mirror of
https://github.com/zhm-real/PathPlanning.git
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zhm-real
88 lines
3.3 KiB
Python
88 lines
3.3 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 Astar:
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def __init__(self, x_start, x_goal, heuristic_type):
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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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self.heuristic_type = heuristic_type
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env.show_map(self.xI, self.xG, self.obs, "a_star searching")
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def searching(self):
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"""
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Searching using A_star.
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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_astar = queue.QueuePrior() # priority queue
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q_astar.put(self.xI, 0)
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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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cost = {self.xI: 0}
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while not q_astar.empty():
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x_current = q_astar.get()
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if x_current == self.xG: # stop condition
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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 self.obs:
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new_cost = cost[x_current] + self.get_cost(x_current, u_next)
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if x_next not in cost or new_cost < cost[x_next]: # conditions for updating cost
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cost[x_next] = new_cost
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priority = new_cost + self.Heuristic(x_next, self.xG, self.heuristic_type)
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q_astar.put(x_next, priority) # put node into queue using priority "f+h"
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parent[x_next] = x_current
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action[x_next] = u_next
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[path_astar, actions_astar] = tools.extract_path(self.xI, self.xG, parent, action)
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return path_astar, actions_astar
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def get_cost(self, x, u):
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"""
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Calculate cost for this motion
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:param x: current node
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:param u: input
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:return: cost for this motion
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:note: cost function could be more complicate!
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"""
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return 1
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def Heuristic(self, state, goal, heuristic_type):
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"""
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Calculate heuristic.
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:param state: current node (state)
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:param goal: goal node (state)
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:param heuristic_type: choosing different heuristic functions
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:return: heuristic
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"""
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if heuristic_type == "manhattan":
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return abs(goal[0] - state[0]) + abs(goal[1] - state[1])
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elif heuristic_type == "euclidean":
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return ((goal[0] - state[0]) ** 2 + (goal[1] - state[1]) ** 2) ** (1 / 2)
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else:
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print("Please choose right heuristic type!")
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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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astar = Astar(x_Start, x_Goal, "manhattan")
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[path_astar, actions_astar] = astar.searching()
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tools.showPath(x_Start, x_Goal, path_astar) # Plot path and visited nodes |