mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
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177 lines
4.9 KiB
Python
177 lines
4.9 KiB
Python
"""
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A* grid based planning
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author: Nikos Kanargias (nkana@tee.gr)
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See Wikipedia article (https://en.wikipedia.org/wiki/A*_search_algorithm)
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"""
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import heapq
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import math
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import matplotlib.pyplot as plt
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show_animation = False
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class Node:
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def __init__(self, x, y, cost, parent_index):
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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_index = parent_index
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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_index)
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def calc_final_path(goal_node, closed_node_set, resolution):
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# generate final course
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rx, ry = [goal_node.x * resolution], [goal_node.y * resolution]
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parent_index = goal_node.parent_index
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while parent_index != -1:
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n = closed_node_set[parent_index]
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rx.append(n.x * resolution)
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ry.append(n.y * resolution)
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parent_index = n.parent_index
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return rx, ry
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def calc_distance_heuristic(gx, gy, ox, oy, resolution, rr):
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"""
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gx: goal x position [m]
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gx: goal x position [m]
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ox: x position list of Obstacles [m]
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oy: y position list of Obstacles [m]
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resolution: grid resolution [m]
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rr: robot radius[m]
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"""
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goal_node = Node(round(gx / resolution), round(gy / resolution), 0.0, -1)
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ox = [iox / resolution for iox in ox]
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oy = [ioy / resolution for ioy in oy]
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obstacle_map, min_x, min_y, max_x, max_y, x_w, y_w = calc_obstacle_map(
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ox, oy, resolution, rr)
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motion = get_motion_model()
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open_set, closed_set = dict(), dict()
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open_set[calc_index(goal_node, x_w, min_x, min_y)] = goal_node
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priority_queue = [(0, calc_index(goal_node, x_w, min_x, min_y))]
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while True:
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if not priority_queue:
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break
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cost, c_id = heapq.heappop(priority_queue)
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if c_id in open_set:
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current = open_set[c_id]
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closed_set[c_id] = current
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open_set.pop(c_id)
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else:
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continue
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# show graph
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if show_animation: # pragma: no cover
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plt.plot(current.x * resolution, current.y * resolution, "xc")
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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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if len(closed_set.keys()) % 10 == 0:
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plt.pause(0.001)
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# Remove the item from the open set
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# expand search grid based on motion model
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for i, _ in enumerate(motion):
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node = Node(current.x + motion[i][0],
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current.y + motion[i][1],
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current.cost + motion[i][2], c_id)
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n_id = calc_index(node, x_w, min_x, min_y)
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if n_id in closed_set:
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continue
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if not verify_node(node, obstacle_map, min_x, min_y, max_x, max_y):
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continue
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if n_id not in open_set:
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open_set[n_id] = node # Discover a new node
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heapq.heappush(
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priority_queue,
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(node.cost, calc_index(node, x_w, min_x, min_y)))
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else:
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if open_set[n_id].cost >= node.cost:
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# This path is the best until now. record it!
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open_set[n_id] = node
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heapq.heappush(
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priority_queue,
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(node.cost, calc_index(node, x_w, min_x, min_y)))
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return closed_set
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def verify_node(node, obstacle_map, min_x, min_y, max_x, max_y):
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if node.x < min_x:
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return False
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elif node.y < min_y:
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return False
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elif node.x >= max_x:
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return False
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elif node.y >= max_y:
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return False
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if obstacle_map[node.x][node.y]:
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return False
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return True
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def calc_obstacle_map(ox, oy, resolution, vr):
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min_x = round(min(ox))
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min_y = round(min(oy))
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max_x = round(max(ox))
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max_y = round(max(oy))
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x_width = round(max_x - min_x)
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y_width = round(max_y - min_y)
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# obstacle map generation
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obstacle_map = [[False for _ in range(y_width)] for _ in range(x_width)]
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for ix in range(x_width):
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x = ix + min_x
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for iy in range(y_width):
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y = iy + min_y
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# print(x, y)
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for iox, ioy in zip(ox, oy):
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d = math.hypot(iox - x, ioy - y)
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if d <= vr / resolution:
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obstacle_map[ix][iy] = True
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break
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return obstacle_map, min_x, min_y, max_x, max_y, x_width, y_width
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def calc_index(node, x_width, x_min, y_min):
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return (node.y - y_min) * x_width + (node.x - x_min)
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def get_motion_model():
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# dx, dy, cost
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motion = [[1, 0, 1],
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[0, 1, 1],
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[-1, 0, 1],
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[0, -1, 1],
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[-1, -1, math.sqrt(2)],
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[-1, 1, math.sqrt(2)],
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[1, -1, math.sqrt(2)],
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[1, 1, math.sqrt(2)]]
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return motion
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