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+"""
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+LRTA_star 2D
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+@author: huiming zhou
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+"""
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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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+
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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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+
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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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+
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+
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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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+
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+ self.Env = env.Env() # class Env
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+
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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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+
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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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+
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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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+
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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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+
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+ while True:
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+ s = parent[s]
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+ path.append(s)
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+
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+ if s == self.xI:
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+ break
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+
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+ return path
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+
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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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+
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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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+
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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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+
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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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+
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+ return 1
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+
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+
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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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+
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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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+
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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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+
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+
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+if __name__ == '__main__':
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+ main()
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yue qi