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https://github.com/zhm-real/PathPlanning.git
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zhm-real
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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@author: huiming zhou
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"""
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import queue
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import tools
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import env
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import motion_model
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class DFS:
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"""
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DFS -> Depth-first Searching
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"""
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def __init__(self, x_start, x_goal):
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self.u_set = motion_model.motions # feasible input set
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self.xI, self.xG = x_start, x_goal
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self.obs = env.obs_map() # position of obstacles
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env.show_map(self.xI, self.xG, self.obs, "depth-first searching")
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def searching(self):
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"""
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Searching using DFS.
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:return: planning path, action in each node, visited nodes in the planning process
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"""
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q_dfs = queue.QueueLIFO() # last-in-first-out queue
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q_dfs.put(self.xI)
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parent = {self.xI: self.xI} # record parents of nodes
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action = {self.xI: (0, 0)} # record actions of nodes
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while not q_dfs.empty():
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x_current = q_dfs.get()
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if x_current == self.xG:
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break
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if x_current != self.xI:
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tools.plot_dots(x_current, len(parent))
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for u_next in self.u_set: # explore neighborhoods of current node
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x_next = tuple([x_current[i] + u_next[i] for i in range(len(x_current))])
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if x_next not in parent and x_next not in self.obs: # node not visited and not in obstacles
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q_dfs.put(x_next)
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parent[x_next] = x_current
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action[x_next] = u_next
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[path_dfs, action_dfs] = tools.extract_path(self.xI, self.xG, parent, action)
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return path_dfs, action_dfs
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if __name__ == '__main__':
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x_Start = (5, 5) # Starting node
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x_Goal = (49, 5) # Goal node
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dfs = DFS(x_Start, x_Goal)
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[path_dfs, action_dfs] = dfs.searching()
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tools.showPath(x_Start, x_Goal, path_dfs) |