Path_Planning_Algo_with_Animations/Search-based Planning/dfs.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: huiming zhou
"""

import queue
import tools
import env
import motion_model

class DFS:
    """
    DFS -> Depth-first Searching
    """

    def __init__(self, x_start, x_goal, x_range, y_range):
        self.u_set = motion_model.motions                       # feasible input set
        self.xI, self.xG = x_start, x_goal
        self.x_range, self.y_range = x_range, y_range
        self.obs = env.obs_map()                                # position of obstacles

        env.show_map(self.xI, self.xG, self.obs, "depth-first searching")

    def searching(self):
        """
        Searching using DFS.

        :return: planning path, action in each node, visited nodes in the planning process
        """

        q_dfs = queue.QueueLIFO()                               # last-in-first-out queue
        q_dfs.put(self.xI)
        parent = {self.xI: self.xI}                             # record parents of nodes
        action = {self.xI: (0, 0)}                              # record actions of nodes

        while not q_dfs.empty():
            x_current = q_dfs.get()
            if x_current == self.xG:
                break
            if x_current != self.xI:
                tools.plot_dots(x_current, len(parent))
            for u_next in self.u_set:                                   # explore neighborhoods of current node
                x_next = tuple([x_current[i] + u_next[i] for i in range(len(x_current))])
                if x_next not in parent and x_next not in self.obs:     # node not visited and not in obstacles
                    q_dfs.put(x_next)
                    parent[x_next] = x_current
                    action[x_next] = u_next
        [path_dfs, action_dfs] = tools.extract_path(self.xI, self.xG, parent, action)
        return path_dfs, action_dfs


if __name__ == '__main__':
    x_Start = (5, 5)                # Starting node
    x_Goal = (49, 5)                # Goal node
    dfs = DFS(x_Start, x_Goal, env.x_range, env.y_range)
    [path_dfs, action_dfs] = dfs.searching()
    tools.showPath(x_Start, x_Goal, path_dfs)