mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 14:48:02 -05:00
239 lines
7.1 KiB
Python
239 lines
7.1 KiB
Python
"""
|
|
Path planning Sample Code with RRT with Dubins path
|
|
|
|
author: AtsushiSakai(@Atsushi_twi)
|
|
|
|
"""
|
|
import copy
|
|
import math
|
|
import random
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import sys
|
|
import pathlib
|
|
sys.path.append(str(pathlib.Path(__file__).parent.parent.parent)) # root dir
|
|
sys.path.append(str(pathlib.Path(__file__).parent.parent))
|
|
|
|
from RRT.rrt import RRT
|
|
from DubinsPath import dubins_path_planner
|
|
from utils.plot import plot_arrow
|
|
|
|
show_animation = True
|
|
|
|
|
|
class RRTDubins(RRT):
|
|
"""
|
|
Class for RRT planning with Dubins path
|
|
"""
|
|
|
|
class Node(RRT.Node):
|
|
"""
|
|
RRT Node
|
|
"""
|
|
|
|
def __init__(self, x, y, yaw):
|
|
super().__init__(x, y)
|
|
self.cost = 0
|
|
self.yaw = yaw
|
|
self.path_yaw = []
|
|
|
|
def __init__(self, start, goal, obstacle_list, rand_area,
|
|
goal_sample_rate=10,
|
|
max_iter=200,
|
|
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.goal_sample_rate = goal_sample_rate
|
|
self.max_iter = max_iter
|
|
self.obstacle_list = obstacle_list
|
|
|
|
self.curvature = 1.0 # for dubins path
|
|
self.goal_yaw_th = np.deg2rad(1.0)
|
|
self.goal_xy_th = 0.5
|
|
self.robot_radius = robot_radius
|
|
|
|
def planning(self, animation=True, search_until_max_iter=True):
|
|
"""
|
|
execute 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):
|
|
self.node_list.append(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 draw_graph(self, rnd=None): # pragma: no cover
|
|
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): # pragma: no cover
|
|
plot_arrow(self.start.x, self.start.y, self.start.yaw)
|
|
plot_arrow(self.end.x, self.end.y, self.end.yaw)
|
|
|
|
def steer(self, from_node, to_node):
|
|
|
|
px, py, pyaw, mode, course_lengths = \
|
|
dubins_path_planner.plan_dubins_path(
|
|
from_node.x, from_node.y, from_node.yaw,
|
|
to_node.x, to_node.y, to_node.yaw, self.curvature)
|
|
|
|
if len(px) <= 1: # cannot find a dubins path
|
|
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(c) for c in course_lengths])
|
|
new_node.parent = from_node
|
|
|
|
return new_node
|
|
|
|
def calc_new_cost(self, from_node, to_node):
|
|
|
|
_, _, _, _, course_length = dubins_path_planner.plan_dubins_path(
|
|
from_node.x, from_node.y, from_node.yaw,
|
|
to_node.x, to_node.y, to_node.yaw, self.curvature)
|
|
|
|
return from_node.cost + course_length
|
|
|
|
def get_random_node(self):
|
|
|
|
if random.randint(0, 100) > self.goal_sample_rate:
|
|
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)
|
|
)
|
|
else: # goal point sampling
|
|
rnd = self.Node(self.end.x, self.end.y, self.end.yaw)
|
|
|
|
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)
|
|
|
|
# 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)
|
|
|
|
if not final_goal_indexes:
|
|
return None
|
|
|
|
min_cost = min([self.node_list[i].cost for i in final_goal_indexes])
|
|
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):
|
|
print("final")
|
|
path = [[self.end.x, self.end.y]]
|
|
node = self.node_list[goal_index]
|
|
while node.parent:
|
|
for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
|
|
path.append([ix, iy])
|
|
node = node.parent
|
|
path.append([self.start.x, self.start.y])
|
|
return path
|
|
|
|
|
|
def main():
|
|
|
|
print("Start " + __file__)
|
|
# ====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, np.deg2rad(0.0)]
|
|
goal = [10.0, 10.0, np.deg2rad(0.0)]
|
|
|
|
rrt_dubins = RRTDubins(start, goal, obstacleList, [-2.0, 15.0])
|
|
path = rrt_dubins.planning(animation=show_animation)
|
|
|
|
# Draw final path
|
|
if show_animation: # pragma: no cover
|
|
rrt_dubins.draw_graph()
|
|
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()
|