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Greedy Best-First Search (#315)

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This commit is contained in:
Erwin Lejeune
2020-05-05 07:06:01 +02:00
committed by GitHub
parent ab49acb1ac
commit 734f3aed20
5 changed files with 326 additions and 46 deletions
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78
PathPlanning/BidirectionalAStar/bidirectional_a_star.py
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@@ -100,8 +100,9 @@ class BidirectionalAStarPlanner:
self.calc_grid_position(current_B.y, self.miny), "xc")
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event',
lambda event: [exit(
0) if event.key == 'escape' else None])
lambda event:
[exit(0) if event.key == 'escape'
else None])
if len(closed_set_A.keys()) % 10 == 0:
plt.pause(0.001)
@@ -121,61 +122,50 @@ class BidirectionalAStarPlanner:
# expand_grid search grid based on motion model
for i, _ in enumerate(self.motion):
continue_A = False
continue_B = False
child_node_A = self.Node(current_A.x + self.motion[i][0],
current_A.y + self.motion[i][1],
current_A.cost + self.motion[i][2],
c_id_A)
c_nodes = [self.Node(current_A.x + self.motion[i][0],
current_A.y + self.motion[i][1],
current_A.cost + self.motion[i][2],
c_id_A),
self.Node(current_B.x + self.motion[i][0],
current_B.y + self.motion[i][1],
current_B.cost + self.motion[i][2],
c_id_B)]
child_node_B = self.Node(current_B.x + self.motion[i][0],
current_B.y + self.motion[i][1],
current_B.cost + self.motion[i][2],
c_id_B)
n_id_A = self.calc_grid_index(child_node_A)
n_id_B = self.calc_grid_index(child_node_B)
n_ids = [self.calc_grid_index(c_nodes[0]),
self.calc_grid_index(c_nodes[1])]
# If the node is not safe, do nothing
if not self.verify_node(child_node_A):
continue_A = True
continue_ = self.check_nodes_and_sets(c_nodes, closed_set_A,
closed_set_B, n_ids)
if not self.verify_node(child_node_B):
continue_B = True
if n_id_A in closed_set_A:
continue_A = True
if n_id_B in closed_set_B:
continue_B = True
if not continue_A:
if n_id_A not in open_set_A:
if not continue_[0]:
if n_ids[0] not in open_set_A:
# discovered a new node
open_set_A[n_id_A] = child_node_A
open_set_A[n_ids[0]] = c_nodes[0]
else:
if open_set_A[n_id_A].cost > child_node_A.cost:
if open_set_A[n_ids[0]].cost > c_nodes[0].cost:
# This path is the best until now. record it
open_set_A[n_id_A] = child_node_A
open_set_A[n_ids[0]] = c_nodes[0]
if not continue_B:
if n_id_B not in open_set_B:
if not continue_[1]:
if n_ids[1] not in open_set_B:
# discovered a new node
open_set_B[n_id_B] = child_node_B
open_set_B[n_ids[1]] = c_nodes[1]
else:
if open_set_B[n_id_B].cost > child_node_B.cost:
if open_set_B[n_ids[1]].cost > c_nodes[1].cost:
# This path is the best until now. record it
open_set_B[n_id_B] = child_node_B
open_set_B[n_ids[1]] = c_nodes[1]
rx, ry = self.calc_final_bidirectional_path(
meetpointA, meetpointB, closed_set_A, closed_set_B)
return rx, ry
def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A)
rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B)
# takes two sets and two meeting nodes and return the optimal path
def calc_final_bidirectional_path(self, n1, n2, setA, setB):
rx_A, ry_A = self.calc_final_path(n1, setA)
rx_B, ry_B = self.calc_final_path(n2, setB)
rx_A.reverse()
ry_A.reverse()
@@ -198,6 +188,16 @@ class BidirectionalAStarPlanner:
return rx, ry
def check_nodes_and_sets(self, c_nodes, closedSet_A, closedSet_B, n_ids):
continue_ = [False, False]
if not self.verify_node(c_nodes[0]) or n_ids[0] in closedSet_A:
continue_[0] = True
if not self.verify_node(c_nodes[1]) or n_ids[1] in closedSet_B:
continue_[1] = True
return continue_
@staticmethod
def calc_heuristic(n1, n2):
w = 1.0 # weight of heuristic