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rrt_star_reeds_shepp.py
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"""
Path planning Sample Code with RRT with Reeds-Shepp path
author: AtsushiSakai(@Atsushi_twi)
"""
import copy
import math
import random
import sys
import pathlib
import matplotlib.pyplot as plt
import numpy as np
sys.path.append(str(pathlib.Path(__file__).parent.parent))
from ReedsSheppPath import reeds_shepp_path_planning
from RRTStar.rrt_star import RRTStar
show_animation = True
class RRTStarReedsShepp(RRTStar):
"""
Class for RRT star planning with Reeds Shepp path
"""
class Node(RRTStar.Node):
"""
RRT Node
"""
def __init__(self, x, y, yaw):
super().__init__(x, y)
self.yaw = yaw
self.path_yaw = []
def __init__(self, start, goal, obstacle_list, rand_area,
max_iter=200, step_size=0.2,
connect_circle_dist=50.0,
robot_radius=0.0
):
"""
Setting Parameter
start:Start Position [x,y]
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])
self.end = self.Node(goal[0], goal[1], goal[2])
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
self.curvature = 1.0
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
animation: flag for animation on or off
"""
self.node_list = [self.start]
for i in range(self.max_iter):
print("Iter:", i, ", number of nodes:", len(self.node_list))
rnd = self.get_random_node()
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, self.robot_radius):
near_indexes = self.find_near_nodes(new_node)
new_node = self.choose_parent(new_node, near_indexes)
if new_node:
self.node_list.append(new_node)
self.rewire(new_node, near_indexes)
self.try_goal_path(new_node)
if animation and i % 5 == 0:
self.plot_start_goal_arrow()
self.draw_graph(rnd)
if (not search_until_max_iter) and new_node: # check reaching the goal
last_index = self.search_best_goal_node()
if last_index:
return self.generate_final_course(last_index)
print("reached max iteration")
last_index = self.search_best_goal_node()
if last_index:
return self.generate_final_course(last_index)
else:
print("Cannot find path")
return None
def try_goal_path(self, node):
goal = self.Node(self.end.x, self.end.y, self.end.yaw)
new_node = self.steer(node, goal)
if new_node is None:
return
if self.check_collision(
new_node, self.obstacle_list, self.robot_radius):
self.node_list.append(new_node)
def draw_graph(self, 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])
if rnd is not None:
plt.plot(rnd.x, rnd.y, "^k")
for node in self.node_list:
if node.parent:
plt.plot(node.path_x, node.path_y, "-g")
for (ox, oy, size) in self.obstacle_list:
plt.plot(ox, oy, "ok", ms=30 * size)
plt.plot(self.start.x, self.start.y, "xr")
plt.plot(self.end.x, self.end.y, "xr")
plt.axis([-2, 15, -2, 15])
plt.grid(True)
self.plot_start_goal_arrow()
plt.pause(0.01)
def plot_start_goal_arrow(self):
reeds_shepp_path_planning.plot_arrow(
self.start.x, self.start.y, self.start.yaw)
reeds_shepp_path_planning.plot_arrow(
self.end.x, self.end.y, self.end.yaw)
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, self.step_size)
if not px:
return None
new_node = copy.deepcopy(from_node)
new_node.x = px[-1]
new_node.y = py[-1]
new_node.yaw = pyaw[-1]
new_node.path_x = px
new_node.path_y = py
new_node.path_yaw = pyaw
new_node.cost += sum([abs(l) for l in course_lengths])
new_node.parent = from_node
return new_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, self.step_size)
if not course_lengths:
return float("inf")
return from_node.cost + sum([abs(l) for l in course_lengths])
def get_random_node(self):
rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
random.uniform(self.min_rand, self.max_rand),
random.uniform(-math.pi, math.pi)
)
return rnd
def search_best_goal_node(self):
goal_indexes = []
for (i, node) in enumerate(self.node_list):
if self.calc_dist_to_goal(node.x, node.y) <= self.goal_xy_th:
goal_indexes.append(i)
print("goal_indexes:", len(goal_indexes))
# angle check
final_goal_indexes = []
for i in goal_indexes:
if abs(self.node_list[i].yaw - self.end.yaw) <= self.goal_yaw_th:
final_goal_indexes.append(i)
print("final_goal_indexes:", len(final_goal_indexes))
if not final_goal_indexes:
return None
min_cost = min([self.node_list[i].cost for i in final_goal_indexes])
print("min_cost:", min_cost)
for i in final_goal_indexes:
if self.node_list[i].cost == min_cost:
return i
return None
def generate_final_course(self, goal_index):
path = [[self.end.x, self.end.y, self.end.yaw]]
node = self.node_list[goal_index]
while node.parent:
for (ix, iy, iyaw) in zip(reversed(node.path_x), reversed(node.path_y), reversed(node.path_yaw)):
path.append([ix, iy, iyaw])
node = node.parent
path.append([self.start.x, self.start.y, self.start.yaw])
return path
def main(max_iter=100):
print("Start " + __file__)
# ====Search Path with RRT====
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)]
# Set Initial parameters
start = [0.0, 0.0, np.deg2rad(0.0)]
goal = [6.0, 7.0, np.deg2rad(90.0)]
rrt_star_reeds_shepp = RRTStarReedsShepp(start, goal,
obstacleList,
[-2.0, 15.0], max_iter=max_iter)
path = rrt_star_reeds_shepp.planning(animation=show_animation)
# Draw final path
if path and show_animation: # pragma: no cover
rrt_star_reeds_shepp.draw_graph()
plt.plot([x for (x, y, yaw) in path], [y for (x, y, yaw) in path], '-r')
plt.grid(True)
plt.pause(0.001)
plt.show()
if __name__ == '__main__':
main()