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223 lines
6.7 KiB
223 lines
6.7 KiB
"""
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ARA_star 2D (Anytime Repairing A*)
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@author: huiming zhou
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@description: local inconsistency: g-value decreased.
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g(s) decreased introduces a local inconsistency between s and its successors.
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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 plotting, env
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class AraStar:
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def __init__(self, s_start, s_goal, e, 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.e = e # weight
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self.g = dict() # Cost to come
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self.OPEN = dict() # priority queue / OPEN set
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self.CLOSED = set() # CLOSED set
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self.INCONS = {} # INCONSISTENT set
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self.PARENT = dict() # relations
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self.path = [] # planning path
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self.visited = [] # order of visited nodes
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def init(self):
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"""
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initialize each set.
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"""
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self.g[self.s_start] = 0.0
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self.g[self.s_goal] = math.inf
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self.OPEN[self.s_start] = self.f_value(self.s_start)
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self.PARENT[self.s_start] = self.s_start
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def searching(self):
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self.init()
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self.ImprovePath()
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self.path.append(self.extract_path())
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while self.update_e() > 1: # continue condition
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self.e -= 0.4 # increase weight
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self.OPEN.update(self.INCONS)
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self.OPEN = {s: self.f_value(s) for s in self.OPEN} # update f_value of OPEN set
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self.INCONS = dict()
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self.CLOSED = set()
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self.ImprovePath() # improve path
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self.path.append(self.extract_path())
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return self.path, self.visited
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def ImprovePath(self):
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"""
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:return: a e'-suboptimal path
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"""
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visited_each = []
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while True:
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s, f_small = self.calc_smallest_f()
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if self.f_value(self.s_goal) <= f_small:
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break
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self.OPEN.pop(s)
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self.CLOSED.add(s)
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for s_n in self.get_neighbor(s):
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if s_n in self.obs:
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continue
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new_cost = self.g[s] + self.cost(s, s_n)
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if s_n not in self.g or new_cost < self.g[s_n]:
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self.g[s_n] = new_cost
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self.PARENT[s_n] = s
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visited_each.append(s_n)
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if s_n not in self.CLOSED:
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self.OPEN[s_n] = self.f_value(s_n)
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else:
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self.INCONS[s_n] = 0.0
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self.visited.append(visited_each)
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def calc_smallest_f(self):
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"""
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:return: node with smallest f_value in OPEN set.
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"""
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s_small = min(self.OPEN, key=self.OPEN.get)
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return s_small, self.OPEN[s_small]
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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 update_e(self):
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v = float("inf")
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if self.OPEN:
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v = min(self.g[s] + self.h(s) for s in self.OPEN)
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if self.INCONS:
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v = min(v, min(self.g[s] + self.h(s) for s in self.INCONS))
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return min(self.e, self.g[self.s_goal] / v)
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def f_value(self, x):
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"""
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f = g + e * h
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f = cost-to-come + weight * cost-to-go
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:param x: current state
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:return: f_value
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"""
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return self.g[x] + self.e * self.h(x)
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def extract_path(self):
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"""
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Extract the path based on the PARENT set.
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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 h(self, s):
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"""
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Calculate heuristic.
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:param s: current node (state)
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:return: heuristic function value
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"""
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heuristic_type = self.heuristic_type # heuristic type
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goal = self.s_goal # goal node
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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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s_start = (5, 5)
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s_goal = (45, 25)
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arastar = AraStar(s_start, s_goal, 2.5, "euclidean")
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plot = plotting.Plotting(s_start, s_goal)
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path, visited = arastar.searching()
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plot.animation_ara_star(path, visited, "Anytime Repairing A* (ARA*)")
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if __name__ == '__main__':
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main()
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