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+"""
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+Bidirectional_a_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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+
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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 BidirectionalAstar:
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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_fore = {self.xI: 0, self.xG: float("inf")}
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+ self.g_back = {self.xG: 0, self.xI: float("inf")}
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+
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+ self.OPEN_fore = queue.QueuePrior()
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+ self.OPEN_fore.put(self.xI, self.g_fore[self.xI] + self.h(self.xI, self.xG))
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+ self.OPEN_back = queue.QueuePrior()
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+ self.OPEN_back.put(self.xG, self.g_back[self.xG] + self.h(self.xG, self.xI))
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+
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+ self.CLOSED_fore = []
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+ self.CLOSED_back = []
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+
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+ self.Parent_fore = {self.xI: self.xI}
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+ self.Parent_back = {self.xG: self.xG}
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+
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+ def searching(self):
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+ visited_fore, visited_back = [], []
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+ s_meet = self.xI
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+
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+ while not self.OPEN_fore.empty() and not self.OPEN_back.empty():
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+
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+ # solve foreward-search
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+ s_fore = self.OPEN_fore.get()
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+ if s_fore in self.Parent_back:
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+ s_meet = s_fore
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+ break
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+ visited_fore.append(s_fore)
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+ for u in self.u_set:
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+ s_next = tuple([s_fore[i] + u[i] for i in range(len(s_fore))])
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+ if s_next not in self.obs:
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+ new_cost = self.g_fore[s_fore] + self.get_cost(s_fore, u)
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+ if s_next not in self.g_fore:
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+ self.g_fore[s_next] = float("inf")
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+ if new_cost < self.g_fore[s_next]:
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+ self.g_fore[s_next] = new_cost
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+ self.Parent_fore[s_next] = s_fore
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+ self.OPEN_fore.put(s_next, new_cost + self.h(s_next, self.xG))
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+
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+ # solve backward-search
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+ s_back = self.OPEN_back.get()
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+ if s_back in self.Parent_fore:
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+ s_meet = s_back
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+ break
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+ visited_back.append(s_back)
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+ for u in self.u_set:
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+ s_next = tuple([s_back[i] + u[i] for i in range(len(s_back))])
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+ if s_next not in self.obs:
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+ new_cost = self.g_back[s_back] + self.get_cost(s_back, u)
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+ if s_next not in self.g_back:
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+ self.g_back[s_next] = float("inf")
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+ if new_cost < self.g_back[s_next]:
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+ self.g_back[s_next] = new_cost
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+ self.Parent_back[s_next] = s_back
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+ self.OPEN_back.put(s_next, new_cost + self.h(s_next, self.xI))
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+
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+ return self.extract_path(s_meet), visited_fore, visited_back
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+
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+ def extract_path(self, s):
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+ path_back_fore = [s]
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+ s_current = s
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+
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+ while True:
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+ s_current = self.Parent_fore[s_current]
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+ path_back_fore.append(s_current)
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+
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+ if s_current == self.xI:
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+ break
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+
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+ path_back_back = []
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+ s_current = s
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+
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+ while True:
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+ s_current = self.Parent_back[s_current]
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+ path_back_back.append(s_current)
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+
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+ if s_current == self.xG:
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+ break
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+
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+ return list(reversed(path_back_fore)) + list(path_back_back)
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+
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+ def h(self, state, goal):
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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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+ :return: heuristic
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+ """
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+
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+ heuristic_type = self.heuristic_type
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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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+
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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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+
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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 = (5, 5) # Starting node
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+ x_goal = (49, 25) # Goal node
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+
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+ bastar = BidirectionalAstar(x_start, x_goal, "euclidean")
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+ plot = plotting.Plotting(x_start, x_goal) # class Plotting
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+
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+ fig_name = "Bidirectional-A* Algorithm"
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+ path, v_fore, v_back = bastar.searching()
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+ plot.animation_bi_astar(path, v_fore, v_back, fig_name) # animation generate
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+
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+
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+if __name__ == '__main__':
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+ main()
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zhm-real
