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@@ -1,279 +0,0 @@
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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.OPEN = {}, {}, {}
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- self.parent = {}
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- self.cknbr = {}
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- self.ccknbr = {}
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- self.bptr = {}
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- self.init_table()
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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.bptr[(i, j)] = (i, j)
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-
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- self.rhs[self.s_goal] = 0.0
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- self.OPEN[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 init_table(self):
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- for i in range(1, self.Env.x_range - 1):
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- for j in range(1, self.Env.y_range - 1):
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- s_neighbor = self.get_neighbor_pure((i, j))
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- s_neighbor.append(s_neighbor[0])
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- for k in range(8):
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- self.cknbr[((i, j), s_neighbor[k])] = s_neighbor[k + 1]
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- s_neighbor = list(reversed(s_neighbor))
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- for k in range(8):
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- self.ccknbr[((i, j), s_neighbor[k])] = s_neighbor[k + 1]
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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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-
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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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- sn_list = self.get_neighbor((x, y))
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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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- sn_list = [(x, y)]
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- sn_list += self.get_neighbor((x, y))
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-
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- for s in sn_list:
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- v_list = []
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- for sn in self.get_neighbor(s):
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- v_list.append(self.ComputeCost(s, sn, self.ccknbr[(s, sn)]))
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- self.rhs[s] = min(v_list)
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- self.UpdateVertex(s)
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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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- if self.g[s] > self.rhs[s]:
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- self.g[s] = self.rhs[s]
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- self.OPEN.pop(s)
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- for sn in self.get_neighbor(s):
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- if self.rhs[sn] > self.ComputeCost(sn, s, self.ccknbr[(sn, s)]):
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- self.rhs[sn] = self.ComputeCost(sn, s, self.ccknbr[(sn, s)])
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- self.bptr[sn] = s
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- if self.rhs[sn] > self.ComputeCost(sn, s, self.cknbr[(sn, s)]):
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- self.rhs[sn] = self.ComputeCost(sn, self.cknbr[(sn, s)], s)
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- self.bptr[sn] = self.cknbr[(sn, s)]
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- self.UpdateVertex(sn)
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- else:
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- self.g[s] = float("inf")
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- for sn in self.get_neighbor(s):
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- if self.bptr[sn] == s or self.bptr[sn] == self.cknbr[(sn, s)]:
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- v_list = []
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- ssn_list = self.get_neighbor(sn)
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- for ssn in ssn_list:
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- v_list.append(self.ComputeCost(sn, ssn, self.ccknbr[(sn, ssn)]))
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- self.rhs[sn] = min(v_list)
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- self.bptr[sn] = ssn_list[v_list.index(min(v_list))]
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- self.UpdateVertex(sn)
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- self.UpdateVertex(s)
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-
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- def UpdateVertex(self, s):
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- if self.g[s] != self.rhs[s]:
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- self.OPEN[s] = self.CalculateKey(s)
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- elif s in self.OPEN:
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- self.OPEN.pop(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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-
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- if c != float("inf"):
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- c = c / math.sqrt(2)
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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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- return vs
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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.OPEN, key=self.OPEN.get)
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- return s, self.OPEN[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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- s = self.bptr[s]
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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