mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
128 lines
3.4 KiB
Python
128 lines
3.4 KiB
Python
"""
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Best-First Searching
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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 math
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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 BestFirst:
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def __init__(self, s_start, s_goal):
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self.s_start, self.s_goal = s_start, s_goal
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self.Env = env.Env()
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self.plotting = plotting.Plotting(self.s_start, self.s_goal)
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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.OPEN = queue.QueuePrior() # OPEN set
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self.OPEN.put(self.s_start, self.Heuristic(self.s_start))
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self.CLOSED = [] # CLOSED set / visited order
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self.PARENT = {self.s_start: self.s_start}
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def searching(self):
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"""
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Best-first Searching
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:return: planning path, visited order
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"""
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while self.OPEN:
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s = self.OPEN.get()
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if s == self.s_goal:
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break
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self.CLOSED.append(s)
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for s_n in self.get_neighbor(s):
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if s_n not in self.PARENT: # node not explored
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self.OPEN.put(s_n, self.Heuristic(s_n))
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self.PARENT[s_n] = s
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return self.extract_path(), self.CLOSED
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def Heuristic(self, s):
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"""
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estimated distance between current state and goal state.
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:param s: current state
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:return: estimated distance
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"""
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h = math.hypot(s[0] - self.s_goal[0], s[1] - self.s_goal[1])
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return h
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def get_neighbor(self, s):
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"""
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find neighbors of state s that not in obstacles.
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:param s: state
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:return: neighbors
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"""
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s_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(2)])
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if not self.is_collision(s, s_next):
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s_list.append(s_next)
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return s_list
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def is_collision(self, s_start, s_end):
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if s_start in self.obs or s_end in self.obs:
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return True
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if s_start[0] != s_end[0] and s_start[1] != s_end[1]:
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if s_end[0] - s_start[0] == s_start[1] - s_end[1]:
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s1 = (min(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
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s2 = (max(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
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else:
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s1 = (min(s_start[0], s_end[0]), max(s_start[1], s_end[1]))
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s2 = (max(s_start[0], s_end[0]), min(s_start[1], s_end[1]))
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if s1 in self.obs or s2 in self.obs:
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return True
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return False
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def extract_path(self):
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"""
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Extract the path based on the relationship of nodes.
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:return: The planning path
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"""
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path = [self.s_goal]
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s = self.s_goal
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while True:
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s = self.PARENT[s]
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path.append(s)
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if s == self.s_start:
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break
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return list(path)
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def main():
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s_start = (5, 5)
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s_goal = (45, 25)
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BF = BestFirst(s_start, s_goal)
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plot = plotting.Plotting(s_start, s_goal)
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path, visited = BF.searching()
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plot.animation(path, visited, "Best-first Searching") # animation
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if __name__ == '__main__':
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main()
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