add clrrt sample

Author: AtsushiSakai

PathPlanning/CRRRTStar/cr_rrt_star_car.py

@@ -0,0 +1,393 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+"""
+@brief: Path Planning Sample Code with RRT for car like robot.
+
+@author: AtsushiSakai(@Atsushi_twi)
+
+@license: MIT
+
+"""
+
+import random
+import math
+import copy
+import numpy as np
+import dubins_path_planning
+import pure_pursuit
+import unicycle_model
+
+
+class RRT():
+    u"""
+    Class for RRT Planning
+    """
+
+    def __init__(self, start, goal, obstacleList, randArea,
+                 goalSampleRate=10, maxIter=100):
+        u"""
+        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], start[2])
+        self.end = Node(goal[0], goal[1], goal[2])
+        self.minrand = randArea[0]
+        self.maxrand = randArea[1]
+        self.goalSampleRate = goalSampleRate
+        self.obstacleList = obstacleList
+        self.maxIter = maxIter
+
+    def Planning(self, animation=True):
+        u"""
+        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)
+            #  print(newNode.cost)
+
+            if self.CollisionCheck(newNode, obstacleList):
+                nearinds = self.find_near_nodes(newNode)
+                newNode = self.choose_parent(newNode, nearinds)
+                self.nodeList.append(newNode)
+                self.rewire(newNode, nearinds)
+
+            if animation and i % 5 == 0:
+                self.DrawGraph(rnd=rnd)
+                matplotrecorder.save_frame()  # save each frame
+
+        # generate coruse
+        path_indexs = self.get_best_last_indexs()
+
+        # pure pursuit tracking
+        for ind in path_indexs:
+            path = self.gen_final_course(ind)
+
+            flag, x, y, yaw, v, t = self.check_tracking_path_is_feasible(path)
+
+            if flag:
+                print("feasible path is found")
+                break
+
+        return x, y, yaw, v, t
+
+    def check_tracking_path_is_feasible(self, path):
+        print("check_tracking_path_is_feasible")
+
+        init_speed = 0.0
+        target_speed = 10.0 / 3.6
+
+        path = np.matrix(path[::-1])
+
+        state = unicycle_model.State(
+            x=self.start.x, y=self.start.y, yaw=self.start.yaw, v=init_speed)
+
+        target_ind = pure_pursuit.calc_nearest_index(
+            state, path[:, 0], path[:, 1])
+
+        lastIndex = len(path[:, 0]) - 2
+
+        x = [state.x]
+        y = [state.y]
+        yaw = [state.yaw]
+        v = [state.v]
+        t = [0.0]
+        a = [0.0]
+        d = [0.0]
+        time = 0.0
+
+        while lastIndex > target_ind:
+            print(lastIndex, target_ind)
+            ai = pure_pursuit.PIDControl(target_speed, state.v)
+            di, target_ind = pure_pursuit.pure_pursuit_control(
+                state, path[:, 0], path[:, 1], target_ind)
+            state = unicycle_model.update(state, ai, di)
+
+            time = time + unicycle_model.dt
+
+            x.append(state.x)
+            y.append(state.y)
+            yaw.append(state.yaw)
+            v.append(state.v)
+            t.append(time)
+            a.append(ai)
+            d.append(di)
+
+        if self.CollisionCheckWithXY(path[:, 0], path[:, 1], self.obstacleList):
+            #  print("OK")
+            return True, x, y, yaw, v, t, a, d
+        else:
+            #  print("NG")
+            return False, x, y, yaw, v, t, a, d
+
+        #  plt.plot(x, y, '-r')
+        #  plt.plot(path[:, 0], path[:, 1], '-g')
+        #  plt.show()
+
+        #  return True
+
+    def choose_parent(self, newNode, nearinds):
+        if len(nearinds) == 0:
+            return newNode
+
+        dlist = []
+        for i in nearinds:
+            tNode = self.steer(newNode, i)
+            if self.CollisionCheck(tNode, 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 steer(self, rnd, nind):
+        #  print(rnd)
+        curvature = 1.0
+
+        nearestNode = self.nodeList[nind]
+
+        px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+            nearestNode.x, nearestNode.y, nearestNode.yaw, rnd.x, rnd.y, rnd.yaw, curvature)
+
+        newNode = copy.deepcopy(nearestNode)
+        newNode.x = px[-1]
+        newNode.y = py[-1]
+        newNode.yaw = pyaw[-1]
+
+        newNode.path_x = px
+        newNode.path_y = py
+        newNode.path_yaw = pyaw
+        newNode.cost += 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
+            rnd = [self.end.x, self.end.y, self.end.yaw]
+
+        node = Node(rnd[0], rnd[1], rnd[2])
+
+        return node
+
+    def get_best_last_indexs(self):
+        #  print("get_best_last_index")
+
+        YAWTH = math.radians(1.0)
+        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)
+
+        # angle check
+        fgoalinds = []
+        for i in goalinds:
+            if abs(self.nodeList[i].yaw - self.end.yaw) <= YAWTH:
+                fgoalinds.append(i)
+
+        return fgoalinds
+
+        #  mincost = min([self.nodeList[i].cost for i in fgoalinds])
+        #  for i in fgoalinds:
+        #  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))
+        #  r = self.expandDis * 5.0
+        dlist = [(node.x - newNode.x) ** 2 +
+                 (node.y - newNode.y) ** 2 +
+                 (node.yaw - newNode.yaw) ** 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)
+
+            obstacleOK = self.CollisionCheck(tNode, obstacleList)
+            imporveCost = nearNode.cost > tNode.cost
+
+            if obstacleOK and imporveCost:
+                #  print("rewire")
+                self.nodeList[i] = tNode
+
+    def DrawGraph(self, rnd=None):
+        u"""
+        Draw Graph
+        """
+        import matplotlib.pyplot as plt
+        #  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")
+                #  plt.plot([node.x, self.nodeList[node.parent].x], [
+                #  node.y, self.nodeList[node.parent].y], "-g")
+
+        for (ox, oy, size) in obstacleList:
+            plt.plot(ox, oy, "ok", ms=30 * size)
+
+        dubins_path_planning.plot_arrow(
+            self.start.x, self.start.y, self.start.yaw)
+        dubins_path_planning.plot_arrow(
+            self.end.x, self.end.y, self.end.yaw)
+
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        plt.pause(0.01)
+
+        #  plt.show()
+        #  input()
+
+    def GetNearestListIndex(self, nodeList, rnd):
+        dlist = [(node.x - rnd.x) ** 2 +
+                 (node.y - rnd.y) ** 2 +
+                 (node.yaw - rnd.yaw) ** 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
+
+    def CollisionCheckWithXY(self, x, y, obstacleList):
+
+        for (ox, oy, size) in obstacleList:
+            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
+
+
+class Node():
+    u"""
+    RRT Node
+    """
+
+    def __init__(self, x, y, yaw):
+        self.x = x
+        self.y = y
+        self.yaw = yaw
+        self.path_x = []
+        self.path_y = []
+        self.path_yaw = []
+        self.cost = 0.0
+        self.parent = None
+
+
+if __name__ == '__main__':
+    print("Start rrt start planning")
+    import matplotlib.pyplot as plt
+    import matplotrecorder
+    matplotrecorder.donothing = True
+
+    # ====Search Path with RRT====
+    obstacleList = [
+        (5, 5, 1),
+        (3, 6, 2),
+        (3, 8, 2),
+        (3, 10, 2),
+        (7, 5, 2),
+        (9, 5, 2)
+    ]  # [x,y,size(radius)]
+
+    # Set Initial parameters
+    start = [0.0, 0.0, math.radians(0.0)]
+    goal = [10.0, 10.0, math.radians(0.0)]
+
+    rrt = RRT(start, goal, randArea=[-2.0, 15.0], obstacleList=obstacleList)
+    x, y, yaw, v, t = rrt.Planning(animation=False)
+
+    flg, ax = plt.subplots(1)
+    # Draw final path
+    rrt.DrawGraph()
+    plt.plot(x, y, '-r')
+    plt.grid(True)
+    plt.pause(0.001)
+
+    for i in range(10):
+        matplotrecorder.save_frame()  # save each frame
+
+    flg, ax = plt.subplots(1)
+    plt.plot(t, yaw, '-r')
+
+    flg, ax = plt.subplots(1)
+    plt.plot(t, v, '-r')
+
+    plt.show()
+
+    matplotrecorder.save_movie("animation.gif", 0.1)