mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
132 lines
3.6 KiB
Python
132 lines
3.6 KiB
Python
"""
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A_star 2D
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@author: huiming zhou
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"""
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import os
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import sys
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
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"/../../Search-based Planning/")
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from Search_2D import queue
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from Search_2D import plotting
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from Search_2D import env
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class Astar:
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def __init__(self, x_start, x_goal, e, heuristic_type):
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self.xI, self.xG = x_start, x_goal
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self.heuristic_type = heuristic_type
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self.Env = env.Env() # class Env
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self.e = e
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self.u_set = self.Env.motions # feasible input set
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self.obs = self.Env.obs # position of obstacles
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self.g = {self.xI: 0, self.xG: float("inf")}
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self.OPEN = queue.QueuePrior() # priority queue / OPEN
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self.OPEN.put(self.xI, self.fvalue(self.xI))
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self.CLOSED = []
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self.Parent = {self.xI: self.xI}
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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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while not self.OPEN.empty():
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s = self.OPEN.get()
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self.CLOSED.append(s)
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if s == self.xG: # stop condition
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break
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for u_next in self.u_set: # explore neighborhoods of current node
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s_next = tuple([s[i] + u_next[i] for i in range(len(s))])
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if s_next not in self.obs and s_next not in self.CLOSED:
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new_cost = self.g[s] + self.get_cost(s, u_next)
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if s_next not in self.g:
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self.g[s_next] = float("inf")
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if new_cost < self.g[s_next]: # conditions for updating cost
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self.g[s_next] = new_cost
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self.Parent[s_next] = s
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self.OPEN.put(s_next, self.fvalue(s_next))
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return self.extract_path(), self.CLOSED
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def fvalue(self, x):
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h = self.e * self.Heuristic(x)
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return self.g[x] + h
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def extract_path(self):
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"""
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Extract the path based on the relationship of nodes.
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:return: The planning path
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"""
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path_back = [self.xG]
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x_current = self.xG
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while True:
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x_current = self.Parent[x_current]
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path_back.append(x_current)
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if x_current == self.xI:
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break
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return list(path_back)
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@staticmethod
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def get_cost(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):
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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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heuristic_type = self.heuristic_type
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goal = self.xG
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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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def main():
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x_start = (5, 5) # Starting node
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x_goal = (49, 25) # Goal node
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astar = Astar(x_start, x_goal, 1, "euclidean")
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plot = plotting.Plotting(x_start, x_goal) # class Plotting
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fig_name = "A* Algorithm"
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path, visited = astar.searching()
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plot.animation(path, visited, fig_name) # animation generate
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if __name__ == '__main__':
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main()
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