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@@ -1,6 +1,10 @@
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"""
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ARA_star 2D (Anytime Repairing A*)
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@author: huiming zhou
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+
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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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"""
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import os
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@@ -10,8 +14,7 @@ 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
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-from Search_2D import env
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+from Search_based_Planning.Search_2D import plotting, env
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class AraStar:
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@@ -23,27 +26,37 @@ class AraStar:
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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 # initial weight
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- self.g = {self.s_start: 0, self.s_goal: float("inf")} # Cost to come
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+ self.e = e # weight
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- self.OPEN = {self.s_start: self.fvalue(self.s_start)} # priority queue / OPEN set
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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 = {} # INCONS set
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- self.PARENT = {self.s_start: self.s_start} # relations
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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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+
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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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+
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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 -= 0.5 # increase weight
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+ self.e -= 0.4 # increase weight
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self.OPEN.update(self.INCONS)
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- for s in self.OPEN:
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- self.OPEN[s] = self.fvalue(s)
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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 = {}
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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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@@ -58,38 +71,40 @@ class AraStar:
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visited_each = []
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while True:
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- s, f_small = self.get_smallest_f()
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- if self.fvalue(self.s_goal) <= f_small:
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+ s, f_small = self.calc_smallest_f()
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+
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+ if self.f_value(self.s_goal) <= f_small:
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break
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+
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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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+
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new_cost = self.g[s] + self.cost(s, s_n)
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+
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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.fvalue(s_n)
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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
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+ self.INCONS[s_n] = 0.0
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self.visited.append(visited_each)
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- def get_smallest_f(self):
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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_list = {}
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- for s in self.OPEN:
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- s_list[s] = self.fvalue(s)
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- s_small = min(s_list, key=s_list.get)
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+ s_small = min(self.OPEN, key=self.OPEN.get)
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- self.OPEN.pop(s_small)
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-
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- return s_small, s_list[s_small]
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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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@@ -98,14 +113,7 @@ class AraStar:
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:return: neighbors
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"""
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- s_list = set()
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-
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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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-
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- return s_list
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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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@@ -117,7 +125,14 @@ class AraStar:
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return min(self.e, self.g[self.s_goal] / v)
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- def fvalue(self, x):
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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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+
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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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@@ -163,11 +178,18 @@ class AraStar:
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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.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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+
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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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