mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-29 16:40:46 +08:00
yue qi
122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
# this is the three dimensional near-sighted 1 neighborhood LRTA* 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.Astar3D import Weighted_A_star
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from Search_3D.utils3D import getAABB, getDist, getRay, StateSpace, Heuristic, getNearest, isCollide, hash3D, dehash, cost
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from Search_3D.plot_util3D import visualization
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import queue
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# class LRT_A_star1(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],[-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)
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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 # this is g
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# self.OPEN = queue.QueuePrior()
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# self.h = Heuristic(self.Space,self.goal) # 1. initialize heuristic h = h0
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# self.Child = {}
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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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# 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 step(self, xi, strxi):
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# childs = self.children(xi) # 4. generate depth 1 neighborhood S(s,1) = {s' in S | norm(s,s') = 1}
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# fvals = [cost(xi,i) + self.h[hash3D(i)] for i in childs]
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# xj , fmin = childs[np.argmin(fvals)], min(fvals) # 5. compute h'(s) = min(dist(s,s') + h(s'))
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# strxj = hash3D(xj)
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# # add the child of xi
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# self.Child[strxi] = xj
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# if fmin >= self.h[strxi]: # 6. if h'(s) > h(s) then update h(s) = h'(s)
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# self.h[strxi] = fmin
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# # TODO: action to move to xj
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# self.OPEN.put(strxj, self.h[strxj]) # 7. update current state s = argmin (dist(s,s') + h(s'))
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# def run(self):
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# x0 = hash3D(self.start)
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# xt = hash3D(self.goal)
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# self.OPEN.put(x0, self.Space[x0] + self.h[x0]) # 2. reset the current state
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# self.ind = 0
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# while xt not in self.CLOSED and self.OPEN: # 3. while s not in Sg do
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# strxi = self.OPEN.get()
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# xi = dehash(strxi)
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# self.CLOSED.add(strxi)
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# self.V.append(xi)
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# visualization(self)
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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.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 path(self):
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# # this is a suboptimal path.
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# path = []
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# strgoal = hash3D(self.goal)
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# strx = hash3D(self.start)
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# ind = 0
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# while strx != strgoal:
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# path.append([dehash(strx),self.Child[strx]])
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# strx = hash3D(self.Child[strx])
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# ind += 1
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# if ind == 1000:
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# return np.flip(path,axis=0)
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# path = np.flip(path,axis=0)
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# return path
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class LRT_A_star2():
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def __init__(self,resolution=0.5, N=7):
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self.lookahead = N
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self.Astar = Weighted_A_star()
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self.Astar.env.resolution = resolution
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def expand(self):
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self.Astar.run(self.lookahead)
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def updateHeuristic(self):
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for strxi in self.Astar.CLOSED:
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self.Astar.h[strxi] = np.inf
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xi = dehash(strxi)
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self.Astar.h[strxi] = min([cost(xi,xj) + self.Astar.h[hash3D(xj)] for xj in self.Astar.children(xi)])
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def move(self):
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print(np.argmin([j[0] for j in self.Astar.OPEN.enumerate()]))
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def run(self):
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xt = hash3D(self.Astar.goal)
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while xt not in self.Astar.CLOSED:
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self.expand()
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#self.updateHeuristic()
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if __name__ == '__main__':
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T = LRT_A_star2(resolution = 1, N = 2)
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T.run()
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