mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
yue qi
111 lines
4.4 KiB
Python
111 lines
4.4 KiB
Python
# this is the three dimensional bidirectional A* algo
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# !/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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@author: yue qi
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from collections import defaultdict
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import os
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import sys
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Search-based Planning/")
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from Search_3D.env3D import env
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from Search_3D.utils3D import getDist, getRay, g_Space, Heuristic, getNearest, isCollide, cost, children, heuristic_fun
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from Search_3D.plot_util3D import visualization
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import queue
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class Weighted_A_star(object):
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def __init__(self,resolution=0.5):
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self.Alldirec = {(1, 0, 0): 1, (0, 1, 0): 1, (0, 0, 1): 1, \
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(-1, 0, 0): 1, (0, -1, 0): 1, (0, 0, -1): 1, \
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(1, 1, 0): np.sqrt(2), (1, 0, 1): np.sqrt(2), (0, 1, 1): np.sqrt(2), \
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(-1, -1, 0): np.sqrt(2), (-1, 0, -1): np.sqrt(2), (0, -1, -1): np.sqrt(2), \
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(1, -1, 0): np.sqrt(2), (-1, 1, 0): np.sqrt(2), (1, 0, -1): np.sqrt(2), \
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(-1, 0, 1): np.sqrt(2), (0, 1, -1): np.sqrt(2), (0, -1, 1): np.sqrt(2), \
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(1, 1, 1): np.sqrt(3), (-1, -1, -1) : np.sqrt(3), \
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(1, -1, -1): np.sqrt(3), (-1, 1, -1): np.sqrt(3), (-1, -1, 1): np.sqrt(3), \
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(1, 1, -1): np.sqrt(3), (1, -1, 1): np.sqrt(3), (-1, 1, 1): np.sqrt(3)}
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self.env = env(resolution = resolution)
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self.start, self.goal = tuple(self.env.start), tuple(self.env.goal)
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self.g = {self.start:0,self.goal:0}
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self.OPEN1 = queue.MinheapPQ() # store [point,priority]
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self.OPEN2 = queue.MinheapPQ()
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self.Parent1, self.Parent2 = {}, {}
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self.CLOSED1, self.CLOSED2 = set(), set()
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self.V = []
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self.done = False
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self.Path = []
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def run(self):
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x0, xt = self.start, self.goal
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self.OPEN1.put(x0, self.g[x0] + heuristic_fun(self,x0,xt)) # item, priority = g + h
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self.OPEN2.put(xt, self.g[xt] + heuristic_fun(self,xt,x0)) # item, priority = g + h
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self.ind = 0
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while not self.CLOSED1.intersection(self.CLOSED2): # while xt not reached and open is not empty
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xi1, xi2 = self.OPEN1.get(), self.OPEN2.get()
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self.CLOSED1.add(xi1) # add the point in CLOSED set
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self.CLOSED2.add(xi2)
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self.V.append(xi1)
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self.V.append(xi2)
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# visualization(self)
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allchild1, allchild2 = children(self,xi1), children(self,xi2)
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self.evaluation(allchild1,xi1,conf=1)
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self.evaluation(allchild2,xi2,conf=2)
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if self.ind % 100 == 0: print('iteration number = '+ str(self.ind))
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self.ind += 1
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self.common = self.CLOSED1.intersection(self.CLOSED2)
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self.done = True
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self.Path = self.path()
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visualization(self)
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plt.show()
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def evaluation(self, allchild, xi, conf):
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for xj in allchild:
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if conf == 1:
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if xj not in self.CLOSED1:
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if xj not in self.g:
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self.g[xj] = np.inf
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else:
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pass
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gi = self.g[xi]
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a = gi + cost(self,xi,xj)
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if a < self.g[xj]:
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self.g[xj] = a
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self.Parent1[xj] = xi
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self.OPEN1.put(xj, a+1*heuristic_fun(self,xj,self.goal))
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if conf == 2:
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if xj not in self.CLOSED2:
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if xj not in self.g:
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self.g[xj] = np.inf
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else:
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pass
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gi = self.g[xi]
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a = gi + cost(self,xi,xj)
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if a < self.g[xj]:
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self.g[xj] = a
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self.Parent2[xj] = xi
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self.OPEN2.put(xj, a+1*heuristic_fun(self,xj,self.start))
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def path(self):
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# TODO: fix path
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path = []
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goal = self.goal
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start = self.start
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x = list(self.common)[0]
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while x != start:
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path.append([x,self.Parent1[x]])
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x = self.Parent1[x]
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x = list(self.common)[0]
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while x != goal:
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path.append([x,self.Parent2[x]])
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x = self.Parent2[x]
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path = np.flip(path,axis=0)
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return path
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if __name__ == '__main__':
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Astar = Weighted_A_star(0.5)
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Astar.run() |