mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 11:37:54 -05:00
fix rrt star reeds shepp rewire and test
This commit is contained in:
Atsushi Sakai
@@ -1,5 +1,5 @@
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"""
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Path planning Sample Code with RRT for car like robot.
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Path planning Sample Code with RRT with Dubins path
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author: AtsushiSakai(@Atsushi_twi)
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@@ -100,7 +100,7 @@ class RRTStar(RRT):
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for i in near_inds:
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near_node = self.node_list[i]
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t_node = self.steer(near_node, new_node)
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if self.check_collision(t_node, self.obstacle_list):
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if t_node and self.check_collision(t_node, self.obstacle_list):
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costs.append(self.calc_new_cost(near_node, new_node))
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else:
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costs.append(float("inf")) # the cost of collision node
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@@ -143,6 +143,8 @@ class RRTStar(RRT):
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for i in near_inds:
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near_node = self.node_list[i]
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edge_node = self.steer(new_node, near_node)
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if not edge_node:
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continue
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edge_node.cost = self.calc_new_cost(new_node, near_node)
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no_collision = self.check_collision(edge_node, self.obstacle_list)
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@@ -1,289 +1,219 @@
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"""
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Path Planning Sample Code with RRT for car like robot.
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Path planning Sample Code with RRT with Reeds-Shepp path
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author: AtsushiSakai(@Atsushi_twi)
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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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 os
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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__)) +
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"/../ReedsSheppPath/")
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
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"/../RRTStar/")
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try:
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import reeds_shepp_path_planning
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except:
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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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STEP_SIZE = 0.1
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curvature = 1.0
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class RRT():
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class RRTStarReedsShepp(RRTStar):
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"""
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Class for RRT Planning
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Class for RRT star planning with Reeds Shepp path
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"""
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def __init__(self, start, goal, obstacleList, randArea,
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goalSampleRate=10, maxIter=400):
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class Node(RRTStar.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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super().__init__(x, y)
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self.yaw = yaw
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self.path_yaw = []
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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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):
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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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randArea:Random Sampling 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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self.start = self.Node(start[0], start[1], start[2])
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self.end = self.Node(goal[0], goal[1], goal[2])
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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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def Planning(self, animation=True):
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self.curvature = 1.0
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self.goal_yaw_th = np.deg2rad(1.0)
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self.goal_xy_th = 0.5
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def planning(self, animation=True, search_until_max_iter=True):
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"""
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Pathplanning
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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.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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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_list_index(self.node_list, rnd)
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new_node = self.steer(self.node_list[nearest_ind], rnd)
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newNode = self.steer(rnd, nind)
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if newNode is None:
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continue
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if self.CollisionCheck(newNode, self.obstacleList):
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nearinds = self.find_near_nodes(newNode)
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newNode = self.choose_parent(newNode, nearinds)
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if newNode is None:
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continue
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self.nodeList.append(newNode)
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self.rewire(newNode, nearinds)
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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.DrawGraph(rnd=rnd)
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self.plot_start_goal_arrow()
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self.draw_graph(rnd)
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# generate coruse
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lastIndex = self.get_best_last_index()
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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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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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def choose_parent(self, newNode, nearinds):
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if not nearinds:
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return newNode
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print("reached max iteration")
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dlist = []
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for i in nearinds:
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tNode = self.steer(newNode, i)
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if tNode is None:
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continue
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if self.CollisionCheck(tNode, self.obstacleList):
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dlist.append(tNode.cost)
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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 = self.steer(newNode, 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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nearestNode = self.nodeList[nind]
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px, py, pyaw, mode, clen = reeds_shepp_path_planning.reeds_shepp_path_planning(
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nearestNode.x, nearestNode.y, nearestNode.yaw, rnd.x, rnd.y, rnd.yaw, curvature, STEP_SIZE)
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if px is None:
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return None
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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 += sum([abs(c) for c in 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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node = Node(rnd[0], rnd[1], rnd[2])
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return node
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def get_best_last_index(self):
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# print("get_best_last_index")
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YAWTH = np.deg2rad(3.0)
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XYTH = 0.5
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goalinds = []
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for (i, node) in enumerate(self.nodeList):
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if self.calc_dist_to_goal(node.x, node.y) <= XYTH:
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goalinds.append(i)
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# print("OK XY TH num is")
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# print(len(goalinds))
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# angle check
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fgoalinds = []
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for i in goalinds:
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if abs(self.nodeList[i].yaw - self.end.yaw) <= YAWTH:
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fgoalinds.append(i)
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# print("OK YAW TH num is")
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# print(len(fgoalinds))
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if not fgoalinds:
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return None
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mincost = min([self.nodeList[i].cost for i in fgoalinds])
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for i in fgoalinds:
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if self.nodeList[i].cost == mincost:
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return i
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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 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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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 find_near_nodes(self, newNode):
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nnode = len(self.nodeList)
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r = 50.0 * math.sqrt((math.log(nnode) / nnode))
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# r = self.expandDis * 5.0
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dlist = [(node.x - newNode.x) ** 2 +
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(node.y - newNode.y) ** 2 +
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(node.yaw - newNode.yaw) ** 2
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for node in self.nodeList]
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nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
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return nearinds
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def rewire(self, newNode, nearinds):
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nnode = len(self.nodeList)
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for i in nearinds:
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nearNode = self.nodeList[i]
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tNode = self.steer(nearNode, nnode - 1)
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if tNode is None:
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continue
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obstacleOK = self.CollisionCheck(tNode, self.obstacleList)
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imporveCost = nearNode.cost > tNode.cost
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if obstacleOK and imporveCost:
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# print("rewire")
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self.nodeList[i] = tNode
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def DrawGraph(self, rnd=None): # pragma: no cover
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def draw_graph(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.x, rnd.y, "^k")
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for node in self.nodeList:
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if node.parent is not None:
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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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# plt.plot([node.x, self.nodeList[node.parent].x], [
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# node.y, self.nodeList[node.parent].y], "-g")
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for (ox, oy, size) in self.obstacleList:
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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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self.plot_start_goal_arrow()
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plt.pause(0.01)
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def plot_start_goal_arrow(self):
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reeds_shepp_path_planning.plot_arrow(
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self.start.x, self.start.y, self.start.yaw)
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reeds_shepp_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 steer(self, from_node, to_node):
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# plt.show()
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# input()
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px, py, pyaw, mode, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning(
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from_node.x, from_node.y, from_node.yaw,
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to_node.x, to_node.y, to_node.yaw, self.curvature)
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def GetNearestListIndex(self, nodeList, rnd):
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dlist = [(node.x - rnd.x) ** 2 +
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(node.y - rnd.y) ** 2 +
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(node.yaw - rnd.yaw) ** 2 for node in nodeList]
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minind = dlist.index(min(dlist))
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if not px:
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return None
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return minind
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new_node = copy.deepcopy(from_node)
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new_node.x = px[-1]
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new_node.y = py[-1]
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new_node.yaw = pyaw[-1]
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def CollisionCheck(self, node, obstacleList):
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new_node.path_x = px
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new_node.path_y = py
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new_node.path_yaw = pyaw
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new_node.cost += sum(course_lengths)
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new_node.parent = from_node
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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 new_node
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return True # safe
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def calc_new_cost(self, from_node, to_node):
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_, _, _, _, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning(
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from_node.x, from_node.y, from_node.yaw,
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to_node.x, to_node.y, to_node.yaw, self.curvature)
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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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random.uniform(-math.pi, math.pi)
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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, self.end.yaw)
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return rnd
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def search_best_goal_node(self):
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goal_indexes = []
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for (i, node) in enumerate(self.node_list):
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if self.calc_dist_to_goal(node.x, node.y) <= self.goal_xy_th:
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goal_indexes.append(i)
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# angle check
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final_goal_indexes = []
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for i in goal_indexes:
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if abs(self.node_list[i].yaw - self.end.yaw) <= self.goal_yaw_th:
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final_goal_indexes.append(i)
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if not final_goal_indexes:
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return None
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min_cost = min([self.node_list[i].cost for i in final_goal_indexes])
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for i in final_goal_indexes:
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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 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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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(maxIter=200):
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def main():
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print("Start " + __file__)
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# ====Search Path with RRT====
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||||
@@ -303,14 +233,14 @@ def main(maxIter=200):
|
||||
start = [0.0, 0.0, np.deg2rad(0.0)]
|
||||
goal = [6.0, 7.0, np.deg2rad(90.0)]
|
||||
|
||||
rrt = RRT(start, goal, randArea=[-2.0, 15.0],
|
||||
obstacleList=obstacleList,
|
||||
maxIter=maxIter)
|
||||
path = rrt.Planning(animation=show_animation)
|
||||
rrt_star_reeds_shepp = RRTStarReedsShepp(start, goal,
|
||||
obstacleList,
|
||||
[-2.0, 15.0])
|
||||
path = rrt_star_reeds_shepp.planning(animation=show_animation)
|
||||
|
||||
# Draw final path
|
||||
if show_animation: # pragma: no cover
|
||||
rrt.DrawGraph()
|
||||
if path and show_animation: # pragma: no cover
|
||||
rrt_star_reeds_shepp.draw_graph()
|
||||
plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
|
||||
plt.grid(True)
|
||||
plt.pause(0.001)
|
||||
|
||||
@@ -5,10 +5,10 @@ Reeds Shepp path planner sample code
|
||||
author Atsushi Sakai(@Atsushi_twi)
|
||||
|
||||
"""
|
||||
import numpy as np
|
||||
import math
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
show_animation = True
|
||||
|
||||
@@ -353,7 +353,7 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
|
||||
|
||||
|
||||
def reeds_shepp_path_planning(sx, sy, syaw,
|
||||
gx, gy, gyaw, maxc, step_size):
|
||||
gx, gy, gyaw, maxc, step_size=0.2):
|
||||
|
||||
paths = calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size)
|
||||
|
||||
|
||||
@@ -1,7 +1,9 @@
|
||||
from unittest import TestCase
|
||||
from PathPlanning.DubinsPath import dubins_path_planning
|
||||
|
||||
import numpy as np
|
||||
|
||||
from PathPlanning.DubinsPath import dubins_path_planning
|
||||
|
||||
|
||||
class Test(TestCase):
|
||||
|
||||
@@ -19,8 +21,8 @@ class Test(TestCase):
|
||||
px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
|
||||
start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
|
||||
|
||||
assert(abs(px[-1] - end_x) <= 0.1)
|
||||
assert(abs(py[-1] - end_y) <= 0.1)
|
||||
assert (abs(px[-1] - end_x) <= 0.5)
|
||||
assert (abs(py[-1] - end_y) <= 0.5)
|
||||
assert(abs(pyaw[-1] - end_yaw) <= 0.1)
|
||||
|
||||
def test2(self):
|
||||
|
||||
Reference in New Issue
Block a user