mirror of
https://github.com/AtsushiSakai/PythonRobotics.git
synced 2026-01-13 16:47:55 -05:00
Atsushi Sakai
@@ -271,6 +271,7 @@ def main():
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if show_animation: # pragma: no cover
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plt.plot(rx, ry, "-r")
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plt.show()
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plt.pause(0.001)
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if __name__ == '__main__':
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342
PathPlanning/BidirectionalAStar/bidirectional_a_star.py
Normal file
342
PathPlanning/BidirectionalAStar/bidirectional_a_star.py
Normal file
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"""
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Bidirectional A* grid planning
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author: Erwin Lejeune (@spida_rwin)
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See Wikipedia article (https://en.wikipedia.org/wiki/Bidirectional_search)
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"""
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import math
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import matplotlib.pyplot as plt
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show_animation = True
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class BidirectionalAStarPlanner:
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def __init__(self, ox, oy, reso, rr):
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"""
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Initialize grid map for a star planning
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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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reso: grid resolution [m]
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rr: robot radius[m]
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"""
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self.reso = reso
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self.rr = rr
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self.calc_obstacle_map(ox, oy)
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self.motion = self.get_motion_model()
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class Node:
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def __init__(self, x, y, cost, pind):
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self.x = x # index of grid
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self.y = y # index of grid
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self.cost = cost
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self.pind = pind
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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.pind)
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def planning(self, sx, sy, gx, gy):
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"""
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Bidirectional A star path search
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input:
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sx: start x position [m]
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sy: start y position [m]
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gx: goal x position [m]
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gy: goal y position [m]
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output:
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rx: x position list of the final path
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ry: y position list of the final path
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"""
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nstart = self.Node(self.calc_xyindex(sx, self.minx),
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self.calc_xyindex(sy, self.miny), 0.0, -1)
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ngoal = self.Node(self.calc_xyindex(gx, self.minx),
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self.calc_xyindex(gy, self.miny), 0.0, -1)
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open_set_A, closed_set_A = dict(), dict()
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open_set_B, closed_set_B = dict(), dict()
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open_set_A[self.calc_grid_index(nstart)] = nstart
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open_set_B[self.calc_grid_index(ngoal)] = ngoal
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current_A = nstart
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current_B = ngoal
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while 1:
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if len(open_set_A) == 0:
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print("Open set A is empty..")
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break
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if len(open_set_B) == 0:
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print("Open set B is empty..")
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break
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c_id_A = min(
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open_set_A,
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key=lambda o: self.find_total_cost(open_set_A, o, current_B))
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current_A = open_set_A[c_id_A]
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c_id_B = min(
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open_set_B,
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key=lambda o: self.find_total_cost(open_set_B, o, current_A))
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current_B = open_set_B[c_id_B]
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# show graph
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if show_animation: # pragma: no cover
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plt.plot(self.calc_grid_position(current_A.x, self.minx),
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self.calc_grid_position(current_A.y, self.miny), "xc")
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plt.plot(self.calc_grid_position(current_B.x, self.minx),
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self.calc_grid_position(current_B.y, self.miny), "xc")
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# for stopping simulation with the esc key.
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plt.gcf().canvas.mpl_connect('key_release_event',
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lambda event: [exit(
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0) if event.key == 'escape' else None])
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if len(closed_set_A.keys()) % 10 == 0:
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plt.pause(0.001)
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if current_A.x == current_B.x and current_A.y == current_B.y:
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print("Found goal")
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meetpointA = current_A
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meetpointB = current_B
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break
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# Remove the item from the open set
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del open_set_A[c_id_A]
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del open_set_B[c_id_B]
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# Add it to the closed set
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closed_set_A[c_id_A] = current_A
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closed_set_B[c_id_B] = current_B
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# expand_grid search grid based on motion model
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for i, _ in enumerate(self.motion):
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continue_A = False
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continue_B = False
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child_node_A = self.Node(current_A.x + self.motion[i][0],
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current_A.y + self.motion[i][1],
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current_A.cost + self.motion[i][2],
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c_id_A)
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child_node_B = self.Node(current_B.x + self.motion[i][0],
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current_B.y + self.motion[i][1],
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current_B.cost + self.motion[i][2],
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c_id_B)
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n_id_A = self.calc_grid_index(child_node_A)
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n_id_B = self.calc_grid_index(child_node_B)
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# If the node is not safe, do nothing
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if not self.verify_node(child_node_A):
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continue_A = True
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if not self.verify_node(child_node_B):
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continue_B = True
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if n_id_A in closed_set_A:
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continue_A = True
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if n_id_B in closed_set_B:
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continue_B = True
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if not continue_A:
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if n_id_A not in open_set_A:
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# discovered a new node
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open_set_A[n_id_A] = child_node_A
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else:
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if open_set_A[n_id_A].cost > child_node_A.cost:
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# This path is the best until now. record it
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open_set_A[n_id_A] = child_node_A
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if not continue_B:
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if n_id_B not in open_set_B:
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# discovered a new node
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open_set_B[n_id_B] = child_node_B
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else:
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if open_set_B[n_id_B].cost > child_node_B.cost:
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# This path is the best until now. record it
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open_set_B[n_id_B] = child_node_B
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rx, ry = self.calc_final_bidirectional_path(
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meetpointA, meetpointB, closed_set_A, closed_set_B)
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return rx, ry
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def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
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rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A)
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rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B)
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rx_A.reverse()
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ry_A.reverse()
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rx = rx_A + rx_B
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ry = ry_A + ry_B
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return rx, ry
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def calc_final_path(self, ngoal, closedset):
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# generate final course
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rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
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self.calc_grid_position(ngoal.y, self.miny)]
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pind = ngoal.pind
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while pind != -1:
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n = closedset[pind]
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rx.append(self.calc_grid_position(n.x, self.minx))
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ry.append(self.calc_grid_position(n.y, self.miny))
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pind = n.pind
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return rx, ry
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@staticmethod
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def calc_heuristic(n1, n2):
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w = 1.0 # weight of heuristic
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d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
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return d
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def find_total_cost(self, open_set, lambda_, n1):
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g_cost = open_set[lambda_].cost
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h_cost = self.calc_heuristic(n1, open_set[lambda_])
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f_cost = g_cost + h_cost
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return f_cost
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def calc_grid_position(self, index, minp):
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"""
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calc grid position
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:param index:
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:param minp:
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:return:
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"""
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pos = index * self.reso + minp
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return pos
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def calc_xyindex(self, position, min_pos):
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return round((position - min_pos) / self.reso)
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def calc_grid_index(self, node):
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return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
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def verify_node(self, node):
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px = self.calc_grid_position(node.x, self.minx)
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py = self.calc_grid_position(node.y, self.miny)
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if px < self.minx:
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return False
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elif py < self.miny:
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return False
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elif px >= self.maxx:
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return False
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elif py >= self.maxy:
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return False
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# collision check
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if self.obmap[node.x][node.y]:
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return False
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return True
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def calc_obstacle_map(self, ox, oy):
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self.minx = round(min(ox))
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self.miny = round(min(oy))
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self.maxx = round(max(ox))
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self.maxy = round(max(oy))
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print("minx:", self.minx)
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print("miny:", self.miny)
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print("maxx:", self.maxx)
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print("maxy:", self.maxy)
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self.xwidth = round((self.maxx - self.minx) / self.reso)
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self.ywidth = round((self.maxy - self.miny) / self.reso)
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print("xwidth:", self.xwidth)
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print("ywidth:", self.ywidth)
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# obstacle map generation
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self.obmap = [[False for _ in range(self.ywidth)]
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for _ in range(self.xwidth)]
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for ix in range(self.xwidth):
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x = self.calc_grid_position(ix, self.minx)
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for iy in range(self.ywidth):
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y = self.calc_grid_position(iy, self.miny)
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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 <= self.rr:
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self.obmap[ix][iy] = True
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break
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@staticmethod
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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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def main():
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print(__file__ + " start!!")
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# start and goal position
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sx = 10.0 # [m]
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sy = 10.0 # [m]
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gx = 50.0 # [m]
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gy = 50.0 # [m]
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grid_size = 2.0 # [m]
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robot_radius = 1.0 # [m]
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# set obstacle positions
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ox, oy = [], []
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for i in range(-10, 60):
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ox.append(i)
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oy.append(-10.0)
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for i in range(-10, 60):
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ox.append(60.0)
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oy.append(i)
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for i in range(-10, 61):
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ox.append(i)
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oy.append(60.0)
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for i in range(-10, 61):
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ox.append(-10.0)
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oy.append(i)
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for i in range(-10, 40):
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ox.append(20.0)
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oy.append(i)
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for i in range(0, 40):
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ox.append(40.0)
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oy.append(60.0 - i)
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if show_animation: # pragma: no cover
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plt.plot(ox, oy, ".k")
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plt.plot(sx, sy, "og")
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plt.plot(gx, gy, "ob")
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plt.grid(True)
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plt.axis("equal")
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bidir_a_star = BidirectionalAStarPlanner(ox, oy, grid_size, robot_radius)
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rx, ry = bidir_a_star.planning(sx, sy, gx, gy)
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if show_animation: # pragma: no cover
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plt.plot(rx, ry, "-r")
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plt.pause(.0001)
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plt.show()
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if __name__ == '__main__':
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main()
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Block a user