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230 lines
6.8 KiB
230 lines
6.8 KiB
"""
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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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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_2D import plotting, 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 = s_start
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self.s_goal = 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.OPEN_fore = [] # OPEN set for forward searching
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self.OPEN_back = [] # OPEN set for backward searching
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self.CLOSED_fore = [] # CLOSED set for forward
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self.CLOSED_back = [] # CLOSED set for backward
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self.PARENT_fore = dict() # recorded parent for forward
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self.PARENT_back = dict() # recorded parent for backward
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self.g_fore = dict() # cost to come for forward
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self.g_back = dict() # cost to come for backward
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def init(self):
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"""
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initialize parameters
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"""
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self.g_fore[self.s_start] = 0.0
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self.g_fore[self.s_goal] = math.inf
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self.g_back[self.s_goal] = 0.0
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self.g_back[self.s_start] = math.inf
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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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heapq.heappush(self.OPEN_fore,
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(self.f_value_fore(self.s_start), self.s_start))
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heapq.heappush(self.OPEN_back,
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(self.f_value_back(self.s_goal), self.s_goal))
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def searching(self):
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"""
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Bidirectional A*
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:return: connected path, visited order of forward, visited order of backward
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"""
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self.init()
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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 = heapq.heappop(self.OPEN_fore)
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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] = math.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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heapq.heappush(self.OPEN_fore,
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(self.f_value_fore(s_n), s_n))
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# solve backward-search
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_, s_back = heapq.heappop(self.OPEN_back)
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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] = math.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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heapq.heappush(self.OPEN_back,
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(self.f_value_back(s_n), s_n))
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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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return [(s[0] + u[0], s[1] + u[1]) for u in self.u_set]
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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 f_value_fore(self, s):
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"""
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forward searching: f = g + h. (g: Cost to come, h: heuristic value)
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:param s: current state
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:return: f
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"""
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return self.g_fore[s] + self.h(s, self.s_goal)
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def f_value_back(self, s):
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"""
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backward searching: f = g + h. (g: Cost to come, h: heuristic value)
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:param s: current state
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:return: f
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"""
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return self.g_back[s] + self.h(s, self.s_start)
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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 math.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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"""
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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 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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