mirror of
https://github.com/zhm-real/PathPlanning.git
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zhm-real
97 lines
2.4 KiB
Python
97 lines
2.4 KiB
Python
"""
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IDA_Star 2D (Iteratively Deepening A*)
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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 plotting
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from Search_2D import env
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class IdaStar:
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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.visited = []
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def ida_star(self):
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bound = self.h(self.xI)
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path = [self.xI]
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while True:
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t = self.searching(path, 0, bound)
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if t == self.xG:
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return path, self.visited
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if t == float("inf"):
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return [], self.visited
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bound = t
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def searching(self, path, g, bound):
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s = path[-1]
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self.visited.append(s)
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f = g + self.h(s)
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if f > bound:
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return f
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if s == self.xG:
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return s
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res_min = float("inf")
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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 and s_next not in path:
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path.append(s_next)
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t = self.searching(path, g + 1, bound)
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if t == self.xG:
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return self.xG
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if t < res_min:
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res_min = t
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path.pop()
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return res_min
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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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def main():
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x_start = (5, 5)
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x_goal = (15, 20)
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ida_star = IdaStar(x_start, x_goal, "manhattan")
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plot = plotting.Plotting(x_start, x_goal)
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path, visited = ida_star.ida_star()
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if path:
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plot.plot_grid("Iteratively Deepening A*")
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plot.plot_path(visited, 'gray', True)
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plot.plot_path(path)
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plt.show()
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else:
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print("Path not found!")
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if __name__ == '__main__':
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main()
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