mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-14 16:08:05 -05:00
fix LQR rrt star
This commit is contained in:
Atsushi Sakai
@@ -6,19 +6,20 @@ author: Atsushi Sakai (@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 scipy.linalg as la
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import math
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import random
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show_animation = True
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import matplotlib.pyplot as plt
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import numpy as np
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import scipy.linalg as la
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SHOW_ANIMATION = True
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MAX_TIME = 100.0 # Maximum simulation time
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DT = 0.1 # Time tick
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def LQRplanning(sx, sy, gx, gy):
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def LQRplanning(sx, sy, gx, gy, show_animation=SHOW_ANIMATION):
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rx, ry = [sx], [sy]
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@@ -129,7 +130,7 @@ def main():
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rx, ry = LQRplanning(sx, sy, gx, gy)
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if show_animation: # pragma: no cover
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if SHOW_ANIMATION: # pragma: no cover
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plt.plot(sx, sy, "or")
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plt.plot(gx, gy, "ob")
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plt.plot(rx, ry, "-r")
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@@ -5,117 +5,164 @@ 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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import copy
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import math
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import random
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import sys
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import os
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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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import LQRplanner
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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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LQRplanner.show_animation = False
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STEP_SIZE = 0.05 # step size of local path
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XYTH = 0.5 # [m] acceptance xy distance in final paths
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class RRT():
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class LQRRRTStar(RRTStar):
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"""
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Class for RRT Planning
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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, obstacleList, randArea,
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goalSampleRate=10, maxIter=200):
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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: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])
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self.end = Node(goal[0], goal[1])
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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])
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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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def planning(self, animation=True):
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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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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.get_nearest_index(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.check_collision(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.draw_graph(rnd=rnd)
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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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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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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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path = self.gen_final_course(lastIndex)
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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 = LQRplanner.LQRplanning(
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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 choose_parent(self, newNode, nearinds):
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if not nearinds:
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return newNode
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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.check_collision(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 sample_path(self, wx, wy, step):
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px, py, clen = [], [], []
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@@ -129,160 +176,30 @@ class RRT():
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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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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, rnd, nind):
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nearestNode = self.nodeList[nind]
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def steer(self, from_node, to_node):
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wx, wy = LQRplanner.LQRplanning(
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nearestNode.x, nearestNode.y, rnd.x, rnd.y)
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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, clen = self.sample_path(wx, wy, STEP_SIZE)
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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(nearestNode)
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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 clen])
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newNode.parent = nind
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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 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]
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node = Node(rnd[0], rnd[1])
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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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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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if not goalinds:
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return None
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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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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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dlist = [(node.x - newNode.x) ** 2
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+ (node.y - newNode.y) ** 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.check_collision(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 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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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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plt.plot(self.start.x, self.start.y, "or")
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plt.plot(self.end.x, self.end.y, "or")
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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 get_nearest_index(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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for node in nodeList]
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minind = dlist.index(min(dlist))
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return minind
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def check_collision(self, node, obstacleList):
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px = np.array(node.path_x)
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py = np.array(node.path_y)
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for (ox, oy, size) in obstacleList:
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dx = ox - px
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dy = oy - py
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d = dx ** 2 + dy ** 2
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dmin = min(d)
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if dmin <= 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):
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self.x = x
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self.y = y
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self.path_x = []
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self.path_y = []
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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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print("Start " + __file__)
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@@ -301,14 +218,14 @@ def main(maxIter=200):
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start = [0.0, 0.0]
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goal = [6.0, 7.0]
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rrt = RRT(start, goal, randArea=[-2.0, 15.0],
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obstacleList=obstacleList,
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maxIter=maxIter)
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path = rrt.planning(animation=show_animation)
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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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rrt.draw_graph()
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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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@@ -213,7 +213,7 @@ class RRTStarReedsShepp(RRTStar):
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return path
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def main():
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def main(max_iter=200):
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print("Start " + __file__)
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# ====Search Path with RRT====
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@@ -235,7 +235,7 @@ def main():
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rrt_star_reeds_shepp = RRTStarReedsShepp(start, goal,
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obstacleList,
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[-2.0, 15.0])
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[-2.0, 15.0], max_iter=max_iter)
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path = rrt_star_reeds_shepp.planning(animation=show_animation)
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# Draw final path
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||||
@@ -1,7 +1,7 @@
|
||||
import os
|
||||
import sys
|
||||
from unittest import TestCase
|
||||
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__))
|
||||
+ "/../PathPlanning/RRTStarReedsShepp/")
|
||||
sys.path.append(os.path.dirname(os.path.abspath(__file__))
|
||||
@@ -20,7 +20,7 @@ class Test(TestCase):
|
||||
|
||||
def test1(self):
|
||||
m.show_animation = False
|
||||
m.main(maxIter=5)
|
||||
m.main(max_iter=5)
|
||||
|
||||
|
||||
if __name__ == '__main__': # pragma: no cover
|
||||
|
||||
Reference in New Issue
Block a user