""" IDA_Star 2D @author: huiming zhou """ import os import sys sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../../Search-based Planning/") from Search_2D import queue from Search_2D import plotting from Search_2D import env class IdaStar: def __init__(self, x_start, x_goal, heuristic_type): self.xI, self.xG = x_start, x_goal self.heuristic_type = heuristic_type self.Env = env.Env() # class Env self.u_set = self.Env.motions # feasible input set self.obs = self.Env.obs # position of obstacles def ida_star(self): bound = self.h(self.xI) path = [self.xI] while True: t = self.searching(path, 0, bound) if t == self.xG: return path if t == float("inf"): return None bound = t def searching(self, path, g, bound): s = path[-1] f = g + self.h(s) if f > bound: return f if s == self.xG: return s res_min = float("inf") for u in self.u_set: s_next = tuple([s[i] + u[i] for i in range(len(s))]) if s_next not in self.obs and s_next not in path: path.append(s_next) t = self.searching(path, g + 1, bound) if t == self.xG: return self.xG if t < res_min: res_min = t path.pop() return res_min def h(self, s): heuristic_type = self.heuristic_type goal = self.xG if heuristic_type == "manhattan": return abs(goal[0] - s[0]) + abs(goal[1] - s[1]) elif heuristic_type == "euclidean": return ((goal[0] - s[0]) ** 2 + (goal[1] - s[1]) ** 2) ** (1 / 2) else: print("Please choose right heuristic type!") def main(): x_start = (5, 5) # Starting node x_goal = (15, 25) # Goal node ida_star = IdaStar(x_start, x_goal, "manhattan") plot = plotting.Plotting(x_start, x_goal) path = ida_star.ida_star() if path: plot.animation(path, [], "IDA_Star") else: print("Path not found!") if __name__ == '__main__': main()