Path_Planning_Algo_with_Animations/Search-based Planning/Search_3D/Anytime_Dstar3D.py

# check paper of 
# [Likhachev2005]
import numpy as np
import matplotlib.pyplot as plt

import os
import sys
from collections import defaultdict

sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Search-based Planning/")
from Search_3D.env3D import env
from Search_3D.utils3D import getDist, heuristic_fun, getNearest, isinbound, \
     cost, children, StateSpace
from Search_3D.plot_util3D import visualization
from Search_3D import queue
import time

class Anytime_Dstar(object):

    def __init__(self, resolution=1):
        self.Alldirec = {(1, 0, 0): 1, (0, 1, 0): 1, (0, 0, 1): 1, \
                        (-1, 0, 0): 1, (0, -1, 0): 1, (0, 0, -1): 1, \
                        (1, 1, 0): np.sqrt(2), (1, 0, 1): np.sqrt(2), (0, 1, 1): np.sqrt(2), \
                        (-1, -1, 0): np.sqrt(2), (-1, 0, -1): np.sqrt(2), (0, -1, -1): np.sqrt(2), \
                        (1, -1, 0): np.sqrt(2), (-1, 1, 0): np.sqrt(2), (1, 0, -1): np.sqrt(2), \
                        (-1, 0, 1): np.sqrt(2), (0, 1, -1): np.sqrt(2), (0, -1, 1): np.sqrt(2), \
                        (1, 1, 1): np.sqrt(3), (-1, -1, -1) : np.sqrt(3), \
                        (1, -1, -1): np.sqrt(3), (-1, 1, -1): np.sqrt(3), (-1, -1, 1): np.sqrt(3), \
                        (1, 1, -1): np.sqrt(3), (1, -1, 1): np.sqrt(3), (-1, 1, 1): np.sqrt(3)}
        self.env = env(resolution=resolution)
        self.settings = 'CollisionChecking' # for collision checking
        self.x0, self.xt = tuple(self.env.start), tuple(self.env.goal)
        self.OPEN = queue.MinheapPQ()
        self.km = 0
        self.g = {} # all g initialized at inf
        self.rhs = {self.xt:0} # rhs(x0) = 0
        self.h = {}
        self.OPEN.put(self.xt, self.key(self.xt))
        self.INCONS = set()
        self.CLOSED = set()
        
        # init children set:
        self.CHILDREN = {}
        # init cost set
        self.COST = defaultdict(lambda: defaultdict(dict))
        
        # for visualization
        self.V = set()  # vertice in closed
        self.ind = 0
        self.Path = []
        self.done = False

    def getcost(self, xi, xj):
        # use a LUT for getting the costd
        if xi not in self.COST:
            for (xj,xjcost) in children(self, xi, settings=1):
                self.COST[xi][xj] = cost(self, xi, xj, xjcost)
        # this might happen when there is a node changed. 
        if xj not in self.COST[xi]:
            self.COST[xi][xj] = cost(self, xi, xj)
        return self.COST[xi][xj]

    def getchildren(self, xi):
        if xi not in self.CHILDREN:
            allchild = children(self, xi)
            self.CHILDREN[xi] = set(allchild)
        return self.CHILDREN[xi]

    def geth(self, xi):
        # when the heurisitic is first calculated
        if xi not in self.h:
            self.h[xi] = heuristic_fun(self, xi, self.x0)
        return self.h[xi]

    def getg(self, xi):
        if xi not in self.g:
            self.g[xi] = np.inf
        return self.g[xi]

    def getrhs(self, xi):
        if xi not in self.rhs:
            self.rhs[xi] = np.inf
        return self.rhs[xi]

#--------------main functions for Anytime D star

    def key(self, s, epsilon=1):
        if self.getg(s) > self.getrhs(s):
            return [self.rhs[s] + epsilon * heuristic_fun(self, s, self.x0), self.rhs[s]]
        else:
            return [self.getg(s) + heuristic_fun(self, s, self.x0), self.getg(s)]

    def UpdateState(self, s):
        if s not in self.CLOSED:
        # TODO if s is not visited before
            self.g[s] = np.inf
        if getDist(s, self.xt) <= self.env.resolution:
            self.rhs[s] = min([self.getcost(s, s_p) + self.getg(s_p) for s_p in self.getchildren(s)]) 
        self.OPEN.check_remove(s)
        if self.getg(s) != self.getrhs(s):
            if s not in self.CLOSED:
                self.OPEN.put(s, self.key(s))
            else:
                self.INCONS.add(s)

    def ComputeorImprovePath(self):
        pass

    def Main(self):
        pass

if __name__ == '__main__':
    AD = Anytime_Dstar(resolution = 1)
    AD.Main()