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+"""
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+Field D* 2D
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+@author: huiming zhou
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+"""
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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 matplotlib.pyplot as plt
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+
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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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+
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+from Search_2D import plotting
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+from Search_2D import env
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+
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+
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+class FieldDStar:
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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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+
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+ self.Env = env.Env() # class Env
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+ self.Plot = plotting.Plotting(s_start, s_goal)
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+
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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.x = self.Env.x_range
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+ self.y = self.Env.y_range
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+
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+ self.g, self.rhs, self.U = {}, {}, {}
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+ self.parent = {}
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+
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+ for i in range(self.Env.x_range):
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+ for j in range(self.Env.y_range):
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+ self.rhs[(i, j)] = float("inf")
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+ self.g[(i, j)] = float("inf")
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+ self.parent[(i, j)] = (0, 0)
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+
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+ self.rhs[self.s_goal] = 0.0
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+ self.U[self.s_goal] = self.CalculateKey(self.s_goal)
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+ self.visited = set()
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+ self.count = 0
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+ self.fig = plt.figure()
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+
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+ def run(self):
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+ self.Plot.plot_grid("Field D*")
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+ self.ComputeShortestPath()
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+ self.plot_path(self.extract_path())
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+ self.fig.canvas.mpl_connect('button_press_event', self.on_press)
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+ plt.show()
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+
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+ def on_press(self, event):
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+ x, y = event.xdata, event.ydata
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+ if x < 0 or x > self.x - 1 or y < 0 or y > self.y - 1:
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+ print("Please choose right area!")
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+ else:
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+ x, y = int(x), int(y)
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+ print("Change position: x =", x, ",", "y =", y)
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+ self.visited = set()
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+ self.count += 1
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+ if (x, y) not in self.obs:
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+ self.obs.add((x, y))
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+ plt.plot(x, y, 'sk')
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+ else:
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+ self.obs.remove((x, y))
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+ plt.plot(x, y, marker='s', color='white')
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+ self.UpdateVertex((x, y))
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+
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+ for s_n in self.get_neighbor((x, y)):
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+ self.UpdateVertex(s_n)
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+
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+ self.ComputeShortestPath()
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+ self.plot_visited(self.visited)
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+ self.plot_path(self.extract_path())
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+ self.fig.canvas.draw_idle()
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+
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+ def ComputeShortestPath(self):
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+ while True:
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+ s, v = self.TopKey()
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+ if v >= self.CalculateKey(self.s_start) and \
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+ self.rhs[self.s_start] == self.g[self.s_start]:
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+ break
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+
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+ k_old = v
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+ self.U.pop(s)
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+ self.visited.add(s)
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+
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+ if k_old < self.CalculateKey(s):
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+ self.U[s] = self.CalculateKey(s)
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+ elif self.g[s] > self.rhs[s]:
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+ self.g[s] = self.rhs[s]
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+ for x in self.get_neighbor(s):
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+ self.UpdateVertex(x)
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+ else:
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+ self.g[s] = float("inf")
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+ self.UpdateVertex(s)
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+ for x in self.get_neighbor(s):
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+ self.UpdateVertex(x)
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+
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+ def UpdateVertex(self, s):
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+ if s != self.s_goal:
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+ value = []
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+ s_plist = []
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+ sn_list = self.get_neighbor_pure(s)
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+ sn_list.append(sn_list[0])
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+ for k in range(8):
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+ v, sp = self.ComputeCost(s, sn_list[k], sn_list[k + 1])
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+ value.append(v)
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+ s_plist.append(sp)
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+ self.rhs[s] = min(value)
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+ self.parent[s] = s_plist[value.index(min(value))]
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+
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+ if s in self.U:
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+ self.U.pop(s)
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+
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+ if self.g[s] != self.rhs[s]:
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+ self.U[s] = self.CalculateKey(s)
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+
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+ def get_neighbor_pure(self, s):
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+ s_list = []
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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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+ s_list.append(s_next)
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+
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+ return s_list
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+
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+ def CalculateKey(self, s):
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+ return [min(self.g[s], self.rhs[s]) + self.h(self.s_start, s),
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+ min(self.g[s], self.rhs[s])]
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+
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+ def ComputeCost(self, s, sa, sb):
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+ if sa[0] != s[0] and sa[1] != s[1]:
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+ s1, s2 = sb, sa
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+ else:
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+ s1, s2 = sa, sb
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+
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+ c = self.cost(s, s2)
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+ b = self.cost(s, s1)
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+ y = 0
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+
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+ if min(c, b) == float("inf"):
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+ vs = float("inf")
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+ elif self.g[s1] <= self.g[s2]:
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+ vs = min(c, b) + self.g[s1]
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+ else:
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+ f = self.g[s1] - self.g[s2]
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+ if f <= b:
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+ if c <= f:
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+ vs = math.sqrt(2) * c + self.g[s2]
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+ else:
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+ y = min(f / (math.sqrt(c ** 2 - f ** 2)), 1)
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+ vs = c * math.sqrt(1 + y ** 2) + f * (1 - y) + self.g[s2]
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+ else:
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+ if c <= b:
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+ vs = math.sqrt(2) * c + self.g[s2]
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+ else:
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+ x = 1 - min(b / (math.sqrt(c ** 2 - b ** 2)), 1)
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+ vs = c * math.sqrt(1 + (1 - x) ** 2) + b * x + self.g[s2]
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+
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+ ss = (y * s1[0] + (1 - y) * s2[0], y * s1[1] + (1 - y) * s2[1])
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+
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+ return vs, ss
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+
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+ def TopKey(self):
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+ """
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+ :return: return the min key and its value.
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+ """
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+
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+ s = min(self.U, key=self.U.get)
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+ return s, self.U[s]
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+
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+ def h(self, s_start, s_goal):
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+ heuristic_type = self.heuristic_type # heuristic type
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+
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+ if heuristic_type == "manhattan":
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+ return abs(s_goal[0] - s_start[0]) + abs(s_goal[1] - s_start[1])
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+ else:
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+ return math.hypot(s_goal[0] - s_start[0], s_goal[1] - s_start[1])
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+
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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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+
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+ if self.is_collision(s_start, s_goal):
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+ return float("inf")
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+
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+ return math.hypot(s_goal[0] - s_start[0], s_goal[1] - s_start[1])
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+
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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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+
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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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+
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+ if s1 in self.obs or s2 in self.obs:
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+ return True
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+
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+ return False
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+
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+ def get_neighbor(self, s):
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+ s_list = []
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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.append(s_next)
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+
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+ return s_list
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+
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+ def extract_path(self):
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+ path = [self.s_start]
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+ s = self.s_start
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+ count = 0
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+ while True:
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+ count += 1
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+ g_list = {}
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+ for x in self.get_neighbor(s):
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+ if not self.is_collision(s, x):
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+ g_list[x] = self.g[x]
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+ ss = self.parent[s]
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+ s = min(g_list, key=g_list.get)
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+ path.append(s)
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+
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+ if s == self.s_goal or count > 100:
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+ return list(reversed(path))
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+
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+ def plot_path(self, path):
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+ px = [x[0] for x in path]
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+ py = [x[1] for x in path]
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+ plt.plot(px, py, linewidth=2)
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+ plt.plot(self.s_start[0], self.s_start[1], "bs")
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+ plt.plot(self.s_goal[0], self.s_goal[1], "gs")
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+
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+ def plot_visited(self, visited):
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+ color = ['gainsboro', 'lightgray', 'silver', 'darkgray',
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+ 'bisque', 'navajowhite', 'moccasin', 'wheat',
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+ 'powderblue', 'skyblue', 'lightskyblue', 'cornflowerblue']
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+
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+ if self.count >= len(color) - 1:
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+ self.count = 0
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+
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+ for x in visited:
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+ plt.plot(x[0], x[1], marker='s', color=color[self.count])
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+
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+
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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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+
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+ fielddstar = FieldDStar(s_start, s_goal, "euclidean")
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+ fielddstar.run()
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+
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+
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+if __name__ == '__main__':
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+ main()
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zhm-real