Path_Planning_Algo_with_Animations/Search-based Planning/Search_2D/ARAstar.py

"""
ARA_star 2D (Anytime Repairing A*)
@author: huiming zhou
"""

import os
import sys
import math

sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                "/../../Search-based Planning/")

from Search_2D import plotting
from Search_2D import env


class AraStar:
    def __init__(self, s_start, s_goal, e, heuristic_type):
        self.s_start, self.s_goal = s_start, s_goal
        self.heuristic_type = heuristic_type

        self.Env = env.Env()                                                # class Env

        self.u_set = self.Env.motions                                       # feasible input set
        self.obs = self.Env.obs                                             # position of obstacles
        self.e = e                                                          # initial weight
        self.g = {self.s_start: 0, self.s_goal: float("inf")}               # cost to come

        self.OPEN = {self.s_start: self.fvalue(self.s_start)}               # priority queue / OPEN set
        self.CLOSED = set()                                                 # CLOSED set
        self.INCONS = {}                                                    # INCONS set
        self.PARENT = {self.s_start: self.s_start}                          # relations
        self.path = []                                                      # planning path
        self.visited = []                                                   # order of visited nodes

    def searching(self):
        self.ImprovePath()
        self.path.append(self.extract_path())

        while self.update_e() > 1:                                          # continue condition
            self.e -= 0.5                                                   # increase weight
            self.OPEN.update(self.INCONS)
            for s in self.OPEN:
                self.OPEN[s] = self.fvalue(s)

            self.INCONS = {}
            self.CLOSED = set()
            self.ImprovePath()                                              # improve path
            self.path.append(self.extract_path())

        return self.path, self.visited

    def ImprovePath(self):
        """
        :return: a e'-suboptimal path
        """

        visited_each = []

        while True:
            s, f_small = self.get_smallest_f()
            if self.fvalue(self.s_goal) <= f_small:
                break
            self.CLOSED.add(s)

            for s_n in self.get_neighbor(s):
                new_cost = self.g[s] + self.cost(s, s_n)
                if s_n not in self.g or new_cost < self.g[s_n]:
                    self.g[s_n] = new_cost
                    self.PARENT[s_n] = s
                    visited_each.append(s_n)

                    if s_n not in self.CLOSED:
                        self.OPEN[s_n] = self.fvalue(s_n)
                    else:
                        self.INCONS[s_n] = 0

        self.visited.append(visited_each)

    def get_smallest_f(self):
        """
        :return: node with smallest f_value in OPEN set.
        """
        s_list = {}

        for s in self.OPEN:
            s_list[s] = self.fvalue(s)
        s_small = min(s_list, key=s_list.get)

        self.OPEN.pop(s_small)

        return s_small, s_list[s_small]

    def get_neighbor(self, s):
        """
        find neighbors of state s that not in obstacles.
        :param s: state
        :return: neighbors
        """

        s_list = set()

        for u in self.u_set:
            s_next = tuple([s[i] + u[i] for i in range(2)])
            if s_next not in self.obs:
                s_list.add(s_next)

        return s_list

    def update_e(self):
        v = float("inf")

        if self.OPEN:
            v = min(self.g[s] + self.h(s) for s in self.OPEN)
        if self.INCONS:
            v = min(v, min(self.g[s] + self.h(s) for s in self.INCONS))

        return min(self.e, self.g[self.s_goal] / v)

    def fvalue(self, x):
        return self.g[x] + self.e * self.h(x)

    def extract_path(self):
        """
        Extract the path based on the PARENT set.
        :return: The planning path
        """

        path = [self.s_goal]
        s = self.s_goal

        while True:
            s = self.PARENT[s]
            path.append(s)

            if s == self.s_start:
                break

        return list(path)

    def h(self, s):
        """
        Calculate heuristic.
        :param s: current node (state)
        :return: heuristic function value
        """

        heuristic_type = self.heuristic_type                                # heuristic type
        goal = self.s_goal                                                  # goal node

        if heuristic_type == "manhattan":
            return abs(goal[0] - s[0]) + abs(goal[1] - s[1])
        else:
            return math.hypot(goal[0] - s[0], goal[1] - s[1])

    @staticmethod
    def cost(s_start, s_goal):
        """
        Calculate cost for this motion
        :param s_start: starting node
        :param s_goal: end node
        :return:  cost for this motion
        :note: cost function could be more complicate!
        """

        return 1


def main():
    s_start = (5, 5)
    s_goal = (45, 25)

    arastar = AraStar(s_start, s_goal, 2.5, "euclidean")
    plot = plotting.Plotting(s_start, s_goal)

    path, visited = arastar.searching()
    plot.animation_ara_star(path, visited, "Anytime Repairing A* (ARA*)")


if __name__ == '__main__':
    main()