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DOFBOT SE📖
1.First Trial
  • 1.First Trial
  • 2.Analysis of common problems
  • 3.Calibration offset
  • 4.Precautions for DIY Robotic Arm
  • 5.Robotic arm gripping anomaly solutions
2.Assembly Course
  • 1.Installation video
  • 2.Calibrate the Robotic Arm
  • 3.Install the map
3.Remote Control Course
  • 1.Mobile APP control
  • 2.USB handle control
  • 3.PC software control
4.Preparation
  • 1.Manage APP control service
  • 2.Manage JupyterLab service
  • 3.Write STM32 firmware
  • 4.Introduction to expansion board
5.Basic Control Course
  • 1.Control RGB light
  • 2.Control buzzer
  • 3.Control single servo
  • 4.Read servo angle
  • 5.Control all servo
  • 6.DOFBOT up down left right
  • 7.DOFBOT dancing
  • 8.DOFBOT memory action
  • 9.DOFBOT grab block
  • 10.Porter
  • 11.Stack block
  • 12.USB camera display
  • 13.Synchronous action
6.AI Vision Basic Course
  • 1.Gesture Recognition
  • 2.Color threshold adjustment Color calibration
  • 3.Color recognition
  • 4.Face recognition
  • 5.Garbage recognition
  • 6.Yolo object recognition
  • 7.Real-time web view
7.2D Visual Tracking Course
  • 1.PID algorithm basics
  • 2.Color block positioning
  • 3.Color block tracking
  • 4.Face positioning
  • 5.Face tracking
  • 6.Gesture recognition fixed action
8.2D Visual Grasping Course
  • 0.Visual positioning
  • 1.Gesture recognition stack blocks
  • 2.Color recognition grab blocks
  • 3.Catch game
  • 4.Color sorting and stacking
  • 5.Garbage sorting
  • 6.Color interaction
9.2D Mediapipe Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall inspection
  • 4.Face detection
  • 5.Face recognition
  • 6.Face effects
  • 7.3D object recognition
  • 8.Paintbrush
  • 9.Finger control
  • 10.Gesture recognition
  • 11.Gesture control robotic arm action group
  • 12.Arm posture control robotic arm
  • 13.Palm Targeting
  • 14.Robotic arm tracks the palm
  • 15.Fingertip trajectory recognition
  • 16.Fingertip trajectory control robotic arm
  • 17.Gesture Grasp and Release Objects
  • 18.Finger Control Robot Arm
10.AI Large Model Voice Module (Must Read)
  • 1. Introduction to AI Large Model Voice Module
  • 2. Flash the firmware (Must read)
  • 3. Modify the wake-up word and command word
11.2D Voice Control Course
  • 1.Simple Voice Control
  • 2.Smart Voice Control
  • 3.Voice Action Control
  • 4.Memory Learning Mode
  • 5.Color Recognition with Voice Broadcast
  • 6.Color Recognition and Grasping with Voice Broadcast
  • 7.Color Recognition and Sorting with Voice Broadcast
  • 8.Voice Recognition Target Tracking
  • 9.Voice Control Stacking
  • 10.Voice Control Color Sorting
  • 11.Garbage Recognition with Voice Broadcast
  • 12.Garbage Sorting with Voice Broadcast
12.Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
13.3D Sorting and Grasping Course
  • 1.AprilTag ID sorting
  • 2.MediaPipe Gesture-AprilTag ID sorting
  • 3.MediaPipe Gesture-AprilTag distance sorting
  • 4.Block color sorting
  • 5.Block different shape sorting
  • 6.Yolov11 garbage sorting
14.3D Tracking Course
  • 1.AprilTag Tracking
  • 2.Color Block Tracking
  • 3.KCF Object Tracking
15.3D Voice Control for Sorting & Tracking Course
  • 1.Voice Control AprilTag ID Sorting
  • 2.Voice Control Color Block Sorting
  • 3.Voice Control Yolov11 Garbage Recognition Sorting
  • 4.Voice Control AprilTag Tracking
  • 5.Voice Control Color Block Tracking
  • 6.Voice Control Object Tracking
16.AI Model Basics
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
17.AI Model Development
  • 1. Register a model service provider account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. AI Large Model Development - chatbot
  • 6. Deploy the RAG knowledge base
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
18.AI Model – Text Version
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Text Large Model + robotic arm control
  • 4.Multimodal Large Model+video understanding
  • 5.Multimodal Large Model+robotic arm gripping
  • 6.Multimodal Large Model+robotic arm tracking
  • 7.Multimodal Large Model+robotic arm sorting
  • 8.Multimodal Large Model+robotic arm handling
  • 9.Multimodal Large Model+Color Blocks Back In Place
  • 10.Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11.Text Large Model + YOLOv11 + Intelligent Garbage Classification
19.AI Model – Voice Version
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Text Large Model + robotic arm control
  • 4.Multimodal Large Model+video understanding
  • 5.Multimodal Large Model+robotic arm gripping
  • 6.Multimodal Large Model+robotic arm tracking
  • 7.Multimodal Large Model+robotic arm sorting
  • 8.Multimodal Large Model+robotic arm handling
  • 9.Multimodal Large Model+Color Blocks Back In Place
  • 10.Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11.Text Large Model + YOLOv11 + Intelligent Garbage Classification
20.AI Model – Fun Applications
  • 1.AI Butler-Intent Inference
  • 2.AI Encyclopedia-Arithmetic Master
  • 3.AI Encyclopedia-You asked I Pointed
  • 4.AI Encyclopedia-Nutritional Analysis
  • 5.AI Encyclopedia-Problem Solving Expert
  • 6.AI Encyclopedia-English Spelling
  • 7.AI Butler-Garbage Sorting
  • 8.AI Top Chef
21.MoveIt2 Course
  • 1. MoveIt Configuration
  • 2. MoveIt Usage
  • 3. MoveIt Drive Real Robot
  • 4. MoveIt Random Movement
  • 5. MoveIt Forward Kinematics Design
  • 6. MoveIt Inverse Kinematics Design
  • 7. MoveIt Cartesian Path
  • 8. MoveIt Scene Design
  • 9. MoveIt Trajectory Planning
  • 10. MoveIt Collision Detection
22.YOLOv11 Development Course
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Model Prediction
23. Linux Operating System
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
24.OpenCV Course
  • 1.Introduction to Open Source CV
  • 2.Image reading and display
  • 3.Picture writing
  • 4.Image Quality
  • 5.Pixel operations
  • 6.Image zoom
  • 7.Picture cut
  • 8.Picture pan
  • 9.Picture mirroring
  • 10.Affine transformation
  • 11.Picture rotation
  • 12.perspective transformation
  • 13.Grayscale processing
  • 14.Binary image
  • 15.Edge green detection
  • 16.Line segment drawing
  • 17.Rectangular circle drawing
  • 18.Text and picture drawing
25.Advanced Visuals Course
  • 1. QR Code
  • 2. Human Pose Estimation
  • 3. Object Detection
  • 4. AR Vision
  • 5. ROS + OpenCV Basics
26.ROS2 Basic Course
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.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
27.Jupyter Lab Usage Course
  • 0.Read the document before operation
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Widgets
  • 3.Launch JupyterLab
  • 4.How to Use JupyterLab
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Welcome to DOFBOT SE repository

9.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:
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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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