mirror of
https://github.com/zhm-real/PathPlanning.git
synced 2026-08-30 00:50:46 +08:00
zhm-real
188 lines
6.1 KiB
Python
188 lines
6.1 KiB
Python
"""
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Bidirectional_a_star 2D
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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 BidirectionalAstar:
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def __init__(self, s_start, s_goal, heuristic_type):
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self.s_start, self.s_goal = s_start, s_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.g_fore = {self.s_start: 0, self.s_goal: float("inf")} # cost to come: from s_start
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self.g_back = {self.s_goal: 0, self.s_start: float("inf")} # cost to come: form s_goal
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self.OPEN_fore = queue.QueuePrior() # OPEN set for foreward searching
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self.OPEN_fore.put(self.s_start,
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self.g_fore[self.s_start] + self.h(self.s_start, self.s_goal))
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self.OPEN_back = queue.QueuePrior() # OPEN set for backward searching
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self.OPEN_back.put(self.s_goal,
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self.g_back[self.s_goal] + self.h(self.s_goal, self.s_start))
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self.CLOSED_fore = [] # CLOSED set for foreward
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self.CLOSED_back = [] # CLOSED set for backward
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self.PARENT_fore = {self.s_start: self.s_start}
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self.PARENT_back = {self.s_goal: self.s_goal}
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def searching(self):
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s_meet = self.s_start
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while self.OPEN_fore and self.OPEN_back:
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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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self.CLOSED_fore.append(s_fore)
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for s_n in self.get_neighbor(s_fore):
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new_cost = self.g_fore[s_fore] + self.cost(s_fore, s_n)
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if s_n not in self.g_fore:
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self.g_fore[s_n] = float("inf")
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if new_cost < self.g_fore[s_n]:
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self.g_fore[s_n] = new_cost
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self.PARENT_fore[s_n] = s_fore
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self.OPEN_fore.put(s_n, new_cost + self.h(s_n, self.s_goal))
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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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self.CLOSED_back.append(s_back)
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for s_n in self.get_neighbor(s_back):
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new_cost = self.g_back[s_back] + self.cost(s_back, s_n)
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if s_n not in self.g_back:
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self.g_back[s_n] = float("inf")
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if new_cost < self.g_back[s_n]:
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self.g_back[s_n] = new_cost
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self.PARENT_back[s_n] = s_back
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self.OPEN_back.put(s_n, new_cost + self.h(s_n, self.s_start))
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return self.extract_path(s_meet), self.CLOSED_fore, self.CLOSED_back
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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 = set()
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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 s_next not in self.obs:
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s_list.add(s_next)
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return s_list
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def extract_path(self, s_meet):
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"""
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extract path from start and goal
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:param s_meet: meet point of bi-direction a*
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:return: path
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"""
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# extract path for foreward part
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path_fore = [s_meet]
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s = s_meet
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while True:
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s = self.PARENT_fore[s]
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path_fore.append(s)
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if s == self.s_start:
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break
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# extract path for backward part
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path_back = []
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s = s_meet
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while True:
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s = self.PARENT_back[s]
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path_back.append(s)
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if s == self.s_goal:
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break
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return list(reversed(path_fore)) + list(path_back)
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def h(self, s, goal):
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"""
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Calculate heuristic value.
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:param s: current node (state)
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:param goal: goal node (state)
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:return: heuristic value
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"""
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heuristic_type = self.heuristic_type
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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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else:
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return math.hypot(goal[0] - s[0], goal[1] - s[1])
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def cost(self, s_start, s_goal):
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"""
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Calculate cost for this motion
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:param s_start: starting node
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:param s_goal: end node
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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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if self.is_collision(s_start, s_goal):
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return float("inf")
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return math.hypot(s_goal[0] - s_start[0], s_goal[1] - s_start[1])
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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 main():
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x_start = (5, 5)
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x_goal = (45, 25)
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bastar = BidirectionalAstar(x_start, x_goal, "euclidean")
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plot = plotting.Plotting(x_start, x_goal)
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path, visited_fore, visited_back = bastar.searching()
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plot.animation_bi_astar(path, visited_fore, visited_back, "Bidirectional-A*") # animation
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if __name__ == '__main__':
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main()
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