使用优化算法,如JPS(Jump Point Search)或Theta *算法,来加速A *的执行速度。
示例代码:
JPS实现:
def jump(point, direction, end_point)
# 跳跃到下一个相邻节点
next_point = point + direction
if not end_point.encompasses(next_point):
# 当前位置不在终点范围内,终止跳跃
return None
if next_point == end_point:
# 找到了终点
return next_point
if self.grid.is_obstacle(next_point):
# 下一点是障碍,终止跳跃
return None
# 当前节点沿目标线方向跳跃
if direction.is_diagonal():
if any((
self.jump(next_point, Direction(direction.x, 0), end_point),
self.jump(next_point, Direction(0, direction.y), end_point)
)):
# 投影到x, y轴,是否有可达节点
return next_point
return None
else:
return self.jump(next_point, direction, end_point)
def get_successors(current_node, end_node):
successors = []
# 寻找可到达的节点,如果找到加进successors
for direction in Direction.diagonal_directions() + Direction.cardinal_directions():
neighbor = jump(current_node.position, direction, end_node.position)
if neighbor is None:
continue
cost = current_node.g + current_node.position.distance_to(neighbor) # 计算代价g
successor = Node(neighbor, current_node, cost, neighbor.distance_to(end_node.position)) # 计算代价f
successors.append(successor)
return successors
Theta *实现:
def walk(current_node, end_node):
direction = current_node.position.direction_to(end_node.position)
if direction is None or not self.line_of_sight(current_node.position, end_node.position):
return None
if current_node.position == end_node.position:
return end_node
neighbor_pos = current_node.position + direction
neighbor = self.grid.node_at(neighbor_pos)
# 计算代价
cost = current_node.g + current_node.position.distance_to(neighbor_pos)
successor = Node(neighbor_pos, current_node, cost, neighbor_pos.distance_to(end_node.position))
return successor
def get_successors(current_node, end_node):
successors = []
# 寻找可到达的节点,如果找到加进successors