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252 lines
6.7 KiB
Python
252 lines
6.7 KiB
Python
"""
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Path Planning Sample Code with RRT for car like robot.
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author: AtsushiSakai(@Atsushi_twi)
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"""
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import random
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import math
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import copy
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import numpy as np
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import dubins_path_planning
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import matplotlib.pyplot as plt
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show_animation = True
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class RRT():
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"""
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Class for RRT Planning
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"""
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def __init__(self, start, goal, obstacleList, randArea,
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goalSampleRate=10, maxIter=1000):
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"""
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Setting Parameter
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start:Start Position [x,y]
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goal:Goal Position [x,y]
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obstacleList:obstacle Positions [[x,y,size],...]
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randArea:Ramdom Samping Area [min,max]
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"""
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self.start = Node(start[0], start[1], start[2])
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self.end = Node(goal[0], goal[1], goal[2])
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self.minrand = randArea[0]
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self.maxrand = randArea[1]
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self.goalSampleRate = goalSampleRate
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self.maxIter = maxIter
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self.obstacleList = obstacleList
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def Planning(self, animation=False):
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"""
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Pathplanning
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animation: flag for animation on or off
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"""
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self.nodeList = [self.start]
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for i in range(self.maxIter):
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rnd = self.get_random_point()
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nind = self.GetNearestListIndex(self.nodeList, rnd)
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newNode = self.steer(rnd, nind)
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if self.__CollisionCheck(newNode, self.obstacleList):
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self.nodeList.append(newNode)
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if animation and i % 5 == 0:
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self.DrawGraph(rnd=rnd)
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# generate coruse
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lastIndex = self.get_best_last_index()
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# print(lastIndex)
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if lastIndex is None:
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return None
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path = self.gen_final_course(lastIndex)
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return path
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def choose_parent(self, newNode, nearinds):
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if len(nearinds) == 0:
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return newNode
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dlist = []
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for i in nearinds:
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dx = newNode.x - self.nodeList[i].x
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dy = newNode.y - self.nodeList[i].y
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d = math.sqrt(dx ** 2 + dy ** 2)
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theta = math.atan2(dy, dx)
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if self.check_collision_extend(self.nodeList[i], theta, d):
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dlist.append(self.nodeList[i].cost + d)
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else:
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dlist.append(float("inf"))
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mincost = min(dlist)
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minind = nearinds[dlist.index(mincost)]
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if mincost == float("inf"):
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print("mincost is inf")
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return newNode
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newNode.cost = mincost
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newNode.parent = minind
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return newNode
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def pi_2_pi(self, angle):
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return (angle + math.pi) % (2 * math.pi) - math.pi
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def steer(self, rnd, nind):
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# print(rnd)
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curvature = 1.0
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nearestNode = self.nodeList[nind]
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px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
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nearestNode.x, nearestNode.y, nearestNode.yaw, rnd[0], rnd[1], rnd[2], curvature)
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newNode = copy.deepcopy(nearestNode)
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newNode.x = px[-1]
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newNode.y = py[-1]
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newNode.yaw = pyaw[-1]
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newNode.path_x = px
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newNode.path_y = py
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newNode.path_yaw = pyaw
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newNode.cost += clen
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newNode.parent = nind
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return newNode
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def get_random_point(self):
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if random.randint(0, 100) > self.goalSampleRate:
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rnd = [random.uniform(self.minrand, self.maxrand),
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random.uniform(self.minrand, self.maxrand),
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random.uniform(-math.pi, math.pi)
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]
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else: # goal point sampling
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rnd = [self.end.x, self.end.y, self.end.yaw]
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return rnd
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def get_best_last_index(self):
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# print("get_best_last_index")
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disglist = [self.calc_dist_to_goal(
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node.x, node.y) for node in self.nodeList]
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goalinds = [disglist.index(i) for i in disglist if i <= 0.1]
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# print(goalinds)
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mincost = min([self.nodeList[i].cost for i in goalinds])
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for i in goalinds:
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if self.nodeList[i].cost == mincost:
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return i
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return None
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def gen_final_course(self, goalind):
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path = [[self.end.x, self.end.y]]
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while self.nodeList[goalind].parent is not None:
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node = self.nodeList[goalind]
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for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
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path.append([ix, iy])
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# path.append([node.x, node.y])
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goalind = node.parent
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path.append([self.start.x, self.start.y])
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return path
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def calc_dist_to_goal(self, x, y):
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return np.linalg.norm([x - self.end.x, y - self.end.y])
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def DrawGraph(self, rnd=None):
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plt.clf()
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if rnd is not None:
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plt.plot(rnd[0], rnd[1], "^k")
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for node in self.nodeList:
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if node.parent is not None:
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plt.plot(node.path_x, node.path_y, "-g")
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for (ox, oy, size) in self.obstacleList:
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plt.plot(ox, oy, "ok", ms=30 * size)
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dubins_path_planning.plot_arrow(
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self.start.x, self.start.y, self.start.yaw)
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dubins_path_planning.plot_arrow(
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self.end.x, self.end.y, self.end.yaw)
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plt.axis([-2, 15, -2, 15])
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plt.grid(True)
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plt.pause(0.01)
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def GetNearestListIndex(self, nodeList, rnd):
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dlist = [(node.x - rnd[0]) ** 2 +
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(node.y - rnd[1]) ** 2 +
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(node.yaw - rnd[2] ** 2) for node in nodeList]
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minind = dlist.index(min(dlist))
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return minind
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def __CollisionCheck(self, node, obstacleList):
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for (ox, oy, size) in obstacleList:
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for (ix, iy) in zip(node.path_x, node.path_y):
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dx = ox - ix
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dy = oy - iy
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d = dx * dx + dy * dy
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if d <= size ** 2:
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return False # collision
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return True # safe
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class Node():
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"""
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RRT Node
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"""
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def __init__(self, x, y, yaw):
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self.x = x
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self.y = y
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self.yaw = yaw
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self.path_x = []
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self.path_y = []
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self.path_yaw = []
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self.cost = 0.0
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self.parent = None
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def main():
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print("Start rrt planning")
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# ====Search Path with RRT====
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obstacleList = [
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(5, 5, 1),
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(3, 6, 2),
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(3, 8, 2),
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(3, 10, 2),
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(7, 5, 2),
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(9, 5, 2)
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] # [x,y,size(radius)]
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# Set Initial parameters
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start = [0.0, 0.0, np.deg2rad(0.0)]
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goal = [10.0, 10.0, np.deg2rad(0.0)]
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rrt = RRT(start, goal, randArea=[-2.0, 15.0], obstacleList=obstacleList)
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path = rrt.Planning(animation=show_animation)
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# Draw final path
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if show_animation:
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rrt.DrawGraph()
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plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
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plt.grid(True)
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plt.pause(0.001)
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plt.show()
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if __name__ == '__main__':
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main()
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