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ROSMASTER M3📖
00.Configuration and Operation Guide
  • 1.Quick start handle to control the car
  • 2.Log in to the car and view the code
  • 3.Enter the Docker (For RPI5)
  • 4.Camera and Radar Configuration (Must Read)
  • 5.Assembly video
  • 6.Modify Control Board Firmware Parameters
01.Chassis Control Course
  • 01.ROS control
  • 02.Handle control
  • 03.Keyboard control
  • 04.Robot state estimation
  • 05.Data remote visualization
  • 06.Line velocity calibration
  • 07.Angular velocity calibration
  • 08.Robot URDF model
  • 09.Line patrol automatic driving
  • 10.Patrol
02.Lidar Course
  • 01.Lidar introduction and use
  • 02.Lidar point cloud fusion and filtering
  • 03.Lidar obstacle avoidance
  • 04.Lidar tracking
  • 05.Lidar guard
  • 06.Gmapping-SLAM mapping
  • 07.Cartographer-SLAM mapping
  • 08.slam_toolbox mapping
  • 09.Lifelong mapping
  • 10.Navigation2 single-point navigation avoid
  • 11.Navigation2 multi-point navigation avoid
  • 12.Cartographer relocalization navigation
  • 13.slam_toolbox localization and navigation
  • 14.Synchronous mapping and navigation
  • 15.RTAB-Map mapping
  • 16.RTAB-Map navigation
  • 17.APP mapping and navigation
03.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
04.AI Model Development
  • 01.Register a model service provider account
  • 02.Configuring API-KEY
  • 03.Introduction to Dify
  • 04.Basic Dify Features
  • 05.AI Large Model Development - chatbot
  • 06.Deploy the RAG knowledge base
  • 07.RAG knowledge base + chatbot
  • 08.AI agent workflow
  • 09.Custom wake-up response
  • 10.Core module testing tools
05.AI Model - Voice Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understanding
  • 3.Multimodal visual understanding+SLAM navigation
  • 4.Intention estimation
06.AI Model - Text Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understanding
  • 3.Multimodal visual understanding+SLAM navigation
  • 4.Intention estimation
07.Road Network Planning and Navigation
  • 1.Introduction and Quick Start
  • 2.Constructing a Pose Map
  • 3.Road Network Labelling
  • 4.Inter-Road Network Planning and Navigation
  • 5.Free-Pose Road Network Navigation
  • 6.Path Replanning
08.Autonomous Driving expand(Need purchase track map)
  • 01.Environment Setup & Precautions
  • 02.YOLO Environment Configuration
  • 03.Data Collection
  • 04.Data Annotation
  • 05.YOLOv11 Model Training
  • 06.Model Format Conversion
  • 07.Vehicle Centerline Calibration
  • 08.Lane Detection
  • 09.Lane-Keeping Autonomous Driving
  • 10.Traffic Sign Recognition
  • 11.Comprehensive Autonomous Driving Applications
  • 12.Autonomous Driving Debugging Module
  • 13.Road Network Planning with Track Map
  • 14.Road Network Planning with Traffic Sign Recognition
  • 15.AI Model with Road Network Planning
  • 16.AI Model with Track Map and SLAM Navigation
  • 17.AI Model with Road Network Planning and Autonomous Driving
09.Depth Camera Course
  • 01.Dabai_DCW2 Camera Introduction
  • 02.Nuwa-HP60C Camera Introduction
  • 03.Depth Pseudo-Color Image
  • 04.Depth Camera Distance Measurement
  • 05.Wood Block Volume Measurement
  • 06.Edge Detection
  • 07.YOLOv11 Object Detection
  • 08.YOLOv11 Object Tracking
  • 09.Color Following
  • 10.Face Following
  • 11.QR Code Following
  • 12.Machine Code Following
  • 13.KCF Object Tracking
  • 14.QR Code Motion Control
  • 15.AR Vision
10.Mediapipe Visual Course
  • 01.Hand Detection
  • 02.Posture Detection
  • 03.Overall Detection
  • 04.Facial Landmark Detection
  • 05.3D Object Recognition
  • 06.Face Detection
  • 07.Gesture Recognition
  • 08.Face Special Effects
  • 09.Brush
  • 10.Finger Control
  • 11.Palm Target Positioning
  • 12.Fingertip Trajectory Recognition
  • 13.Gesture Following
  • 14.Posture Following
  • 15.Gesture Motion Control
11.Multi-vehicle Course
  • 1.Multi-vehicle Control
  • 2.Multi-vehicle Navigation
  • 3.Multi-vehicle Platoon
12.AI Large Model Basic Course
Offline large model (Text Interaction)
  • 0. AI Large Model Mirroring Explanation
  • 1. Deployment of large-scale AI models
  • 2. Installation of the large model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 Model
  • 5. DeepSeek DeepSeek-R1 model
  • 6. Microsoft Phi-4 mini model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Llava Visual Multimodal Large Model
  • 10. Multimodal Visual Understanding Applications
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Text-Based Image Application
  • 13. Multimodal Video Analysis Application
  • 14. Multimodal table scanning application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
Online large model (Text Interaction)
  • 1. OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal visual understanding applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
13.Control Board Course
1.Development environment setup
  • 1.Introduction to the Control Board
  • 2.Set up the STM32CUBEIDE development environment
  • 3.Burning STM32 firmware using SWD
  • 4.Burning STM32 firmware using serial port
  • 5.Compile the microros driver library
  • 6.Install and start the microros agent
  • 7.Import Project
2.Control board STM32 basic routine
  • 1.Turn on the LED light
  • 2.Button functions
  • 3.Drive the buzzer
  • 4.Serial communication
  • 5.Battery voltage detection
  • 6.Driving PWM Servo
  • 7.Drive motor
  • 8.Read motor encoder data
  • 9.PID control of motor speed
  • 10.Robot kinematics analysis theory
  • 11.Read IMU data
  • 12.Read Lidar data
  • 13.Flash access data
  • 14.Driving OLED Displays
  • 15.Driving RGB light strip
  • 16.SBUS model aircraft remote control
  • 17.USB controller remote control
  • 18.CAN bus communication
3.Control board micro_ros basic routine
  • 1.Post a topic
  • 2.Subscribe to a topic
  • 3.Multi-topic subscription and publishing
  • 4.Subscribe to the buzzer topic
  • 5.Subscribe to the bus servo topic
  • 6.Subscribe to the rate control topic
  • 7.Release Speed Topic
  • 8.Publish IMU data topic
  • 9.Publish radar data topic
14.Main Control Course
Jetson Orin Nano_NX
  • 1.Jetson Orin board introduction
  • 2.Restore the factory image system
  • 3.Backup SSD system
  • 4.SSD expansion
  • 5.Network configuration
  • 6.SSH remote login
  • 7.VNC remote control
  • 8.Remote file transfer
  • 9.Jtop tool
  • 10.Exchange space expansion
Raspberry Pi
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Update and upgrade operating system
  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
  • 8. Network Configuration
  • 9. remote access
  • 10. Transfer files remotely
  • 11. Set display resolution and rotation
  • 12. Set screen to sleep
  • 13. Play audio and video
  • 14. Using USB camera
  • 15. Using MIPI camera
  • 16. Get real-time temperature of Raspberry Pi
RDKX5
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Network configuration
  • 6.Remote login
  • 7.Update system
  • 8.srpi-config user guide
  • 9.config. txt configuration file
  • 10.Self-startup setting
  • 11.RDK exclusive command usage
  • 12.Linux command usage
  • 13.GPIO readwrite operation
  • 14.Image classification algorithm
  • 15.Use USB camera
  • 16.Use MIPI camera
  • 17.Binocular camera depth algorithm
15.YOLOv11 Development (ORIN)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Dataset annotation
  • 10.Model Training_Conversion
  • 11.Model prediction
16.YOLOv11 Development(RDKX5)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model prediction
  • 9.Model Quantization
17.YOLOv11 Development (RPI)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Model prediction
18.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
19.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
20.Docker Course
  • 1、Docker overview and docker installation
  • 2、Common commands for docker image containers
  • 3、Docker images deeply understand and publish images
  • 4、Docker hardware interaction and data processing
  • 5、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
21.Image Processing Basics Course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Image cutting
  • 8. Image translation
  • 9. Image Mirroring
  • 10. Affine Transformation
  • 11. Image Rotation
  • 12. Perspective Transformation
  • 13. Grayscale processing
  • 14. Image Binarization
  • 15. Edge Detection
  • 16. Line drawing
  • 17. Rectangle and circle drawing
  • 18. Drawing text and pictures
Download
  • Mapping_APP(iOS_Android)
  • MainBoard_AI_Pure_System
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • VM_File
  • Software_Tools
  • Firmware_DriverFile
  • 3D_Model_File
  • ROS2_Basic_VM
  • Other_Annex
Download ZIP

Welcome to ROSMASTER M3 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:

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#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:
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'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:

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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

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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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