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Jetson Orin NX SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notes
  • 04.Disabling automatic updates and troubleshooting issues
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Write Jetson original system
  • 03.Installing Jetson environment
  • 04.Backup SSD system
  • 05.Restore initial image system
  • 06.Upgrade to SUPER version
  • 07.Write SUPER boot
  • 08.SSD expansion
  • 09.Jetpack old system upgraded to Yahboom SUPER factory system
  • 10.Write the system onto your own solid-state drive
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.Vision basic course
  • 01.CSI camera preview
  • 02.USB camera preview
  • 03.Use Jupyter Lab
  • 04.Use JetCam
06.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
  • 17.Preview USB camera image
  • 18.Preview CSI camera image
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.QR code recognition
06.YOLO26 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detectionnew
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS1-Melodic
01.ROS1 basic course
  • 01. Introduction to ROS
  • 02. Project file structure
  • 03. Common commands and tools
  • 04. Publisher
  • 05. Subscribers
  • 06. Customize topic messages and use
  • 07. Client
  • 08. Server
  • 09. Customize service messages and usage
  • 10. TF release and monitoring
02.ROS1 vision course
  • 00.Must read before running
  • 01.Camera preview
  • 02.QR code
  • 03.Human posture estimation
  • 04.Target detection
  • 05.AR vision
  • 06.Opencv application
  • 07.ROS+Opencv basics
  • 08.Mediapipe development
10.ROS2-Humble
01.ROS2 basic course
  • 01.Introduction to ROS2
  • 02.ROS2 install Humble
  • 03.ROS2 development environment
  • 04.ROS2 workspace
  • 05.ROS2 function package
  • 06.ROS2 node
  • 07.ROS2 topic communication
  • 08.ROS2 service communication
  • 09.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera preview (CSI)
  • 03.Camera calibration (USB)
  • 04.Camera calibration (CSI)
  • 05.AR vision (USB)
  • 06.AR vision (CSI)
11.Offline AI model coursenew
Offline large model (Text Interaction)
  • 1.AI large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
  • 10.AI large model offline voice assistant
12.Online AI model coursenew
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
13.NVIDIA lsaac ROS coursenew
  • 1.Build lsaac ROS environment
  • 2.Deep segmentation
  • 3.DNN stereoscopic depth
  • 4.Free space segmentation
  • 5.Image distortion processing
  • 6.Image segmentation
  • 7.3D scene reconstruction and mapping
  • 8.Object detection
  • 9.3D attitude estimation
  • 10.Visual SLAM
14.OpenClaw Deployment and Basic Usagenew
  • 01.OpenClaw Quick Start on Jetson Platform
  • 02.OpenClaw Deployment
  • 03.OpenClaw WhatsApp Plugin Application
  • 04.OpenClaw WebChat Interaction
  • 05.OpenClaw TUI Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawHub Introduction (Skill Installation)
  • 10.OpenClaw Application - File Management
  • 11.OpenClaw Application - Camera
  • 12.OpenClaw Application - Browser Control
  • 13.OpenClaw Application - Script Execution
  • 14.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 15.OpenClaw Application - Dedicated AI Assistant
  • 16.OpenClaw Web Search Capability Configuration
15.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 1. Peripheral Hardware Configuration
  • 2. OpenClaw API Key Configuration
  • 3. OpenClaw Model Switching
  • 4. OpenClaw Prompts
  • 5. AI Voice Interaction Configuration
  • 6. Three Dialogue Configuration Tests
2.OpenClaw AI Programming Peripheral Control
  • 1. Servo Control
  • 2. RGB Strip
  • 3. OLED Display
  • 4. Temperature and Humidity Sensor
  • 5. Camera Application
  • 6. AI Script Peripheral Control
3.OpenClaw Peripheral Integration Applications
  • 1. Plant Care Assistant
  • 2. AI Calorie Estimation
  • 3. AI Rock Paper Scissors Game
  • 4. AI Pet
  • 5. AI Weather Station
  • 6. Temperature and Humidity Dashboard
  • 7. Scheduled Tasks
Download
  • System_File
  • VM_File
  • 3D_Model_File
  • Data_Sheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
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Welcome to Jetson Orin NX SUPER repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

09.ROS2 action communication

9.ROS2 action communication

 

1. Introduction to Action Communication

Action communication is a communication model with continuous feedback. Between the two communicating parties, the client sends request data to the server, and the server responds to the client. However, from the time the server receives the request to the time it generates the final response, it sends continuous feedback to the client.

The action communication client/server model is as follows:

image8

2. Example Introduction

The action client submits an integer data type N. The action server receives the request data and accumulates all integers between 1 and N, returning the final result to the action client. Each addition calculates the current computation progress and provides feedback to the action client.

3. Create a new function package

3.1. Create an action communication interface function package

  1. Action communication requires creating the action communication interface first.
  • Create a new pkg_interfaces function package in the src directory of the workspace.
xxxxxxxxxx
ros2 pkg create --build-type ament_cmake pkg_interfaces
  1. Next, create an action folder under the pkg_interfaces function package and create a new file called [Progress.action] within the action folder. The file contents are as follows:
xxxxxxxxxx
int64 num
---
int64 sum
---
float64 progress

image-20231030154108201

  1. Add some dependency packages to package.xml. The specific content is as follows:
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<buildtool_depend>rosidl_default_generators</buildtool_depend>
<exec_depend>rosidl_default_runtime</exec_depend>
<depend>action_msgs</depend>
<member_of_group>rosidl_interface_packages</member_of_group>
  1. Add the following configuration to CMakeLists.txt:
xxxxxxxxxx
find_package(rosidl_default_generators REQUIRED)
​
rosidl_generate_interfaces(${PROJECT_NAME}
  "action/Progress.action"
)

image-20231030154828498

  1. Compile the package:
xxxxxxxxxx
colcon build --packages-select pkg_interfaces

image-20250905142323197

  1. After compilation is complete, the C++ and Python files corresponding to the Progress.action file will be generated in the install directory of the workspace. You can also access the workspace in a terminal and verify the file definitions and compilation by running the following command:
xxxxxxxxxx
source install/setup.bash
ros2 interface show pkg_interfaces/action/Progress

Under normal circumstances, the terminal will output the same content as the Progress.action file.

image-20250820112138938

3.2. Creating the Action Communication Function Package

Create a new package called pkg_action in the src directory of the workspace.

xxxxxxxxxx
ros2 pkg create pkg_action --build-type ament_python --dependencies rclpy pkg_interfaces --node-name action_server_demo

After executing the above command, the pkg_action function package will be created, along with an action_server_demo node and the relevant configuration files.

image-20231030155311734

4. Server Implementation

4.1 Creating the Server

Next, edit [action_server_demo.py] to implement the server functionality and add the following code:

xxxxxxxxxx
import time
import rclpy
from rclpy.action import ActionServer
from rclpy.node import Node
​
from pkg_interfaces.action import Progress
​
​
class Action_Server(Node):
​
    def __init__(self):
        super().__init__('progress_action_server')
        # Creating an action server
        self._action_server = ActionServer(
            self,
            Progress,
            'get_sum',
            self.execute_callback)
        self.get_logger().info('动作服务已经启动!')
​
    def execute_callback(self, goal_handle):
        self.get_logger().info('开始执行任务....')
​
​
        # Generate continuous feedback;
        feedback_msg = Progress.Feedback()
​
        sum = 0
        for i in range(1, goal_handle.request.num + 1):
            sum += i
            feedback_msg.progress = i / goal_handle.request.num
            self.get_logger().info('连续反馈: %.2f' % feedback_msg.progress)
            goal_handle.publish_feedback(feedback_msg)
            time.sleep(1)
​
        # Generate the final response.
        goal_handle.succeed()
        result = Progress.Result()
        result.sum = sum
        self.get_logger().info('任务完成!')
​
        return result
​
​
def main(args=None):
​
    rclpy.init(args=args)
    # Call the spin function and pass in the node object
    Progress_action_server = Action_Server()
    rclpy.spin(Progress_action_server)
    Progress_action_server.destroy_node() 
    # Release resources
    rclpy.shutdown()

 

4.2 Edit the Configuration File

  • Open setup.py and add the following line to the console_scripts list:
xxxxxxxxxx
'action_server_demo = pkg_action.action_server_demo:main',

image-20231030164238858

4.3 Compile the Package

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colcon build --packages-select pkg_action

4.4 Run the Program

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ros2 run pkg_action action_server_demo

image-20231030164959631

In another terminal, enter:

xxxxxxxxxx
ros2 action list

image-20231030165029444

/get_sum is the action we need to call. To do so, enter the following command in the terminal:

xxxxxxxxxx
ros2 action send_goal /get_sum pkg_interfaces/action/Progress "{num: 10}"

Here we calculate the sum of the values ​​from 1 to 10:

image-20231030165504071

The upper part of the image above shows the server, and the lower part shows the client. You can see that the server continuously reports progress while calculating the sum from 1 to 10. Finally, the task is completed, and the client receives feedback that the sum is 55.

5. Client Implementation

5.1 Creating the Client

Create a new file, action_client_demo.py, in the same directory as action_server_demo.py.

image-20231030170243256

Next, edit action_client_demo.py to implement the server functionality and add the following code:

xxxxxxxxxx
import rclpy
from rclpy.action import ActionClient
from rclpy.node import Node
from pkg_interfaces.action import Progress
​
class Action_Client(Node):
    def __init__(self):
        super().__init__('progress_action_client')
        # Create an action client;
        self._action_client = ActionClient(self, Progress, 'get_sum')
​
    def send_goal(self, num):
        # Send a request;
        goal_msg = Progress.Goal()
        goal_msg.num = num
        self._action_client.wait_for_server()
        self._send_goal_future = self._action_client.send_goal_async(goal_msg, feedback_callback=self.feedback_callback)
        self._send_goal_future.add_done_callback(self.goal_response_callback)
​
    def goal_response_callback(self, future):
        # Process the feedback after the target sends it;
        goal_handle = future.result()
        if not goal_handle.accepted:
            self.get_logger().info('请求被拒绝')
            return
​
        self.get_logger().info('请求被接收,开始执行任务!')
​
        self._get_result_future = goal_handle.get_result_async()
        self._get_result_future.add_done_callback(self.get_result_callback)
​
    #Process the final response.
    def get_result_callback(self, future):
        result = future.result().result
        self.get_logger().info('最终计算结果:sum = %d' % result.sum)
        # 5. Release resources.
        rclpy.shutdown()
​
    # Processing continuous feedback;
    def feedback_callback(self, feedback_msg):
        feedback = (int)(feedback_msg.feedback.progress * 100)
        self.get_logger().info('当前进度: %d%%' % feedback)
​
​
def main(args=None):
    rclpy.init(args=args)
    action_client = Action_Client()
    action_client.send_goal(10)
    rclpy.spin(action_client)

 

5.2 Edit the Configuration File

  • Open setup.py and add the following line to the console_scripts list:
xxxxxxxxxx
'action_client_demo = pkg_action.action_client_demo:main'

image-20231030171045187

5.3 Compile the Package

xxxxxxxxxx
colcon build --packages-select pkg_action

image-20250905143944896

5.4 Run the Program

Execute the following command in a separate terminal:

xxxxxxxxxx
# Start the server node
ros2 run pkg_action action_server_demo
# Start the client node
ros2 run pkg_action action_client_demo

image-20231030171150033

The image above shows the server on the top and the client on the bottom. Here, we're calculating the sum of 1 to 10. You can see that the server continuously reports progress during the calculation. Finally, the task is completed, and the client receives feedback that the sum is 55.

ROS2 service communication
ROS2 custom interface message

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