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

"""
LPA_star 2D
@author: huiming zhou
"""

import os
import sys
import math
import matplotlib.pyplot as plt

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

from Search_2D import plotting
from Search_2D import env


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

        self.Env = env.Env()
        self.Plot = plotting.Plotting(self.s_start, self.s_goal)

        self.u_set = self.Env.motions
        self.obs = self.Env.obs
        self.x = self.Env.x_range
        self.y = self.Env.y_range

        self.U = {}
        self.g, self.rhs = {}, {}

        for i in range(self.Env.x_range):
            for j in range(self.Env.y_range):
                self.rhs[(i, j)] = float("inf")
                self.g[(i, j)] = float("inf")

        self.rhs[self.s_start] = 0
        self.U[self.s_start] = self.CalculateKey(self.s_start)

        self.fig = plt.figure()

    def run(self):
        self.Plot.plot_grid("Lifelong Planning A*")

        self.ComputePath()
        self.plot_path(self.extract_path())
        self.fig.canvas.mpl_connect('button_press_event', self.on_press)

        plt.show()

    def on_press(self, event):
        x, y = event.xdata, event.ydata
        if x < 0 or x > self.x - 1 or y < 0 or y > self.y - 1:
            print("Please choose right area!")
        else:
            x, y = int(x), int(y)
            print("Change position: x =", x, ",", "y =", y)
            if (x, y) not in self.obs:
                self.obs.add((x, y))
                plt.plot(x, y, 'sk')
            else:
                self.obs.remove((x, y))
                plt.plot(x, y, marker='s', color='white')
                self.UpdateVertex((x, y))

            for s_n in self.get_neighbor((x, y)):
                self.UpdateVertex(s_n)

            self.ComputePath()
            self.plot_path(self.extract_path())
            self.fig.canvas.draw_idle()

    def ComputePath(self):
        while True:
            s, v = self.TopKey()
            if v >= self.CalculateKey(self.s_goal) and \
                    self.rhs[self.s_goal] == self.g[self.s_goal]:
                break
            self.U.pop(s)

            if self.g[s] > self.rhs[s]:                         # over-consistent: deleted obstacles
                self.g[s] = self.rhs[s]
            else:                                               # under-consistent: added obstacles
                self.g[s] = float("inf")
                self.UpdateVertex(s)
            for s_n in self.get_neighbor(s):
                self.UpdateVertex(s_n)

    def UpdateVertex(self, s):
        if s != self.s_start:
            self.rhs[s] = min(self.g[s_n] + self.cost(s_n, s)
                              for s_n in self.get_neighbor(s))
        if s in self.U:
            self.U.pop(s)

        if self.g[s] != self.rhs[s]:
            self.U[s] = self.CalculateKey(s)

    def TopKey(self):
        """
        :return: return the min key and its value.
        """

        s = min(self.U, key=self.U.get)
        return s, self.U[s]

    def CalculateKey(self, s):
        return [min(self.g[s], self.rhs[s]) + self.h(s),
                min(self.g[s], self.rhs[s])]

    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 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])

    def cost(self, s_start, s_end):
        """
        calculate edge cost: (s_start, s_end)
        :param s_start: start node
        :param s_end: end node
        :return: cost
        """

        # if one of the vertex in obstacles: return infinity.
        if s_start in self.obs or s_end in self.obs:
            return float("inf")

        return 1

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

        path = []
        s = self.s_goal

        for k in range(100):
            g_list = {}
            for x in self.get_neighbor(s):
                g_list[x] = self.g[x]
            s = min(g_list, key=g_list.get)
            if s == self.s_start:
                break
            path.append(s)

        return list(reversed(path))

    @staticmethod
    def plot_path(path):
        px = [x[0] for x in path]
        py = [x[1] for x in path]
        plt.plot(px, py, marker='o')


def main():
    x_start = (5, 5)
    x_goal = (45, 25)

    lpastar = LpaStar(x_start, x_goal, "manhattan")
    lpastar.run()


if __name__ == '__main__':
    main()