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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

8.ROS2 service communication

8. ROS2 Service Communication

1. Introduction to Service Communication

Service communication is a request-response communication model. On both sides of the communication chain, the client sends a request to the server, and the server responds to the client.

The client/server model is as follows:

image8

From the perspective of service implementation, this Q&A format is called the client/server model, or CS model for short. When a client needs certain data, it sends a request to a specific service. After receiving the request, the server processes the request and returns a response.

This communication mechanism is also very common in everyday life. For example, in the various web pages we frequently browse, your computer browser is the client. It sends a request to the website server using a domain name or various operations. The server then responds with the page data to be displayed.

2. Create a new package

  • In the src directory of the workspace:
xxxxxxxxxx
ros2 pkg create pkg_service --build-type ament_python --dependencies rclpy --node-name server_demo

After executing the above command, the pkg_service package will be created, along with a server_demo node and the relevant configuration files.

3. Server Implementation

3.1 Creating the Server

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

xxxxxxxxxx
#Import related libraries
import rclpy
from rclpy.node import Node
from example_interfaces.srv import AddTwoInts
​
class Service_Server(Node):
    def __init__(self,name):
        super().__init__(name)
        #Create a server using the create_service function. The parameters passed in are:
        #Service data type, service name, service callback function (i.e., service content)
        self.srv = self.create_service(AddTwoInts, '/add_two_ints', self.Add2Ints_callback)
    #The service callback function here adds two integers and returns the result.   
    def Add2Ints_callback(self,request,response):
        response.sum = request.a + request.b
        print("response.sum = ",response.sum)
        return response
def main():
    rclpy.init()
    server_demo = Service_Server("publisher_node")
    rclpy.spin(server_demo)
    server_demo.destroy_node()                     #Destroy the node object
    rclpy.shutdown()                               #Shut down the ROS2 Python interface

Focus on the service callback function, Add2Ints_callback. Besides self, the required parameters here are request and response. Request is the parameter required by the service, and response is the service's feedback. Request.a and request.b are the contents of the request, and response.sum is the contents of the response. First, let's take a look at what the data type AddTwoInts looks like.

  • You can use the following command to view it:
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ros2 interface show example_interfaces/srv/AddTwoInts

image-20231023183333410

The "---" character divides the data type into two parts: the top part represents the request, and the bottom part represents the response. Each field then contains its own variable, such as int64 a and int64 b. When passing parameters, you must specify the values ​​of a and b. Similarly, the feedback result also needs to specify the sum value.

3.2 Editing the Configuration File

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

image-20231023183130612

3.3 Compiling the Package

  • Compiling the Package
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colcon build --packages-select pkg_service

image-20250905121820983

3.4 Running the Program

  • Refresh the environment variables and run the node.
xxxxxxxxxx
ros2 run pkg_service server_demo

image-20250905121914903

After running, since the service is not called, there is no feedback. You can call the service through the command line. First, query the current services. In another terminal, enter:

xxxxxxxxxx
ros2 service list

image-20231023183459015

/add_two_ints is the service we need to call. Call it with the following command. In the terminal, enter:

xxxxxxxxxx
ros2 service call /add_two_ints example_interfaces/srv/AddTwoInts "{a: 1, b: 4}"

Here, we assign the value of a to 1 and the value of b to 4, effectively calling the service to calculate the sum of 1 and 4:

image-20231023183636466

As shown in the image above, after calling the service, the result returned is 5, and the terminal running the server also prints the returned value.

4. Client Implementation

4.1 Creating the Client

Create a new file, [client_demo.py], in the same directory as [server_demo.py].

image-20231023184928672

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

xxxxxxxxxx
#Import related libraries
import rclpy
from rclpy.node import Node
from example_interfaces.srv import AddTwoInts
​
class Service_Client(Node):
    def __init__(self,name):
        super().__init__(name)
        #Create the client using the create_client function, passing in the service data type and service topic name as parameters.
        self.client = self.create_client(AddTwoInts,'/add_two_ints')
        #Loop waiting for the server to successfully start
        while not self.client.wait_for_service(timeout_sec=1.0):
            print("service not available, waiting again...")
        #Create the service request data object
        self.request = AddTwoInts.Request()
        
    def send_request(self): 
        self.request.a = 10
        self.request.b = 90
        #Send the service request
        self.future = self.client.call_async(self.request)
        
def main():
    rclpy.init() #Node initialization
    service_client = Service_Client("client_node") #Create object
    service_client.send_request() #Send service request
    while rclpy.ok():
        rclpy.spin_once(service_client)
        #Determine if data processing is complete
        if service_client.future.done():
            try:
                #Get service feedback and print it
                response = service_client.future.result()
                print("service_client.request.a = ",service_client.request.a)
                print("service_client.request.b = ",service_client.request.b)
                print("Result = ",response.sum)
            except Exception as e:
                service_client.get_logger().info('Service call failed %r' % (e,))
        break
    service_client.destroy_node()                    
    rclpy.shutdown()                              

 

4.2 Edit the Configuration File

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

image-20231023185118116

4.3 Compile the Package

xxxxxxxxxx
colcon build --packages-select pkg_service

image-20250905121820983

4.4 Run the Program

  • Refresh the environment variables and run the node
xxxxxxxxxx
# Start the server node
ros2 run pkg_service server_demo
# Start the client node
ros2 run pkg_service client_demo

image-20250905122940643

First run the server, then the client. The client provides a=10 and b=90. The server performs the sum and obtains 100. The result is printed on both terminals.

ROS2 topic communication
ROS2 action communication

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