mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
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244 lines
6.9 KiB
Python
244 lines
6.9 KiB
Python
"""
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Path planning code with LQR RRT*
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author: AtsushiSakai(@Atsushi_twi)
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"""
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import copy
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import math
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import os
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import random
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import sys
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import matplotlib.pyplot as plt
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import numpy as np
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../LQRPlanner/")
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRTStar/")
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try:
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from LQRplanner import LQRPlanner
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from rrt_star import RRTStar
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except ImportError:
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raise
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show_animation = True
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class LQRRRTStar(RRTStar):
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"""
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Class for RRT star planning with LQR planning
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"""
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def __init__(self, start, goal, obstacle_list, rand_area,
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goal_sample_rate=10,
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max_iter=200,
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connect_circle_dist=50.0,
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step_size=0.2
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):
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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:Random Sampling Area [min,max]
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"""
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self.start = self.Node(start[0], start[1])
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self.end = self.Node(goal[0], goal[1])
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self.min_rand = rand_area[0]
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self.max_rand = rand_area[1]
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self.goal_sample_rate = goal_sample_rate
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self.max_iter = max_iter
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self.obstacle_list = obstacle_list
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self.connect_circle_dist = connect_circle_dist
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self.curvature = 1.0
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self.goal_xy_th = 0.5
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self.step_size = step_size
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self.lqr_planner = LQRPlanner()
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def planning(self, animation=True, search_until_max_iter=True):
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"""
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RRT Star planning
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animation: flag for animation on or off
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"""
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self.node_list = [self.start]
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for i in range(self.max_iter):
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print("Iter:", i, ", number of nodes:", len(self.node_list))
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rnd = self.get_random_node()
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nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
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new_node = self.steer(self.node_list[nearest_ind], rnd)
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if self.check_collision(new_node, self.obstacle_list):
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near_indexes = self.find_near_nodes(new_node)
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new_node = self.choose_parent(new_node, near_indexes)
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if new_node:
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self.node_list.append(new_node)
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self.rewire(new_node, near_indexes)
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if animation and i % 5 == 0:
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self.draw_graph(rnd)
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if (not search_until_max_iter) and new_node: # check reaching the goal
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last_index = self.search_best_goal_node()
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if last_index:
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return self.generate_final_course(last_index)
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print("reached max iteration")
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last_index = self.search_best_goal_node()
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if last_index:
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return self.generate_final_course(last_index)
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else:
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print("Cannot find path")
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return None
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def draw_graph(self, rnd=None):
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plt.clf()
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# for stopping simulation with the esc key.
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plt.gcf().canvas.mpl_connect('key_release_event',
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lambda event: [exit(0) if event.key == 'escape' else None])
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if rnd is not None:
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plt.plot(rnd.x, rnd.y, "^k")
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for node in self.node_list:
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if node.parent:
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plt.plot(node.path_x, node.path_y, "-g")
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for (ox, oy, size) in self.obstacle_list:
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plt.plot(ox, oy, "ok", ms=30 * size)
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plt.plot(self.start.x, self.start.y, "xr")
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plt.plot(self.end.x, self.end.y, "xr")
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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 search_best_goal_node(self):
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dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list]
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goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.goal_xy_th]
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if not goal_inds:
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return None
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min_cost = min([self.node_list[i].cost for i in goal_inds])
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for i in goal_inds:
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if self.node_list[i].cost == min_cost:
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return i
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return None
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def calc_new_cost(self, from_node, to_node):
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wx, wy = self.lqr_planner.lqr_planning(
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from_node.x, from_node.y, to_node.x, to_node.y, show_animation=False)
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px, py, course_lengths = self.sample_path(wx, wy, self.step_size)
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if not course_lengths:
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return float("inf")
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return from_node.cost + sum(course_lengths)
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def get_random_node(self):
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if random.randint(0, 100) > self.goal_sample_rate:
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rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
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random.uniform(self.min_rand, self.max_rand)
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)
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else: # goal point sampling
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rnd = self.Node(self.end.x, self.end.y)
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return rnd
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def generate_final_course(self, goal_index):
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print("final")
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path = [[self.end.x, self.end.y]]
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node = self.node_list[goal_index]
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while node.parent:
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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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node = 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 sample_path(self, wx, wy, step):
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px, py, clen = [], [], []
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for i in range(len(wx) - 1):
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for t in np.arange(0.0, 1.0, step):
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px.append(t * wx[i + 1] + (1.0 - t) * wx[i])
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py.append(t * wy[i + 1] + (1.0 - t) * wy[i])
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dx = np.diff(px)
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dy = np.diff(py)
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clen = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)]
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return px, py, clen
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def steer(self, from_node, to_node):
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wx, wy = self.lqr_planner.lqr_planning(
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from_node.x, from_node.y, to_node.x, to_node.y, show_animation=False)
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px, py, course_lens = self.sample_path(wx, wy, self.step_size)
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if px is None:
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return None
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newNode = copy.deepcopy(from_node)
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newNode.x = px[-1]
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newNode.y = py[-1]
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newNode.path_x = px
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newNode.path_y = py
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newNode.cost += sum([abs(c) for c in course_lens])
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newNode.parent = from_node
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return newNode
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def main(maxIter=200):
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print("Start " + __file__)
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# ====Search Path with RRT====
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obstacleList = [
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(5, 5, 1),
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(4, 6, 1),
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(4, 7.5, 1),
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(4, 9, 1),
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(6, 5, 1),
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(7, 5, 1)
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] # [x,y,size]
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# Set Initial parameters
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start = [0.0, 0.0]
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goal = [6.0, 7.0]
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lqr_rrt_star = LQRRRTStar(start, goal,
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obstacleList,
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[-2.0, 15.0])
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path = lqr_rrt_star.planning(animation=show_animation)
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# Draw final path
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if show_animation: # pragma: no cover
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lqr_rrt_star.draw_graph()
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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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print("Done")
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if __name__ == '__main__':
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main()
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