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https://github.com/zhm-real/PathPlanning.git
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zhm-real
125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
# this is the three dimensional 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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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 getAABB, getDist, getRay, StateSpace, Heuristic, getNearest, isCollide, hash3D, dehash, \
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cost
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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 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1], [0, 1, 1], [1, 1, 1],
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[-1, 0, 0], [0, -1, 0], [0, 0, -1], [-1, -1, 0], [-1, 0, -1], [0, -1, -1],
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[-1, -1, -1],
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[1, -1, 0], [-1, 1, 0], [1, 0, -1], [-1, 0, 1], [0, 1, -1], [0, -1, 1],
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[1, -1, -1], [-1, 1, -1], [-1, -1, 1], [1, 1, -1], [1, -1, 1], [-1, 1, 1]])
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self.env = env(resolution=resolution)
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self.Space = StateSpace(self) # key is the point, store g value
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self.start, self.goal = getNearest(self.Space, self.env.start), getNearest(self.Space, self.env.goal)
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self.AABB = getAABB(self.env.blocks)
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self.Space[hash3D(getNearest(self.Space, self.start))] = 0 # set g(x0) = 0
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self.h = Heuristic(self.Space, self.goal)
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self.Parent = {}
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self.CLOSED = set()
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self.V = []
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self.done = False
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self.Path = []
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self.ind = 0
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self.x0, self.xt = hash3D(self.start), hash3D(self.goal)
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self.OPEN = queue.QueuePrior() # store [point,priority]
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self.OPEN.put(self.x0, self.Space[self.x0] + self.h[self.x0]) # item, priority = g + h
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self.lastpoint = self.x0
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def children(self, x):
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allchild = []
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for j in self.Alldirec:
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collide, child = isCollide(self, x, j)
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if not collide:
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allchild.append(child)
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return allchild
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def run(self, N=None):
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xt = self.xt
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strxi = self.x0
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while xt not in self.CLOSED and self.OPEN: # while xt not reached and open is not empty
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strxi = self.OPEN.get()
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xi = dehash(strxi)
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if strxi not in self.CLOSED:
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self.V.append(xi)
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self.CLOSED.add(strxi) # add the point in CLOSED set
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visualization(self)
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allchild = self.children(xi)
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for xj in allchild:
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strxj = hash3D(xj)
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if strxj not in self.CLOSED:
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gi, gj = self.Space[strxi], self.Space[strxj]
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a = gi + cost(xi, xj)
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if a < gj:
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self.Space[strxj] = a
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self.Parent[strxj] = xi
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if (a, strxj) in self.OPEN.enumerate():
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# update priority of xj
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self.OPEN.put(strxj, a + 1 * self.h[strxj])
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else:
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# add xj in to OPEN set
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self.OPEN.put(strxj, a + 1 * self.h[strxj])
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# For specified expanded nodes, used primarily in LRTA*
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if N:
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if len(self.CLOSED) % N == 0:
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break
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if self.ind % 100 == 0: print('number node expanded = ' + str(len(self.V)))
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self.ind += 1
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self.lastpoint = strxi
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# if the path finding is finished
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if xt in self.CLOSED:
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self.done = True
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self.Path = self.path()
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if N is None:
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visualization(self)
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plt.show()
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return True
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return False
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def path(self):
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path = []
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strx = self.lastpoint
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# strstart = hash3D(getNearest(self.Space, self.env.start))
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strstart = self.x0
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while strx != strstart:
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path.append([dehash(strx), self.Parent[strx]])
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strx = hash3D(self.Parent[strx])
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# path = np.flip(path, axis=0)
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return path
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# utility used in LRTA*
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def reset(self, xj):
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self.Space = StateSpace(self) # key is the point, store g value
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self.start = xj
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self.Space[hash3D(getNearest(self.Space, self.start))] = 0 # set g(x0) = 0
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self.x0 = hash3D(xj)
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self.OPEN.put(self.x0, self.Space[self.x0] + self.h[self.x0]) # item, priority = g + h
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self.CLOSED = set()
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# self.h = Heuristic(self.Space, self.goal)
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if __name__ == '__main__':
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Astar = Weighted_A_star(1)
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Astar.run()
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