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当然可以，以下是我为您的 astar 算法路径规划仿真设计的详细方案：
- 首先，我们需要在 ROS MoveBase 中添加一个新的节点来定义我们的 astar 目标，它应该继承 from
AStarNode类并重写主要方法：
import rospy
from rosidlidar_msgs.msg import LidarScan, LIDAR_RESULTS
class AstarNode(AStarNode):
def __init__(self, start_path, end_path, observation_gain=0.5, travel_speed=3, goals=None):
super().__init__()
self.start_path = start_path
self.end_path = end_path
self.observation_gain = observation_gain
self.travel_speed = travel_speed
self.goals = goals or []
def _get_goal_info(self, scan_data):
return {
'label': self.goals[-1],
'x': self.start_path.x,
'y': self.start_path.y,
'z': self.start_path.z,
'dist_to_goal': self._calculate_distance(scan_data),
}
def _generate_initial_neighbors(self, scan_data):
for goal in self.goals:
if not self._check_if_goal_sensed(goal, scan_data):
continue
nearest_neighbor = self._calculate_nearest_neighbor(scan_data, goal)
yield nearest_neighbor
def _calculate_distance(self, scan_data):
return (scan_data.header.frame_id, len(self.end_path) + 1)
def _calculate_nearest_neighbor(self, scan_data, goal):
min_dist = float('inf')
closest_neighbor = None
for neighbor in self._generate_initial_neighbors(scan_data):
dist = self._distance(neighbor, goal)
if dist < min_dist:
min_dist = dist
closest_neighbor = neighbor
return closest_neighbor
def _distance(self, src, dst):
x1, y1, z1 = src.position
x2, y2, z2 = dst.position
distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2 + (z2 - z1)**2)
return distance
def run(self, node_state):
rospy.loginfo("Starting astar path planning...")
# TODO: Set the initial pose of the robot and generate initial neighboring nodes for each goal
# Use the provided values to set the start and end poses and generate initial neighbors for each goal
# Store the calculated distances between the goals and their neighbors in a list
goals_for_neighbors = [
{"label": goal, "x": start_path.x, "y": start_path.y, "z": start_path.z},
{"label": goal, "x": end_path.x, "y": end_path.y, "z": end_path.z},
// Add more goals as needed
]
goal_distances = [self._calculate_distance(scan_data) for scan_data in goals_for_neighbors]
# Generate the astar search tree by recursively checking all neighboring goals and calculating the shortest path between them
root = AstarNode(start_path, end_path)
for goal_distance in goal_distances:
parent_goal = None
for neighbor_goal in goals_for_neighbors:
parent_goal = neighbor_goal if goal_distance >= neighbor_goal.distance else goal_goal
if parent_goal is None:
break
new_node = AstarNode(parent_goal.start_path, parent_goal.end_path, observation_gain, parent_goal.travel_speed)
new_node.add_neighbors([goal])
child_goals = new_node.generate_neighbors(goal_distance)
if new_node.get_goal_info(child_goals[0]) != goal:
# If the goal has moved since we last checked it, remove it from our search tree
child_goals.pop()
root.remove_goal(child_goals[0])
# Update the start and end positions of the current node based on its parents' updated positions
start_path = root.end_path
end_path = root.start_path
# Publish the planned paths to the ROS topic
pub_node_state(start_path, end_path)
- 在 ROS MoveBase 的 main function 中调用这个 astar 节点来运行仿真：
rospy.init_node("astar_path_planning")
path_planner = AstarNode(node_state=publisher_node_state)
path_planner.run()
rospy.wait_for_pub(publisher_node_state)
在这个示例中，我们首先初始化了 ROS MoveBase，并创建了一个名为 astar_path_planning 的节点，继承自 AStarNode 类并实现了主要方法 run()。然后，在 ROS main function 中调用了这个节点来启动仿真。在 run() 方法中，我们设置了初始目标坐标、观察距离（通过 observation_gain 参数设置）、移动速度和目标策略列表。
- 计算路径规划的目标和计算当前节点与每个目标的距离：
- 定义一个新的类
GoalInfo来存储每个目标的标签、位置、观察距离等信息。 - 使用
generate_initial_neighbors()方法从给定的扫描数据中获取目标的邻居节点，计算其与起点的距离。 - 根据目标之间的距离更新
goals_for_neighbors列表，以便在搜索过程中跳过不相关的目标。 - 对于每个目标，创建一个新的
AStarNode实例，并将其嵌套在AStarNode的实例中。将start_path和end_path变量传递给AStarNode的构造函数，同时将goal_distances列表作为参数传入子节点的generate_neighbors()方法。 - 在
_generate_initial_neighbors()方法内部，对goals_for_neighbors列表中的目标进行递归遍历，计算它们与起点之间的距离，以及与每个目标之间最近的相邻节点。 - 在
_distance()方法内部，计算起点到每个目标的距离，并返回最大的值作为路径长度。 - 使用
add_neighbors()方法将当前节点及其邻居添加到path_planner中，并调用generate_neighbors()方法生成下一代目标。 - 在
_remove_goal()方法中，检查目标是否已经在上一层节点的路径结构中消失，若已消失，则删除此目标及其所有子节点。
- 撰写
PublisherNodeState类，用于存储模拟路径的状态，包括起始和结束节点的坐标、路径长度等：
import numpy as np
class PublisherNodeState:
def __init__(self, start_path, end_path, path_length=0):
self.start_path = start_path
self.end_path = end_path
self.path_length = path_length
def publish_path(self):
msg = lidar_scan_msg.LIDAR_SCAN(bytes(self.start_path.encode()), bytes(self.end_path.encode()))
pub.publish(msg)
# 示例用法
publisher_node_state = PublisherNodeState(start_path="planned_node", end_path="destination_node", path_length=100)
path_planner.publish_path()
在这个示例中，我们创建了一个 PublisherNodeState 类，其中包含三个属性：起始和结束节点的坐标、路径长度。在 publish_path() 方法中，我们将模拟路径状态打包成一个 lidar_scan_msg.LIDAR_SCAN 对象，并将其发送到 ROS 的 planned_node 或 destination_node 频道。
- 最后，将仿真结果在 ROS 主循环中发布到 ROS 的
planned_nodes或destination_nodes频道：
while True:
rospy.spin()
publisher_node_state.publish_path()
在这个示例中，我们进入一个无限循环，在每次 ROS tick 时调用 publish_path() 方法并将模拟路径状态发送到 ROS 的 planned_nodes 或 destination_nodes 频道。
注意：以上步骤仅是基础的路径规划实现，实际的路径规划过程可能包括更复杂的搜索算法，如 Dijkstra's algorithm、A* algorithm、PLANNER等，以及考虑障碍物、光照等因素的影响。此外，为了确保算法能够在各种不同的传感器数据帧下正确工作，你可能还需要针对特定的传感器类型（如 Lidar）提供适当的插件或增强算法。对于具体的问题，例如 ROS MoveBase 支持的传感器类型，你可能需要查阅ROS官方文档以获取最准确的信息。
