mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
118 lines
3.1 KiB
Python
118 lines
3.1 KiB
Python
"""
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LRTA_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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import matplotlib.pyplot as plt
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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 LrtAstar:
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def __init__(self, x_start, x_goal, 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.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.h(self.xI))
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self.CLOSED = set()
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self.Parent = {self.xI: self.xI}
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def searching(self):
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h = {self.xI: self.h(self.xI)}
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s = self.xI
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parent = {self.xI: self.xI}
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visited = []
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count = 0
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while s != self.xG:
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count += 1
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print(count)
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visited.append(s)
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h_list = {}
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for u in self.u_set:
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s_next = tuple([s[i] + u[i] for i in range(len(s))])
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if s_next not in self.obs:
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if s_next not in h:
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h[s_next] = self.h(s_next)
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h_list[s_next] = self.get_cost(s, s_next) + h[s_next]
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h_new = min(h_list.values())
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if h_new > h[s]:
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h[s] = h_new
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s_child = min(h_list, key=h_list.get)
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parent[s_child] = s
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s = s_child
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# path_get = self.extract_path(parent)
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return [], visited
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def extract_path(self, parent):
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path = [self.xG]
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s = self.xG
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while True:
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s = parent[s]
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path.append(s)
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if s == self.xI:
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break
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return path
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def h(self, s):
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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] - s[0]) + abs(goal[1] - s[1])
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elif heuristic_type == "euclidean":
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return ((goal[0] - s[0]) ** 2 + (goal[1] - s[1]) ** 2) ** (1 / 2)
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else:
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print("Please choose right heuristic type!")
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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 main():
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x_start = (10, 5) # Starting node
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x_goal = (45, 25) # Goal node
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lrtastar = LrtAstar(x_start, x_goal, "manhattan")
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plot = plotting.Plotting(x_start, x_goal) # class Plotting
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path, visited = lrtastar.searching()
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pathx = [x[0] for x in path]
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pathy = [x[1] for x in path]
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vx = [x[0] for x in visited]
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vy = [x[1] for x in visited]
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plot.plot_grid("test")
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plt.plot(pathx, pathy, 'r')
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plt.plot(vx, vy, 'gray')
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plt.show()
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if __name__ == '__main__':
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main()
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