mirror of
https://github.com/zhm-real/PathPlanning.git
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zhm-real
132 lines
3.7 KiB
Python
132 lines
3.7 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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import heapq
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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_based_Planning.Search_2D import plotting, 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 = s_start
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self.s_goal = 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 = [] # OPEN set: visited nodes
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self.CLOSED = [] # CLOSED set / visited order
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self.PARENT = dict() # recorded parent
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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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self.PARENT[self.s_start] = self.s_start
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heapq.heappush(self.OPEN,
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(self.heuristic(self.s_start), self.s_start))
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while self.OPEN:
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_, s = heapq.heappop(self.OPEN)
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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 self.is_collision(s, s_n):
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continue
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if s_n not in self.PARENT: # node not explored
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heapq.heappush(self.OPEN, (self.heuristic(s_n), 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: Euclidean distance
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"""
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return math.hypot(s[0] - self.s_goal[0], s[1] - self.s_goal[1])
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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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return [(s[0] + u[0], s[1] + u[1]) for u in self.u_set]
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def is_collision(self, s_start, s_end):
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"""
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check if the line segment (s_start, s_end) is collision.
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:param s_start: start node
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:param s_end: end node
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:return: True: is collision / False: not collision
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"""
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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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