mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
yue qi
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
# this is the three dimensional LRTA* algo
|
|
# !/usr/bin/env python3
|
|
# -*- coding: utf-8 -*-
|
|
"""
|
|
@author: yue qi
|
|
"""
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
import os
|
|
import sys
|
|
|
|
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Search-based Planning/")
|
|
from Search_3D.env3D import env
|
|
from Search_3D.utils3D import getAABB, getDist, getRay, StateSpace, Heuristic, getNearest, isCollide, hash3D, dehash, cost
|
|
from Search_3D.plot_util3D import visualization
|
|
import queue
|
|
|
|
|
|
class LRT_A_star(object):
|
|
def __init__(self,resolution=0.5):
|
|
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],\
|
|
[-1,0,0],[0,-1,0],[0,0,-1],[-1,-1,0],[-1,0,-1],[0,-1,-1],[-1,-1,-1],\
|
|
[1,-1,0],[-1,1,0],[1,0,-1],[-1,0, 1],[0,1, -1],[0, -1,1],\
|
|
[1,-1,-1],[-1,1,-1],[-1,-1,1],[1,1,-1],[1,-1,1],[-1,1,1]])
|
|
self.env = env(resolution = resolution)
|
|
self.Space = StateSpace(self) # key is the point, store g value
|
|
self.start, self.goal = getNearest(self.Space,self.env.start), getNearest(self.Space,self.env.goal)
|
|
self.AABB = getAABB(self.env.blocks)
|
|
self.Space[hash3D(getNearest(self.Space,self.start))] = 0 # set g(x0) = 0
|
|
self.OPEN = queue.QueuePrior() # store [point,priority]
|
|
self.h = Heuristic(self.Space,self.goal) # initialize heuristic
|
|
self.Child = {}
|
|
self.CLOSED = set()
|
|
self.V = []
|
|
self.done = False
|
|
self.Path = []
|
|
|
|
def children(self,x):
|
|
allchild = []
|
|
for j in self.Alldirec:
|
|
collide,child = isCollide(self,x,j)
|
|
if not collide:
|
|
allchild.append(child)
|
|
return allchild
|
|
|
|
def step(self, xi, strxi):
|
|
childs = self.children(xi) # find all childs within one move
|
|
fvals = [cost(xi,i) + self.h[hash3D(i)] for i in childs]# f = g + h
|
|
xj , fmin = childs[np.argmin(fvals)], min(fvals)
|
|
strxj = hash3D(xj)
|
|
# add the child of xi
|
|
self.Child[strxi] = xj
|
|
if fmin >= self.h[strxi]:
|
|
self.h[strxi] = fmin # update h(xt) to f(xj) if f is greater
|
|
# TODO: action to move to xj
|
|
self.OPEN.put(strxj, fmin+1*self.h[strxj])
|
|
|
|
def run(self):
|
|
x0 = hash3D(self.start)
|
|
xt = hash3D(self.goal)
|
|
self.OPEN.put(x0, self.Space[x0] + self.h[x0]) # item, priority = g + h
|
|
self.ind = 0
|
|
while xt not in self.CLOSED and self.OPEN: # while xt not reached and open is not empty
|
|
strxi = self.OPEN.get()
|
|
xi = dehash(strxi)
|
|
self.CLOSED.add(strxi) # add the point in CLOSED set
|
|
self.V.append(xi)
|
|
visualization(self)
|
|
self.step(xi , strxi)
|
|
if self.ind % 100 == 0: print('iteration number = '+ str(self.ind))
|
|
self.ind += 1
|
|
self.done = True
|
|
self.Path = self.path()
|
|
visualization(self)
|
|
plt.show()
|
|
|
|
def path(self):
|
|
# this is a suboptimal path.
|
|
path = []
|
|
strgoal = hash3D(self.goal)
|
|
strx = hash3D(self.start)
|
|
ind = 0
|
|
while strx != strgoal:
|
|
path.append([dehash(strx),self.Child[strx]])
|
|
strx = hash3D(self.Child[strx])
|
|
ind += 1
|
|
if ind == 1000:
|
|
return np.flip(path,axis=0)
|
|
path = np.flip(path,axis=0)
|
|
return path
|
|
|
|
if __name__ == '__main__':
|
|
Astar = LRT_A_star(0.5)
|
|
Astar.run() |