/README.md b/README.md
index 2d0ee8d653..28b932baf2 100644
--- a/README.md
+++ b/README.md
@@ -103,8 +103,6 @@ For running each sample code:
  
 - [Matplotlib](https://matplotlib.org/)
  
-- [pandas](https://pandas.pydata.org/)
-
 - [cvxpy](https://www.cvxpy.org/) 
 
 For development:
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 4a19126686..c2be93261b 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -30,7 +30,6 @@ Requirements
 -  `NumPy`_
 -  `SciPy`_
 -  `Matplotlib`_
--  `pandas`_
 -  `cvxpy`_
 
 For development:
@@ -45,7 +44,6 @@ For development:
 .. _`NumPy`: https://numpy.org/
 .. _`SciPy`: https://scipy.org/
 .. _`Matplotlib`: https://matplotlib.org/
-.. _`pandas`: https://pandas.pydata.org/
 .. _`cvxpy`: https://www.cvxpy.org/
 
 
diff --git a/requirements/environment.yml b/requirements/environment.yml
index 109fc57789..202c2693d9 100644
--- a/requirements/environment.yml
+++ b/requirements/environment.yml
@@ -6,6 +6,5 @@ dependencies:
   - pip
   - scipy
   - numpy
-  - pandas
   - cvxpy
   - matplotlib
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 61a73fd2c6..42225b5fbe 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,5 @@
 numpy == 1.23.3
 scipy == 1.9.1
-pandas == 1.4.4
 matplotlib == 3.5.3
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test

From 3814341a10e0d7b97db4d5f5c8057e1d4cb17a2d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 30 Sep 2022 09:24:03 +0900
Subject: [PATCH 179/604] Remove pandas dependency (#727)

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index 28b932baf2..97a7742dca 100644
--- a/README.md
+++ b/README.md
@@ -632,6 +632,11 @@ 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)
+
+They are providing a free license of their 1Password team license for this OSS project.   
+
+
 # Authors
 
 - [Contributors to AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/graphs/contributors)

From aebf8dc3ff6d82af7e02b3ed1cd3167c9aae6683 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Oct 2022 11:14:25 +0900
Subject: [PATCH 180/604] clean up circle_artifact (#728)

* clean up circle_artifact

* Update circleci-artifacts-redirector.yml
---
 .github/workflows/circle_artifacts.yml              | 13 -------------
 .github/workflows/circleci-artifacts-redirector.yml |  2 +-
 2 files changed, 1 insertion(+), 14 deletions(-)
 delete mode 100644 .github/workflows/circle_artifacts.yml

diff --git a/.github/workflows/circle_artifacts.yml b/.github/workflows/circle_artifacts.yml
deleted file mode 100644
index 56ac78ed15..0000000000
--- a/.github/workflows/circle_artifacts.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-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 d988d2c38e..149a087a5d 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -3,7 +3,7 @@ on: [status]
 jobs:
   circleci_artifacts_redirector_job:
     runs-on: ubuntu-latest
-    name: Run CircleCI artifacts redirector
+    name: Run CircleCI artifacts redirector!!
     steps:
       - name: run-circleci-artifacts-redirector
         uses: larsoner/circleci-artifacts-redirector-action@0.3.1

From b607af70c3431bb8e97d17a4805aaadd6ddea857 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Oct 2022 07:27:02 +0900
Subject: [PATCH 181/604] Bump matplotlib from 3.5.3 to 3.6.0 in /requirements
 (#729)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.3 to 3.6.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.3...v3.6.0)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 42225b5fbe..2befd0a9e4 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.3
 scipy == 1.9.1
-matplotlib == 3.5.3
+matplotlib == 3.6.0
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 33f2fa39dc796ab5a9d5e227cece04ffc8297794 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Oct 2022 07:27:22 +0900
Subject: [PATCH 182/604] Bump mypy from 0.971 to 0.982 in /requirements (#730)

Bumps [mypy](https://github.com/python/mypy) from 0.971 to 0.982.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.971...v0.982)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 2befd0a9e4..a85aa121e6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.6.0
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.971 # For unit test
+mypy == 0.982 # For unit test
 flake8 == 5.0.4 # For unit test

From 1c165262f738b41d91acdb03ca546e82c7313fcc Mon Sep 17 00:00:00 2001
From: Abinash Satapathy <iamabinash98@gmail.com>
Date: Mon, 10 Oct 2022 02:15:43 +0200
Subject: [PATCH 183/604] Update SECURITY.md (#733)

Typo correction
---
 SECURITY.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SECURITY.md b/SECURITY.md
index 5047a6fc3d..53dcafa450 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -10,4 +10,4 @@ In this project, we are only support latest code.
 
 ## Reporting a Vulnerability
 
-If you find any sequrity related problem, let us know by creating an issue about it.
+If you find any security related problem, let us know by creating an issue about it.

From 028a0a4d8d9eb53be1291fd0db3930d0c8e351c4 Mon Sep 17 00:00:00 2001
From: Abinash Satapathy <iamabinash98@gmail.com>
Date: Mon, 10 Oct 2022 02:16:31 +0200
Subject: [PATCH 184/604] Update pull_request_template.md (#735)

---
 .github/pull_request_template.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index c8790ad9ec..b6ac52efa2 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -19,6 +19,6 @@ Again, thanks for contributing!
 <!--Any additional information you think is important.-->
 
 #### 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 185/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 186/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 187/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 188/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 <iamabinash98@gmail.com>
Date: Sun, 30 Oct 2022 06:28:58 +0100
Subject: [PATCH 189/604] 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
+# <a id="support"></a>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
+## <a id="sponsors"></a>Sponsors
 
-### [JetBrains](https://www.jetbrains.com/)
+### <a id="JetBrains"></a>[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 190/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 191/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 192/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 193/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 194/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Thu, 10 Nov 2022 21:29:11 +0900
Subject: [PATCH 195/604] 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 196/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 <KohMat@users.noreply.github.com>
Date: Sun, 20 Nov 2022 13:19:42 +0900
Subject: [PATCH 197/604] 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 198/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Mon, 28 Nov 2022 21:27:22 +0900
Subject: [PATCH 199/604] 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 200/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 201/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 202/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 203/604] 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 <cclauss@me.com>
Date: Sat, 31 Dec 2022 07:51:17 +0100
Subject: [PATCH 204/604] 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 <andybozhko@gmail.com>
Date: Sun, 1 Jan 2023 05:44:34 -0600
Subject: [PATCH 205/604] 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 206/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Tue, 3 Jan 2023 22:27:55 +0900
Subject: [PATCH 207/604] 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 b/docs/modules/mapping/point_cloud_sampling/farthest_point_sampling.png
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diff --git a/docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst b/docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst
new file mode 100644
index 0000000000..cbb5652f56
--- /dev/null
+++ b/docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst
@@ -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 <https://en.wikipedia.org/wiki/Voxel>`_ 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
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QvA~aloRVyz^y_#32jv8rPXGV_

literal 0
HcmV?d00001

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 208/604] 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 209/604] 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 <kadir.haspalamutgil@togethertech.com>
---
 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 210/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 211/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 212/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 213/604] 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] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Thu, 26 Jan 2023 21:56:42 +0900
Subject: [PATCH 214/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 26 Jan 2023 22:46:00 +0900
Subject: [PATCH 215/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 29 Jan 2023 22:22:11 +0900
Subject: [PATCH 216/604] 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 <asakai.amsl+github@gmail.com>
Date: Mon, 30 Jan 2023 21:33:49 +0900
Subject: [PATCH 217/604] 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 218/604] 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] <support@github.com>
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 219/604] 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] <support@github.com>
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 220/604] 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] <support@github.com>
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 221/604] 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] <support@github.com>
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 222/604] 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] <support@github.com>
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 223/604] 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] <support@github.com>
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 224/604] 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] <support@github.com>
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 225/604] 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] <support@github.com>
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 226/604] 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] <support@github.com>
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 227/604] 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] <support@github.com>
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 228/604] 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] <support@github.com>
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 229/604] 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] <support@github.com>
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 230/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Sat, 11 Mar 2023 17:54:18 +0900
Subject: [PATCH 231/604] 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
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diff --git a/docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst b/docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst
new file mode 100644
index 0000000000..a4d1bf0df2
--- /dev/null
+++ b/docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst
@@ -0,0 +1,74 @@
+Normal vector estimation
+-------------------------
+
+
+Normal vector calculation of a 3D triangle
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A 3D point is as a vector:
+
+.. math:: p = [x, y, z]
+
+When there are 3 points in 3D space, :math:`p_1, p_2, p_3`,
+
+we can calculate a normal vector n of a 3D triangle which is consisted of the points.
+
+.. math:: n = \frac{v1 \times v2}{|v1 \times v2|}
+
+where
+
+.. 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 <https://stackoverflow.com/a/44315221/8387766>`_
+
+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) <https://en.wikipedia.org/wiki/Random_sample_consensus>`_
+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
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literal 0
HcmV?d00001

diff --git a/ruff.toml b/ruff.toml
index fd02032f48..578864b33e 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,7 +1,7 @@
 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 232/604] 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] <support@github.com>
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 233/604] 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] <support@github.com>
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 234/604] 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] <support@github.com>
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 235/604] 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] <support@github.com>
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 236/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Wed, 22 Mar 2023 22:47:12 +0900
Subject: [PATCH 237/604] 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 238/604] 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] <support@github.com>
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 239/604] 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] <support@github.com>
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 240/604] 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] <support@github.com>
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 241/604] 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] <support@github.com>
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 242/604] 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] <support@github.com>
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 243/604] 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] <support@github.com>
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 244/604] 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] <support@github.com>
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 245/604] 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] <support@github.com>
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 246/604] 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 <asakai.amsl+github@gmail.com>
Date: Tue, 2 May 2023 20:54:45 +0900
Subject: [PATCH 247/604] 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 248/604] 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] <support@github.com>
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 249/604] 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] <support@github.com>
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 250/604] 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] <support@github.com>
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 251/604] 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] <support@github.com>
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 252/604] 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?= <yangshen.d@outlook.com>
Date: Tue, 16 May 2023 20:43:57 +0800
Subject: [PATCH 253/604] 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 254/604] 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] <support@github.com>
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 255/604] 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] <support@github.com>
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 256/604] 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 257/604] 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 258/604] 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] <support@github.com>
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 259/604] 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] <support@github.com>
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 260/604] 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] <support@github.com>
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 261/604] 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 262/604] 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] <support@github.com>
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 263/604] 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] <support@github.com>
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 264/604] 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] <support@github.com>
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 265/604] 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
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="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 <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jul 2023 08:29:34 +0900
Subject: [PATCH 266/604] 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 <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
+
+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 <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
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 <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jul 2023 15:30:32 +0900
Subject: [PATCH 267/604] 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 <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jul 2023 18:40:03 +0900
Subject: [PATCH 268/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jul 2023 22:12:25 +0900
Subject: [PATCH 269/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jul 2023 22:25:55 +0900
Subject: [PATCH 270/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jul 2023 22:27:45 +0900
Subject: [PATCH 271/604] 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 272/604] 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] <support@github.com>
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 273/604] 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] <support@github.com>
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 274/604] 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] <support@github.com>
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 275/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Thu, 6 Jul 2023 23:12:43 +0900
Subject: [PATCH 276/604] 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
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diff --git a/docs/modules/mapping/ndt_map/ndt_map_main.rst b/docs/modules/mapping/ndt_map/ndt_map_main.rst
new file mode 100644
index 0000000000..bfc3936314
--- /dev/null
+++ b/docs/modules/mapping/ndt_map/ndt_map_main.rst
@@ -0,0 +1,53 @@
+.. _ndt_map:
+
+Normal Distance Transform (NDT) map
+------------------------------------
+
+This is a NDT mapping example.
+
+Normal Distribution Transform (NDT) is a map representation that uses normal distribution for observation point modeling.
+
+Normal Distribution
+~~~~~~~~~~~~~~~~~~~~~
+
+Normal distribution consists of two parameters: mean :math:`\mu` and covariance :math:`\Sigma`.
+
+:math:`\mathbf{X} \sim \mathcal{N}(\boldsymbol{\mu}, \boldsymbol{\Sigma})`
+
+In the 2D case, :math:`\boldsymbol{\mu}` is a 2D vector and :math:`\boldsymbol{\Sigma}` is a 2x2 matrix.
+
+In the matrix form, the probability density function of thr normal distribution is:
+
+:math:`X=\frac{1}{\sqrt{(2 \pi)^2|\Sigma|}} \exp \left\{-\frac{1}{2}^t(x-\mu) \sum^{-1}(x-\mu)\right\}`
+
+Normal Distance Transform mapping steps
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~　
+
+NDT mapping consists of two steps:
+
+When we have a new observation like this:
+
+.. figure:: raw_observations.png
+
+First, we need to cluster the observation points.
+This is done by using a grid based clustering algorithm.
+
+The result is:
+
+.. figure:: grid_clustering.png
+
+Then, we need to fit a normal distribution to each grid cluster.
+
+Black ellipse shows each NDT grid like this:
+
+.. figure:: ndt_map1.png
+
+.. figure:: ndt_map2.png
+
+API
+~~~~~
+
+.. autoclass:: Mapping.ndt_map.ndt_map.NDTMap
+    :members:
+    :class-doc-from: class
+
diff --git a/docs/modules/mapping/ndt_map/raw_observations.png b/docs/modules/mapping/ndt_map/raw_observations.png
new file mode 100644
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Okcxu3e4d=y%l`%7gFUVQ

literal 0
HcmV?d00001

diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
index 92ca67e297..670b357ad3 100644
--- a/tests/test_grid_map_lib.py
+++ b/tests/test_grid_map_lib.py
@@ -25,5 +25,16 @@ def test_polygon_set():
                                     1.0, inside=False)
 
 
+def test_xy_and_grid_index_conversion():
+    grid_map = GridMap(100, 120, 0.5, 10.0, -0.5)
+
+    for x_ind in range(grid_map.width):
+        for y_ind in range(grid_map.height):
+            grid_ind = grid_map.calc_grid_index_from_xy_index(x_ind, y_ind)
+            x_ind_2, y_ind_2 = 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 277/604] 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] <support@github.com>
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 278/604] 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] <support@github.com>
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 279/604] 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] <support@github.com>
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 280/604] 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] <support@github.com>
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 <emmanuelferdman@gmail.com>
Date: Wed, 19 Jul 2023 16:04:35 +0300
Subject: [PATCH 281/604] docs: update the code style test script (#880)

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
---
 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 282/604] 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] <support@github.com>
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 283/604] 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] <support@github.com>
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 284/604] 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] <support@github.com>
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 285/604] 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] <support@github.com>
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 286/604] 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] <support@github.com>
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 287/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Tue, 15 Aug 2023 12:40:52 +0900
Subject: [PATCH 288/604] 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 <asakai.amsl+github@gmail.com>
Date: Tue, 15 Aug 2023 13:04:17 +0900
Subject: [PATCH 289/604] 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 290/604] 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] <support@github.com>
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 291/604] 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] <support@github.com>
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 292/604] 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] <support@github.com>
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 293/604] 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] <support@github.com>
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 294/604] 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] <support@github.com>
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 295/604] 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] <support@github.com>
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 296/604] 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] <support@github.com>
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 297/604] 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] <support@github.com>
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 298/604] 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] <support@github.com>
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 299/604] 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] <support@github.com>
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 300/604] 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] <support@github.com>
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 301/604] 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] <support@github.com>
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 302/604] 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] <support@github.com>
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 303/604] 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] <support@github.com>
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 <yuki.st16@gmail.com>
Date: Sun, 15 Oct 2023 10:25:22 +0900
Subject: [PATCH 304/604] 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 305/604] 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] <support@github.com>
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 306/604] 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] <support@github.com>
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 307/604] 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] <support@github.com>
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 308/604] 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] <support@github.com>
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 309/604] 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] <support@github.com>
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 310/604] 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] <support@github.com>
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 <JeffLIrion@users.noreply.github.com>
Date: Wed, 25 Oct 2023 07:02:14 -0700
Subject: [PATCH 311/604] 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 312/604] 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] <support@github.com>
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 313/604] 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] <support@github.com>
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 314/604] 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] <support@github.com>
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 315/604] 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] <support@github.com>
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 316/604] 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] <support@github.com>
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 317/604] 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] <support@github.com>
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 318/604] 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] <support@github.com>
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 319/604] 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] <support@github.com>
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 320/604] 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] <support@github.com>
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 321/604] 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] <support@github.com>
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 322/604] 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] <support@github.com>
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 323/604] 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] <support@github.com>
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 324/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:23:50 +0900
Subject: [PATCH 325/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:35:30 +0900
Subject: [PATCH 326/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:41:48 +0900
Subject: [PATCH 327/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:46:56 +0900
Subject: [PATCH 328/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:53:12 +0900
Subject: [PATCH 329/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:56:04 +0900
Subject: [PATCH 330/604] 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 331/604] 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 332/604] 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] <support@github.com>
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 333/604] 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] <support@github.com>
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 334/604] 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 335/604] 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 336/604] 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] <support@github.com>
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 337/604] 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] <support@github.com>
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 338/604] 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 <asakai.amsl+github@gmail.com>
Date: Fri, 22 Dec 2023 12:13:59 +0900
Subject: [PATCH 339/604] 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 340/604] 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] <support@github.com>
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 341/604] 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] <support@github.com>
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 342/604] 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 <videh.patel@fluxauto.xyz>
---
 .../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 343/604] 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] <support@github.com>
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 344/604] 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 <videh.patel@fluxauto.xyz>
---
 .../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
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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 81e565888e..6d456417fe 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
@@ -5,6 +5,381 @@ A sample code with Reeds Shepp path planning.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
 
+Mathematical Description of Individual Path Types
+=================================================
+Here is an overview of mathematical derivations of formulae for individual path types.
+
+In all the derivations below, radius of curvature of the vehicle is assumed to be of unit length and start pose is considered to be at origin.  (*In code we are removing the offset due to start position and normalising the lengths before passing the values to these functions.*)
+
+Also, (t, u, v) respresent the measure of each motion requried. Thus, in case of a turning maneuver, they represent the angle inscribed at the centre of turning circle and in case of straight maneuver, they represent the distance to be travelled. 
+
+1. **Left-Straight-Left**
+
+.. image:: LSL.png
+
+We can deduce the following facts using geometry.
+
+- AGHC is a rectangle.
+- :math:`∠LAC = ∠BAG = t`
+- :math:`t + v = φ`
+- :math:`C(x - sin(φ), y + cos(φ))`
+- :math:`A(0, 1)`
+- :math:`u, t = polar(vector<AC>)`
+
+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<AC>)`
+- :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<BD>)`
+- :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<BD>)`
+- :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<BD>)`
+- :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<BG>)`
+- :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<BG>)`
+- :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<BF>)`
+- :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<BH>)`
+- :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<BG>)`
+- :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<BH>)`
+- :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<BH>)`
+- :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 345/604] 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] <support@github.com>
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 346/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Thu, 18 Jan 2024 23:14:51 +0900
Subject: [PATCH 347/604] 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 348/604] 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] <support@github.com>
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 <rsasaki0109@gmail.com>
Date: Mon, 22 Jan 2024 22:02:22 +0900
Subject: [PATCH 349/604] 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 <asakai.amsl+github@gmail.com>
---
 .../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
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diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
index 0ec920c9df..da214b6de5 100644
--- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
+++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
@@ -2,6 +2,9 @@
 Extended Kalman Filter Localization
 -----------------------------------
 
+Position Estimation Kalman Filter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 .. image:: extended_kalman_filter_localization_1_0.png
    :width: 600px
 
@@ -9,6 +12,7 @@ Extended Kalman Filter Localization
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif
 
+
 This is a sensor fusion localization with Extended Kalman Filter(EKF).
 
 The blue line is true trajectory, the black line is dead reckoning
@@ -22,9 +26,26 @@ The red ellipse is estimated covariance ellipse with EKF.
 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>`__
 
+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 <https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py>`__
+
 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 <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L34-L50>`__
 
+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 <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py#L163>`__
+
+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 <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L61-L76>`__
+
+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 350/604] 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] <support@github.com>
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 351/604] 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] <support@github.com>
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 352/604] 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] <support@github.com>
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 <chris7462@gmail.com>
Date: Sat, 27 Jan 2024 23:59:02 -0500
Subject: [PATCH 353/604] 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 <http://www.probabilistic-robotics.org/>`_
-
-

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 354/604] 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] <support@github.com>
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 355/604] 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] <support@github.com>
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 356/604] 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] <support@github.com>
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 357/604] 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] <support@github.com>
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 358/604] 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] <support@github.com>
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 359/604] 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] <support@github.com>
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 360/604] 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] <support@github.com>
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 361/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Sat, 24 Feb 2024 18:26:59 +0900
Subject: [PATCH 362/604] 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 <wlt@o-sinc.com>
Date: Sat, 24 Feb 2024 04:35:13 -0500
Subject: [PATCH 363/604] 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 364/604] 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] <support@github.com>
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 365/604] 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] <support@github.com>
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 366/604] 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] <support@github.com>
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 367/604] 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] <support@github.com>
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 368/604] 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] <support@github.com>
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 <guoguangwu@magic-shield.com>
Date: Wed, 13 Mar 2024 20:20:01 +0800
Subject: [PATCH 369/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 14 Mar 2024 22:01:51 +0900
Subject: [PATCH 370/604] 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 371/604] 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] <support@github.com>
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 372/604] 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] <support@github.com>
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 373/604] 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] <support@github.com>
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 374/604] 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] <support@github.com>
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 375/604] 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] <support@github.com>
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 376/604] 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] <support@github.com>
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 377/604] 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] <support@github.com>
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 378/604] 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] <support@github.com>
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 <xiaoshan3920@gmail.com>
Date: Sat, 27 Apr 2024 20:51:19 +0800
Subject: [PATCH 379/604] 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 <chris7462@gmail.com>
Date: Sun, 28 Apr 2024 07:55:25 -0400
Subject: [PATCH 380/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 28 Apr 2024 21:17:33 +0900
Subject: [PATCH 381/604] 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 382/604] 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] <support@github.com>
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 383/604] 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] <support@github.com>
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 384/604] 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] <support@github.com>
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 385/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Sat, 4 May 2024 23:08:14 +0900
Subject: [PATCH 386/604] 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 387/604] 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] <support@github.com>
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 388/604] 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] <support@github.com>
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 389/604] 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 390/604] 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] <support@github.com>

* fix cvxpy update issue

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../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 391/604] --- (#1020)

updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 392/604] --- (#1019)

updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Sat, 25 May 2024 23:31:54 +0900
Subject: [PATCH 393/604] 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.
+
+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.
+
 References:
 ~~~~~~~~~~~
 -  `ApolloAuto/apollo: An open autonomous driving platform <https://github.com/ApolloAuto/apollo>`_
 
+- `Linear Quadratic Regulator (LQR) <https://en.wikipedia.org/wiki/Linear%E2%80%93quadratic_regulator>`_
+
diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png
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From b1fb7fe24e353852b3ab8cc97511463746eac0e9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 May 2024 23:46:08 +0900
Subject: [PATCH 394/604] improve lqr steering control docs

---
 .../lqr_steering_control_main.rst               |   2 +-
 .../lqr_steering_control_model.jpg              | Bin 0 -> 11721 bytes
 .../lqr_steering_control_model.png              | Bin 17366 -> 0 bytes
 3 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 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 b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.jpg
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diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png
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From 3504db00a666d84de0e4738fad1db78142f34c17 Mon Sep 17 00:00:00 2001
From: RyuYamamoto <ryu.yamamoto@tier4.jp>
Date: Sun, 26 May 2024 15:42:25 +0900
Subject: [PATCH 395/604] 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 396/604] 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 <tim@groundlight.ai>
---
 .../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 397/604] 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] <support@github.com>
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 398/604] 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] <support@github.com>
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 399/604] 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] <support@github.com>
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 400/604] 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] <support@github.com>
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 401/604] 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] <support@github.com>
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 <yukky.saito@gmail.com>
Date: Sat, 15 Jun 2024 07:46:15 +0900
Subject: [PATCH 402/604] fix(README.md): typo in  reeds-shepp path (#1031)

Signed-off-by: Yukihito Saito <yukky.saito@gmail.com>
---
 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 403/604] 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] <support@github.com>
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 404/604] 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] <support@github.com>
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 405/604] 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] <support@github.com>
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 406/604] 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] <support@github.com>
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 407/604] 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] <support@github.com>
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 408/604] 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] <support@github.com>
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 409/604] 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] <support@github.com>
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 410/604] 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] <support@github.com>
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 411/604] 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] <support@github.com>
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 412/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Tue, 16 Jul 2024 23:02:31 +0900
Subject: [PATCH 413/604] 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] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py>`_
+
+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
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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] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_steer_control/lqr_steer_control.py>`_
+
 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 414/604] 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] <support@github.com>
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 415/604] 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] <support@github.com>
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 416/604] 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] <support@github.com>
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 417/604] 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] <support@github.com>
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 418/604] 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] <support@github.com>
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 419/604] 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] <support@github.com>
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 420/604] 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] <support@github.com>
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 421/604] 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] <support@github.com>
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 422/604] 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] <support@github.com>
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 423/604] 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] <support@github.com>
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 424/604] 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] <support@github.com>
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 425/604] 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] <support@github.com>
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 426/604] 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] <support@github.com>
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 427/604] 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] <support@github.com>
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 428/604] 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] <support@github.com>
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 429/604] 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] <support@github.com>
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 430/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Mon, 16 Sep 2024 22:03:54 +0900
Subject: [PATCH 431/604] 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
    Kalmanfilter_basics
    Kalmanfilter_basics_2
 
diff --git a/docs/modules/appendix/steering_motion_model/steering_model.png b/docs/modules/appendix/steering_motion_model/steering_model.png
new file mode 100644
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diff --git a/docs/modules/appendix/steering_motion_model_main.rst b/docs/modules/appendix/steering_motion_model_main.rst
new file mode 100644
index 0000000000..6e444b7909
--- /dev/null
+++ b/docs/modules/appendix/steering_motion_model_main.rst
@@ -0,0 +1,97 @@
+
+Steering Motion Model
+-----------------------
+
+Turning radius calculation by steering motion model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+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 <https://link.springer.com/book/10.1007/978-1-4614-1433-9>`_

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 432/604] 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] <support@github.com>
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 433/604] 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] <support@github.com>
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 434/604] 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] <support@github.com>
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 435/604] 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] <support@github.com>
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 436/604] 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] <support@github.com>
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 437/604] 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] <support@github.com>
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 438/604] 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] <support@github.com>
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 439/604] 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] <support@github.com>
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 440/604] 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] <support@github.com>
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 441/604] 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] <support@github.com>
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 442/604] 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] <support@github.com>
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 443/604] 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] <support@github.com>
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 444/604] 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] <support@github.com>
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 445/604] 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] <support@github.com>
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 446/604] 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] <support@github.com>
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 447/604] 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] <support@github.com>
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 448/604] 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] <support@github.com>
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 449/604] 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] <support@github.com>
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 450/604] 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] <support@github.com>
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 451/604] 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] <support@github.com>
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 452/604] 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] <support@github.com>
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 <weipu.shan@gmail.com>
Date: Fri, 20 Dec 2024 20:32:57 +0800
Subject: [PATCH 453/604] 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 454/604] 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 455/604] 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
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diff --git a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_path_planning.png b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_path_planning.png
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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 <https://en.wikipedia.org/wiki/Centripetal_Catmull%E2%80%93Rom_spline>`__
+-  `Catmull-Rom Splines <http://graphics.cs.cmu.edu/nsp/course/15-462/Fall04/assts/catmullRom.pdf>`__
\ 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 <asakai.amsl+github@gmail.com>
Date: Sat, 21 Dec 2024 15:04:02 +0900
Subject: [PATCH 456/604] 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 457/604] 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 458/604] 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 459/604] 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] <support@github.com>
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 460/604] 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] <support@github.com>
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 461/604] 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] <support@github.com>
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 462/604] 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] <support@github.com>
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 463/604] 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] <support@github.com>
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 464/604] 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] <support@github.com>
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 465/604] 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] <support@github.com>
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 466/604] 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] <support@github.com>
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 467/604]  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 468/604] 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 469/604] 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] <support@github.com>
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 470/604] 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] <support@github.com>
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 471/604] 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 <asakai.amsl+github@gmail.com>
Date: Fri, 24 Jan 2025 13:24:26 +0900
Subject: [PATCH 472/604] 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 473/604] 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 <https://lucacarlone.mit.edu/datasets/>`_ 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 <https://arxiv.org/pdf/1211.3063.pdf>`_. 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 <https://arxiv.org/pdf/1211.3063>`_. 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: <http://www.probabilistic-robotics.org>`_
-- `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000.pdf>`_
+- `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000>`_
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 <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/>`_
 
 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 <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/>`_
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 <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
+   Vehicles <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`
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 <https://sites.google.com/site/ece452bugalgorithms/>`_
+- `ECE452 Bug Algorithms <https://web.archive.org/web/20201103052224/https://sites.google.com/site/ece452bugalgorithms/>`_
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 <http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
+- `Planning paths of complete coverage of an unstructured environment by a mobile robot <https://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
 
 
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 <https://www.jstor.org/stable/2372560?origin=crossref>`__
 -  `Dubins path - Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
--  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__
+-  `15.3.1 Dubins Curves <https://lavalle.pl/planning/node821.html>`__
 -  `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths <https://gieseanw.wordpress.com/2012/10/21/a-comprehensive-step-by-step-tutorial-to-computing-dubins-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 <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
+   robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
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 <http://ieeexplore.ieee.org/document/637936/>`__
+   Positioning <https://ieeexplore.ieee.org/document/637936/>`__
 
 
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 <http://planning.cs.uiuc.edu/node822.html>`__
+   Curves <https://lavalle.pl/planning/node822.html>`__
 
 -  `optimal paths for a car that goes both forwards and
    backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
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 <https://arxiv.org/pdf/1404.2334.pdf>`__
+   Heuristic <https://arxiv.org/pdf/1404.2334>`__
 
 .. _batch-informed-rrt*:
 
@@ -90,10 +90,10 @@ PID is used for speed control.
 Ref:
 
 -  `Motion Planning in Complex Environments using Closed-loop
-   Prediction <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
+   Prediction <https://acl.mit.edu/papers/KuwataGNC08.pdf>`__
 
 -  `Real-time Motion Planning with Applications to Autonomous Urban
-   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
+   Driving <https://acl.mit.edu/papers/KuwataTCST09.pdf>`__
 
 -  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
    Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
@@ -113,6 +113,6 @@ Ref:
 
 -  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
    Derived Extension
-   Heuristics <http://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
+   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>`__
\ 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 <https://arxiv.org/pdf/1105.1186.pdf>`__
+-  `Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/pdf/1105.1186>`__
 -  `Incremental Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/abs/1005.0416>`__
 
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 <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
+   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>`__
+   Environments <https://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
 
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 <https://github.com/Shunichi09/nonlinear_control>`__
+   control <https://github.com/Shunichi09/PythonLinearNonlinearControl>`__
 
 -  `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>`__
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 <http://ieeexplore.ieee.org/document/5940562/>`__
+-  `Towards fully autonomous driving: Systems and algorithms <https://ieeexplore.ieee.org/document/5940562/>`__
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 <http://www.springer.com/us/book/9781461414322>`__
 
--  `MPC Course Material - MPC Lab @
-   UC-Berkeley <http://www.mpc.berkeley.edu/mpc-course-material>`__
+-  `MPC Book - MPC Lab @
+   UC-Berkeley <https://sites.google.com/berkeley.edu/mpc-lab/mpc-course-material>`__
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, <br>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 <asakai.amsl+github@gmail.com>
Date: Sat, 25 Jan 2025 21:06:23 +0900
Subject: [PATCH 474/604] 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 <https://en.wikipedia.org/wiki/Centripetal_Catmull%E2%80%93Rom_spline>`__
 -  `Catmull-Rom Splines <http://graphics.cs.cmu.edu/nsp/course/15-462/Fall04/assts/catmullRom.pdf>`__
\ No newline at end of file

From bf8f1774b753d3b0915960628386bb220de8f0af Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 Jan 2025 23:30:14 +0900
Subject: [PATCH 475/604] Update doc organization (#1126)

---
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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
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similarity index 100%
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diff --git a/docs/modules/appendix/steering_motion_model/steering_model.png b/docs/modules/12_appendix/steering_motion_model/steering_model.png
similarity index 100%
rename from docs/modules/appendix/steering_motion_model/steering_model.png
rename to docs/modules/12_appendix/steering_motion_model/steering_model.png
diff --git a/docs/modules/appendix/steering_motion_model/turning_radius_calc1.png b/docs/modules/12_appendix/steering_motion_model/turning_radius_calc1.png
similarity index 100%
rename from docs/modules/appendix/steering_motion_model/turning_radius_calc1.png
rename to docs/modules/12_appendix/steering_motion_model/turning_radius_calc1.png
diff --git a/docs/modules/appendix/steering_motion_model/turning_radius_calc2.png b/docs/modules/12_appendix/steering_motion_model/turning_radius_calc2.png
similarity index 100%
rename from docs/modules/appendix/steering_motion_model/turning_radius_calc2.png
rename to docs/modules/12_appendix/steering_motion_model/turning_radius_calc2.png
diff --git 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
+
+   1_definition_of_robotics/definition_of_robotics
+   2_software_for_robotics/software_for_robotics
+   3_technology_for_robotics/technology_for_robotics
+
diff --git a/docs/modules/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
similarity index 100%
rename from docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
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diff --git a/docs/modules/introduction_main.rst b/docs/modules/introduction_main.rst
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index f9fb487297..0000000000
--- a/docs/modules/introduction_main.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-
-Introduction
-============
-
-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/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
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From f1b171d88b00d8579e8cd4b0b1ee7cc42e0c8407 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 26 Jan 2025 22:12:28 +0900
Subject: [PATCH 476/604] 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 <asakai.amsl+github@gmail.com>
Date: Mon, 27 Jan 2025 22:11:15 +0900
Subject: [PATCH 477/604] 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 <asakai.amsl+github@gmail.com>
Date: Mon, 27 Jan 2025 22:41:52 +0900
Subject: [PATCH 478/604] 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 479/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Tue, 28 Jan 2025 22:25:53 +0900
Subject: [PATCH 480/604] 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 <asakai.amsl+github@gmail.com>
Date: Wed, 29 Jan 2025 21:54:18 +0900
Subject: [PATCH 481/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 30 Jan 2025 22:25:45 +0900
Subject: [PATCH 482/604] 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 <asakai.amsl+github@gmail.com>
Date: Fri, 31 Jan 2025 21:38:06 +0900
Subject: [PATCH 483/604] update getting started (#1134)

Co-authored-by: Atsushi Sakai <atsushi_sakai@AtsushiSakainonotobukkukonpyuta.local>
---
 .../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 <asakai.amsl+github@gmail.com>
Date: Sat, 1 Feb 2025 22:47:52 +0900
Subject: [PATCH 484/604] update getting started (#1135)

Co-authored-by: Atsushi Sakai <atsushi_sakai@AtsushiSakainonotobukkukonpyuta.local>
---
 .../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 <asakai.amsl+github@gmail.com>
Date: Sun, 2 Feb 2025 21:34:26 +0900
Subject: [PATCH 485/604] update getting started (#1136)

Co-authored-by: Atsushi Sakai <atsushi_sakai@AtsushiSakainonotobukkukonpyuta.local>
---
 .../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 <asakai.amsl+github@gmail.com>
Date: Mon, 3 Feb 2025 22:19:10 +0900
Subject: [PATCH 486/604] 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 487/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Wed, 5 Feb 2025 13:41:28 +0900
Subject: [PATCH 488/604] 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 <https://www.python.org/>`_ is an general-purpose programming language developed by
+`Guido van Rossum <https://en.wikipedia.org/wiki/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 <https://numpy.org/>`_ is the fundamental package for scientific computing with Python.
+- `SciPy <https://www.scipy.org/>`_ 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 <https://matplotlib.org/>`_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
+- `Pandas <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
+- `SymPy <https://www.sympy.org/>`_ is a Python library for symbolic mathematics.
+
+And more domain-specific libraries have been developed:
+- `Scikit-learn <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
+- `Scikit-image <https://scikit-image.org/>`_ 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 489/604] 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
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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 <https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf>`_ 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 490/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 6 Feb 2025 15:16:17 +0900
Subject: [PATCH 491/604] 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 <https://www.python.org/>`_ is an general-purpose programming language developed by
-`Guido van Rossum <https://en.wikipedia.org/wiki/Guido_van_Rossum>`_ in the late 1980s.
+`Guido van Rossum <https://en.wikipedia.org/wiki/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 <https://numpy.org/>`_ is the fundamental package for scientific computing with Python.
 - `SciPy <https://www.scipy.org/>`_ 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 <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
 - `SymPy <https://www.sympy.org/>`_ 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 <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
 - `Scikit-image <https://scikit-image.org/>`_ is a collection of algorithms for image processing.
+- `Networkx <https://networkx.org/>`_ is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
+- `SunPy <https://sunpy.org/>`_ is a community-developed free and open-source software package for solar physics.
+- `Astropy <https://www.astropy.org/>`_ 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 <asakai.amsl+github@gmail.com>
Date: Fri, 7 Feb 2025 13:32:11 +0900
Subject: [PATCH 492/604] 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 <https://matplotlib.org/>`_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
 - `Pandas <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
 - `SymPy <https://www.sympy.org/>`_ is a Python library for symbolic mathematics.
+- `CVXPy <https://www.cvxpy.org/>`_ 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 <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
 - `Scikit-image <https://scikit-image.org/>`_ is a collection of algorithms for image processing.
-- `Networkx <https://networkx.org/>`_ is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
+- `Networkx <https://networkx.org/>`_ is a package for the creation, manipulation for complex networks.
 - `SunPy <https://sunpy.org/>`_ is a community-developed free and open-source software package for solar physics.
 - `Astropy <https://www.astropy.org/>`_ 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 <asakai.amsl+github@gmail.com>
Date: Fri, 7 Feb 2025 13:42:56 +0900
Subject: [PATCH 493/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 9 Feb 2025 21:00:02 +0900
Subject: [PATCH 494/604] 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 <asakai.amsl+github@gmail.com>
Date: Mon, 10 Feb 2025 11:21:19 +0900
Subject: [PATCH 495/604] 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 496/604] 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] <support@github.com>
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 497/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Tue, 11 Feb 2025 21:15:34 +0900
Subject: [PATCH 498/604] 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 <asakai.amsl+github@gmail.com>
Date: Wed, 12 Feb 2025 21:51:29 +0900
Subject: [PATCH 499/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 13 Feb 2025 17:19:03 +0900
Subject: [PATCH 500/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 13 Feb 2025 17:56:17 +0900
Subject: [PATCH 501/604] 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 <asakai.amsl+github@gmail.com>
Date: Fri, 14 Feb 2025 21:20:35 +0900
Subject: [PATCH 502/604] 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 <asakai.amsl+github@gmail.com>
Date: Sat, 15 Feb 2025 16:08:23 +0900
Subject: [PATCH 503/604] 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 <asakai.amsl+github@gmail.com>
Date: Sun, 16 Feb 2025 21:41:22 +0900
Subject: [PATCH 504/604] 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 505/604] 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
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diff --git a/PathPlanning/ElasticBands/path.npy b/PathPlanning/ElasticBands/path.npy
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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 <http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf>`__
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 506/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Tue, 18 Feb 2025 20:06:34 +0900
Subject: [PATCH 507/604] 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 <asakai.amsl+github@gmail.com>
Date: Tue, 18 Feb 2025 21:49:28 +0900
Subject: [PATCH 508/604] 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 <http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf>`__

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 509/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Wed, 19 Feb 2025 13:36:07 +0900
Subject: [PATCH 510/604] 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 <asakai.amsl+github@gmail.com>
Date: Thu, 20 Feb 2025 12:11:04 +0900
Subject: [PATCH 511/604] 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 512/604] 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 <asakai.amsl+github@gmail.com>
Date: Fri, 21 Feb 2025 21:40:21 +0900
Subject: [PATCH 513/604] =?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 514/604] 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] <support@github.com>
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 <jonbs98@gmail.com>
Date: Tue, 25 Feb 2025 06:53:36 -0500
Subject: [PATCH 515/604] 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 <asakai.amsl+github@gmail.com>
---
 .../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 <https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf>`__
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 516/604] 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_<state>' and 'on_exit_<state>' 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_<state>' 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_<state>' 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 a/docs/modules/13_mission_planning/state_machine/robot_behavior_case.png b/docs/modules/13_mission_planning/state_machine/robot_behavior_case.png
new file mode 100644
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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 517/604] 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] <support@github.com>
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 518/604] 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] <support@github.com>
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 519/604] 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] <support@github.com>
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 520/604] 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 521/604] 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
+
+            <Sequence>
+                <Action1 />
+                <Action2 />
+            </Sequence>
+    """
+
+    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
+
+            <Selector>
+                <Action1 />
+                <Action2 />
+            </Selector>
+    """
+
+    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
+
+            <WhileDoElse>
+                <Condition />
+                <Do />
+                <Else />
+            </WhileDoElse>
+    """
+
+    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
+
+            <Sleep sec="1.5" />
+    """
+
+    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
+
+            <Echo message="Hello, World!" />
+    """
+
+    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
+
+            <Inverter>
+                <Action />
+            </Inverter>
+    """
+
+    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
+
+            <Timeout sec="1.5">
+                <Action />
+            </Timeout>
+    """
+
+    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
+
+            <Delay sec="1.5">
+                <Action />
+            </Delay>
+    """
+
+    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: <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 = """
+    <Sequence name="Sequence">
+        <Echo name="Echo0" message="Hello, World0!" />
+        <Delay name="Delay" sec="1.5">
+            <Echo name="Echo1" message="Hello, World1!" />
+        </Delay>
+        <Echo name="Echo2" message="Hello, World2!" />
+    </Sequence>
+   """
+
+
+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 @@
+<Selector name="Robot Main Controller">
+    <!-- Charge battery when power is low -->
+    <Sequence name="Battery Management">
+        <Inverter name="Low Battery Detection">
+            <CheckBattery name="Check Battery" threshold="30" />
+        </Inverter>
+        <Echo name="Low Battery Warning" message="Battery level low! Charging needed" />
+        <ChargeBattery name="Charge Battery" charge_rate="20" />
+    </Sequence>
+    <!-- Main task sequence -->
+    <Sequence name="Patrol Task">
+        <Echo name="Start Task" message="Starting patrol task" />
+        <!-- Move to position A -->
+        <Sequence name="Move to Position A">
+            <MoveToPosition name="Move to A" position="A" move_duration="2" />
+            <!-- Handle obstacles -->
+            <Selector name="Obstacle Handling A">
+                <Sequence name="Obstacle Present">
+                    <DetectObstacle name="Detect Obstacle" obstacle_probability="0.3" />
+                    <AvoidObstacle name="Avoid Obstacle" avoid_duration="1.5" />
+                </Sequence>
+                <Echo name="No Obstacle" message="Path clear" />
+            </Selector>
+            <PerformTask name="Position A Task" task_name="Check Device Status" task_duration="2" />
+        </Sequence>
+        <!-- Move to position B -->
+        <Sequence name="Move to Position B">
+            <Delay name="Short Wait" sec="1">
+                <Echo name="Prepare Movement" message="Preparing to move to next position" />
+            </Delay>
+            <MoveToPosition name="Move to B" position="B" move_duration="3" />
+            <!-- Handle obstacles with timeout -->
+            <Timeout name="Limited Time Obstacle Handling" sec="2">
+                <Sequence name="Obstacle Present">
+                    <DetectObstacle name="Detect Obstacle" obstacle_probability="0.4" />
+                    <AvoidObstacle name="Avoid Obstacle" avoid_duration="1.8" />
+                </Sequence>
+            </Timeout>
+            <PerformTask name="Position B Task" task_name="Data Collection" task_duration="2.5" />
+        </Sequence>
+        <!-- Move to position C -->
+        <WhileDoElse name="Conditional Move to C">
+            <CheckBattery name="Check Sufficient Battery" threshold="50" />
+            <Sequence name="Perform Position C Task">
+                <MoveToPosition name="Move to C" position="C" move_duration="2.5" />
+                <ForceSuccess name="Ensure Completion">
+                    <PerformTask name="Position C Task" task_name="Environment Monitoring"
+                        task_duration="2" />
+                </ForceSuccess>
+            </Sequence>
+            <Echo name="Skip Position C" message="Insufficient power, skipping position C task" />
+        </WhileDoElse>
+        <Echo name="Complete Patrol" message="Patrol task completed, returning to charging station" />
+        <MoveToPosition name="Return to Charging Station" position="Charging Station"
+            move_duration="4" />
+    </Sequence>
+</Selector> 
\ 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 <https://xml-tree-visual.vercel.app/>`_.
+
+.. image:: ./robot_behavior_case.svg
+
+Print the behavior tree
+
+.. code-block:: text
+
+    Behavior Tree
+    [Robot Main Controller]
+        [Battery Management]
+            (Low Battery Detection)
+                <Check Battery>
+            <Low Battery Warning>
+            <Charge Battery>
+        [Patrol Task]
+            <Start Task>
+            [Move to Position A]
+                <Move to A>
+                [Obstacle Handling A]
+                    [Obstacle Present]
+                        <Detect Obstacle>
+                        <Avoid Obstacle>
+                    <No Obstacle>
+                <Position A Task>
+            [Move to Position B]
+                (Short Wait)
+                    <Prepare Movement>
+                <Move to B>
+                (Limited Time Obstacle Handling)
+                    [Obstacle Present]
+                        <Detect Obstacle>
+                        <Avoid Obstacle>
+                <Position B Task>
+            [Conditional Move to C]
+                <Check Sufficient Battery>
+                [Perform Position C Task]
+                    <Move to C>
+                    (Ensure Completion)
+                        <Position C Task>
+                <Skip Position C>
+            <Complete Patrol>
+            <Return to Charging Station>
+    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 @@
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+      .node rect { 
+        fill: #fff; 
+        stroke-width: 1px; 
+      }
+      .node text { 
+        font: 12px sans-serif; 
+      }
+      .node-attributes { 
+        font: 10px sans-serif; 
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+      .link { 
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dy="25" x="-105" text-anchor="start">name: Move to A</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">position: A</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">move_duration: 2</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(-135,360)"><rect x="-115" y="-15" width="230" height="50" stroke="#4A90E2" style="stroke: rgb(74, 144, 226);"/><text dy="5" text-anchor="middle">Selector</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Obstacle Handling A</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(135,360)"><rect x="-115" y="-15" width="230" height="90" 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x="-105" text-anchor="start">name: Short Wait</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">sec: 1</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(855,360)"><rect x="-115" y="-15" width="230" height="90" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">MoveToPosition</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Move to B</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">position: B</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">move_duration: 3</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1125,360)"><rect x="-115" y="-15" width="230" height="90" stroke="#F5A623" style="stroke: rgb(245, 166, 35);"/><text dy="5" text-anchor="middle">Timeout</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Limited Time Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">Handling</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">sec: 2</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1395,360)"><rect x="-115" y="-15" width="230" height="90" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">PerformTask</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Position B Task</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">task_name: Data Collection</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">task_duration: 2.5</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1845,360)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">CheckBattery</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Check Sufficient Battery</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">threshold: 50</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(2115,360)"><rect x="-115" y="-15" width="230" height="50" stroke="#4A90E2" style="stroke: rgb(74, 144, 226);"/><text dy="5" text-anchor="middle">Sequence</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Perform Position C Task</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(2385,360)"><rect x="-115" y="-15" width="230" height="90" stroke="#D0021B" style="stroke: rgb(208, 2, 27);"/><text dy="5" text-anchor="middle">Echo</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Skip Position C</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">message: Insufficient power,</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">skipping position C task</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(-270,480)"><rect x="-115" y="-15" width="230" height="50" stroke="#4A90E2" style="stroke: rgb(74, 144, 226);"/><text dy="5" text-anchor="middle">Sequence</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Obstacle Present</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(0,480)"><rect x="-115" y="-15" width="230" height="70" stroke="#D0021B" style="stroke: rgb(208, 2, 27);"/><text dy="5" text-anchor="middle">Echo</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: No Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">message: Path clear</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(585,480)"><rect x="-115" y="-15" width="230" height="90" stroke="#D0021B" style="stroke: rgb(208, 2, 27);"/><text dy="5" text-anchor="middle">Echo</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Prepare Movement</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">message: Preparing to move to</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">next position</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1125,480)"><rect x="-115" y="-15" width="230" height="50" stroke="#4A90E2" style="stroke: rgb(74, 144, 226);"/><text dy="5" text-anchor="middle">Sequence</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Obstacle Present</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1980,480)"><rect x="-115" y="-15" width="230" height="90" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">MoveToPosition</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Move to C</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">position: C</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">move_duration: 2.5</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(2250,480)"><rect x="-115" y="-15" width="230" height="50" stroke="#F5A623" style="stroke: rgb(245, 166, 35);"/><text dy="5" text-anchor="middle">ForceSuccess</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Ensure Completion</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(-405,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">DetectObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Detect Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">obstacle_probability: 0.3</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(-135,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">AvoidObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Avoid Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">avoid_duration: 1.5</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(990,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">DetectObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Detect Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">obstacle_probability: 0.4</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1260,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">AvoidObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Avoid Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">avoid_duration: 1.8</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(2250,600)"><rect x="-115" y="-15" width="230" height="110" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">PerformTask</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Position C Task</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">task_name: Environment</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">Monitoring</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="85" x="-105" text-anchor="start">task_duration: 2</text><line x1="-105" y1="90" x2="105" y2="90" stroke="#ddd" stroke-width="1"/></g></g></svg>
\ 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 = """
+        <Sequence>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <ForceFailure name="Force Failure">
+                <Echo name="Echo 3" message="Hello, World3!" />
+            </ForceFailure>
+        </Sequence>
+    """
+    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 = """
+        <Sequence>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <ForceSuccess name="Force Success">
+                <Echo name="Echo 3" message="Hello, World3!" />
+            </ForceSuccess>
+        </Sequence>
+    """
+    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 = """
+        <Selector>
+            <ForceFailure name="Force Failure">
+                <Echo name="Echo 1" message="Hello, World1!" />
+            </ForceFailure>
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <Echo name="Echo 3" message="Hello, World3!" />
+        </Selector>
+    """
+    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 = """
+        <Selector>
+            <ForceFailure name="Force Success">
+                <Echo name="Echo 1" message="Hello, World1!" />
+            </ForceFailure>
+            <ForceFailure name="Force Failure">
+                <Echo name="Echo 2" message="Hello, World2!" />
+            </ForceFailure>
+        </Selector>
+    """
+    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 = """
+        <WhileDoElse>
+            <Count name="Count" count_threshold="3" />
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+        </WhileDoElse>
+    """
+
+    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 = """
+        <Sequence>
+        </Sequence>
+    """
+    bt_factory = BehaviorTreeFactory()
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # DecoratorNode Must have child
+    xml_string = """
+        <Inverter>
+        </Inverter>
+    """
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # DecoratorNode Must have only one child
+    xml_string = """
+        <Inverter>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+        </Inverter>
+    """
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # ActionNode Must have no children
+    xml_string = """
+        <Echo name="Echo 1" message="Hello, World1!">
+            <Echo name="Echo 2" message="Hello, World2!" />
+        </Echo>
+    """
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # WhileDoElse Must have exactly 2 or 3 children
+    xml_string = """
+        <WhileDoElse>
+            <Echo name="Echo 1" message="Hello, World1!" />
+        </WhileDoElse>
+    """
+    with pytest.raises(ValueError):
+        bt = bt_factory.build_tree(xml_string)
+        bt.tick()
+
+    xml_string = """
+        <WhileDoElse>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <Echo name="Echo 3" message="Hello, World3!" />
+            <Echo name="Echo 4" message="Hello, World4!" />
+        </WhileDoElse>
+    """
+    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 <asakai.amsl+github@gmail.com>
Date: Sun, 9 Mar 2025 20:21:24 +0900
Subject: [PATCH 522/604] 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 523/604] 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] <support@github.com>
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 <jonbs98@gmail.com>
Date: Thu, 13 Mar 2025 10:24:53 -0400
Subject: [PATCH 524/604] 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 <https://github.com/AtsushiSakai/PythonRobotics/pull/1183>`__ 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 <yangtzech@qq.com>
Date: Sun, 16 Mar 2025 21:40:11 +0800
Subject: [PATCH 525/604] 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 <jonbs98@gmail.com>
Date: Mon, 17 Mar 2025 09:01:07 -0400
Subject: [PATCH 526/604] 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 <asakai.amsl+github@gmail.com>

* 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 <asakai.amsl+github@gmail.com>
---
 .../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 <https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf>`__
+
+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 <https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf>`__
+-  `SIPP: Safe Interval Path Planning for Dynamic Environments <https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf>`__
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 527/604] 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] <support@github.com>
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 528/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Mon, 28 Apr 2025 08:51:09 +0900
Subject: [PATCH 529/604] 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 files changed, 13 insertions(+)
 create mode 100644 docs/_static/img/source_link_1.png
 create mode 100644 docs/_static/img/source_link_2.png

diff --git a/docs/_static/img/source_link_1.png b/docs/_static/img/source_link_1.png
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HcmV?d00001

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 1e61760649..30e3001f36 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
@@ -107,6 +107,18 @@ 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.
 
+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 530/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Thu, 1 May 2025 13:08:29 +0900
Subject: [PATCH 531/604] 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 532/604] 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] <support@github.com>
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 <asakai.amsl+github@gmail.com>
Date: Thu, 1 May 2025 21:53:12 +0900
Subject: [PATCH 533/604] 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 <asakai.amsl+github@gmail.com>
Date: Fri, 2 May 2025 10:01:19 +0900
Subject: [PATCH 534/604] 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
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="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
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640" alt="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 <https://link.springer.com/book/10.1007/978-1-4614-1433-9>`_
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 <https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter.py>`__
+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 <https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py>`__
+: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 <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L34-L50>`__
 
-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 <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py#L163>`__
-
-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 <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py#L163>`__
+
+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 <http://www.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: <http://www.probabilistic-robotics.org>`_
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: <http://www.probabilistic-robotics.org>`_
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 <https://www.researchgate.net/publication/267963417_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 <http://www.probabilistic-robotics.org/>`_
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 <http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf>`_
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 <https://ieeexplore.ieee.org/document/4339545/>`__
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 <https://en.wikipedia.org/wiki/D*>`__
 
@@ -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 <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
 
@@ -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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
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 <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
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 <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
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 <https://lavalle.pl/planning/node822.html>`__
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 <https://acl.mit.edu/papers/KuwataGNC08.pdf>`__
@@ -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 <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
 
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 <https://ieeexplore.ieee.org/document/5940562/>`__
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 <https://github.com/ApolloAuto/apollo>`_
 
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 <https://arxiv.org/abs/1604.07446>`__
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 <asakai.amsl+github@gmail.com>
Date: Sat, 3 May 2025 09:25:12 +0900
Subject: [PATCH 535/604] 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 536/604] 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 <asakai.amsl+github@gmail.com>
---
 .../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) <https://en.wikipedia.org/wiki/Inertial_measurement_unit>`_
+  - `Accelerometer <https://en.wikipedia.org/wiki/Accelerometer>`_
+  - `Gyroscope <https://en.wikipedia.org/wiki/Gyroscope>`_
+  - `Magnetometer <https://en.wikipedia.org/wiki/Magnetometer>`_
+  - `Pressure sensor <https://en.wikipedia.org/wiki/Pressure_sensor>`_
+  - `Temperature sensor <https://en.wikipedia.org/wiki/Thermometer>`_
+- `Adafruit Sensor Guides <https://learn.adafruit.com/>`_
\ No newline at end of file

From d2fe5ae8f0e414f394c00570771c07807d167e68 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 4 May 2025 09:50:49 +0900
Subject: [PATCH 537/604] 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 <https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf>`_
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 <asakai.amsl+github@gmail.com>
Date: Sun, 4 May 2025 20:32:11 +0900
Subject: [PATCH 538/604] 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] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py>`_
+
+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] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_steer_control/lqr_steer_control.py>`_
+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 <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py>`__
-
 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 <http://www.cvxpy.org/>`__
 
+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 <asakai.amsl+github@gmail.com>
Date: Mon, 5 May 2025 17:29:45 +0900
Subject: [PATCH 539/604] 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 <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`
+   Vehicles <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`__
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 <https://web.archive.org/web/20201103052224/https://sites.google.com/site/ece452bugalgorithms/>`_
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 <https://ieeexplore.ieee.org/abstract/document/1013479>`_
 
 
@@ -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 <https://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
 
 
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 <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
 
-.. 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 <http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf>`__
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 <https://ieeexplore.ieee.org/document/4339545/>`__
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 <https://en.wikipedia.org/wiki/D*>`__
 
@@ -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 <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
 
@@ -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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
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 <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
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 <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
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 <https://lavalle.pl/planning/node822.html>`__
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 <https://arxiv.org/abs/1405.5848>`__
 
+
 .. _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 <https://acl.mit.edu/papers/KuwataGNC08.pdf>`__
@@ -98,6 +144,7 @@ Reference
 -  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
    Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
 
+
 .. _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 <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
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 <https://en.wikipedia.org/wiki/Visibility_graph>`_
 
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 <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
 
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 540/604] 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] <support@github.com>
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 541/604] 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 @@
 <!-- 
 Thanks for contributing a pull request! 
 Please check this document before submitting:
-- [How to contribute](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html#adding-a-new-algorithm-example)
+- [How to contribute](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/3_how_to_contribute.html#adding-a-new-algorithm-example)
 
 Note that this is my hobby project; I appreciate your
 patience during the review process.
diff --git a/README.md b/README.md
index 05fd0262df..85be274adb 100644
--- a/README.md
+++ b/README.md
@@ -87,7 +87,7 @@ Features:
 
 See this documentation 
 
-- [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/getting_started.html#what-is-pythonrobotics)
+- [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/1_what_is_python_robotics.html)
 
 or this Youtube video:
 
@@ -170,7 +170,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
 
 ## Particle filter localization
 
@@ -490,7 +490,7 @@ Reference
 
 This is a simulation of moving to a pose control
 
-![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif)
+![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/move_to_pose/animation.gif)
 
 Reference
 
@@ -541,7 +541,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_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)
 
@@ -553,7 +553,7 @@ A motion planning and path tracking simulation with NMPC of C-GMRES
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc.html)
 
 
 # Arm Navigation
@@ -593,7 +593,7 @@ This is a 3d trajectory generation simulation for a rocket powered landing.
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
 
 # Bipedal
 
diff --git a/users_comments.md b/users_comments.md
index 1674f21377..30dc2d2d85 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -6,7 +6,7 @@ This is an electric wheelchair control demo by [Katsushun89](https://github.com/
 
 [WHILL Model CR](https://whill.jp/model-cr) is the control target, [M5Stack](https://m5stack.com/) is used for the controller, and [toio](https://toio.io/) is used for the control input device.
 
-[Move to a Pose Control — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/control/move_to_a_pose_control/move_to_a_pose_control.html) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
+[Move to a Pose Control — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_path_tracking/move_to_a_pose/move_to_a_pose.html) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
 
 ![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)
 

From cfaab296a4212948333f6316e98575ef904a0932 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 May 2025 21:55:01 +0900
Subject: [PATCH 542/604] build(deps): bump ruff from 0.11.8 to 0.11.9 in
 /requirements (#1218)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.8 to 0.11.9.
- [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/0.11.8...0.11.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.9
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 e78b9b8d51..32a9b31938 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 == 0.11.8 # For unit test
+ruff == 0.11.9 # For unit test

From c9051758e74896d35c633d506c1df59d49220463 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 May 2025 22:32:30 +0900
Subject: [PATCH 543/604] build(deps): bump ruff from 0.11.9 to 0.11.10 in
 /requirements (#1220)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.9 to 0.11.10.
- [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/0.11.9...0.11.10)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.10
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 32a9b31938..4463aacb36 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 == 0.11.9 # For unit test
+ruff == 0.11.10 # For unit test

From 453c25e461a0cda5c8a572c388307b41c92759a2 Mon Sep 17 00:00:00 2001
From: Yutharsan <yutharsan.22@cse.mrt.ac.lk>
Date: Sun, 25 May 2025 10:27:02 +0530
Subject: [PATCH 544/604] Add root directory to system path for lattice planner
 (#1221)

---
 PathPlanning/StateLatticePlanner/state_lattice_planner.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index 05f8df78f8..554cd0f3b7 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -22,7 +22,9 @@
 import numpy as np
 import math
 import pathlib
+
 sys.path.append(str(pathlib.Path(__file__).parent.parent))
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import ModelPredictiveTrajectoryGenerator.trajectory_generator as planner
 import ModelPredictiveTrajectoryGenerator.motion_model as motion_model

From bdf50dffcdc72d923b5e3d2e25be5e237d0849f6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 May 2025 21:36:36 +0900
Subject: [PATCH 545/604] build(deps): bump
 larsoner/circleci-artifacts-redirector-action (#1224)

Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 1.0.0 to 1.1.0.
- [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases)
- [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/v1.0.0...v1.1.0)

---
updated-dependencies:
- dependency-name: larsoner/circleci-artifacts-redirector-action
  dependency-version: 1.1.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 78eb479aa1..692942cddd 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@v1.0.0
+        uses: larsoner/circleci-artifacts-redirector-action@v1.1.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_TOKEN }}

From 74610fd56bfe7eee02054c48afaa7328bec0d6b7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 May 2025 21:36:59 +0900
Subject: [PATCH 546/604] build(deps): bump ruff from 0.11.10 to 0.11.11 in
 /requirements (#1225)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.10 to 0.11.11.
- [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/0.11.10...0.11.11)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.11
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 4463aacb36..cc15d4e335 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 == 0.11.10 # For unit test
+ruff == 0.11.11 # For unit test

From 26264a343269cbde9f4d3c6d9016684ed4f32d5c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Jun 2025 07:32:07 +0900
Subject: [PATCH 547/604] build(deps): bump pytest-xdist from 3.6.1 to 3.7.0 in
 /requirements (#1226)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.6.1 to 3.7.0.
- [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/v3.6.1...v3.7.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-version: 3.7.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 cc15d4e335..78ca18f42d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,6 +4,6 @@ matplotlib == 3.10.1
 cvxpy == 1.6.5
 ecos == 2.0.14
 pytest == 8.3.5 # For unit test
-pytest-xdist == 3.6.1 # For unit test
+pytest-xdist == 3.7.0 # For unit test
 mypy == 1.15.0 # For unit test
 ruff == 0.11.11 # For unit test

From 4b4d377a541656f6f063c05cbc5e8f0883ad3fd3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Jun 2025 09:26:11 +0900
Subject: [PATCH 548/604] build(deps): bump pytest from 8.3.5 to 8.4.0 in
 /requirements (#1228)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.5 to 8.4.0.
- [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/8.3.5...8.4.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-version: 8.4.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 78ca18f42d..5a21fd2b27 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.15.2
 matplotlib == 3.10.1
 cvxpy == 1.6.5
 ecos == 2.0.14
-pytest == 8.3.5 # For unit test
+pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.15.0 # For unit test
 ruff == 0.11.11 # For unit test

From eee0031eb862e22f5d108d32a023327d87ededec Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Jun 2025 23:11:13 +0900
Subject: [PATCH 549/604] build(deps): bump mypy from 1.15.0 to 1.16.0 in
 /requirements (#1227)

Bumps [mypy](https://github.com/python/mypy) from 1.15.0 to 1.16.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.15.0...v1.16.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.16.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 5a21fd2b27..f154142331 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.6.5
 ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
-mypy == 1.15.0 # For unit test
+mypy == 1.16.0 # For unit test
 ruff == 0.11.11 # For unit test

From ef8c154c73a562f9a90857559e4f380df6968893 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 4 Jun 2025 12:59:20 +0900
Subject: [PATCH 550/604] build(deps): bump ruff from 0.11.11 to 0.11.12 in
 /requirements (#1229)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.11 to 0.11.12.
- [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/0.11.11...0.11.12)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.12
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 f154142331..0c0719cca5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.0 # For unit test
-ruff == 0.11.11 # For unit test
+ruff == 0.11.12 # For unit test

From a8a45033c6daffcb6e5ccc8b02727d407314ee6a Mon Sep 17 00:00:00 2001
From: Zichao Yang <yangtzech@qq.com>
Date: Sat, 7 Jun 2025 19:05:32 +0800
Subject: [PATCH 551/604] feat: Add reverse mode in pure pursuit (#1194)

* feat: Add vehicle model rendering function and pause simulation function

* feat: Add support for reverse mode and related function adjustments

* feat: Add reverse mode test case

* feat: Limit the maximum steering angle to improve path tracking control

* feat: Add a newline for improved readability in plot_arrow function

* fix: Replace max/min with np.clip for steering angle limitation in pure pursuit control
---
 PathTracking/pure_pursuit/pure_pursuit.py | 168 +++++++++++++++++++---
 tests/test_pure_pursuit.py                |   4 +
 2 files changed, 155 insertions(+), 17 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index ff995033a9..48453927ab 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -10,6 +10,11 @@
 import math
 import matplotlib.pyplot as plt
 
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+from utils.angle import angle_mod
+
 # Parameters
 k = 0.1  # look forward gain
 Lfc = 2.0  # [m] look-ahead distance
@@ -17,26 +22,37 @@
 dt = 0.1  # [s] time tick
 WB = 2.9  # [m] wheel base of vehicle
 
-show_animation = True
 
+# Vehicle parameters
+LENGTH = WB + 1.0  # Vehicle length
+WIDTH = 2.0  # Vehicle width
+WHEEL_LEN = 0.6  # Wheel length
+WHEEL_WIDTH = 0.2  # Wheel width
+MAX_STEER = math.pi / 4  # Maximum steering angle [rad]
 
-class State:
 
-    def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
+show_animation = True
+pause_simulation = False  # Flag for pause simulation
+is_reverse_mode = False   # Flag for reverse driving mode
+
+class State:
+    def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0, is_reverse=False):
         self.x = x
         self.y = y
         self.yaw = yaw
         self.v = v
-        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
+        self.direction = -1 if is_reverse else 1  # Direction based on reverse flag
+        self.rear_x = self.x - self.direction * ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - self.direction * ((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 / WB * math.tan(delta) * dt
+        self.yaw += self.direction * self.v / WB * math.tan(delta) * dt
+        self.yaw = angle_mod(self.yaw)
         self.v += a * dt
-        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
+        self.rear_x = self.x - self.direction * ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - self.direction * ((WB / 2) * math.sin(self.yaw))
 
     def calc_distance(self, point_x, point_y):
         dx = self.rear_x - point_x
@@ -51,6 +67,7 @@ def __init__(self):
         self.y = []
         self.yaw = []
         self.v = []
+        self.direction = []
         self.t = []
 
     def append(self, t, state):
@@ -58,6 +75,7 @@ def append(self, t, state):
         self.y.append(state.y)
         self.yaw.append(state.yaw)
         self.v.append(state.v)
+        self.direction.append(state.direction)
         self.t.append(t)
 
 
@@ -117,14 +135,18 @@ def pure_pursuit_steer_control(state, trajectory, pind):
     if ind < len(trajectory.cx):
         tx = trajectory.cx[ind]
         ty = trajectory.cy[ind]
-    else:  # toward goal
+    else:
         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
 
-    delta = math.atan2(2.0 * WB * math.sin(alpha) / Lf, 1.0)
+    # Reverse steering angle when reversing
+    delta = state.direction * math.atan2(2.0 * WB * math.sin(alpha) / Lf, 1.0)
+
+    # Limit steering angle to max value
+    delta = np.clip(delta, -MAX_STEER, MAX_STEER)
 
     return delta, ind
 
@@ -142,10 +164,111 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
                   fc=fc, ec=ec, head_width=width, head_length=width)
         plt.plot(x, y)
 
+def plot_vehicle(x, y, yaw, steer=0.0, color='blue', is_reverse=False):
+    """
+    Plot vehicle model with four wheels
+    Args:
+        x, y: Vehicle center position
+        yaw: Vehicle heading angle
+        steer: Steering angle
+        color: Vehicle color
+        is_reverse: Flag for reverse mode
+    """
+    # Adjust heading angle in reverse mode
+    if is_reverse:
+        yaw = angle_mod(yaw + math.pi)  # Rotate heading by 180 degrees
+        steer = -steer  # Reverse steering direction
+
+    def plot_wheel(x, y, yaw, steer=0.0, color=color):
+        """Plot single wheel"""
+        wheel = np.array([
+            [-WHEEL_LEN/2, WHEEL_WIDTH/2],
+            [WHEEL_LEN/2, WHEEL_WIDTH/2],
+            [WHEEL_LEN/2, -WHEEL_WIDTH/2],
+            [-WHEEL_LEN/2, -WHEEL_WIDTH/2],
+            [-WHEEL_LEN/2, WHEEL_WIDTH/2]
+        ])
+
+        # Rotate wheel if steering
+        if steer != 0:
+            c, s = np.cos(steer), np.sin(steer)
+            rot_steer = np.array([[c, -s], [s, c]])
+            wheel = wheel @ rot_steer.T
+
+        # Apply vehicle heading rotation
+        c, s = np.cos(yaw), np.sin(yaw)
+        rot_yaw = np.array([[c, -s], [s, c]])
+        wheel = wheel @ rot_yaw.T
+
+        # Translate to position
+        wheel[:, 0] += x
+        wheel[:, 1] += y
+
+        # Plot wheel with color
+        plt.plot(wheel[:, 0], wheel[:, 1], color=color)
+
+    # Calculate vehicle body corners
+    corners = np.array([
+        [-LENGTH/2, WIDTH/2],
+        [LENGTH/2, WIDTH/2],
+        [LENGTH/2, -WIDTH/2],
+        [-LENGTH/2, -WIDTH/2],
+        [-LENGTH/2, WIDTH/2]
+    ])
+
+    # Rotation matrix
+    c, s = np.cos(yaw), np.sin(yaw)
+    Rot = np.array([[c, -s], [s, c]])
+
+    # Rotate and translate vehicle body
+    rotated = corners @ Rot.T
+    rotated[:, 0] += x
+    rotated[:, 1] += y
+
+    # Plot vehicle body
+    plt.plot(rotated[:, 0], rotated[:, 1], color=color)
+
+    # Plot wheels (darker color for front wheels)
+    front_color = 'darkblue'
+    rear_color = color
+    
+    # Plot four wheels
+    # Front left
+    plot_wheel(x + LENGTH/4 * c - WIDTH/2 * s,
+              y + LENGTH/4 * s + WIDTH/2 * c,
+              yaw, steer, front_color)
+    # Front right  
+    plot_wheel(x + LENGTH/4 * c + WIDTH/2 * s,
+              y + LENGTH/4 * s - WIDTH/2 * c,
+              yaw, steer, front_color)
+    # Rear left
+    plot_wheel(x - LENGTH/4 * c - WIDTH/2 * s,
+              y - LENGTH/4 * s + WIDTH/2 * c,
+              yaw, color=rear_color)
+    # Rear right
+    plot_wheel(x - LENGTH/4 * c + WIDTH/2 * s,
+              y - LENGTH/4 * s - WIDTH/2 * c,
+              yaw, color=rear_color)
+
+    # Add direction arrow
+    arrow_length = LENGTH/3
+    plt.arrow(x, y,
+             -arrow_length * math.cos(yaw) if is_reverse else arrow_length * math.cos(yaw),
+             -arrow_length * math.sin(yaw) if is_reverse else arrow_length * math.sin(yaw),
+             head_width=WIDTH/4, head_length=WIDTH/4,
+             fc='r', ec='r', alpha=0.5)
+
+# Keyboard event handler
+def on_key(event):
+    global pause_simulation
+    if event.key == ' ':  # Space key
+        pause_simulation = not pause_simulation
+    elif event.key == 'escape':
+        exit(0)
 
 def main():
     #  target course
-    cx = np.arange(0, 50, 0.5)
+    cx = -1 * np.arange(0, 50, 0.5) if is_reverse_mode else 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]
@@ -153,7 +276,7 @@ def main():
     T = 100.0  # max simulation time
 
     # initial state
-    state = State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
+    state = State(x=-0.0, y=-3.0, yaw=math.pi if is_reverse_mode else 0.0, v=0.0, is_reverse=is_reverse_mode)
 
     lastIndex = len(cx) - 1
     time = 0.0
@@ -173,22 +296,33 @@ def main():
 
         time += dt
         states.append(time, 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])
-            plot_arrow(state.x, state.y, state.yaw)
+            plt.gcf().canvas.mpl_connect('key_release_event', on_key)
+            # Pass is_reverse parameter
+            plot_vehicle(state.x, state.y, state.yaw, di, is_reverse=is_reverse_mode)
             plt.plot(cx, cy, "-r", label="course")
             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)
             plt.title("Speed[km/h]:" + str(state.v * 3.6)[:4])
+            plt.legend()  # Add legend display
+
+            # Add pause state display
+            if pause_simulation:
+                plt.text(0.02, 0.95, 'PAUSED', transform=plt.gca().transAxes,
+                        bbox=dict(facecolor='red', alpha=0.5))
+
             plt.pause(0.001)
 
+            # Handle pause state
+            while pause_simulation:
+                plt.pause(0.1)  # Reduce CPU usage
+                if not plt.get_fignums():  # Check if window is closed
+                    exit(0)
+
     # Test
     assert lastIndex >= target_ind, "Cannot goal"
 
diff --git a/tests/test_pure_pursuit.py b/tests/test_pure_pursuit.py
index 0e0b83bf6c..89c1829514 100644
--- a/tests/test_pure_pursuit.py
+++ b/tests/test_pure_pursuit.py
@@ -6,6 +6,10 @@ def test1():
     m.show_animation = False
     m.main()
 
+def test_backward():
+    m.show_animation = False
+    m.is_reverse_mode = True
+    m.main()
 
 if __name__ == '__main__':
     conftest.run_this_test(__file__)

From 4ffd8e76e3429daaeb85a2db0764e7af71604af3 Mon Sep 17 00:00:00 2001
From: atm <72185056+sutatoruta@users.noreply.github.com>
Date: Sat, 7 Jun 2025 20:07:06 +0900
Subject: [PATCH 552/604] Optimize is_obstacle using set (#1223)

---
 PathPlanning/DStarLite/d_star_lite.py | 36 ++++++---------------------
 1 file changed, 7 insertions(+), 29 deletions(-)

diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py
index f099530173..1a44d84fa5 100644
--- a/PathPlanning/DStarLite/d_star_lite.py
+++ b/PathPlanning/DStarLite/d_star_lite.py
@@ -63,9 +63,7 @@ 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.obstacles_xy = {(obstacle.x, obstacle.y) for obstacle in self.obstacles}
         self.start = Node(0, 0)
         self.goal = Node(0, 0)
         self.U = list()  # type: ignore
@@ -73,7 +71,7 @@ def __init__(self, ox: list, oy: list):
         self.kold = 0.0
         self.rhs = self.create_grid(float("inf"))
         self.g = self.create_grid(float("inf"))
-        self.detected_obstacles_xy = np.empty((0, 2))
+        self.detected_obstacles_xy: set[tuple[int, int]] = set()
         self.xy = np.empty((0, 2))
         if show_animation:
             self.detected_obstacles_for_plotting_x = list()  # type: ignore
@@ -84,18 +82,8 @@ def create_grid(self, val: float):
         return np.full((self.x_max, self.y_max), val)
 
     def is_obstacle(self, node: Node):
-        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()
-
+        is_in_obstacles = (node.x, node.y) in self.obstacles_xy
+        is_in_detected_obstacles = (node.x, node.y) in self.detected_obstacles_xy
         return is_in_obstacles or is_in_detected_obstacles
 
     def c(self, node1: Node, node2: Node):
@@ -157,7 +145,7 @@ def initialize(self, start: Node, goal: Node):
             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))
+            self.detected_obstacles_xy = set()
 
     def update_vertex(self, u: Node):
         if not compare_coordinates(u, self.goal):
@@ -215,12 +203,7 @@ def detect_changes(self):
                    compare_coordinates(spoofed_obstacle, self.goal):
                     continue
                 changed_vertices.append(spoofed_obstacle)
-                self.detected_obstacles_xy = np.concatenate(
-                    (
-                        self.detected_obstacles_xy,
-                        [[spoofed_obstacle.x, spoofed_obstacle.y]]
-                    )
-                )
+                self.detected_obstacles_xy.add((spoofed_obstacle.x, spoofed_obstacle.y))
                 if show_animation:
                     self.detected_obstacles_for_plotting_x.append(
                         spoofed_obstacle.x + self.x_min_world)
@@ -241,12 +224,7 @@ def detect_changes(self):
                compare_coordinates(new_obs, self.goal):
                 return changed_vertices
             changed_vertices.append(Node(x, y))
-            self.detected_obstacles_xy = np.concatenate(
-                (
-                    self.detected_obstacles_xy,
-                    [[x, y]]
-                )
-            )
+            self.detected_obstacles_xy.add((x, y))
             if show_animation:
                 self.detected_obstacles_for_plotting_x.append(x +
                                                               self.x_min_world)

From ed02c96abd15f39fbad2bc092c6b46292c0610b4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 11 Jun 2025 12:18:34 +0900
Subject: [PATCH 553/604] build(deps): bump ruff from 0.11.12 to 0.11.13 in
 /requirements (#1233)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.12 to 0.11.13.
- [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/0.11.12...0.11.13)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.13
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 0c0719cca5..5f8bf52415 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.0 # For unit test
-ruff == 0.11.12 # For unit test
+ruff == 0.11.13 # For unit test

From 194be251e1e62faf94b47f6500444f6f5c698dd8 Mon Sep 17 00:00:00 2001
From: Rg Zhong <35895359+phonixer@users.noreply.github.com>
Date: Sun, 15 Jun 2025 21:22:10 +0800
Subject: [PATCH 554/604] Update README.md (#1235)

The link is invalid and needs to be updated.
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 85be274adb..d1b801f219 100644
--- a/README.md
+++ b/README.md
@@ -366,7 +366,7 @@ Reference
 
 - [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](https://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.cs.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf)
 
 
 ### Biased polar sampling

From 0e93ecb66957c5ff6642252c6ca3290b0955d63e Mon Sep 17 00:00:00 2001
From: Shibo Liu <78792689+ShiboWork@users.noreply.github.com>
Date: Sat, 21 Jun 2025 07:04:57 +0800
Subject: [PATCH 555/604] Fix/path smoothing robot radius (#1231)

* Fix: Include robot_radius in path_smoothing collision check

* Test: Add unit test to verify smoothed path respects robot_radius
---
 PathPlanning/RRT/rrt_with_pathsmoothing.py  | 121 +++++++++++++++-----
 tests/test_rrt_with_pathsmoothing_radius.py |  48 ++++++++
 2 files changed, 141 insertions(+), 28 deletions(-)
 create mode 100644 tests/test_rrt_with_pathsmoothing_radius.py

diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 2ed6a366d1..ac68efe23f 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -51,30 +51,93 @@ def get_target_point(path, targetL):
     return [x, y, ti]
 
 
-def line_collision_check(first, second, obstacleList):
-    # Line Equation
-
-    x1 = first[0]
-    y1 = first[1]
-    x2 = second[0]
-    y2 = second[1]
-
-    try:
-        a = y2 - y1
-        b = -(x2 - x1)
-        c = y2 * (x2 - x1) - x2 * (y2 - y1)
-    except ZeroDivisionError:
-        return False
-
-    for (ox, oy, size) in obstacleList:
-        d = abs(a * ox + b * oy + c) / (math.hypot(a, b))
-        if d <= size:
-            return False
-
-    return True  # OK
-
-
-def path_smoothing(path, max_iter, obstacle_list):
+def is_point_collision(x, y, obstacle_list, robot_radius):
+    """
+    Check whether a single point collides with any obstacle.
+
+    This function calculates the Euclidean distance between the given point (x, y)
+    and each obstacle center. If the distance is less than or equal to the sum of
+    the obstacle's radius and the robot's radius, a collision is detected.
+
+    Args:
+        x (float): X-coordinate of the point to check.
+        y (float): Y-coordinate of the point to check.
+        obstacle_list (List[Tuple[float, float, float]]): List of obstacles defined as (ox, oy, radius).
+        robot_radius (float): Radius of the robot, used to inflate the obstacles.
+
+    Returns:
+        bool: True if the point is in collision with any obstacle, False otherwise.
+    """
+    for (ox, oy, obstacle_radius) in obstacle_list:
+        d = math.hypot(ox - x, oy - y)
+        if d <= obstacle_radius + robot_radius:
+            return True  # Collided
+    return False
+
+
+def line_collision_check(first, second, obstacle_list, robot_radius=0.0, sample_step=0.2):
+    """
+    Check if the line segment between `first` and `second` collides with any obstacle.
+    Considers the robot_radius by inflating the obstacle size.
+
+    Args:
+        first (List[float]): Start point of the line [x, y]
+        second (List[float]): End point of the line [x, y]
+        obstacle_list (List[Tuple[float, float, float]]): Obstacles as (x, y, radius)
+        robot_radius (float): Radius of robot
+        sample_step (float): Distance between sampling points along the segment
+
+    Returns:
+        bool: True if collision-free, False otherwise
+    """
+    x1, y1 = first[0], first[1]
+    x2, y2 = second[0], second[1]
+
+    dx = x2 - x1
+    dy = y2 - y1
+    length = math.hypot(dx, dy)
+
+    if length == 0:
+        # Degenerate case: point collision check
+        return not is_point_collision(x1, y1, obstacle_list, robot_radius)
+
+    steps = int(length / sample_step) + 1  # Sampling every sample_step along the segment
+
+    for i in range(steps + 1):
+        t = i / steps
+        x = x1 + t * dx
+        y = y1 + t * dy
+
+        if is_point_collision(x, y, obstacle_list, robot_radius):
+            return False  # Collision found
+
+    return True  # Safe
+
+
+def path_smoothing(path, max_iter, obstacle_list, robot_radius=0.0):
+    """
+    Smooths a given path by iteratively replacing segments with shortcut connections,
+    while ensuring the new segments are collision-free.
+
+    The algorithm randomly picks two points along the original path and attempts to
+    connect them with a straight line. If the line does not collide with any obstacles
+    (considering the robot's radius), the intermediate path points between them are
+    replaced with the direct connection.
+
+    Args:
+        path (List[List[float]]): The original path as a list of [x, y] coordinates.
+        max_iter (int): Number of iterations for smoothing attempts.
+        obstacle_list (List[Tuple[float, float, float]]): List of obstacles represented as
+            (x, y, radius).
+        robot_radius (float, optional): Radius of the robot, used to inflate obstacle size
+            during collision checking. Defaults to 0.0.
+
+    Returns:
+        List[List[float]]: The smoothed path as a list of [x, y] coordinates.
+
+    Example:
+        >>> smoothed = path_smoothing(path, 1000, obstacle_list, robot_radius=0.5)
+    """
     le = get_path_length(path)
 
     for i in range(max_iter):
@@ -94,7 +157,7 @@ def path_smoothing(path, max_iter, obstacle_list):
             continue
 
         # collision check
-        if not line_collision_check(first, second, obstacle_list):
+        if not line_collision_check(first, second, obstacle_list, robot_radius):
             continue
 
         # Create New path
@@ -119,14 +182,16 @@ def main():
         (3, 10, 2),
         (7, 5, 2),
         (9, 5, 2)
-    ]  # [x,y,size]
+    ]  # [x,y,radius]
     rrt = RRT(start=[0, 0], goal=[6, 10],
-              rand_area=[-2, 15], obstacle_list=obstacleList)
+              rand_area=[-2, 15], obstacle_list=obstacleList,
+              robot_radius=0.3)
     path = rrt.planning(animation=show_animation)
 
     # Path smoothing
     maxIter = 1000
-    smoothedPath = path_smoothing(path, maxIter, obstacleList)
+    smoothedPath = path_smoothing(path, maxIter, obstacleList,
+                                  robot_radius=rrt.robot_radius)
 
     # Draw final path
     if show_animation:
diff --git a/tests/test_rrt_with_pathsmoothing_radius.py b/tests/test_rrt_with_pathsmoothing_radius.py
new file mode 100644
index 0000000000..a1159255b5
--- /dev/null
+++ b/tests/test_rrt_with_pathsmoothing_radius.py
@@ -0,0 +1,48 @@
+import conftest
+import math
+
+from PathPlanning.RRT import rrt_with_pathsmoothing as rrt_module
+
+def test_smoothed_path_safety():
+    # Define test environment
+    obstacle_list = [
+        (5, 5, 1.0),
+        (3, 6, 2.0),
+        (3, 8, 2.0),
+        (3, 10, 2.0),
+        (7, 5, 2.0),
+        (9, 5, 2.0)
+    ]
+    robot_radius = 0.5
+
+    # Disable animation for testing
+    rrt_module.show_animation = False
+
+    # Create RRT planner
+    rrt = rrt_module.RRT(
+        start=[0, 0],
+        goal=[6, 10],
+        rand_area=[-2, 15],
+        obstacle_list=obstacle_list,
+        robot_radius=robot_radius
+    )
+
+    # Run RRT
+    path = rrt.planning(animation=False)
+
+    # Smooth the path
+    smoothed = rrt_module.path_smoothing(path, max_iter=1000,
+                                         obstacle_list=obstacle_list,
+                                         robot_radius=robot_radius)
+
+    # Check if all points on the smoothed path are safely distant from obstacles
+    for x, y in smoothed:
+        for ox, oy, obs_radius in obstacle_list:
+            d = math.hypot(x - ox, y - oy)
+            min_safe_dist = obs_radius + robot_radius
+            assert d > min_safe_dist, \
+                f"Point ({x:.2f}, {y:.2f}) too close to obstacle at ({ox}, {oy})"
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From fb667d3d7e635c1b6c6ca13a1b40e8ae623e15a0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 21 Jun 2025 19:01:21 +0900
Subject: [PATCH 556/604] build(deps): bump cvxpy from 1.6.5 to 1.6.6 in
 /requirements (#1238)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.6.5 to 1.6.6.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.6.5...v1.6.6)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.6.6
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 5f8bf52415..98838b4f68 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.6.5
+cvxpy == 1.6.6
 ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test

From e34b90081f05bd92041415593939e819633e7f6d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 21 Jun 2025 19:01:52 +0900
Subject: [PATCH 557/604] build(deps): bump mypy from 1.16.0 to 1.16.1 in
 /requirements (#1237)

Bumps [mypy](https://github.com/python/mypy) from 1.16.0 to 1.16.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.16.0...v1.16.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.16.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 98838b4f68..b61c51850e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.6.6
 ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
-mypy == 1.16.0 # For unit test
+mypy == 1.16.1 # For unit test
 ruff == 0.11.13 # For unit test

From b537376c7619b8ab487e6d1169b4f304e600b358 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 Jun 2025 19:46:10 +0900
Subject: [PATCH 558/604] build(deps): bump ruff from 0.11.13 to 0.12.0 in
 /requirements (#1239)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.13 to 0.12.0.
- [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/0.11.13...0.12.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 b61c51850e..ce89e2f2db 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.11.13 # For unit test
+ruff == 0.12.0 # For unit test

From c50abb38c89451f07ced8687a488105b85064b31 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 27 Jun 2025 22:10:29 +0900
Subject: [PATCH 559/604] build(deps): bump pytest from 8.4.0 to 8.4.1 in
 /requirements (#1240)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.4.0 to 8.4.1.
- [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/8.4.0...8.4.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-version: 8.4.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 ce89e2f2db..30458e03d1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.15.2
 matplotlib == 3.10.1
 cvxpy == 1.6.6
 ecos == 2.0.14
-pytest == 8.4.0 # For unit test
+pytest == 8.4.1 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.1 # For unit test
 ruff == 0.12.0 # For unit test

From e8c6a0f2d81d96e503a7924beadc174b7e73f470 Mon Sep 17 00:00:00 2001
From: Viral Panchal <53462443+Viralpanchal7899@users.noreply.github.com>
Date: Fri, 27 Jun 2025 09:12:20 -0400
Subject: [PATCH 560/604] Enhance external sensors documentation.  (#1236)

* add: external sensors documentation

* add a line
---
 docs/modules/12_appendix/external_sensors_main.rst | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst
index 3597418150..b7caaf2c3a 100644
--- a/docs/modules/12_appendix/external_sensors_main.rst
+++ b/docs/modules/12_appendix/external_sensors_main.rst
@@ -10,9 +10,7 @@ Therefore, we will provide an overview.
 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.
+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
 -------------
@@ -22,7 +20,7 @@ Laser sensors measure distances by utilizing light, commonly referred to as Ligh
 Radar Sensors
 -------------
 
-TBD
+Radar measures distances using radio waves, commonly referred to as Radio Detection and Ranging (RADAR). It operates by transmitting radio signals towards an object and calculating the distance based on the time it takes for the reflected waves to return, using the speed of radio waves as a constant. 
 
 
 Monocular Cameras
@@ -59,4 +57,9 @@ Ultrasonic sensors are commonly used in indoor robots and some automotive autono
 References
 ----------
 
-TBD
+- Wikipedia articles:
+
+  - `Lidar Sensors <https://en.wikipedia.org/wiki/Lidar>`_
+  - `Radar Sensors <https://en.wikipedia.org/wiki/Radar>`_
+  - `Stereo Cameras <https://en.wikipedia.org/wiki/Stereo_camera>`_
+  - `Ultrasonic Sensors <https://en.wikipedia.org/wiki/Ultrasonic_transducer>`_
\ No newline at end of file

From 93228bda8c5a8aaa129a90ca1294e3bf2741e154 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Jul 2025 23:19:42 +0900
Subject: [PATCH 561/604] build(deps): bump ruff from 0.12.0 to 0.12.1 in
 /requirements (#1244)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.0 to 0.12.1.
- [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/0.12.0...0.12.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 30458e03d1..410bf3f39d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.0 # For unit test
+ruff == 0.12.1 # For unit test

From 3a1ffdeb11dab931ff70bbcc3cabb21d879c0eaf Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Jul 2025 22:22:52 +0900
Subject: [PATCH 562/604] build(deps): bump pytest-xdist from 3.7.0 to 3.8.0 in
 /requirements (#1245)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.7.0 to 3.8.0.
- [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/v3.7.0...v3.8.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-version: 3.8.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 410bf3f39d..23c7393dce 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,6 +4,6 @@ matplotlib == 3.10.1
 cvxpy == 1.6.6
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
-pytest-xdist == 3.7.0 # For unit test
+pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
 ruff == 0.12.1 # For unit test

From 19bff8c04b91abb58c024fceb6e10ffab4e7d723 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Jul 2025 22:51:17 +0900
Subject: [PATCH 563/604] build(deps): bump ruff from 0.12.1 to 0.12.2 in
 /requirements (#1246)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.1 to 0.12.2.
- [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/0.12.1...0.12.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 23c7393dce..96734f3c2a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.1 # For unit test
+ruff == 0.12.2 # For unit test

From e9c86ab707ace8fa1407222b99c8946dcb20ec80 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Jul 2025 22:21:03 +0900
Subject: [PATCH 564/604] build(deps): bump ruff from 0.12.2 to 0.12.3 in
 /requirements (#1248)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.2 to 0.12.3.
- [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/0.12.2...0.12.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 96734f3c2a..21bf7b9dec 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.2 # For unit test
+ruff == 0.12.3 # For unit test

From d918947360f7e2393c2f1cef110dab77bbbe50db Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Wed, 16 Jul 2025 08:56:00 -0400
Subject: [PATCH 565/604] Collaborative astar (#1247)

* consolidate Node definition

* add base class for single agent planner

* add base class for single agent planner

* its working

* use single agent plotting util

* cleanup, bug fix, add some results to docs

* remove seeding from sta* - it happens in Node

* remove stale todo

* rename CA* and speed up plotting

* paper

* proper paper (ofc its csail)

* some cleanup

* update docs

* add unit test

* add logic for saving animation as gif

* address github bot

* Revert "add logic for saving animation as gif"

This reverts commit 639167795c80a76a1dbd40a6fbf1f1ba70c14d54.

* fix tests

* docs lint

* add gifs

* copilot review

* appease mypy
---
 .../TimeBasedPathPlanning/BaseClasses.py      |  49 ++++++
 .../GridWithDynamicObstacles.py               |  95 +++++++---
 PathPlanning/TimeBasedPathPlanning/Node.py    |  99 +++++++++++
 .../TimeBasedPathPlanning/Plotting.py         | 135 +++++++++++++++
 .../PriorityBasedPlanner.py                   |  95 ++++++++++
 .../TimeBasedPathPlanning/SafeInterval.py     | 163 ++++--------------
 .../TimeBasedPathPlanning/SpaceTimeAStar.py   | 150 +++-------------
 .../time_based_grid_search_main.rst           |  19 ++
 tests/test_priority_based_planner.py          |  39 +++++
 tests/test_safe_interval_path_planner.py      |   4 +-
 tests/test_space_time_astar.py                |   6 +-
 11 files changed, 566 insertions(+), 288 deletions(-)
 create mode 100644 PathPlanning/TimeBasedPathPlanning/BaseClasses.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/Node.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/Plotting.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py
 create mode 100644 tests/test_priority_based_planner.py

diff --git a/PathPlanning/TimeBasedPathPlanning/BaseClasses.py b/PathPlanning/TimeBasedPathPlanning/BaseClasses.py
new file mode 100644
index 0000000000..745cde45fb
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/BaseClasses.py
@@ -0,0 +1,49 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath
+import random
+import numpy.random as numpy_random
+
+# Seed randomness for reproducibility
+RANDOM_SEED = 50
+random.seed(RANDOM_SEED)
+numpy_random.seed(RANDOM_SEED)
+
+class SingleAgentPlanner(ABC):
+    """
+    Base class for single agent planners
+    """
+    
+    @staticmethod
+    @abstractmethod
+    def plan(grid: Grid, start: Position, goal: Position, verbose: bool = False) -> NodePath:
+        pass
+
+@dataclass
+class StartAndGoal:
+    # Index of this agent
+    index: int
+    # Start position of the robot
+    start: Position
+    # Goal position of the robot
+    goal: Position
+
+    def distance_start_to_goal(self) -> float:
+        return pow(self.goal.x - self.start.x, 2) + pow(self.goal.y - self.start.y, 2)
+
+class MultiAgentPlanner(ABC):
+    """
+    Base class for multi-agent planners
+    """       
+    
+    @staticmethod
+    @abstractmethod
+    def plan(grid: Grid, start_and_goal_positions: list[StartAndGoal], single_agent_planner_class: SingleAgentPlanner, verbose: bool = False) -> tuple[list[StartAndGoal], list[NodePath]]:
+        """
+        Plan for all agents. Returned paths are in order corresponding to the returned list of `StartAndGoal` objects
+        """
+        pass
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
index 2a47023f8c..ccc2989001 100644
--- a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
+++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
@@ -7,31 +7,7 @@
 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)}"
-        )
-
-    def __hash__(self):
-        return hash((self.x, self.y))
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath, Position
 
 @dataclass
 class Interval:
@@ -43,6 +19,8 @@ class ObstacleArrangement(Enum):
     RANDOM = 0
     # Obstacles start in a line in y at center of grid and move side-to-side in x
     ARRANGEMENT1 = 1
+    # Static obstacle arrangement
+    NARROW_CORRIDOR = 2
 
 """
 Generates a 2d numpy array with lists for elements.
@@ -87,6 +65,8 @@ def __init__(
             self.obstacle_paths = self.generate_dynamic_obstacles(num_obstacles)
         elif obstacle_arrangement == ObstacleArrangement.ARRANGEMENT1:
             self.obstacle_paths = self.obstacle_arrangement_1(num_obstacles)
+        elif obstacle_arrangement == ObstacleArrangement.NARROW_CORRIDOR:
+            self.obstacle_paths = self.generate_narrow_corridor_obstacles(num_obstacles)
 
         for i, path in enumerate(self.obstacle_paths):
             obs_idx = i + 1  # avoid using 0 - that indicates free space in the grid
@@ -184,6 +164,26 @@ def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]:
             obstacle_paths.append(path)
 
         return obstacle_paths
+    
+    def generate_narrow_corridor_obstacles(self, obs_count: int) -> list[list[Position]]:
+        obstacle_paths = []
+
+        for y in range(0, self.grid_size[1]):
+            if y > obs_count:
+                break
+
+            if y == self.grid_size[1] // 2:
+                # Skip the middle row
+                continue
+
+            obstacle_path = []
+            x = self.grid_size[0] // 2 # middle of the grid
+            for t in range(0, self.time_limit - 1):
+                obstacle_path.append(Position(x, y))
+
+            obstacle_paths.append(obstacle_path)
+
+        return obstacle_paths
 
     """
     Check if the given position is valid at time t
@@ -196,11 +196,11 @@ def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]:
         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
+        # Check if position is in grid
         if not self.inside_grid_bounds(position):
             return False
 
-        # Check if new position is not occupied at time t
+        # Check if position is not occupied at time t
         return self.reservation_matrix[position.x, position.y, t] == 0
 
     """
@@ -289,9 +289,48 @@ def get_safe_intervals_at_cell(self, cell: Position) -> list[Interval]:
         # 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
+    
+    """
+    Reserve an agent's path in the grid. Raises an exception if the agent's index is 0, or if a position is
+    already reserved by a different agent.
+    """
+    def reserve_path(self, node_path: NodePath, agent_index: int):
+        if agent_index == 0:
+            raise Exception("Agent index cannot be 0")
+        
+        for i, node in enumerate(node_path.path):
+            reservation_finish_time = node.time + 1
+            if i < len(node_path.path) - 1:
+                reservation_finish_time = node_path.path[i + 1].time
 
-show_animation = True
+            self.reserve_position(node.position, agent_index, Interval(node.time, reservation_finish_time))
+
+    """
+    Reserve a position for the provided agent during the provided time interval.
+    Raises an exception if the agent's index is 0, or if the position is already reserved by a different agent during the interval.
+    """
+    def reserve_position(self, position: Position, agent_index: int, interval: Interval):
+        if agent_index == 0:
+            raise Exception("Agent index cannot be 0")
+
+        for t in range(interval.start_time, interval.end_time + 1):
+            current_reserver = self.reservation_matrix[position.x, position.y, t]
+            if current_reserver not in [0, agent_index]:
+                raise Exception(
+                    f"Agent {agent_index} tried to reserve a position already reserved by another agent: {position} at time {t}, reserved by {current_reserver}"
+                )
+            self.reservation_matrix[position.x, position.y, t] = agent_index
+
+    """
+    Clears the initial reservation for an agent by clearing reservations at its start position with its index for
+    from time 0 to the time limit.
+    """
+    def clear_initial_reservation(self, position: Position, agent_index: int):
+        for t in range(self.time_limit):
+            if self.reservation_matrix[position.x, position.y, t] == agent_index:
+                self.reservation_matrix[position.x, position.y, t] = 0
 
+show_animation = True
 
 def main():
     grid = Grid(
diff --git a/PathPlanning/TimeBasedPathPlanning/Node.py b/PathPlanning/TimeBasedPathPlanning/Node.py
new file mode 100644
index 0000000000..728eebb676
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/Node.py
@@ -0,0 +1,99 @@
+from dataclasses import dataclass
+from functools import total_ordering
+import numpy as np
+from typing import Sequence
+
+@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)}"
+        )
+
+    def __hash__(self):
+        return hash((self.x, self.y))
+
+@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)
+
+    """
+    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: Sequence[Node]
+    positions_at_time: dict[int, Position]
+    # Number of nodes expanded while finding this path
+    expanded_node_count: int
+
+    def __init__(self, path: Sequence[Node], expanded_node_count: int):
+        self.path = path
+        self.expanded_node_count = expanded_node_count
+
+        self.positions_at_time = {}
+        for i, node in enumerate(path):
+            reservation_finish_time = node.time + 1
+            if i < len(path) - 1:
+                reservation_finish_time = path[i + 1].time
+
+            for t in range(node.time, reservation_finish_time):
+                self.positions_at_time[t] = 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
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/Plotting.py b/PathPlanning/TimeBasedPathPlanning/Plotting.py
new file mode 100644
index 0000000000..7cd1f615d8
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/Plotting.py
@@ -0,0 +1,135 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.backend_bases import KeyEvent
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import StartAndGoal
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath
+
+'''
+Plot a single agent path.
+'''
+def PlotNodePath(grid: Grid, start: Position, goal: Position, path: NodePath):
+    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.grid_size[0], 1))
+    ax.set_yticks(np.arange(0, grid.grid_size[1], 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]
+        if isinstance(event, KeyEvent) 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)
+        if not path_position:
+            raise Exception(f"Path position not found for time {i}.")
+
+        path_points.set_data([path_position.x], [path_position.y])
+        plt.pause(0.2)
+    plt.show()
+
+'''
+Plot a series of agent paths.
+'''
+def PlotNodePaths(grid: Grid, start_and_goals: list[StartAndGoal], paths: list[NodePath]):
+    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.grid_size[0], 1))
+    ax.set_yticks(np.arange(0, grid.grid_size[1], 1))
+
+    # Plot start and goal positions for each agent
+    colors = [] # generated randomly in loop
+    markers = ['D', 's', '^', 'o', 'p']  # Different markers for visual distinction
+
+    # Create plots for start and goal positions
+    start_and_goal_plots = []
+    for i, path in enumerate(paths):
+        marker_idx = i % len(markers)
+        agent_id = start_and_goals[i].index
+        start = start_and_goals[i].start
+        goal = start_and_goals[i].goal
+        
+        color = np.random.rand(3,)
+        colors.append(color)
+        sg_plot, = ax.plot([], [], markers[marker_idx], c=color, ms=15, 
+                            label=f"Agent {agent_id} Start/Goal")
+        sg_plot.set_data([start.x, goal.x], [start.y, goal.y])
+        start_and_goal_plots.append(sg_plot)
+
+    # Plot for obstacles
+    (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles")
+
+    # Create plots for each agent's path
+    path_plots = []
+    for i, path in enumerate(paths):
+        agent_id = start_and_goals[i].index
+        path_plot, = ax.plot([], [], "o", c=colors[i], ms=10, 
+                            label=f"Agent {agent_id} Path")
+        path_plots.append(path_plot)
+
+    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]
+        if isinstance(event, KeyEvent) else None
+    )
+
+    # Find the maximum time across all paths
+    max_time = max(path.goal_reached_time() for path in paths)
+
+    # Animation loop
+    for i in range(0, max_time + 1):
+        # Update obstacle positions
+        obs_positions = grid.get_obstacle_positions_at_time(i)
+        obs_points.set_data(obs_positions[0], obs_positions[1])
+        
+        # Update each agent's position
+        for (j, path) in enumerate(paths):
+            path_positions = []
+            if i <= path.goal_reached_time():
+                res = path.get_position(i)
+                if not res:
+                    print(path)
+                    print(i)
+                path_position = path.get_position(i)
+                if not path_position:
+                    raise Exception(f"Path position not found for time {i}.")
+
+                # Verify position is valid
+                assert not path_position in obs_positions
+                assert not path_position in path_positions
+                path_positions.append(path_position)
+
+                path_plots[j].set_data([path_position.x], [path_position.y])
+        
+        plt.pause(0.2)
+
+    plt.show()
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py b/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py
new file mode 100644
index 0000000000..2573965cf6
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py
@@ -0,0 +1,95 @@
+"""
+Priority Based Planner for multi agent path planning.
+The planner generates an order to plan in, and generates plans for the robots in that order. Each planned
+path is reserved in the grid, and all future plans must avoid that path.
+
+Algorithm outlined in section III of this paper: https://pure.tudelft.nl/ws/portalfiles/portal/67074672/07138650.pdf
+"""
+
+import numpy as np
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Interval,
+    ObstacleArrangement,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import MultiAgentPlanner, StartAndGoal
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import SingleAgentPlanner
+from PathPlanning.TimeBasedPathPlanning.SafeInterval import SafeIntervalPathPlanner
+from PathPlanning.TimeBasedPathPlanning.Plotting import PlotNodePaths
+import time
+
+class PriorityBasedPlanner(MultiAgentPlanner):
+
+    @staticmethod
+    def plan(grid: Grid, start_and_goals: list[StartAndGoal], single_agent_planner_class: SingleAgentPlanner, verbose: bool = False) -> tuple[list[StartAndGoal], list[NodePath]]:
+        """
+        Generate a path from the start to the goal for each agent in the `start_and_goals` list.
+        Returns the re-ordered StartAndGoal combinations, and a list of path plans. The order of the plans
+        corresponds to the order of the `start_and_goals` list.
+        """
+        print(f"Using single-agent planner: {single_agent_planner_class}")
+
+        # Reserve initial positions
+        for start_and_goal in start_and_goals:
+            grid.reserve_position(start_and_goal.start, start_and_goal.index, Interval(0, 10))
+
+        # Plan in descending order of distance from start to goal
+        start_and_goals = sorted(start_and_goals,
+                    key=lambda item: item.distance_start_to_goal(),
+                    reverse=True)
+
+        paths = []
+        for start_and_goal in start_and_goals:
+            if verbose:
+                print(f"\nPlanning for agent:  {start_and_goal}" )
+
+            grid.clear_initial_reservation(start_and_goal.start, start_and_goal.index)
+            path = single_agent_planner_class.plan(grid, start_and_goal.start, start_and_goal.goal, verbose)
+
+            if path is None:
+                print(f"Failed to find path for {start_and_goal}")
+                return []
+
+            agent_index = start_and_goal.index
+            grid.reserve_path(path, agent_index)
+            paths.append(path)
+
+        return (start_and_goals, paths)
+
+verbose = False
+show_animation = True
+def main():
+    grid_side_length = 21
+
+    start_and_goals = [StartAndGoal(i, Position(1, i), Position(19, 19-i)) for i in range(1, 16)]
+    obstacle_avoid_points = [pos for item in start_and_goals for pos in (item.start, item.goal)]
+
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        num_obstacles=250,
+        obstacle_avoid_points=obstacle_avoid_points,
+        # obstacle_arrangement=ObstacleArrangement.NARROW_CORRIDOR,
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+        # obstacle_arrangement=ObstacleArrangement.RANDOM,
+    )
+
+    start_time = time.time()
+    start_and_goals, paths = PriorityBasedPlanner.plan(grid, start_and_goals, SafeIntervalPathPlanner, verbose)
+
+    runtime = time.time() - start_time
+    print(f"\nPlanning took: {runtime:.5f} seconds")
+
+    if verbose:
+        print(f"Paths:")
+        for path in paths:
+            print(f"{path}\n")
+
+    if not show_animation:
+        return
+
+    PlotNodePaths(grid, start_and_goals, paths)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/SafeInterval.py b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
index 7fea10c67f..446847ac6d 100644
--- a/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
+++ b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
@@ -9,7 +9,6 @@
 """
 
 import numpy as np
-import matplotlib.pyplot as plt
 from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
     Grid,
     Interval,
@@ -17,8 +16,11 @@
     Position,
     empty_2d_array_of_lists,
 )
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import SingleAgentPlanner
+from PathPlanning.TimeBasedPathPlanning.Node import Node, NodePath
+from PathPlanning.TimeBasedPathPlanning.Plotting import PlotNodePath
+
 import heapq
-import random
 from dataclasses import dataclass
 from functools import total_ordering
 import time
@@ -29,116 +31,50 @@
 #     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
+class SIPPNode(Node):
     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)
+class SafeIntervalPathPlanner(SingleAgentPlanner):
 
     """
     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:
+    @staticmethod
+    def plan(grid: Grid, start: Position, goal: Position, verbose: bool = False) -> NodePath:
 
-        safe_intervals = self.grid.get_safe_intervals()
+        safe_intervals = grid.get_safe_intervals()
 
-        open_set: list[Node] = []
-        first_node_interval = safe_intervals[self.start.x, self.start.y][0]
+        open_set: list[SIPPNode] = []
+        first_node_interval = safe_intervals[start.x, start.y][0]
         heapq.heappush(
-            open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1, first_node_interval)
+            open_set, SIPPNode(start, 0, SafeIntervalPathPlanner.calculate_heuristic(start, goal), -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])
+        expanded_list: list[SIPPNode] = []
+        visited_intervals = empty_2d_array_of_lists(grid.grid_size[0], grid.grid_size[1])
         while open_set:
-            expanded_node: Node = heapq.heappop(open_set)
+            expanded_node: SIPPNode = heapq.heappop(open_set)
             if verbose:
                 print("Expanded node:", expanded_node)
 
-            if expanded_node.time + 1 >= self.grid.time_limit:
+            if expanded_node.time + 1 >= 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")
+            if expanded_node.position == goal:
+                if verbose:
+                    print(f"Found path to goal after {len(expanded_list)} expansions")
+
                 path = []
-                path_walker: Node = expanded_node
+                path_walker: SIPPNode = expanded_node
                 while True:
                     path.append(path_walker)
                     if path_walker.parent_index == -1:
@@ -147,14 +83,14 @@ def plan(self, verbose: bool = False) -> NodePath:
 
                 # reverse path so it goes start -> goal
                 path.reverse()
-                return NodePath(path)
+                return NodePath(path, len(expanded_list))
 
             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):
+            for child in SafeIntervalPathPlanner.generate_successors(grid, goal, expanded_node, expanded_idx, safe_intervals, visited_intervals):
                 heapq.heappush(open_set, child)
 
         raise Exception("No path found")
@@ -162,9 +98,10 @@ def plan(self, verbose: bool = False) -> NodePath:
     """
     Generate list of possible successors of the provided `parent_node` that are worth expanding
     """
+    @staticmethod
     def generate_successors(
-        self, parent_node: Node, parent_node_idx: int, intervals: np.ndarray, visited_intervals: np.ndarray
-    ) -> list[Node]:
+        grid: Grid, goal: Position, parent_node: SIPPNode, parent_node_idx: int, intervals: np.ndarray, visited_intervals: np.ndarray
+    ) -> list[SIPPNode]:
         new_nodes = []
         diffs = [
             Position(0, 0),
@@ -175,7 +112,7 @@ def generate_successors(
         ]
         for diff in diffs:
             new_pos = parent_node.position + diff
-            if not self.grid.inside_grid_bounds(new_pos):
+            if not grid.inside_grid_bounds(new_pos):
                 continue
 
             current_interval = parent_node.interval
@@ -203,12 +140,12 @@ def generate_successors(
                 # 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(
+                    if grid.valid_position(new_pos, possible_t):
+                        new_nodes.append(SIPPNode(
                             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),
+                            SafeIntervalPathPlanner.calculate_heuristic(new_pos, goal),
                             parent_node_idx,
                             interval,
                         ))
@@ -220,8 +157,9 @@ def generate_successors(
     """
     Calculate the heuristic for a given position - Manhattan distance to the goal
     """
-    def calculate_heuristic(self, position) -> int:
-        diff = self.goal - position
+    @staticmethod
+    def calculate_heuristic(position: Position, goal: Position) -> int:
+        diff = goal - position
         return abs(diff.x) + abs(diff.y)
 
 
@@ -229,7 +167,7 @@ def calculate_heuristic(self, position) -> int:
 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):
+def add_entry_to_visited_intervals_array(entry_time_and_interval: EntryTimeAndInterval, visited_intervals: np.ndarray, expanded_node: SIPPNode):
     # 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:
@@ -257,8 +195,7 @@ def main():
         # obstacle_arrangement=ObstacleArrangement.RANDOM,
     )
 
-    planner = SafeIntervalPathPlanner(grid, start, goal)
-    path = planner.plan(verbose)
+    path = SafeIntervalPathPlanner.plan(grid, start, goal, verbose)
     runtime = time.time() - start_time
     print(f"Planning took: {runtime:.5f} seconds")
 
@@ -268,35 +205,7 @@ def main():
     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()
+    PlotNodePath(grid, start, goal, path)
 
 
 if __name__ == "__main__":
diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
index c4e2802d37..b85569f5dc 100644
--- a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
+++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
@@ -9,101 +9,25 @@
 """
 
 import numpy as np
-import matplotlib.pyplot as plt
 from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
     Grid,
     ObstacleArrangement,
     Position,
 )
+from PathPlanning.TimeBasedPathPlanning.Node import Node, NodePath
 import heapq
 from collections.abc import Generator
-import random
-from dataclasses import dataclass
-from functools import total_ordering
 import time
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import SingleAgentPlanner
+from PathPlanning.TimeBasedPathPlanning.Plotting import PlotNodePath
 
-# 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
+class SpaceTimeAStar(SingleAgentPlanner):
 
-    """
-    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)
-
-    """
-    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]
-    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
-    # Used to evaluate solutions
-    expanded_node_count: int = -1
-
-    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:
+    @staticmethod
+    def plan(grid: Grid, start: Position, goal: Position, verbose: bool = False) -> NodePath:
         open_set: list[Node] = []
         heapq.heappush(
-            open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1)
+            open_set, Node(start, 0, SpaceTimeAStar.calculate_heuristic(start, goal), -1)
         )
 
         expanded_list: list[Node] = []
@@ -113,13 +37,15 @@ def plan(self, verbose: bool = False) -> NodePath:
             if verbose:
                 print("Expanded node:", expanded_node)
 
-            if expanded_node.time + 1 >= self.grid.time_limit:
+            if expanded_node.time + 1 >= 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")
+            if expanded_node.position == goal:
+                if verbose:
+                    print(f"Found path to goal after {len(expanded_list)} expansions")
+
                 path = []
                 path_walker: Node = expanded_node
                 while True:
@@ -130,14 +56,13 @@ def plan(self, verbose: bool = False) -> NodePath:
 
                 # reverse path so it goes start -> goal
                 path.reverse()
-                self.expanded_node_count = len(expanded_set)
-                return NodePath(path)
+                return NodePath(path, len(expanded_set))
 
             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, expanded_set):
+            for child in SpaceTimeAStar.generate_successors(grid, goal, expanded_node, expanded_idx, verbose, expanded_set):
                 heapq.heappush(open_set, child)
 
         raise Exception("No path found")
@@ -145,8 +70,9 @@ def plan(self, verbose: bool = False) -> NodePath:
     """
     Generate possible successors of the provided `parent_node`
     """
+    @staticmethod
     def generate_successors(
-        self, parent_node: Node, parent_node_idx: int, verbose: bool, expanded_set: set[Node]
+        grid: Grid, goal: Position, parent_node: Node, parent_node_idx: int, verbose: bool, expanded_set: set[Node]
     ) -> Generator[Node, None, None]:
         diffs = [
             Position(0, 0),
@@ -160,20 +86,22 @@ def generate_successors(
             new_node = Node(
                 new_pos,
                 parent_node.time + 1,
-                self.calculate_heuristic(new_pos),
+                SpaceTimeAStar.calculate_heuristic(new_pos, goal),
                 parent_node_idx,
             )
 
             if new_node in expanded_set:
                 continue
 
-            if self.grid.valid_position(new_pos, parent_node.time + 1):
+            # Check if the new node is valid for the next 2 time steps - one step to enter, and another to leave
+            if all([grid.valid_position(new_pos, parent_node.time + dt) for dt in [1, 2]]):
                 if verbose:
                     print("\tNew successor node: ", new_node)
                 yield new_node
 
-    def calculate_heuristic(self, position) -> int:
-        diff = self.goal - position
+    @staticmethod
+    def calculate_heuristic(position: Position, goal: Position) -> int:
+        diff = goal - position
         return abs(diff.x) + abs(diff.y)
 
 
@@ -194,8 +122,7 @@ def main():
         obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
     )
 
-    planner = SpaceTimeAStar(grid, start, goal)
-    path = planner.plan(verbose)
+    path = SpaceTimeAStar.plan(grid, start, goal, verbose)
 
     runtime = time.time() - start_time
     print(f"Planning took: {runtime:.5f} seconds")
@@ -206,36 +133,7 @@ def main():
     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()
-
+    PlotNodePath(grid, start, goal, path)
 
 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 9517e95b31..a44dd20a97 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
@@ -81,9 +81,28 @@ Code Link
 ^^^^^^^^^^^^^
 .. autoclass:: PathPlanning.TimeBasedPathPlanning.SafeInterval.SafeIntervalPathPlanner
 
+Multi-Agent Path Planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Priority Based Planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The planner generates an order to plan in, and generates plans for the robots in that order. Each planned path is reserved in the grid, and all future plans must avoid that path. The robots are planned for in descending order of distance from start to goal.
+
+This is a sub-optimal algorithm because no replanning happens once paths are found. It does not guarantee the shortest path is found for any particular robot, nor that the final set of paths found contains the lowest possible amount of time or movement.
+
+Static Obstacles:
+
+.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner/priority_planner2.gif
+
+Dynamic Obstacles:
+
+.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner/priority_planner.gif
+
 
 References
 ~~~~~~~~~~~
 
 -  `Cooperative Pathfinding <https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf>`__
 -  `SIPP: Safe Interval Path Planning for Dynamic Environments <https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf>`__
+- `Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots <https://pure.tudelft.nl/ws/portalfiles/portal/67074672/07138650.pdf>`__
\ No newline at end of file
diff --git a/tests/test_priority_based_planner.py b/tests/test_priority_based_planner.py
new file mode 100644
index 0000000000..e2ff602d88
--- /dev/null
+++ b/tests/test_priority_based_planner.py
@@ -0,0 +1,39 @@
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    NodePath,
+    ObstacleArrangement,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import StartAndGoal
+from PathPlanning.TimeBasedPathPlanning import PriorityBasedPlanner as m
+from PathPlanning.TimeBasedPathPlanning.SafeInterval import SafeIntervalPathPlanner
+import numpy as np
+import conftest
+
+
+def test_1():
+    grid_side_length = 21
+
+    start_and_goals = [StartAndGoal(i, Position(1, i), Position(19, 19-i)) for i in range(1, 16)]
+    obstacle_avoid_points = [pos for item in start_and_goals for pos in (item.start, item.goal)]
+
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        num_obstacles=250,
+        obstacle_avoid_points=obstacle_avoid_points,
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+    )
+
+    m.show_animation = False
+
+    start_and_goals: list[StartAndGoal]
+    paths: list[NodePath]
+    start_and_goals, paths = m.PriorityBasedPlanner.plan(grid, start_and_goals, SafeIntervalPathPlanner, False)
+
+    # All paths should start at the specified position and reach the goal
+    for i, start_and_goal in enumerate(start_and_goals):
+        assert paths[i].path[0].position == start_and_goal.start
+        assert paths[i].path[-1].position == start_and_goal.goal
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)
diff --git a/tests/test_safe_interval_path_planner.py b/tests/test_safe_interval_path_planner.py
index f1fbf90d2a..bbcd4e427c 100644
--- a/tests/test_safe_interval_path_planner.py
+++ b/tests/test_safe_interval_path_planner.py
@@ -18,9 +18,7 @@ def test_1():
     )
 
     m.show_animation = False
-    planner = m.SafeIntervalPathPlanner(grid, start, goal)
-
-    path = planner.plan(False)
+    path = m.SafeIntervalPathPlanner.plan(grid, start, goal)
 
     # path should have 31 entries
     assert len(path.path) == 31
diff --git a/tests/test_space_time_astar.py b/tests/test_space_time_astar.py
index 390c7732dc..1194c61d2e 100644
--- a/tests/test_space_time_astar.py
+++ b/tests/test_space_time_astar.py
@@ -18,9 +18,7 @@ def test_1():
     )
 
     m.show_animation = False
-    planner = m.SpaceTimeAStar(grid, start, goal)
-
-    path = planner.plan(False)
+    path = m.SpaceTimeAStar.plan(grid, start, goal)
 
     # path should have 28 entries
     assert len(path.path) == 31
@@ -28,7 +26,7 @@ def test_1():
     # path should end at the goal
     assert path.path[-1].position == goal
 
-    assert planner.expanded_node_count < 1000
+    assert path.expanded_node_count < 1000
 
 if __name__ == "__main__":
     conftest.run_this_test(__file__)

From 0a8766929d83dfa90b262f0dba03bef59c84dca7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Jul 2025 23:08:47 +0900
Subject: [PATCH 566/604] build(deps): bump ruff from 0.12.3 to 0.12.5 in
 /requirements (#1251)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.3 to 0.12.5.
- [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/0.12.3...0.12.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.5
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 21bf7b9dec..39519f93c0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.3 # For unit test
+ruff == 0.12.5 # For unit test

From 7e8fbfb635fd7fd8beabbd7fe8f6c876af471201 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 12:57:43 +0900
Subject: [PATCH 567/604] build(deps): bump cvxpy from 1.6.6 to 1.7.1 in
 /requirements (#1253)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.6.6 to 1.7.1.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.6.6...v1.7.1)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.7.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 39519f93c0..6ca0bf9606 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.6.6
+cvxpy == 1.7.1
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test

From f9db30bff1df50b078dd3696ee8ce05f2bc8ebe5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 18:37:08 +0900
Subject: [PATCH 568/604] build(deps): bump mypy from 1.16.1 to 1.17.0 in
 /requirements (#1252)

Bumps [mypy](https://github.com/python/mypy) from 1.16.1 to 1.17.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.16.1...v1.17.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.17.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 6ca0bf9606..66b5202aca 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.1
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.16.1 # For unit test
+mypy == 1.17.0 # For unit test
 ruff == 0.12.5 # For unit test

From 458c52f39024e4970bef420d3cac8cbe0776628e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 7 Aug 2025 07:33:33 +0900
Subject: [PATCH 569/604] build(deps): bump
 larsoner/circleci-artifacts-redirector-action (#1254)

Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 1.1.0 to 1.2.0.
- [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases)
- [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/v1.1.0...v1.2.0)

---
updated-dependencies:
- dependency-name: larsoner/circleci-artifacts-redirector-action
  dependency-version: 1.2.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 692942cddd..f6dd3f2e7c 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@v1.1.0
+        uses: larsoner/circleci-artifacts-redirector-action@v1.2.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_TOKEN }}

From db06b9f9669b216b4a78269c8acfa07abc6d45e7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 7 Aug 2025 21:54:40 +0900
Subject: [PATCH 570/604] build(deps): bump mypy from 1.17.0 to 1.17.1 in
 /requirements (#1255)

Bumps [mypy](https://github.com/python/mypy) from 1.17.0 to 1.17.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.17.0...v1.17.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.17.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 66b5202aca..f8f16dc34d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.1
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.17.0 # For unit test
+mypy == 1.17.1 # For unit test
 ruff == 0.12.5 # For unit test

From 6455a4db8f7e3d995af0b08087f0c9543f2b7ca3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 9 Aug 2025 11:36:45 +0900
Subject: [PATCH 571/604] build(deps): bump ruff from 0.12.5 to 0.12.7 in
 /requirements (#1256)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.5 to 0.12.7.
- [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/0.12.5...0.12.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.7
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 f8f16dc34d..38001d34a9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.5 # For unit test
+ruff == 0.12.7 # For unit test

From b85b5d2ee4420b6f3f3d7f54a3d33cf86fbc90d4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Aug 2025 16:57:02 +0900
Subject: [PATCH 572/604] build(deps): bump actions/checkout from 4 to 5
 (#1257)

Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 5.
- [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/v4...v5)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: '5'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
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 907d36452d..22a8baa4e7 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@v4
+    - uses: actions/checkout@v5
     - run: git fetch --prune --unshallow
 
     - name: Setup python
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 03db65f43d..aa162cde2e 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.13' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - run: git fetch --prune --unshallow
 
       - name: Update bash
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 5cb19699b2..4d83a3a696 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.13' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - run: git fetch --prune --unshallow
 
       - name: Setup python
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 878a4a4435..0ed1d7e90d 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -16,7 +16,7 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v5
       with:
         # We must fetch at least the immediate parents so that if this is
         # a pull request then we can checkout the head.

From cec3a844a5fce464bb5c402720168b5cd2a5ba96 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Aug 2025 22:07:38 +0900
Subject: [PATCH 573/604] build(deps): bump ruff from 0.12.7 to 0.12.8 in
 /requirements (#1258)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.7 to 0.12.8.
- [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/0.12.7...0.12.8)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.8
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 38001d34a9..1a7b43c438 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.7 # For unit test
+ruff == 0.12.8 # For unit test

From 721dd180772f34dd3f07183af8e67ff239c229c8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 20 Aug 2025 01:09:55 +0900
Subject: [PATCH 574/604] build(deps): bump ruff from 0.12.8 to 0.12.9 in
 /requirements (#1259)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.8 to 0.12.9.
- [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/0.12.8...0.12.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.9
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 1a7b43c438..21098a13f9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.8 # For unit test
+ruff == 0.12.9 # For unit test

From ddafd936e705ae83d64b942eae8a1194326bdb5b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Aug 2025 21:19:40 +0900
Subject: [PATCH 575/604] build(deps): bump ruff from 0.12.9 to 0.12.10 in
 /requirements (#1261)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.9 to 0.12.10.
- [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/0.12.9...0.12.10)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.10
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 21098a13f9..18b2a04d04 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.9 # For unit test
+ruff == 0.12.10 # For unit test

From e1685187fb5004b6c177cc65f7e4caf93aec5d5a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Aug 2025 21:20:00 +0900
Subject: [PATCH 576/604] build(deps): bump cvxpy from 1.7.1 to 1.7.2 in
 /requirements (#1262)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.7.1 to 1.7.2.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.7.1...v1.7.2)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.7.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 18b2a04d04..348b54fc76 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.7.1
+cvxpy == 1.7.2
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test

From 1461ac25535274f95ec40aff2ea40dc2638ff1eb Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Sep 2025 20:14:43 +0900
Subject: [PATCH 577/604] build(deps): bump actions/setup-python from 5 to 6
 (#1264)

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v5...v6)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
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 22a8baa4e7..152a3b0682 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@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index aa162cde2e..ab04dc01dc 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@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 4d83a3a696..a4385e595b 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@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index e08c6106c0..84165b9cd2 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@v5
+      uses: actions/setup-python@v6
     - name: Checkout
       uses: actions/checkout@master
       with:

From 1ee1788114b47830eaed7ef028b35ccff73086d6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Sep 2025 20:52:09 +0900
Subject: [PATCH 578/604] build(deps): bump pytest from 8.4.1 to 8.4.2 in
 /requirements (#1265)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.4.1 to 8.4.2.
- [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/8.4.1...8.4.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-version: 8.4.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 348b54fc76..fc11c01101 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.15.2
 matplotlib == 3.10.1
 cvxpy == 1.7.2
 ecos == 2.0.14
-pytest == 8.4.1 # For unit test
+pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
 ruff == 0.12.10 # For unit test

From d241f4a7bb64e8683308202c12165e7f04415f38 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Sep 2025 21:18:59 +0900
Subject: [PATCH 579/604] build(deps): bump ruff from 0.12.10 to 0.12.12 in
 /requirements (#1266)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.10 to 0.12.12.
- [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/0.12.10...0.12.12)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.12
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 fc11c01101..9690665821 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.10 # For unit test
+ruff == 0.12.12 # For unit test

From db3b1ab8738888a9e086ae7c1e35cbb4677ad6fa Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Sat, 13 Sep 2025 22:41:25 +0900
Subject: [PATCH 580/604] Fix typos in comments and documentation (#1260)

* Initial plan

* Fix typos in comments and documentation

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

* Revert filename change in users_comments.md as requested

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>
---
 .../0_getting_started/3_how_to_contribute_main.rst        | 2 +-
 users_comments.md                                         | 8 ++++----
 2 files changed, 5 insertions(+), 5 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 0325aaacae..9e773e930c 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
@@ -176,7 +176,7 @@ Adding the missed documentations for existing examples is also great contributio
 If you check the `Python Robotics Docs`_, you can notice that some of the examples
 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.
+These documents need to be improved.
 
 This doc `how to write doc`_ can be helpful to write documents.
 
diff --git a/users_comments.md b/users_comments.md
index 30dc2d2d85..c814e74c4b 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -101,7 +101,7 @@ Thank you!
 
 ---
 
-Dear AtsushiSakai, <br>Thanks a lot for the wonderful collection of projects.It was really useful. I really appreciate your time in maintaing and building this repository.It will be really great if I get a chance to meet you in person sometime.I got really inspired looking at your work. <br>All the very best for all your future endeavors! <br> Thanks once again, <br>
+Dear AtsushiSakai, <br>Thanks a lot for the wonderful collection of projects.It was really useful. I really appreciate your time in maintaining and building this repository.It will be really great if I get a chance to meet you in person sometime.I got really inspired looking at your work. <br>All the very best for all your future endeavors! <br> Thanks once again, <br>
 
 ---Ezhil Bharathi 
 
@@ -163,7 +163,7 @@ so kind of you can you make videos of it.
 
 ---
 
-Dear AtshshiSakai, <br>You are very wise and smart that you created this library for students wanting to learn Probabilistic Robotics and actually perform robot control. <br>Hats off to you and your work. <br>I am also reading your book titled : Python Robotics
+Dear AtsushiSakai, <br>You are very wise and smart that you created this library for students wanting to learn Probabilistic Robotics and actually perform robot control. <br>Hats off to you and your work. <br>I am also reading your book titled : Python Robotics
 
 --Himanshu
 
@@ -263,7 +263,7 @@ Hey Atsushi <br>We are working on a multiagent simulation game and this project
 
 ---
 
-Thanks a lot for this amazing set of very useful librarires!
+Thanks a lot for this amazing set of very useful libraries!
 
 --Razvan Viorel Mihai
 
@@ -275,7 +275,7 @@ Dear Atsushi Sakai, <br> <br>This is probably one of the best open-source roboti
 
 ---
 
-hanks frnd, for ur contibusion
+Thanks friend, for your contribution
 
 —--
 

From 0b7f8aa22d4be6128dcd1d1db2f69a6479f50494 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 17 Sep 2025 10:44:06 +0900
Subject: [PATCH 581/604] build(deps): bump mypy from 1.17.1 to 1.18.1 in
 /requirements (#1267)

Bumps [mypy](https://github.com/python/mypy) from 1.17.1 to 1.18.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.17.1...v1.18.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.18.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 9690665821..45e86f3cb2 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.2
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.17.1 # For unit test
+mypy == 1.18.1 # For unit test
 ruff == 0.12.12 # For unit test

From c5871248be46388abdd7cb5e0a2e9d52c4492c7e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 17 Sep 2025 21:55:53 +0900
Subject: [PATCH 582/604] build(deps): bump ruff from 0.12.12 to 0.13.0 in
 /requirements (#1268)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.12 to 0.13.0.
- [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/0.12.12...0.13.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 45e86f3cb2..673f1ec179 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.1 # For unit test
-ruff == 0.12.12 # For unit test
+ruff == 0.13.0 # For unit test

From 4a8dbf2199c4bce998ff10f49579d30a5462c82c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 00:09:21 +0900
Subject: [PATCH 583/604] build(deps): bump
 larsoner/circleci-artifacts-redirector-action (#1271)

Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 1.2.0 to 1.3.1.
- [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases)
- [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/v1.2.0...v1.3.1)

---
updated-dependencies:
- dependency-name: larsoner/circleci-artifacts-redirector-action
  dependency-version: 1.3.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 f6dd3f2e7c..0e64bab96c 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@v1.2.0
+        uses: larsoner/circleci-artifacts-redirector-action@v1.3.1
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_TOKEN }}

From d4dd8bbc9bbbe4cbe4a3711992dceaf76f5a2a7d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 12:42:30 +0900
Subject: [PATCH 584/604] build(deps): bump cvxpy from 1.7.2 to 1.7.3 in
 /requirements (#1274)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.7.2 to 1.7.3.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.7.2...v1.7.3)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.7.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 673f1ec179..0a326d3b6e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.7.2
+cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test

From 7a37f2e403f0f2c2a7d29849e4e804ee5ef45012 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 19:32:16 +0900
Subject: [PATCH 585/604] build(deps): bump mypy from 1.18.1 to 1.18.2 in
 /requirements (#1272)

Bumps [mypy](https://github.com/python/mypy) from 1.18.1 to 1.18.2.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.18.1...v1.18.2)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.18.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 0a326d3b6e..8703fb1f18 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.18.1 # For unit test
+mypy == 1.18.2 # For unit test
 ruff == 0.13.0 # For unit test

From ffaa02fb9377efb4649eacb39e76865e2ca920e8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 21:21:05 +0900
Subject: [PATCH 586/604] build(deps): bump ruff from 0.13.0 to 0.13.1 in
 /requirements (#1273)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.0 to 0.13.1.
- [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/0.13.0...0.13.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 8703fb1f18..a7e9eb25f9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.0 # For unit test
+ruff == 0.13.1 # For unit test

From 722efb8dbb5ca9a01fcedfd6b0fe36623d013d39 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Sep 2025 21:49:29 +0900
Subject: [PATCH 587/604] build(deps): bump ruff from 0.13.1 to 0.13.2 in
 /requirements (#1275)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.1 to 0.13.2.
- [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/0.13.1...0.13.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 a7e9eb25f9..050bd1e57c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.1 # For unit test
+ruff == 0.13.2 # For unit test

From c4c7d5957fe88eaf3f3624588cd2123a7de44ce8 Mon Sep 17 00:00:00 2001
From: Sanskar Modi <sansyprog8@gmail.com>
Date: Mon, 6 Oct 2025 19:28:05 +0530
Subject: [PATCH 588/604] fixed the scope of range function (#1276)

---
 .../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 eb2d7b6a73..205f37e62f 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
@@ -318,7 +318,7 @@ def calc_ref_trajectory(state, cx, cy, cyaw, ck, sp, dl, pind):
 
     travel = 0.0
 
-    for i in range(T + 1):
+    for i in range(1, T + 1):
         travel += abs(state.v) * DT
         dind = int(round(travel / dl))
 

From 194dc52083268dec76e51ae0807583437dae753f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Oct 2025 21:59:44 +0900
Subject: [PATCH 589/604] build(deps): bump scipy from 1.15.2 to 1.16.2 in
 /requirements (#1280)

Bumps [scipy](https://github.com/scipy/scipy) from 1.15.2 to 1.16.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.15.2...v1.16.2)

---
updated-dependencies:
- dependency-name: scipy
  dependency-version: 1.16.2
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 050bd1e57c..a8601c982f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 2.2.4
-scipy == 1.15.2
+scipy == 1.16.2
 matplotlib == 3.10.1
 cvxpy == 1.7.3
 ecos == 2.0.14

From 1ff2b862652188756b0351fca6163598f0d5b95a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Oct 2025 18:53:10 +0900
Subject: [PATCH 590/604] build(deps): bump numpy from 2.2.4 to 2.3.3 in
 /requirements (#1282)

Bumps [numpy](https://github.com/numpy/numpy) from 2.2.4 to 2.3.3.
- [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/v2.2.4...v2.3.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-version: 2.3.3
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 a8601c982f..8b2a8dcbf1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.2.4
+numpy == 2.3.3
 scipy == 1.16.2
 matplotlib == 3.10.1
 cvxpy == 1.7.3

From 52c9c07114ab67879e462cd23b39c578c9cafa1b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Oct 2025 19:11:28 +0900
Subject: [PATCH 591/604] build(deps): bump matplotlib from 3.10.1 to 3.10.6 in
 /requirements (#1283)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.10.1 to 3.10.6.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.10.1...v3.10.6)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-version: 3.10.6
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 8b2a8dcbf1..43a16ef1e2 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.3.3
 scipy == 1.16.2
-matplotlib == 3.10.1
+matplotlib == 3.10.6
 cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test

From 6ec297fbb3055abf02030ad5b81bbb149371d161 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Oct 2025 19:22:26 +0900
Subject: [PATCH 592/604] build(deps): bump ruff from 0.13.2 to 0.13.3 in
 /requirements (#1281)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.2 to 0.13.3.
- [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/0.13.2...0.13.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 43a16ef1e2..153add3786 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.2 # For unit test
+ruff == 0.13.3 # For unit test

From 2895d880b296b42470d95906d55cbbc0f6073383 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Oct 2025 07:22:12 +0900
Subject: [PATCH 593/604] build(deps): bump github/codeql-action from 3 to 4
 (#1287)

Bumps [github/codeql-action](https://github.com/github/codeql-action) from 3 to 4.
- [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/v3...v4)

---
updated-dependencies:
- dependency-name: github/codeql-action
  dependency-version: '4'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
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 0ed1d7e90d..dcbcdbaa88 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@v3
+      uses: github/codeql-action/init@v4
       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@v3
+      uses: github/codeql-action/autobuild@v4
 
     # 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@v3
+      uses: github/codeql-action/analyze@v4

From 01a85c3c848975b5efc9b11b6a6a3be1e0a8b8dd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Oct 2025 07:23:37 +0900
Subject: [PATCH 594/604] build(deps): bump matplotlib from 3.10.6 to 3.10.7 in
 /requirements (#1289)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.10.6 to 3.10.7.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.10.6...v3.10.7)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-version: 3.10.7
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 153add3786..166f4e2ddc 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.3.3
 scipy == 1.16.2
-matplotlib == 3.10.6
+matplotlib == 3.10.7
 cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test

From b0c81aedbcfefe43fac7b298dd3b7664dcabd8a9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Oct 2025 19:30:32 +0900
Subject: [PATCH 595/604] build(deps): bump ruff from 0.13.3 to 0.14.0 in
 /requirements (#1288)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.3 to 0.14.0.
- [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/0.13.3...0.14.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.14.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
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 166f4e2ddc..c6223c01df 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.3 # For unit test
+ruff == 0.14.0 # For unit test

From 39e29bbc15cddb86830b391e44f2edf819aa49b8 Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Tue, 14 Oct 2025 20:23:16 +0900
Subject: [PATCH 596/604] Add comprehensive documentation for Ensemble Kalman
 Filter localization (#1278)

* Initial plan

* Add comprehensive documentation for Ensemble Kalman Filter localization

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>
---
 ...samble_kalman_filter_localization_main.rst | 196 +++++++++++++++++-
 1 file changed, 194 insertions(+), 2 deletions(-)

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 214e645d10..6274859002 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
@@ -1,12 +1,204 @@
-Ensamble Kalman Filter Localization
+Ensemble 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).
+This is a sensor fusion localization with Ensemble Kalman Filter(EnKF).
+
+The blue line is true trajectory, the black line is dead reckoning trajectory,
+and the red line is estimated trajectory with EnKF.
+
+The red ellipse is estimated covariance ellipse with EnKF.
+
+It is assumed that the robot can measure distance and bearing angle from landmarks (RFID).
+
+These measurements are used for EnKF localization.
 
 Code Link
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autofunction:: Localization.ensemble_kalman_filter.ensemble_kalman_filter.enkf_localization
 
+
+Ensemble Kalman Filter Algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Ensemble Kalman Filter (EnKF) is a Monte Carlo approach to the Kalman filter that
+uses an ensemble of state estimates to represent the probability distribution. Unlike
+the traditional Kalman filter that propagates the mean and covariance analytically,
+EnKF uses a collection of samples (ensemble members) to estimate the state and its uncertainty.
+
+The EnKF algorithm consists of two main steps:
+
+=== Predict ===
+
+For each ensemble member :math:`i = 1, ..., N`:
+
+1. Add random noise to the control input:
+
+   :math:`\mathbf{u}^i = \mathbf{u} + \epsilon_u`, where :math:`\epsilon_u \sim \mathcal{N}(0, \mathbf{R})`
+
+2. Predict the state:
+
+   :math:`\mathbf{x}^i_{pred} = f(\mathbf{x}^i_t, \mathbf{u}^i)`
+
+3. Predict the observation:
+
+   :math:`\mathbf{z}^i_{pred} = h(\mathbf{x}^i_{pred}) + \epsilon_z`, where :math:`\epsilon_z \sim \mathcal{N}(0, \mathbf{Q})`
+
+=== Update ===
+
+1. Calculate ensemble mean for states:
+
+   :math:`\bar{\mathbf{x}} = \frac{1}{N}\sum_{i=1}^N \mathbf{x}^i_{pred}`
+
+2. Calculate ensemble mean for observations:
+
+   :math:`\bar{\mathbf{z}} = \frac{1}{N}\sum_{i=1}^N \mathbf{z}^i_{pred}`
+
+3. Calculate state deviation:
+
+   :math:`\mathbf{X}' = \mathbf{x}^i_{pred} - \bar{\mathbf{x}}`
+
+4. Calculate observation deviation:
+
+   :math:`\mathbf{Z}' = \mathbf{z}^i_{pred} - \bar{\mathbf{z}}`
+
+5. Calculate covariance matrices:
+
+   :math:`\mathbf{U} = \frac{1}{N-1}\mathbf{X}'\mathbf{Z}'^T`
+
+   :math:`\mathbf{V} = \frac{1}{N-1}\mathbf{Z}'\mathbf{Z}'^T`
+
+6. Calculate Kalman gain:
+
+   :math:`\mathbf{K} = \mathbf{U}\mathbf{V}^{-1}`
+
+7. Update each ensemble member:
+
+   :math:`\mathbf{x}^i_{t+1} = \mathbf{x}^i_{pred} + \mathbf{K}(\mathbf{z}_{obs} - \mathbf{z}^i_{pred})`
+
+8. Calculate final state estimate:
+
+   :math:`\mathbf{x}_{est} = \frac{1}{N}\sum_{i=1}^N \mathbf{x}^i_{t+1}`
+
+9. Calculate covariance estimate:
+
+   :math:`\mathbf{P}_{est} = \frac{1}{N}\sum_{i=1}^N (\mathbf{x}^i_{t+1} - \mathbf{x}_{est})(\mathbf{x}^i_{t+1} - \mathbf{x}_{est})^T`
+
+
+Filter Design
+~~~~~~~~~~~~~
+
+In this simulation, the robot has a state vector with 4 states at time :math:`t`:
+
+.. math:: \mathbf{x}_t = [x_t, y_t, \phi_t, v_t]^T
+
+where:
+
+- :math:`x, y` are 2D position coordinates
+- :math:`\phi` is orientation (yaw angle)
+- :math:`v` is velocity
+
+The filter uses an ensemble of :math:`N=20` particles to represent the state distribution.
+
+Input Vector
+^^^^^^^^^^^^
+
+The robot has a velocity sensor and a gyro sensor, so the control input vector at each time step is:
+
+.. math:: \mathbf{u}_t = [v_t, \omega_t]^T
+
+where:
+
+- :math:`v_t` is linear velocity
+- :math:`\omega_t` is angular velocity (yaw rate)
+
+The input vector includes sensor noise modeled by covariance matrix :math:`\mathbf{R}`:
+
+.. math:: \mathbf{R} = \begin{bmatrix} \sigma_v^2 & 0 \\ 0 & \sigma_\omega^2 \end{bmatrix}
+
+
+Observation Vector
+^^^^^^^^^^^^^^^^^^
+
+The robot can observe landmarks (RFID tags) with distance and bearing measurements:
+
+.. math:: \mathbf{z}_t = [d_t, \theta_t, x_{lm}, y_{lm}]
+
+where:
+
+- :math:`d_t` is the distance to the landmark
+- :math:`\theta_t` is the bearing angle to the landmark
+- :math:`x_{lm}, y_{lm}` are the known landmark positions
+
+The observation noise is modeled by covariance matrix :math:`\mathbf{Q}`:
+
+.. math:: \mathbf{Q} = \begin{bmatrix} \sigma_d^2 & 0 \\ 0 & \sigma_\theta^2 \end{bmatrix}
+
+
+Motion Model
+~~~~~~~~~~~~
+
+The robot motion model is:
+
+.. math::  \dot{x} = v \cos(\phi)
+
+.. math::  \dot{y} = v \sin(\phi)
+
+.. math::  \dot{\phi} = \omega
+
+.. math::  \dot{v} = 0
+
+Discretized with time step :math:`\Delta t`, the motion model becomes:
+
+.. math:: \mathbf{x}_{t+1} = f(\mathbf{x}_t, \mathbf{u}_t) = \mathbf{F}\mathbf{x}_t + \mathbf{B}\mathbf{u}_t
+
+where:
+
+:math:`\begin{equation*} \mathbf{F} = \begin{bmatrix} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} \mathbf{B} = \begin{bmatrix} \cos(\phi) \Delta t & 0\\ \sin(\phi) \Delta t & 0\\ 0 & \Delta t\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
+
+The motion function expands to:
+
+:math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \phi_{t+1} \\ v_{t+1} \end{bmatrix} = \begin{bmatrix} x_t + v_t\cos(\phi_t)\Delta t \\ y_t + v_t\sin(\phi_t)\Delta t \\ \phi_t + \omega_t \Delta t \\ v_t \end{bmatrix} \end{equation*}`
+
+
+Observation Model
+~~~~~~~~~~~~~~~~~
+
+For each landmark at position :math:`(x_{lm}, y_{lm})`, the observation model calculates
+the expected landmark position in the global frame:
+
+.. math:: 
+   \begin{bmatrix} 
+   x_{lm,obs} \\ 
+   y_{lm,obs} 
+   \end{bmatrix} = 
+   \begin{bmatrix} 
+   x + d \cos(\phi + \theta) \\ 
+   y + d \sin(\phi + \theta) 
+   \end{bmatrix}
+
+where :math:`d` and :math:`\theta` are the observed distance and bearing to the landmark.
+
+The observation function projects the robot state to expected landmark positions,
+which are then compared with actual landmark positions for the update step.
+
+
+Advantages of EnKF
+~~~~~~~~~~~~~~~~~~
+
+- **No Jacobian required**: Unlike EKF, EnKF does not need to compute Jacobian matrices, making it easier to implement for nonlinear systems
+- **Handles non-Gaussian distributions**: The ensemble representation can capture non-Gaussian features of the state distribution
+- **Computationally efficient**: For high-dimensional systems, EnKF can be more efficient than maintaining full covariance matrices
+- **Easy to parallelize**: Each ensemble member can be propagated independently
+
+
+Reference
+~~~~~~~~~
+
+- `Ensemble Kalman filtering <https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135>`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
+

From fc6b36e3092f0fefde02ad06a6a95406fd83579d Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Sun, 19 Oct 2025 18:48:21 +0900
Subject: [PATCH 597/604] Add comprehensive documentation for Unscented Kalman
 Filter localization (#1290)

* Initial plan

* Add comprehensive UKF documentation with algorithm details

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

* Add comprehensive docstrings to UKF core functions

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

* Fix codestyle issues - remove trailing whitespace from docstrings

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>
---
 .../unscented_kalman_filter.py                |  77 ++++++
 ...cented_kalman_filter_localization_main.rst | 240 +++++++++++++++++-
 2 files changed, 316 insertions(+), 1 deletion(-)

diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 4af748ec71..2119e1eacc 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -91,6 +91,29 @@ def observation_model(x):
 
 
 def generate_sigma_points(xEst, PEst, gamma):
+    """
+    Generate sigma points for UKF.
+
+    Sigma points are deterministically sampled points used to capture
+    the mean and covariance of the state distribution.
+
+    Parameters
+    ----------
+    xEst : numpy.ndarray
+        Current state estimate (n x 1)
+    PEst : numpy.ndarray
+        Current state covariance estimate (n x n)
+    gamma : float
+        Scaling parameter sqrt(n + lambda)
+
+    Returns
+    -------
+    sigma : numpy.ndarray
+        Sigma points (n x 2n+1)
+        sigma[:, 0] = xEst
+        sigma[:, 1:n+1] = xEst + gamma * sqrt(P)
+        sigma[:, n+1:2n+1] = xEst - gamma * sqrt(P)
+    """
     sigma = xEst
     Psqrt = scipy.linalg.sqrtm(PEst)
     n = len(xEst[:, 0])
@@ -149,6 +172,35 @@ def calc_pxz(sigma, x, z_sigma, zb, wc):
 
 
 def ukf_estimation(xEst, PEst, z, u, wm, wc, gamma):
+    """
+    Unscented Kalman Filter estimation.
+
+    Performs one iteration of UKF state estimation using the unscented transform.
+
+    Parameters
+    ----------
+    xEst : numpy.ndarray
+        Current state estimate [x, y, yaw, v]^T (4 x 1)
+    PEst : numpy.ndarray
+        Current state covariance estimate (4 x 4)
+    z : numpy.ndarray
+        Observation vector [x_obs, y_obs]^T (2 x 1)
+    u : numpy.ndarray
+        Control input [velocity, yaw_rate]^T (2 x 1)
+    wm : numpy.ndarray
+        Weights for calculating mean (1 x 2n+1)
+    wc : numpy.ndarray
+        Weights for calculating covariance (1 x 2n+1)
+    gamma : float
+        Sigma point scaling parameter sqrt(n + lambda)
+
+    Returns
+    -------
+    xEst : numpy.ndarray
+        Updated state estimate (4 x 1)
+    PEst : numpy.ndarray
+        Updated state covariance estimate (4 x 4)
+    """
     #  Predict
     sigma = generate_sigma_points(xEst, PEst, gamma)
     sigma = predict_sigma_motion(sigma, u)
@@ -194,6 +246,31 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
 
 
 def setup_ukf(nx):
+    """
+    Setup UKF parameters and weights.
+
+    Calculates the weights for mean and covariance computation,
+    and the scaling parameter gamma for sigma point generation.
+
+    Parameters
+    ----------
+    nx : int
+        Dimension of the state vector
+
+    Returns
+    -------
+    wm : numpy.ndarray
+        Weights for calculating mean (1 x 2n+1)
+        wm[0] = lambda / (n + lambda)
+        wm[i] = 1 / (2(n + lambda)) for i > 0
+    wc : numpy.ndarray
+        Weights for calculating covariance (1 x 2n+1)
+        wc[0] = lambda / (n + lambda) + (1 - alpha^2 + beta)
+        wc[i] = 1 / (2(n + lambda)) for i > 0
+    gamma : float
+        Sigma point scaling parameter sqrt(n + lambda)
+        where lambda = alpha^2 * (n + kappa) - n
+    """
     lamb = ALPHA ** 2 * (nx + KAPPA) - nx
     # calculate weights
     wm = [lamb / (lamb + nx)]
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 a7a5aab61b..01d161b17e 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
@@ -5,7 +5,10 @@ Unscented Kalman Filter localization
 
 This is a sensor fusion localization with Unscented Kalman Filter(UKF).
 
-The lines and points are same meaning of the EKF simulation.
+The blue line is true trajectory, the black line is dead reckoning trajectory,
+the green points are GPS observations, and the red line is estimated trajectory with UKF.
+
+The red ellipse is estimated covariance ellipse with UKF.
 
 Code Link
 ~~~~~~~~~~~~~
@@ -13,7 +16,242 @@ Code Link
 .. autofunction:: Localization.unscented_kalman_filter.unscented_kalman_filter.ukf_estimation
 
 
+Unscented Kalman Filter Algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Unscented Kalman Filter (UKF) is a nonlinear state estimation technique that uses
+the unscented transform to handle nonlinearities. Unlike the Extended Kalman Filter (EKF)
+that linearizes the nonlinear functions using Jacobians, UKF uses a deterministic sampling
+approach called sigma points to capture the mean and covariance of the state distribution.
+
+The UKF algorithm consists of two main steps:
+
+=== Predict ===
+
+1. Generate sigma points around the current state estimate:
+
+   :math:`\chi_0 = \mathbf{x}_{t}`
+
+   :math:`\chi_i = \mathbf{x}_{t} + \gamma\sqrt{\mathbf{P}_t}_i` for :math:`i = 1, ..., n`
+
+   :math:`\chi_i = \mathbf{x}_{t} - \gamma\sqrt{\mathbf{P}_t}_{i-n}` for :math:`i = n+1, ..., 2n`
+
+   where :math:`\gamma = \sqrt{n + \lambda}` and :math:`\lambda = \alpha^2(n + \kappa) - n`
+
+2. Propagate sigma points through the motion model:
+
+   :math:`\chi^-_i = f(\chi_i, \mathbf{u}_t)`
+
+3. Calculate predicted state mean:
+
+   :math:`\mathbf{x}^-_{t+1} = \sum_{i=0}^{2n} w^m_i \chi^-_i`
+
+4. Calculate predicted state covariance:
+
+   :math:`\mathbf{P}^-_{t+1} = \sum_{i=0}^{2n} w^c_i (\chi^-_i - \mathbf{x}^-_{t+1})(\chi^-_i - \mathbf{x}^-_{t+1})^T + \mathbf{Q}`
+
+=== Update ===
+
+1. Generate sigma points around the predicted state:
+
+   :math:`\chi_i = \text{generate\_sigma\_points}(\mathbf{x}^-_{t+1}, \mathbf{P}^-_{t+1})`
+
+2. Propagate sigma points through the observation model:
+
+   :math:`\mathcal{Z}_i = h(\chi_i)`
+
+3. Calculate predicted observation mean:
+
+   :math:`\mathbf{z}^-_{t+1} = \sum_{i=0}^{2n} w^m_i \mathcal{Z}_i`
+
+4. Calculate innovation covariance:
+
+   :math:`\mathbf{S}_t = \sum_{i=0}^{2n} w^c_i (\mathcal{Z}_i - \mathbf{z}^-_{t+1})(\mathcal{Z}_i - \mathbf{z}^-_{t+1})^T + \mathbf{R}`
+
+5. Calculate cross-correlation matrix:
+
+   :math:`\mathbf{P}_{xz} = \sum_{i=0}^{2n} w^c_i (\chi_i - \mathbf{x}^-_{t+1})(\mathcal{Z}_i - \mathbf{z}^-_{t+1})^T`
+
+6. Calculate Kalman gain:
+
+   :math:`\mathbf{K} = \mathbf{P}_{xz}\mathbf{S}_t^{-1}`
+
+7. Update state estimate:
+
+   :math:`\mathbf{x}_{t+1} = \mathbf{x}^-_{t+1} + \mathbf{K}(\mathbf{z}_t - \mathbf{z}^-_{t+1})`
+
+8. Update covariance estimate:
+
+   :math:`\mathbf{P}_{t+1} = \mathbf{P}^-_{t+1} - \mathbf{K}\mathbf{S}_t\mathbf{K}^T`
+
+
+Sigma Points and Weights
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The UKF uses deterministic sigma points to represent the state distribution. The weights
+for combining sigma points are calculated as:
+
+**Mean weights:**
+
+:math:`w^m_0 = \frac{\lambda}{n + \lambda}`
+
+:math:`w^m_i = \frac{1}{2(n + \lambda)}` for :math:`i = 1, ..., 2n`
+
+**Covariance weights:**
+
+:math:`w^c_0 = \frac{\lambda}{n + \lambda} + (1 - \alpha^2 + \beta)`
+
+:math:`w^c_i = \frac{1}{2(n + \lambda)}` for :math:`i = 1, ..., 2n`
+
+where:
+
+- :math:`\alpha` controls the spread of sigma points around the mean (typically :math:`0.001 \leq \alpha \leq 1`)
+- :math:`\beta` incorporates prior knowledge of the distribution (:math:`\beta = 2` is optimal for Gaussian distributions)
+- :math:`\kappa` is a secondary scaling parameter (typically :math:`\kappa = 0`)
+- :math:`n` is the dimension of the state vector
+
+
+Filter Design
+~~~~~~~~~~~~~
+
+In this simulation, the robot has a state vector with 4 states at time :math:`t`:
+
+.. math:: \mathbf{x}_t = [x_t, y_t, \phi_t, v_t]^T
+
+where:
+
+- :math:`x, y` are 2D position coordinates
+- :math:`\phi` is orientation (yaw angle)
+- :math:`v` is velocity
+
+In the code, "xEst" means the state vector.
+
+The covariance matrices are:
+
+- :math:`\mathbf{P}_t` is the state covariance matrix
+- :math:`\mathbf{Q}` is the process noise covariance matrix
+- :math:`\mathbf{R}` is the observation noise covariance matrix
+
+**Process Noise Covariance** :math:`\mathbf{Q}`:
+
+.. math:: 
+   \mathbf{Q} = \begin{bmatrix} 
+   0.1^2 & 0 & 0 & 0 \\
+   0 & 0.1^2 & 0 & 0 \\
+   0 & 0 & (0.017)^2 & 0 \\
+   0 & 0 & 0 & 1.0^2
+   \end{bmatrix}
+
+**Observation Noise Covariance** :math:`\mathbf{R}`:
+
+.. math:: 
+   \mathbf{R} = \begin{bmatrix} 
+   1.0^2 & 0 \\
+   0 & 1.0^2
+   \end{bmatrix}
+
+
+Input Vector
+^^^^^^^^^^^^
+
+The robot has a velocity sensor and a gyro sensor, so the control input vector at each time step is:
+
+.. math:: \mathbf{u}_t = [v_t, \omega_t]^T
+
+where:
+
+- :math:`v_t` is linear velocity [m/s]
+- :math:`\omega_t` is angular velocity (yaw rate) [rad/s]
+
+The input vector includes sensor noise.
+
+
+Observation Vector
+^^^^^^^^^^^^^^^^^^
+
+The robot has a GNSS (GPS) sensor that can measure x-y position:
+
+.. math:: \mathbf{z}_t = [x_t, y_t]^T
+
+The observation includes GPS noise with covariance :math:`\mathbf{R}`.
+
+
+Motion Model
+~~~~~~~~~~~~
+
+The robot motion model is:
+
+.. math::  \dot{x} = v \cos(\phi)
+
+.. math::  \dot{y} = v \sin(\phi)
+
+.. math::  \dot{\phi} = \omega
+
+.. math::  \dot{v} = 0
+
+Discretized with time step :math:`\Delta t`, the motion model becomes:
+
+.. math:: \mathbf{x}_{t+1} = f(\mathbf{x}_t, \mathbf{u}_t) = \mathbf{F}\mathbf{x}_t + \mathbf{B}\mathbf{u}_t
+
+where:
+
+:math:`\begin{equation*} \mathbf{F} = \begin{bmatrix} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} \mathbf{B} = \begin{bmatrix} \cos(\phi) \Delta t & 0\\ \sin(\phi) \Delta t & 0\\ 0 & \Delta t\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
+
+The motion function expands to:
+
+:math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \phi_{t+1} \\ v_{t+1} \end{bmatrix} = \begin{bmatrix} x_t + v_t\cos(\phi_t)\Delta t \\ y_t + v_t\sin(\phi_t)\Delta t \\ \phi_t + \omega_t \Delta t \\ v_t \end{bmatrix} \end{equation*}`
+
+:math:`\Delta t = 0.1` [s] is the time interval.
+
+
+Observation Model
+~~~~~~~~~~~~~~~~~
+
+The robot can get x-y position information from GPS.
+
+The GPS observation model is:
+
+.. math:: \mathbf{z}_{t} = h(\mathbf{x}_t) = \mathbf{H} \mathbf{x}_t
+
+where:
+
+:math:`\begin{equation*} \mathbf{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_{obs} \\ y_{obs} \end{bmatrix} = h(\mathbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}`
+
+
+UKF Parameters
+~~~~~~~~~~~~~~
+
+The UKF uses three tuning parameters:
+
+- **ALPHA = 0.001**: Controls the spread of sigma points around the mean. Smaller values result in sigma points closer to the mean.
+- **BETA = 2**: Incorporates prior knowledge about the distribution. For Gaussian distributions, the optimal value is 2.
+- **KAPPA = 0**: Secondary scaling parameter. Usually set to 0 or 3-n where n is the state dimension.
+
+These parameters affect the calculation of :math:`\lambda = \alpha^2(n + \kappa) - n`, which determines
+the sigma point spread and weights.
+
+
+Advantages of UKF over EKF
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Unscented Kalman Filter offers several advantages over the Extended Kalman Filter:
+
+- **No Jacobian required**: UKF does not need to compute Jacobian matrices, which can be error-prone and computationally expensive for complex nonlinear systems
+- **Higher accuracy**: UKF captures the mean and covariance to the second order (third order for Gaussian distributions) while EKF only achieves first-order accuracy
+- **Better handling of nonlinearities**: The unscented transform provides a better approximation of the probability distribution after nonlinear transformation
+- **Easier implementation**: For highly nonlinear systems, UKF is often easier to implement since it doesn't require analytical derivatives
+- **Numerical stability**: UKF can be more numerically stable than EKF for strongly nonlinear systems
+
+
 Reference
 ~~~~~~~~~~~
 
 - `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization <https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization>`_
+- `The Unscented Kalman Filter for Nonlinear Estimation <https://www.seas.harvard.edu/courses/cs281/papers/unscented.pdf>`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_

From 1311b94d9b651da8bec85a47f0db2771d99e0c91 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 20 Oct 2025 21:44:33 +0900
Subject: [PATCH 598/604] Potential fix for code scanning alert no. 328:
 Workflow does not contain permissions (#1294)

Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
---
 .github/workflows/codeql.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index dcbcdbaa88..b402f989e8 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -8,6 +8,8 @@ on:
   schedule:
     - cron: '0 19 * * 0'
 
+permissions:
+  contents: read
 jobs:
   CodeQL-Build:
 

From 59d0cc7b4244a9e7fbe7b63ad14a70ff5db8c8c5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Oct 2025 07:56:16 +0900
Subject: [PATCH 599/604] build(deps): bump ruff from 0.14.0 to 0.14.1 in
 /requirements (#1296)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.14.0 to 0.14.1.
- [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/0.14.0...0.14.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.14.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 c6223c01df..a2d843d7b7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.14.0 # For unit test
+ruff == 0.14.1 # For unit test

From 10764c2657ef36e9bcec6108020fd9c22563e93c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Oct 2025 20:05:29 +0900
Subject: [PATCH 600/604] build(deps): bump numpy from 2.3.3 to 2.3.4 in
 /requirements (#1297)

Bumps [numpy](https://github.com/numpy/numpy) from 2.3.3 to 2.3.4.
- [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/v2.3.3...v2.3.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-version: 2.3.4
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
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 a2d843d7b7..306d93b736 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.3.3
+numpy == 2.3.4
 scipy == 1.16.2
 matplotlib == 3.10.7
 cvxpy == 1.7.3

From 2d3cb831c94230017ca5467a6ce2e25ec107f8c5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 21 Oct 2025 21:20:50 +0900
Subject: [PATCH 601/604] =?UTF-8?q?Revert=20"Potential=20fix=20for=20code?=
 =?UTF-8?q?=20scanning=20alert=20no.=20328:=20Workflow=20does=20not=20cont?=
 =?UTF-8?q?=E2=80=A6"=20(#1298)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This reverts commit 1311b94d9b651da8bec85a47f0db2771d99e0c91.
---
 .github/workflows/codeql.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index b402f989e8..dcbcdbaa88 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -8,8 +8,6 @@ on:
   schedule:
     - cron: '0 19 * * 0'
 
-permissions:
-  contents: read
 jobs:
   CodeQL-Build:
 

From 69d07fcc00dc0b8e7b673019f43bcec48fd4d7fb Mon Sep 17 00:00:00 2001
From: Anish Kumar <B24331@STUDENTS.IITMANDI.AC.IN>
Date: Tue, 21 Oct 2025 17:52:18 +0530
Subject: [PATCH 602/604] added pso algorithm  (#1279)

* added pso algorithm also known as bird flocking algorithm

* fix: Resolve linting issues in PSO implementation

* improved code formattings

* added docstring and added pso in main rst file

* fixed paths

* fixed changes requested

* fixed typo

* improved readilbility
---
 .gitignore                                    |   1 +
 .../particle_swarm_optimization.py            | 335 ++++++++++++++++++
 README.md                                     |  19 +
 .../particleSwarmOptimization_main.rst        | 153 ++++++++
 .../5_path_planning/path_planning_main.rst    |   1 +
 tests/test_particle_swarm_optimization.py     |  11 +
 6 files changed, 520 insertions(+)
 create mode 100755 PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py
 create mode 100644 docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst
 create mode 100755 tests/test_particle_swarm_optimization.py

diff --git a/.gitignore b/.gitignore
index c971b8f9c5..8a0b1257b5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,6 +15,7 @@ _build/
 # Distribution / packaging
 .Python
 env/
+venv/
 build/
 develop-eggs/
 dist/
diff --git a/PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py b/PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py
new file mode 100755
index 0000000000..4d1df5e197
--- /dev/null
+++ b/PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py
@@ -0,0 +1,335 @@
+"""
+Particle Swarm Optimization (PSO) Path Planning
+
+author: Anish (@anishk85)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/Particle_swarm_optimization)
+
+References:
+    - Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization"
+    - Shi, Y.; Eberhart, R. (1998). "A Modified Particle Swarm Optimizer"
+    - https://machinelearningmastery.com/a-gentle-introduction-to-particle-swarm-optimization/
+
+This implementation uses PSO to find collision-free paths by treating
+path planning as an optimization problem where particles explore the
+search space to minimize distance to target while avoiding obstacles.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+import matplotlib.patches as patches
+import signal
+import sys
+# Add show_animation flag for consistency with other planners
+show_animation = True
+
+def signal_handler(sig, frame):
+    print("\nExiting...")
+    plt.close("all")
+    sys.exit(0)
+
+signal.signal(signal.SIGINT, signal_handler)
+
+class Particle:
+    """Represents a single particle in the PSO swarm.
+    Each particle maintains its current position, velocity, and personal best
+    position discovered during the search. Particles explore the search space
+    by updating their velocity based on personal experience (cognitive component)
+    and swarm knowledge (social component).
+    Attributes:
+        search_bounds: List of tuples [(x_min, x_max), (y_min, y_max)] defining search space
+        max_velocity: Maximum velocity allowed in each dimension (5% of search space range)
+        position: Current 2D position [x, y] in search space
+        velocity: Current velocity vector [vx, vy]
+        personal_best_position: Personal best position found so far
+        personal_best_value: Fitness value at personal best position
+        path: List of all positions visited by this particle
+    """
+
+    def __init__(self, search_bounds, spawn_bounds):
+        self.search_bounds = search_bounds
+        self.max_velocity = np.array([(b[1] - b[0]) * 0.05 for b in search_bounds])
+        self.position = np.array([np.random.uniform(b[0], b[1]) for b in spawn_bounds])
+        self.velocity = np.random.randn(2) * 0.1
+        self.personal_best_position = self.position.copy()
+        self.personal_best_value = np.inf
+        self.path = [self.position.copy()]
+
+    def update_velocity(self, gbest_pos, w, c1, c2):
+        """Update particle velocity using PSO equation:
+        v = w*v + c1*r1*(personal_best - x) + c2*r2*(gbest - x)
+        """
+        r1 = np.random.rand(2)
+        r2 = np.random.rand(2)
+        cognitive = c1 * r1 * (self.personal_best_position - self.position)
+        social = c2 * r2 * (gbest_pos - self.position)
+        self.velocity = w * self.velocity + cognitive + social
+        self.velocity = np.clip(self.velocity, -self.max_velocity, self.max_velocity)
+
+    def update_position(self):
+        self.position = self.position + self.velocity
+        # Keep in bounds
+        for i in range(2):
+            self.position[i] = np.clip(
+                self.position[i], self.search_bounds[i][0], self.search_bounds[i][1]
+            )
+        self.path.append(self.position.copy())
+
+class PSOSwarm:
+
+    def __init__(
+        self, n_particles, max_iter, target, search_bounds, spawn_bounds, obstacles
+    ):
+        self.n_particles = n_particles
+        self.max_iter = max_iter
+        self.target = np.array(target)
+        self.obstacles = obstacles
+        self.search_bounds = search_bounds
+        # PSO parameters
+        self.w_start = 0.9  # Initial inertia weight
+        self.w_end = 0.4  # Final inertia weight
+        self.c1 = 1.5  # Cognitive coefficient
+        self.c2 = 1.5  # Social coefficient
+        # Initialize particles
+        self.particles = [
+            Particle(search_bounds, spawn_bounds) for _ in range(n_particles)
+        ]
+        self.gbest_position = None
+        self.gbest_value = np.inf
+        self.gbest_path = []
+        self.iteration = 0
+
+    def fitness(self, pos):
+        """Calculate fitness - distance to target + obstacle penalty"""
+        dist = np.linalg.norm(pos - self.target)
+        # Obstacle penalty
+        penalty = 0
+        for ox, oy, r in self.obstacles:
+            obs_dist = np.linalg.norm(pos - np.array([ox, oy]))
+            if obs_dist < r:
+                penalty += 1000  # Inside obstacle
+            elif obs_dist < r + 5:
+                penalty += 50 / (obs_dist - r + 0.1)  # Too close
+        return dist + penalty
+
+    def check_collision(self, start, end, obstacle):
+        """Check if path from start to end hits obstacle using line-circle intersection
+            Args:
+            start: Starting position (numpy array)
+            end: Ending position (numpy array)
+            obstacle: Tuple (ox, oy, r) representing obstacle center and radius
+        Returns:
+            bool: True if collision detected, False otherwise
+        """
+        ox, oy, r = obstacle
+        center = np.array([ox, oy])
+        # Vector math for line-circle intersection
+        d = end - start
+        f = start - center
+        a = np.dot(d, d)
+        # Guard against zero-length steps to prevent ZeroDivisionError
+        if a < 1e-10:  # Near-zero length step
+            # Check if start point is inside obstacle
+            return np.linalg.norm(f) <= r
+        b = 2 * np.dot(f, d)
+        c = np.dot(f, f) - r * r
+        discriminant = b * b - 4 * a * c
+        if discriminant < 0:
+            return False
+        # Check if intersection on segment
+        sqrt_discriminant = np.sqrt(discriminant)
+        t1 = (-b - sqrt_discriminant) / (2 * a)
+        t2 = (-b + sqrt_discriminant) / (2 * a)
+        return (0 <= t1 <= 1) or (0 <= t2 <= 1)
+
+    def step(self):
+        """Run one PSO iteration
+        Returns:
+            bool: True if algorithm should continue, False if completed
+        """
+        if self.iteration >= self.max_iter:
+            return False
+        # Update inertia weight (linear decay)
+        w = self.w_start - (self.w_start - self.w_end) * (
+            self.iteration / self.max_iter
+        )
+        # Evaluate all particles
+        for particle in self.particles:
+            value = self.fitness(particle.position)
+            # Update personal best
+            if value < particle.personal_best_value:
+                particle.personal_best_value = value
+                particle.personal_best_position = particle.position.copy()
+            # Update global best
+            if value < self.gbest_value:
+                self.gbest_value = value
+                self.gbest_position = particle.position.copy()
+        if self.gbest_position is not None:
+            self.gbest_path.append(self.gbest_position.copy())
+        # Update particles
+        for particle in self.particles:
+            particle.update_velocity(self.gbest_position, w, self.c1, self.c2)
+            # Predict next position
+            next_pos = particle.position + particle.velocity
+            # Check collision
+            collision = False
+            for obs in self.obstacles:
+                if self.check_collision(particle.position, next_pos, obs):
+                    collision = True
+                    break
+            if collision:
+                # Reduce velocity if collision detected
+                particle.velocity *= 0.2
+            particle.update_position()
+        self.iteration += 1
+        if show_animation and self.iteration % 20 == 0:
+            print(
+                f"Iteration {self.iteration}/{self.max_iter}, Best: {self.gbest_value:.2f}"
+            )
+        return True
+
+def main():
+    """Run PSO path planning algorithm demonstration.
+    This function demonstrates PSO-based path planning with the following setup:
+    - 15 particles exploring a (-50,50) x (-50,50) search space
+    - Start zone: (-45,-45) to (-35,-35)
+    - Target: (40, 35)
+    - 4 circular obstacles with collision avoidance
+    - Real-time visualization showing particle convergence (if show_animation=True)
+    - Headless mode support for testing (if show_animation=False)
+    The algorithm runs for up to 150 iterations, displaying particle movement,
+    personal/global best positions, and the evolving optimal path.
+    """
+    print(__file__ + " start!!")
+    # Set matplotlib backend for headless environments
+    if not show_animation:
+        plt.switch_backend("Agg")  # Use non-GUI backend for testing
+    # Setup parameters
+    N_PARTICLES = 15
+    MAX_ITER = 150
+    SEARCH_BOUNDS = [(-50, 50), (-50, 50)]
+    TARGET = [40, 35]
+    SPAWN_AREA = [(-45, -35), (-45, -35)]
+    OBSTACLES = [(10, 15, 8), (-20, 0, 12), (20, -25, 10), (-5, -30, 7)]
+    swarm = PSOSwarm(
+        n_particles=N_PARTICLES,
+        max_iter=MAX_ITER,
+        target=TARGET,
+        search_bounds=SEARCH_BOUNDS,
+        spawn_bounds=SPAWN_AREA,
+        obstacles=OBSTACLES,
+    )
+    # pragma: no cover
+    if show_animation:
+        # Visualization setup
+        signal.signal(signal.SIGINT, signal_handler)
+        fig, ax = plt.subplots(figsize=(10, 10))
+        ax.set_xlim(SEARCH_BOUNDS[0])
+        ax.set_ylim(SEARCH_BOUNDS[1])
+        ax.set_title("PSO Path Planning with Collision Avoidance", fontsize=14)
+        ax.grid(True, alpha=0.3)
+        # Draw obstacles
+        for ox, oy, r in OBSTACLES:
+            circle = patches.Circle((ox, oy), r, color="gray", alpha=0.7)
+            ax.add_patch(circle)
+        # Draw spawn area
+        spawn_rect = patches.Rectangle(
+            (SPAWN_AREA[0][0], SPAWN_AREA[1][0]),
+            SPAWN_AREA[0][1] - SPAWN_AREA[0][0],
+            SPAWN_AREA[1][1] - SPAWN_AREA[1][0],
+            linewidth=2,
+            edgecolor="green",
+            facecolor="green",
+            alpha=0.2,
+            label="Start Zone",
+        )
+        ax.add_patch(spawn_rect)
+        # Draw target
+        ax.plot(TARGET[0], TARGET[1], "r*", markersize=20, label="Target")
+        # Initialize plot elements
+        particles_scatter = ax.scatter(
+            [], [], c="blue", s=50, alpha=0.6, label="Particles"
+        )
+        gbest_scatter = ax.plot([], [], "yo", markersize=12, label="Best Position")[0]
+        particle_paths = [
+            ax.plot([], [], "b-", lw=0.5, alpha=0.2)[0] for _ in range(N_PARTICLES)
+        ]
+        gbest_path_line = ax.plot([], [], "y--", lw=2, alpha=0.8, label="Best Path")[0]
+        iteration_text = ax.text(
+            0.02,
+            0.95,
+            "",
+            transform=ax.transAxes,
+            fontsize=12,
+            verticalalignment="top",
+            bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5),
+        )
+        ax.legend(loc="upper right")
+        def animate(frame):
+            """Animation function for matplotlib FuncAnimation"""
+            if not swarm.step():
+                return (
+                    particles_scatter,
+                    gbest_scatter,
+                    gbest_path_line,
+                    iteration_text,
+                    *particle_paths,
+                )
+            # Update particle positions
+            positions = np.array([p.position for p in swarm.particles])
+            particles_scatter.set_offsets(positions)
+            # Update particle paths
+            for i, particle in enumerate(swarm.particles):
+                if len(particle.path) > 1:
+                    path = np.array(particle.path)
+                    particle_paths[i].set_data(path[:, 0], path[:, 1])
+            # Update global best
+            if swarm.gbest_position is not None:
+                gbest_scatter.set_data(
+                    [swarm.gbest_position[0]], [swarm.gbest_position[1]]
+                )
+                if len(swarm.gbest_path) > 1:
+                    gbest = np.array(swarm.gbest_path)
+                    gbest_path_line.set_data(gbest[:, 0], gbest[:, 1])
+            # Update text
+            iteration_text.set_text(
+                f"Iteration: {swarm.iteration}/{MAX_ITER}\n"
+                f"Best Fitness: {swarm.gbest_value:.2f}"
+            )
+            return (
+                particles_scatter,
+                gbest_scatter,
+                gbest_path_line,
+                iteration_text,
+                *particle_paths,
+            )
+        # Create animation and store reference to prevent garbage collection
+        animation_ref = animation.FuncAnimation(
+            fig, animate, frames=MAX_ITER, interval=100, blit=True, repeat=False
+        )
+        plt.tight_layout()
+        plt.show()
+        # Keep reference to prevent garbage collection
+        return animation_ref
+    else:
+        # Run without animation for testing
+        print("Running PSO algorithm without animation...")
+        iteration_count = 0
+        while swarm.step():
+            iteration_count += 1
+            if iteration_count >= MAX_ITER:
+                break
+        print(f"PSO completed after {iteration_count} iterations")
+        print(f"Best fitness: {swarm.gbest_value:.2f}")
+        if swarm.gbest_position is not None:
+            print(
+                f"Best position: [{swarm.gbest_position[0]:.2f}, {swarm.gbest_position[1]:.2f}]"
+            )
+        return None
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\nProgram interrupted by user")
+        plt.close("all")
+        sys.exit(0)
\ No newline at end of file
diff --git a/README.md b/README.md
index d1b801f219..65445fa4ce 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,7 @@ Python codes and [textbook](https://atsushisakai.github.io/PythonRobotics/index.
          * [D* Lite algorithm](#d-lite-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
+         * [Particle Swarm Optimization (PSO)](#particle-swarm-optimization-pso)  
       * [State Lattice Planning](#state-lattice-planning)
          * [Biased polar sampling](#biased-polar-sampling)
          * [Lane sampling](#lane-sampling)
@@ -356,6 +357,24 @@ This is a 2D grid based coverage path planning simulation.
 
 ![PotentialField](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif)
 
+### Particle Swarm Optimization (PSO)
+
+This is a 2D path planning simulation using the Particle Swarm Optimization algorithm.
+
+![PSO](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ParticleSwarmOptimization/animation.gif)
+
+PSO is a metaheuristic optimization algorithm inspired by bird flocking behavior. In path planning, particles explore the search space to find collision-free paths while avoiding obstacles.
+
+The animation shows particles (blue dots) converging towards the optimal path (yellow line) from start (green area) to goal (red star).
+
+References
+
+- [Particle swarm optimization - Wikipedia](https://en.wikipedia.org/wiki/Particle_swarm_optimization)
+
+- [Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization"](https://ieeexplore.ieee.org/document/488968)
+
+
+
 ## State Lattice Planning
 
 This script is a path planning code with state lattice planning.
diff --git a/docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst b/docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst
new file mode 100644
index 0000000000..b430faec1e
--- /dev/null
+++ b/docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst
@@ -0,0 +1,153 @@
+.. _particle_swarm_optimization:
+
+Particle Swarm Optimization Path Planning
+------------------------------------------
+
+This is a 2D path planning simulation using the Particle Swarm Optimization algorithm.
+
+PSO is a metaheuristic optimization algorithm inspired by the social behavior of bird flocking or fish schooling. In path planning, particles represent potential solutions that explore the search space to find collision-free paths from start to goal.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ParticleSwarmOptimization/animation.gif
+
+Algorithm Overview
+++++++++++++++++++
+
+The PSO algorithm maintains a swarm of particles that move through the search space according to simple mathematical rules:
+
+1. **Initialization**: Particles are randomly distributed near the start position
+2. **Evaluation**: Each particle's fitness is calculated based on distance to goal and obstacle penalties
+3. **Update**: Particles adjust their velocities based on:
+   - Personal best position (cognitive component)
+   - Global best position (social component)  
+   - Current velocity (inertia component)
+4. **Movement**: Particles move to new positions and check for collisions
+5. **Convergence**: Process repeats until maximum iterations or goal is reached
+
+Mathematical Foundation
++++++++++++++++++++++++
+
+The core PSO velocity update equation is:
+
+.. math::
+
+   v_{i}(t+1) = w \cdot v_{i}(t) + c_1 \cdot r_1 \cdot (p_{i} - x_{i}(t)) + c_2 \cdot r_2 \cdot (g - x_{i}(t))
+
+Where:
+- :math:`v_{i}(t)` = velocity of particle i at time t
+- :math:`x_{i}(t)` = position of particle i at time t  
+- :math:`w` = inertia weight (controls exploration vs exploitation)
+- :math:`c_1` = cognitive coefficient (attraction to personal best)
+- :math:`c_2` = social coefficient (attraction to global best)
+- :math:`r_1, r_2` = random numbers in [0,1]
+- :math:`p_{i}` = personal best position of particle i
+- :math:`g` = global best position
+
+Position update:
+
+.. math::
+
+   x_{i}(t+1) = x_{i}(t) + v_{i}(t+1)
+
+Fitness Function
+++++++++++++++++
+
+The fitness function combines distance to target with obstacle penalties:
+
+.. math::
+
+   f(x) = ||x - x_{goal}|| + \sum_{j} P_{obs}(x, O_j)
+
+Where:
+- :math:`||x - x_{goal}||` = Euclidean distance to goal
+- :math:`P_{obs}(x, O_j)` = penalty for obstacle j
+- :math:`O_j` = obstacle j with position and radius
+
+The obstacle penalty function is defined as:
+
+.. math::
+
+   P_{obs}(x, O_j) = \begin{cases}
+   1000 & \text{if } ||x - O_j|| < r_j \text{ (inside obstacle)} \\
+   \frac{50}{||x - O_j|| - r_j + 0.1} & \text{if } r_j \leq ||x - O_j|| < r_j + R_{influence} \text{ (near obstacle)} \\
+   0 & \text{if } ||x - O_j|| \geq r_j + R_{influence} \text{ (safe distance)}
+   \end{cases}
+
+Where:
+- :math:`r_j` = radius of obstacle j
+- :math:`R_{influence}` = influence radius (typically 5 units)
+
+Collision Detection
++++++++++++++++++++
+
+Line-circle intersection is used to detect collisions between particle paths and circular obstacles:
+
+.. math::
+
+   ||P_0 + t \cdot \vec{d} - C|| = r
+
+Where:
+- :math:`P_0` = start point of path segment
+- :math:`\vec{d}` = direction vector of path
+- :math:`C` = obstacle center
+- :math:`r` = obstacle radius
+- :math:`t \in [0,1]` = parameter along line segment
+
+Algorithm Parameters
+++++++++++++++++++++
+
+Key parameters affecting performance:
+
+- **Number of particles** (n_particles): More particles = better exploration but slower
+- **Maximum iterations** (max_iter): More iterations = better convergence but slower  
+- **Inertia weight** (w): High = exploration, Low = exploitation
+- **Cognitive coefficient** (c1): Attraction to personal best
+- **Social coefficient** (c2): Attraction to global best
+
+Typical values:
+- n_particles: 20-50
+- max_iter: 100-300
+- w: 0.9 → 0.4 (linearly decreasing)
+- c1, c2: 1.5-2.0
+
+Advantages
+++++++++++
+
+- **Global optimization**: Can escape local minima unlike gradient-based methods
+- **No derivatives needed**: Works with non-differentiable fitness landscapes
+- **Parallel exploration**: Multiple particles search simultaneously
+- **Simple implementation**: Few parameters and straightforward logic
+- **Flexible**: Easily adaptable to different environments and constraints
+
+Disadvantages
++++++++++++++
+
+- **Stochastic**: Results may vary between runs
+- **Parameter sensitive**: Performance heavily depends on parameter tuning
+- **No optimality guarantee**: Metaheuristic without convergence proof
+- **Computational cost**: Requires many fitness evaluations
+- **Prone to stagnation**: Premature convergence where the entire swarm can get trapped in a local minimum if exploration is insufficient
+
+Code Link
++++++++++
+
+.. autofunction:: PathPlanning.ParticleSwarmOptimization.particle_swarm_optimization.main
+
+Usage Example
++++++++++++++
+
+.. code-block:: python
+
+   import matplotlib.pyplot as plt
+   from PathPlanning.ParticleSwarmOptimization.particle_swarm_optimization import main
+   
+   # Run PSO path planning with visualization
+   main()
+
+References
+++++++++++
+
+- `Particle swarm optimization - Wikipedia <https://en.wikipedia.org/wiki/Particle_swarm_optimization>`__
+- Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.
+- Shi, Y.; Eberhart, R. (1998). "A Modified Particle Swarm Optimizer". IEEE International Conference on Evolutionary Computation.
+- `A Gentle Introduction to Particle Swarm Optimization <https://machinelearningmastery.com/a-gentle-introduction-to-particle-swarm-optimization/>`__
+- Clerc, M.; Kennedy, J. (2002). "The particle swarm - explosion, stability, and convergence in a multidimensional complex space". IEEE Transactions on Evolutionary Computation. 6 (1): 58–73.
\ No newline at end of file
diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst
index 0c84a19c22..5132760dc5 100644
--- a/docs/modules/5_path_planning/path_planning_main.rst
+++ b/docs/modules/5_path_planning/path_planning_main.rst
@@ -19,6 +19,7 @@ Path planning is the ability of a robot to search feasible and efficient path to
    visibility_road_map_planner/visibility_road_map_planner
    vrm_planner/vrm_planner
    rrt/rrt
+   particleSwarmOptimization/particleSwarmOptimization
    cubic_spline/cubic_spline
    bspline_path/bspline_path
    catmull_rom_spline/catmull_rom_spline
diff --git a/tests/test_particle_swarm_optimization.py b/tests/test_particle_swarm_optimization.py
new file mode 100755
index 0000000000..95720022f6
--- /dev/null
+++ b/tests/test_particle_swarm_optimization.py
@@ -0,0 +1,11 @@
+import conftest  # Add root path to sys.path
+from PathPlanning.ParticleSwarmOptimization import particle_swarm_optimization as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
\ No newline at end of file

From 99af1f391686cc7fa10b14bc5ed667cbe7b3f5eb Mon Sep 17 00:00:00 2001
From: Musab Kasbati <43878981+Musab1Blaser@users.noreply.github.com>
Date: Wed, 22 Oct 2025 15:03:13 +0500
Subject: [PATCH 603/604] Add theta star planner (#1293)

* Theta Star path compression

* add tests and documentation for theta star algorithm

* remove erraneous nested loop

* improve documentation for theta star planner

* add reference for theta star algorithm

* use built-in function for norm

* Add Bresenham line algorithm description to docs
---
 PathPlanning/ThetaStar/theta_star.py          | 345 ++++++++++++++++++
 .../grid_base_search_main.rst                 |  29 ++
 tests/test_theta_star.py                      |  11 +
 3 files changed, 385 insertions(+)
 create mode 100644 PathPlanning/ThetaStar/theta_star.py
 create mode 100644 tests/test_theta_star.py

diff --git a/PathPlanning/ThetaStar/theta_star.py b/PathPlanning/ThetaStar/theta_star.py
new file mode 100644
index 0000000000..73a1448b28
--- /dev/null
+++ b/PathPlanning/ThetaStar/theta_star.py
@@ -0,0 +1,345 @@
+"""
+
+Theta* grid planning
+
+author: Musab Kasbati (@Musab1Blaser)
+
+See Wikipedia article (https://cdn.aaai.org/AAAI/2007/AAAI07-187.pdf)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+use_theta_star = True
+
+
+class ThetaStarPlanner:
+
+    def __init__(self, ox, oy, resolution, rr):
+        """
+        Initialize grid map for theta star planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        resolution: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.resolution = resolution
+        self.rr = rr
+        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, parent_index):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.parent_index = parent_index
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.parent_index)
+
+    def planning(self, sx, sy, gx, gy): 
+        """
+        Theta star path search
+
+        input:
+            s_x: start x position [m]
+            s_y: 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
+        """
+
+        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(start_node)] = start_node
+
+        while True:
+            if len(open_set) == 0:
+                print("Open set is empty..")
+                break
+
+            c_id = min(
+                open_set,
+                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
+                x = self.calc_grid_position(current.x, self.min_x)
+                y = self.calc_grid_position(current.y, self.min_y)
+
+                # Draw an arrow toward the parent
+                if current.parent_index != -1 and current.parent_index in closed_set:
+                    parent = closed_set[current.parent_index]
+                    px = self.calc_grid_position(parent.x, self.min_x)
+                    py = self.calc_grid_position(parent.y, self.min_y)
+
+                    # Vector from current to parent
+                    dx = px - x
+                    dy = py - y
+
+                    # scale down vector for visibility
+                    norm = math.hypot(dx, dy)
+                    dx /= norm
+                    dy /= norm
+
+                    # Draw a small arrow (scale it down for visibility)
+                    plt.arrow(x, y, dx, dy,
+                            head_width=0.5, head_length=0.5,
+                            fc='c', ec='c', alpha=0.7)
+
+                # 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 == goal_node.x and current.y == goal_node.y:
+                print("Find goal")
+                goal_node.parent_index = current.parent_index
+                goal_node.cost = current.cost
+                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
+            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)  # cost may later be updated by theta star path compression
+                n_id = self.calc_grid_index(node)
+
+                if not self.verify_node(node):
+                    continue
+
+                if n_id in closed_set:
+                    continue
+
+                # Theta* modification:
+                if use_theta_star and current.parent_index != -1 and current.parent_index in closed_set:
+                    grandparent = closed_set[current.parent_index]
+                    if self.line_of_sight(grandparent, node):
+                        # If parent(current) has line of sight to neighbor
+                        node.cost = grandparent.cost + math.hypot(node.x - grandparent.x, node.y - grandparent.y)
+                        node.parent_index = current.parent_index # compress path directly to grandparent
+
+                if n_id not in open_set:
+                    open_set[n_id] = node
+                else:
+                    if open_set[n_id].cost > node.cost:
+                        # This path is the best until now. record it
+                        open_set[n_id] = node 
+
+
+        rx, ry = self.calc_final_path(goal_node, closed_set)
+
+        return rx, ry
+
+    def calc_final_path(self, goal_node, closed_set):
+        # generate final course
+        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
+
+    @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, min_position):
+        """
+        calc grid position
+
+        :param index:
+        :param min_position:
+        :return:
+        """
+        pos = index * self.resolution + min_position
+        return pos
+
+    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.min_y) * self.x_width + (node.x - self.min_x)
+
+    def line_of_sight(self, node1, node2):
+        """
+        Check if there is a direct line of sight between two nodes.
+        Uses Bresenham’s line algorithm for grid traversal.
+        """
+        x0 = node1.x
+        y0 = node1.y
+        x1 = node2.x
+        y1 = node2.y
+
+        dx = abs(x1 - x0)
+        dy = abs(y1 - y0)
+        sx = 1 if x0 < x1 else -1
+        sy = 1 if y0 < y1 else -1
+
+        err = dx - dy
+
+        while True:
+            if not self.verify_node(self.Node(x0, y0, 0, -1)):
+                return False
+            if x0 == x1 and y0 == y1:
+                break
+            e2 = 2 * err
+            if e2 > -dy:
+                err -= dy
+                x0 += sx
+            if e2 < dx:
+                err += dx
+                y0 += sy
+        return True
+
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.min_x)
+        py = self.calc_grid_position(node.y, self.min_y)
+
+        if px < self.min_x:
+            return False
+        elif py < self.min_y:
+            return False
+        elif px >= self.max_x:
+            return False
+        elif py >= self.max_y:
+            return False
+
+        # collision check
+        if self.obstacle_map[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        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.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.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.obstacle_map[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")
+
+    theta_star = ThetaStarPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = theta_star.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(0.001)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
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 c4aa6882aa..e2f4d8832d 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
@@ -82,6 +82,35 @@ Code Link
 .. autofunction:: PathPlanning.BidirectionalAStar.bidirectional_a_star.BidirectionalAStarPlanner
 
 
+.. _Theta*-algorithm:
+
+Theta\* algorithm
+~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with Theta star algorithm.
+
+It offers an optimization over the A star algorithm to generate any-angle paths. Unlike A star, which always assigns the current node as the parent of the successor, Theta star checks for a line-of-sight (unblocked path) from an earlier node (typically the parent of the current node) to the successor, and directly assigns it as a parent if visible. This reduces cost by replacing staggered segments with straight lines. 
+
+Checking for line of sight involves verifying that no obstacles block the straight line between two nodes. On a grid, this means identifying all the discrete cells that the line passes through to determine if they are empty. Bresenham’s line algorithm is commonly used for this purpose. Starting from one endpoint, it incrementally steps along one axis, while considering the gradient to determine the position on the other axis. Because it relies only on integer addition and subtraction, it is both efficient and precise for grid-based visibility checks in Theta*.
+
+As a result, Theta star produces shorter, smoother paths than A star, ideal for ground or aerial robots operating in continuous environments where smoother motion enables higher acceleration and reduced travel time.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ThetaStar/animation.gif
+
+In the animation, each cyan arrow represents a node pointing to its parent.
+
+Reference
+++++++++++++
+
+-  `Theta*: Any-Angle Path Planning on Grids <https://cdn.aaai.org/AAAI/2007/AAAI07-187.pdf>`__
+-  `Bresenham's line algorithm <https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm>`__
+
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.ThetaStar.theta_star.ThetaStarPlanner
+
+
 .. _D*-algorithm:
 
 D\* algorithm
diff --git a/tests/test_theta_star.py b/tests/test_theta_star.py
new file mode 100644
index 0000000000..10ceae44e3
--- /dev/null
+++ b/tests/test_theta_star.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.ThetaStar import theta_star as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
\ No newline at end of file

From 2e6baeb2d326b152a28d185338935746468de151 Mon Sep 17 00:00:00 2001
From: Musab Kasbati <43878981+Musab1Blaser@users.noreply.github.com>
Date: Sat, 25 Oct 2025 20:49:06 +0500
Subject: [PATCH 604/604] corrected obvious typos in text and mathematical
 notations as well as grammatical corrections (#1304)

---
 .../0_getting_started/2_how_to_run_sample_codes_main.rst  | 2 +-
 .../0_getting_started/3_how_to_contribute_main.rst        | 2 +-
 .../10_inverted_pendulum/inverted_pendulum_main.rst       | 4 ++--
 docs/modules/12_appendix/Kalmanfilter_basics_main.rst     | 2 +-
 .../behavior_tree/behavior_tree_main.rst                  | 4 ++--
 .../2_python_for_robotics/python_for_robotics_main.rst    | 2 +-
 .../technologies_for_robotics_main.rst                    | 2 +-
 .../histogram_filter_localization_main.rst                | 2 +-
 .../particle_filter_localization_main.rst                 | 2 +-
 .../lidar_to_grid_map_tutorial_main.rst                   | 2 +-
 docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst          | 4 ++--
 docs/modules/4_slam/ekf_slam/ekf_slam_main.rst            | 2 +-
 docs/modules/4_slam/graph_slam/graphSLAM_doc.rst          | 4 ++--
 .../5_path_planning/dubins_path/dubins_path_main.rst      | 2 +-
 .../5_path_planning/elastic_bands/elastic_bands_main.rst  | 2 +-
 .../grid_base_search/grid_base_search_main.rst            | 2 +-
 .../reeds_shepp_path/reeds_shepp_path_main.rst            | 2 +-
 .../5_path_planning/vrm_planner/vrm_planner_main.rst      | 2 +-
 .../model_predictive_speed_and_steering_control_main.rst  | 8 ++++----
 .../move_to_a_pose/move_to_a_pose_main.rst                | 2 +-
 .../n_joint_arm_to_point_control_main.rst                 | 2 +-
 docs/modules/7_arm_navigation/planar_two_link_ik_main.rst | 2 +-
 22 files changed, 29 insertions(+), 29 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 b92fc9bde0..33440bb137 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
@@ -46,7 +46,7 @@ using pip :
 
 5. Execute python script in each directory.
 
-For example, to run the sample code of `Extented Kalman Filter` in the
+For example, to run the sample code of `Extended Kalman Filter` in the
 `localization` directory, execute the following command:
 
 .. code-block::
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 9e773e930c..f975127107 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
@@ -132,7 +132,7 @@ 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.
+you can access your PR doc with clicking the [Details] of the "ci/circleci: build_doc artifact" CI.
 
 .. image:: /_static/img/doc_ci.png
 
diff --git a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
index 58dc0f2e57..47ae034195 100644
--- a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
+++ b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
@@ -4,7 +4,7 @@ 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.
+horizontally moving base as shown in the adjacent figure.
 
 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.
 
@@ -97,7 +97,7 @@ Code Link
 
 MPC control
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The MPC controller minimize this cost function defined as:
+The MPC controller minimizes this cost function defined as:
 
 .. math:: J = x^T Q x + u^T R u
 
diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
index a1d99d47ef..1ed5790d52 100644
--- a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
+++ b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
@@ -55,7 +55,7 @@ Variance, Covariance and Correlation
 Variance
 ^^^^^^^^
 
-Variance is the spread of the data. The mean does’nt tell much **about**
+Variance is the spread of the data. The mean doesn’t tell much **about**
 the data. Therefore the variance tells us about the **story** about the
 data meaning the spread of the data.
 
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 22849f7c54..4b0ef82c12 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
@@ -2,8 +2,8 @@ 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)
+It presents 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 Trees have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863)
 
 Code Link
 ~~~~~~~~~~~~~
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 c47c122853..e3f38a55fd 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
@@ -1,7 +1,7 @@
 Python for Robotics
 ----------------------
 
-A programing language, Python is used for this `PythonRobotics` project
+A programming 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 and robotics.
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 0ed51e961b..dd3cd1d86c 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
@@ -3,7 +3,7 @@ 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.
+This project, `PythonRobotics`, only focuses on computer science and artificial intelligence.
 
 The technologies for robotics are categorized as following 3 categories:
 
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 3a175b1316..b5697d2dd9 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
@@ -68,7 +68,7 @@ But, the probability is getting uncertain without observations:
 
 
 The `gaussian filter <https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html>`_
-is used in the simulation for adding noize.
+is used in the simulation for adding noise.
 
 Step3: Update probability by observation
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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 d648d8e080..8bf22a246b 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
@@ -13,7 +13,7 @@ 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.
+These measurements are used for PF localization.
 
 Code Link
 ~~~~~~~~~~~~~
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 29f5878e48..c97d204a82 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
@@ -19,7 +19,7 @@ unknown (unobserved) areas, which are close to 0.5.
 .. 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
+discretise the values. But, first we need to ``import`` some
 necessary packages.
 
 .. code:: ipython3
diff --git a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
index b6bafa0982..296f1766de 100644
--- a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
+++ b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
@@ -112,7 +112,7 @@ will converge to the correct estimate.
     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
     
@@ -409,7 +409,7 @@ probably will die out.
 3- Resampling
 ~~~~~~~~~~~~~
 
-In the reseampling steps a new set of particles are chosen from the old
+In the resampling steps a new set of particles are chosen from the old
 set. This is done according to the weight of each particle.
 
 The figure shows 100 particles distributed uniformly between [-0.5, 0.5]
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 3967a7ae4d..05bd636ef5 100644
--- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
+++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
@@ -32,7 +32,7 @@ Introduction
 
 EKF SLAM models the SLAM problem in a single EKF where the modeled state
 is both the pose :math:`(x, y, \theta)` and an array of landmarks
-:math:`[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]` for :math:`n`
+:math:`[(x_1, y_1), (x_2, y_2), ... , (x_n, y_n)]` for :math:`n`
 landmarks. The covariance between each of the positions and landmarks
 are also tracked.
 
diff --git a/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst b/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst
index 5297604809..3d329ae131 100644
--- a/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst
+++ b/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst
@@ -28,7 +28,7 @@ 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
+environment to create a least squares problem that can be solved using
 standard optimization techniques.
 
 Minimal Example
@@ -331,7 +331,7 @@ between node 0 and 1 will be created. The equations for the error is as follows:
 
 :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`
+:math:`e_{ij}^{\psi} = \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
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 5a3d14464b..74cb757886 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
@@ -13,7 +13,7 @@ It can generates a shortest path between two 2D poses (x, y, yaw) with maximum c
 
 Generated paths 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).' 
+Each segment type can be categorized by 3 types: '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.
 
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 d0109d4ec3..e37cbd4e77 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
@@ -67,7 +67,7 @@ Dynamic Path Maintenance
    .. 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}})`
+   where :math:`\alpha` is a step-size parameter, which is often proportional to :math:`\rho(b_i^{\text{old}})`
 
 2. **Overlap Enforcement**:
 - Insert new nodes if adjacent nodes are too far apart
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 e2f4d8832d..b7450107b6 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
@@ -91,7 +91,7 @@ This is a 2D grid based shortest path planning with Theta star algorithm.
 
 It offers an optimization over the A star algorithm to generate any-angle paths. Unlike A star, which always assigns the current node as the parent of the successor, Theta star checks for a line-of-sight (unblocked path) from an earlier node (typically the parent of the current node) to the successor, and directly assigns it as a parent if visible. This reduces cost by replacing staggered segments with straight lines. 
 
-Checking for line of sight involves verifying that no obstacles block the straight line between two nodes. On a grid, this means identifying all the discrete cells that the line passes through to determine if they are empty. Bresenham’s line algorithm is commonly used for this purpose. Starting from one endpoint, it incrementally steps along one axis, while considering the gradient to determine the position on the other axis. Because it relies only on integer addition and subtraction, it is both efficient and precise for grid-based visibility checks in Theta*.
+Checking for line of sight involves verifying that no obstacles block the straight line between two nodes. On a grid, this means identifying all the discrete cells that the line passes through to determine if they are empty. Bresenham’s line algorithm is commonly used for this purpose. Starting from one endpoint, it incrementally steps along one axis, while considering the gradient to determine the position on the other axis. Because it relies only on integer addition and subtraction, it is both efficient and precise for grid-based visibility checks in Theta star.
 
 As a result, Theta star produces shorter, smoother paths than A star, ideal for ground or aerial robots operating in continuous environments where smoother motion enables higher acceleration and reduced travel time.
 
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 4dd54d7c97..bedcfc33d8 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
@@ -17,7 +17,7 @@ Here is an overview of mathematical derivations of formulae for individual path
 
 In all the derivations below, radius of curvature of the vehicle is assumed to be of unit length and start pose is considered to be at origin.  (*In code we are removing the offset due to start position and normalising the lengths before passing the values to these functions.*)
 
-Also, (t, u, v) respresent the measure of each motion requried. Thus, in case of a turning maneuver, they represent the angle inscribed at the centre of turning circle and in case of straight maneuver, they represent the distance to be travelled. 
+Also, (t, u, v) represent the measure of each motion required. Thus, in case of a turning maneuver, they represent the angle inscribed at the centre of turning circle and in case of straight maneuver, they represent the distance to be travelled. 
 
 1. **Left-Straight-Left**
 
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 a9a41aab74..46b86123f3 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
@@ -9,7 +9,7 @@ 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.
+The red line is the final path of Voronoi Road-Map.
 
 Code Link
 ~~~~~~~~~~~~~~~
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 76a6819a46..cc5a6812ca 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
@@ -34,17 +34,17 @@ Input vector is:
 
 .. math::  u = [a, \delta]
 
-a: accellation, δ: steering angle
+a: acceleration, δ: steering angle
 
-The MPC cotroller minimize this cost function for path tracking:
+The MPC cotroller minimizes this cost function for path tracking:
 
 .. math:: 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
 
-z_ref come from target path and speed.
+z_ref comes from target path and speed.
 
 subject to:
 
--  Linearlied vehicle model
+-  Linearized vehicle model
 
 .. math:: z_{t+1}=Az_t+Bu+C
 
diff --git a/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
index 19bb0e4c14..b3d23ab2be 100644
--- a/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
+++ b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
@@ -32,7 +32,7 @@ Constructor
 
    PathFinderController(Kp_rho, Kp_alpha, Kp_beta)
 
-Constructs an instantiate of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane.
+Constructs an instance of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane.
 
 Parameters:
 
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 56900acde1..6c1db7d2a5 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
@@ -3,7 +3,7 @@ N joint arm to point control
 
 N joint arm to a point control simulation.
 
-This is a interactive simulation.
+This is an interactive simulation.
 
 You can set the goal position of the end effector with left-click on the
 plotting area.
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 5b2712eb48..8d3b709e47 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
@@ -7,7 +7,7 @@ Two joint arm to point control
 
 This is two joint arm to a point control simulation.
 
-This is a interactive simulation.
+This is an interactive simulation.
 
 You can set the goal position of the end effector with left-click on the plotting area.