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https://github.com/AtsushiSakai/PythonRobotics.git
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141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
"""
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Dijkstra Search library
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author: Atsushi Sakai (@Atsushi_twi)
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"""
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import matplotlib.pyplot as plt
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import math
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import numpy as np
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class DijkstraSearch:
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class Node:
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"""
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Node class for dijkstra search
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"""
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def __init__(self, x, y, cost=None, parent=None, edge_ids=None):
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self.x = x
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self.y = y
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self.cost = cost
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self.parent = parent
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self.edge_ids = edge_ids
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def __str__(self):
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return str(self.x) + "," + str(self.y) + "," + str(
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self.cost) + "," + str(self.parent)
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def __init__(self, show_animation):
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self.show_animation = show_animation
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def search(self, sx, sy, gx, gy, node_x, node_y, edge_ids_list):
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"""
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Search shortest path
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s_x: start x positions [m]
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s_y: start y positions [m]
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gx: goal x position [m]
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gx: goal x position [m]
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node_x: node x position
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node_y: node y position
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edge_ids_list: edge_list each item includes a list of edge ids
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"""
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start_node = self.Node(sx, sy, 0.0, -1)
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goal_node = self.Node(gx, gy, 0.0, -1)
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current_node = None
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open_set, close_set = dict(), dict()
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open_set[self.find_id(node_x, node_y, start_node)] = start_node
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while True:
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if self.has_node_in_set(close_set, goal_node):
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print("goal is found!")
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goal_node.parent = current_node.parent
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goal_node.cost = current_node.cost
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break
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elif not open_set:
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print("Cannot find path")
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break
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current_id = min(open_set, key=lambda o: open_set[o].cost)
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current_node = open_set[current_id]
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# show graph
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if self.show_animation and len(
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close_set.keys()) % 2 == 0: # pragma: no cover
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plt.plot(current_node.x, current_node.y, "xg")
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# for stopping simulation with the esc key.
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plt.gcf().canvas.mpl_connect(
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'key_release_event',
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lambda event: [exit(0) if event.key == 'escape' else None])
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plt.pause(0.1)
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# Remove the item from the open set
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del open_set[current_id]
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# Add it to the closed set
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close_set[current_id] = current_node
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# expand search grid based on motion model
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for i in range(len(edge_ids_list[current_id])):
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n_id = edge_ids_list[current_id][i]
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dx = node_x[n_id] - current_node.x
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dy = node_y[n_id] - current_node.y
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d = math.hypot(dx, dy)
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node = self.Node(node_x[n_id], node_y[n_id],
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current_node.cost + d, current_id)
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if n_id in close_set:
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continue
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# Otherwise if it is already in the open set
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if n_id in open_set:
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if open_set[n_id].cost > node.cost:
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open_set[n_id] = node
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else:
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open_set[n_id] = node
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# generate final course
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rx, ry = self.generate_final_path(close_set, goal_node)
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return rx, ry
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@staticmethod
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def generate_final_path(close_set, goal_node):
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rx, ry = [goal_node.x], [goal_node.y]
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parent = goal_node.parent
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while parent != -1:
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n = close_set[parent]
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rx.append(n.x)
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ry.append(n.y)
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parent = n.parent
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rx, ry = rx[::-1], ry[::-1] # reverse it
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return rx, ry
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def has_node_in_set(self, target_set, node):
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for key in target_set:
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if self.is_same_node(target_set[key], node):
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return True
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return False
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def find_id(self, node_x_list, node_y_list, target_node):
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for i, _ in enumerate(node_x_list):
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if self.is_same_node_with_xy(node_x_list[i], node_y_list[i],
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target_node):
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return i
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return None
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@staticmethod
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def is_same_node_with_xy(node_x, node_y, node_b):
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dist = np.hypot(node_x - node_b.x,
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node_y - node_b.y)
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return dist <= 0.1
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@staticmethod
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def is_same_node(node_a, node_b):
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dist = np.hypot(node_a.x - node_b.x,
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node_a.y - node_b.y)
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return dist <= 0.1
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