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tf2 库允许你在 ROS 节点中查询两个帧之间的转换。这个查询可以是阻塞的,也可以是非阻塞的,取决于你的需求。下面是一个基本的 Python 示例,展示如何在 ROS 节点中使用 tf2 查询帧转换。
本教程假设您已完成tf2 静态广播器教程 (Python)和tf2 广播器教程 (Python)。在上一个教程中,我们创建了一个learning_tf2_py包。机器人系统ros2-开发实践06-将静态坐标系广播到 tf2(Python)-定义机器人底座与其传感器或非移动部件之间的关系
1 编写监听节点
我们首先创建源文件。转到learning_tf2_py我们在上一教程中创建的包。在src/learning_tf2_py/learning_tf2_py目录中新建turtle_tf2_listener.py,用vscode 打开文件,将下面的代码贴入文件中。
代码如下:
import math
from geometry_msgs.msg import Twist
import rclpy
from rclpy.node import Node
from tf2_ros import TransformException
from tf2_ros.buffer import Buffer
from tf2_ros.transform_listener import TransformListener
from turtlesim.srv import Spawn
class FrameListener(Node):
def __init__(self):
super().__init__('turtle_tf2_frame_listener')
# Declare and acquire `target_frame` parameter
self.target_frame = self.declare_parameter(
'target_frame', 'turtle1').get_parameter_value().string_value
self.tf_buffer = Buffer()
self.tf_listener = TransformListener(self.tf_buffer, self)
# Create a client to spawn a turtle
self.spawner = self.create_client(Spawn, 'spawn')
# Boolean values to store the information
# if the service for spawning turtle is available
self.turtle_spawning_service_ready = False
# if the turtle was successfully spawned
self.turtle_spawned = False
# Create turtle2 velocity publisher
self.publisher = self.create_publisher(Twist, 'turtle2/cmd_vel', 1)
# Call on_timer function every second
self.timer = self.create_timer(1.0, self.on_timer)
def on_timer(self):
# Store frame names in variables that will be used to
# compute transformations
from_frame_rel = self.target_frame
to_frame_rel = 'turtle2'
if self.turtle_spawning_service_ready:
if self.turtle_spawned:
# Look up for the transformation between target_frame and turtle2 frames
# and send velocity commands for turtle2 to reach target_frame
try:
t = self.tf_buffer.lookup_transform(
to_frame_rel,
from_frame_rel,
rclpy.time.Time())
except TransformException as ex:
self.get_logger().info(
f'Could not transform {to_frame_rel} to {from_frame_rel}: {ex}')
return
msg = Twist()
scale_rotation_rate = 1.0
msg.angular.z = scale_rotation_rate * math.atan2(
t.transform.translation.y,
t.transform.translation.x)
scale_forward_speed = 0.5
msg.linear.x = scale_forward_speed * math.sqrt(
t.transform.translation.x ** 2 +
t.transform.translation.y ** 2)
self.publisher.publish(msg)
else:
if self.result.done():
self.get_logger().info(
f'Successfully spawned {self.result.result().name}')
self.turtle_spawned = True
else:
self.get_logger().info('Spawn is not finished')
else:
if self.spawner.service_is_ready():
# Initialize request with turtle name and coordinates
# Note that x, y and theta are defined as floats in turtlesim/srv/Spawn
request = Spawn.Request()
request.name = 'turtle2'
request.x = float(4)
request.y = float(2)
request.theta = float(0)
# Call request
self.result = self.spawner.call_async(request)
self.turtle_spawning_service_ready = True
else:
# Check if the service is ready
self.get_logger().info('Service is not ready')
def main():
rclpy.init()
node = FrameListener()
try:
rclpy.spin(node)
except KeyboardInterrupt:
pass
rclpy.shutdown()
TransformListener以帮助简化接收转换的任务。
from tf2_ros.transform_listener import TransformListener
在这里,我们创建一个TransformListener对象。创建侦听器后,它开始通过线路接收 tf2 转换,并将它们缓冲最多 10 秒。
self.tf_listener = TransformListener(self.tf_buffer, self)
最后,我们向侦听器查询特定的转换。我们lookup_transform使用以下参数调用方法:
提供rclpy.time.Time()只会为我们提供最新的可用转换。所有这些都包含在 try- except 块中以处理可能的异常
t = self.tf_buffer.lookup_transform(
to_frame_rel,
from_frame_rel,
rclpy.time.Time())
要允许命令运行您的节点,您必须将入口点添加到src/learning_tf2_py 的setup.py ,在setup.py 文件里找到console_scripts,在括号之间添加以下行
'turtle_tf2_listener = learning_tf2_py.turtle_tf2_listener:main',
使用文本编辑器打开目录中调用的启动文件src/learning_tf2_py/launch,向启动描述turtle_tf2_demo.launch.py添加两个新节点,添加启动参数,然后添加导入。生成的新的文件应如下所示:
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.substitutions import LaunchConfiguration
from launch_ros.actions import Node
def generate_launch_description():
return LaunchDescription([
Node(
package='turtlesim',
executable='turtlesim_node',
name='sim'
),
Node(
package='learning_tf2_py',
executable='turtle_tf2_broadcaster',
name='broadcaster1',
parameters=[
{'turtlename': 'turtle1'}
]
),
DeclareLaunchArgument(
'target_frame', default_value='turtle1',
description='Target frame name.'
),
Node(
package='learning_tf2_py',
executable='turtle_tf2_broadcaster',
name='broadcaster2',
parameters=[
{'turtlename': 'turtle2'}
]
),
Node(
package='learning_tf2_py',
executable='turtle_tf2_listener',
name='listener',
parameters=[
{'target_frame': LaunchConfiguration('target_frame')}
]
),
])
这将声明一个target_frame启动参数,为我们将生成的第二只海龟启动一个广播器,以及将订阅这些转换的坐标侦监听器。
在工作区的根目录中运行rosdep以检查是否缺少依赖项。
rosdep install -i --from-path src --rosdistro humble -y
仍然在工作区的根目录中构建您的包:
colcon build --packages-select learning_tf2_py
打开一个新终端,导航到工作区的根目录,然后获取安装文件:
. install/setup.bash
现在您已准备好开始完整的海龟演示:
在工作区的根目录执行
ros2 launch learning_tf2_py turtle_tf2_demo.launch.py
看到有两个海龟,另外一个会向其中一个自动靠齐
您应该会看到海龟模拟卡上有两只海龟。在第二个终端窗口中键入以下命令:
ros2 run turtlesim turtle_teleop_key
要查看是否有效,只需使用箭头键围绕第一只乌龟行驶(确保您的终端窗口处于活动状态,而不是模拟器窗口),您将看到第二只乌龟紧随第一只乌龟!
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