first release LQR RRT Star

Author: AtsushiSakai

PathPlanning/LQRPlanner/LQRplanner.py

@@ -42,7 +42,7 @@ def LQRplanning(sx, sy, gx, gy):
 
         d = math.sqrt((gx - rx[-1])**2 + (gy - ry[-1])**2)
         if d <= 0.1:
-            print("Goal!!")
+            #  print("Goal!!")
             found_path = True
             break
 

PathPlanning/LQRRRTStar/lqr_rrt_star.py

@@ -0,0 +1,320 @@
+"""
+
+Path planning code with LQR RRT*
+
+author: AtsushiSakai(@Atsushi_twi)
+
+"""
+
+import sys
+sys.path.append("../LQRPlanner/")
+
+import random
+import math
+import copy
+import numpy as np
+import matplotlib.pyplot as plt
+import LQRplanner
+
+show_animation = True
+
+LQRplanner.show_animation = False
+
+STEP_SIZE = 0.05
+
+
+class RRT():
+    """
+    Class for RRT Planning
+    """
+
+    def __init__(self, start, goal, obstacleList, randArea,
+                 goalSampleRate=10, maxIter=100):
+        """
+        Setting Parameter
+
+        start:Start Position [x,y]
+        goal:Goal Position [x,y]
+        obstacleList:obstacle Positions [[x,y,size],...]
+        randArea:Ramdom Samping Area [min,max]
+
+        """
+        self.start = Node(start[0], start[1])
+        self.end = Node(goal[0], goal[1])
+        self.minrand = randArea[0]
+        self.maxrand = randArea[1]
+        self.goalSampleRate = goalSampleRate
+        self.maxIter = maxIter
+        self.obstacleList = obstacleList
+
+    def Planning(self, animation=True):
+        """
+        Pathplanning
+
+        animation: flag for animation on or off
+        """
+
+        self.nodeList = [self.start]
+        for i in range(self.maxIter):
+            rnd = self.get_random_point()
+            nind = self.GetNearestListIndex(self.nodeList, rnd)
+
+            newNode = self.steer(rnd, nind)
+            if newNode is None:
+                continue
+
+            if self.CollisionCheck(newNode, self.obstacleList):
+                nearinds = self.find_near_nodes(newNode)
+                newNode = self.choose_parent(newNode, nearinds)
+                if newNode is None:
+                    continue
+                self.nodeList.append(newNode)
+                self.rewire(newNode, nearinds)
+
+            if animation and i % 5 == 0:
+                self.DrawGraph(rnd=rnd)
+
+        # generate coruse
+        lastIndex = self.get_best_last_index()
+        if lastIndex is None:
+            return None
+        path = self.gen_final_course(lastIndex)
+        return path
+
+    def choose_parent(self, newNode, nearinds):
+        if len(nearinds) == 0:
+            return newNode
+
+        dlist = []
+        for i in nearinds:
+            tNode = self.steer(newNode, i)
+            if tNode is None:
+                continue
+
+            if self.CollisionCheck(tNode, self.obstacleList):
+                dlist.append(tNode.cost)
+            else:
+                dlist.append(float("inf"))
+
+        mincost = min(dlist)
+        minind = nearinds[dlist.index(mincost)]
+
+        if mincost == float("inf"):
+            print("mincost is inf")
+            return newNode
+
+        newNode = self.steer(newNode, minind)
+
+        return newNode
+
+    def pi_2_pi(self, angle):
+        while(angle > math.pi):
+            angle = angle - 2.0 * math.pi
+
+        while(angle < -math.pi):
+            angle = angle + 2.0 * math.pi
+
+        return angle
+
+    def sample_path(self, wx, wy, step):
+
+        px, py, clen = [], [], []
+
+        for i in range(len(wx) - 1):
+
+            for t in np.arange(0.0, 1.0, step):
+                px.append(t * wx[i + 1] + (1.0 - t) * wx[i])
+                py.append(t * wy[i + 1] + (1.0 - t) * wy[i])
+
+        dx = np.diff(px)
+        dy = np.diff(py)
+
+        clen = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)]
+
+        return px, py, clen
+
+    def steer(self, rnd, nind):
+
+        nearestNode = self.nodeList[nind]
+
+        wx, wy = LQRplanner.LQRplanning(
+            nearestNode.x, nearestNode.y, rnd.x, rnd.y)
+
+        px, py, clen = self.sample_path(wx, wy, STEP_SIZE)
+
+        if px is None:
+            return None
+
+        newNode = copy.deepcopy(nearestNode)
+        newNode.x = px[-1]
+        newNode.y = py[-1]
+
+        newNode.path_x = px
+        newNode.path_y = py
+        newNode.cost += sum([abs(c) for c in clen])
+        newNode.parent = nind
+
+        return newNode
+
+    def get_random_point(self):
+
+        if random.randint(0, 100) > self.goalSampleRate:
+            rnd = [random.uniform(self.minrand, self.maxrand),
+                   random.uniform(self.minrand, self.maxrand),
+                   random.uniform(-math.pi, math.pi)
+                   ]
+        else:  # goal point sampling
+            #  print("goal sample")
+            rnd = [self.end.x, self.end.y]
+
+        node = Node(rnd[0], rnd[1])
+
+        return node
+
+    def get_best_last_index(self):
+        #  print("get_best_last_index")
+
+        XYTH = 0.5
+
+        goalinds = []
+        for (i, node) in enumerate(self.nodeList):
+            if self.calc_dist_to_goal(node.x, node.y) <= XYTH:
+                goalinds.append(i)
+
+        if len(goalinds) == 0:
+            return None
+
+        mincost = min([self.nodeList[i].cost for i in goalinds])
+        for i in goalinds:
+            if self.nodeList[i].cost == mincost:
+                return i
+
+        return None
+
+    def gen_final_course(self, goalind):
+        path = [[self.end.x, self.end.y]]
+        while self.nodeList[goalind].parent is not None:
+            node = self.nodeList[goalind]
+            for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
+                path.append([ix, iy])
+            #  path.append([node.x, node.y])
+            goalind = node.parent
+        path.append([self.start.x, self.start.y])
+        return path
+
+    def calc_dist_to_goal(self, x, y):
+        return np.linalg.norm([x - self.end.x, y - self.end.y])
+
+    def find_near_nodes(self, newNode):
+        nnode = len(self.nodeList)
+        r = 50.0 * math.sqrt((math.log(nnode) / nnode))
+        dlist = [(node.x - newNode.x) ** 2 +
+                 (node.y - newNode.y) ** 2
+                 for node in self.nodeList]
+        nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
+        return nearinds
+
+    def rewire(self, newNode, nearinds):
+
+        nnode = len(self.nodeList)
+
+        for i in nearinds:
+            nearNode = self.nodeList[i]
+            tNode = self.steer(nearNode, nnode - 1)
+            if tNode is None:
+                continue
+
+            obstacleOK = self.CollisionCheck(tNode, self.obstacleList)
+            imporveCost = nearNode.cost > tNode.cost
+
+            if obstacleOK and imporveCost:
+                #  print("rewire")
+                self.nodeList[i] = tNode
+
+    def DrawGraph(self, rnd=None):
+        """
+        Draw Graph
+        """
+        plt.clf()
+        if rnd is not None:
+            plt.plot(rnd.x, rnd.y, "^k")
+        for node in self.nodeList:
+            if node.parent is not None:
+                plt.plot(node.path_x, node.path_y, "-g")
+
+        for (ox, oy, size) in self.obstacleList:
+            plt.plot(ox, oy, "ok", ms=30 * size)
+
+        plt.plot(self.start.x, self.start.y, "or")
+        plt.plot(self.end.x, self.end.y, "or")
+
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        plt.pause(0.01)
+
+    def GetNearestListIndex(self, nodeList, rnd):
+        dlist = [(node.x - rnd.x) ** 2 +
+                 (node.y - rnd.y) ** 2
+                 for node in nodeList]
+        minind = dlist.index(min(dlist))
+
+        return minind
+
+    def CollisionCheck(self, node, obstacleList):
+
+        for (ox, oy, size) in obstacleList:
+            for (ix, iy) in zip(node.path_x, node.path_y):
+                dx = ox - ix
+                dy = oy - iy
+                d = dx * dx + dy * dy
+                if d <= size ** 2:
+                    return False  # collision
+
+        return True  # safe
+
+
+class Node():
+    """
+    RRT Node
+    """
+
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        self.path_x = []
+        self.path_y = []
+        self.cost = 0.0
+        self.parent = None
+
+
+def main():
+    print("Start rrt start planning")
+
+    # ====Search Path with RRT====
+    obstacleList = [
+        (5, 5, 1),
+        (4, 6, 1),
+        (4, 7.5, 1),
+        (4, 9, 1),
+        (6, 5, 1),
+        (7, 5, 1)
+    ]  # [x,y,size]
+
+    # Set Initial parameters
+    start = [0.0, 0.0]
+    goal = [6.0, 7.0]
+
+    rrt = RRT(start, goal, randArea=[-2.0, 15.0], obstacleList=obstacleList)
+    path = rrt.Planning(animation=show_animation)
+
+    # Draw final path
+    if show_animation:
+        rrt.DrawGraph()
+        plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
+        plt.grid(True)
+        plt.pause(0.001)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()