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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

14.ROS2 DDS

14. ROS2 DDS

1. Introduction to DDS

DDS stands for Data Distribution Service. Published and maintained by the Object Management Group (OMG) in 2004, it is a data distribution/subscription standard designed specifically for real-time systems. It was first adopted by the US Navy to address compatibility issues with large-scale software upgrades within the complex network environments of ships. It has now become a mandatory standard.

DDS emphasizes data-centricity and provides a rich set of quality-of-service policies to ensure real-time, efficient, and flexible data distribution, meeting the needs of various distributed real-time communication applications.

References

  • Fast DDS Official Documentation: 3.3.0
  • ROS2 official document DDS advanced functions: Using Fast DDS Discovery Server as discovery protocol community-contributed] — ROS 2 Documentation: Humble documentation

2. Communication Model

The topics, services, and actions we learned about in the previous course all rely on DDS for their underlying communication implementation. DDS is equivalent to the neural network in the ROS robot system.

The core of DDS is communication. Numerous models and software frameworks exist to implement this communication. Here we list four commonly used models.

image-20220528020740057

  • The first, the Peer-to-Peer model, involves many clients connecting to a single server. Each communication requires a connection between the two parties. As the number of communicating nodes increases, the number of connections also increases. Furthermore, each client needs to know the server's specific address and the services it provides. If the server's address changes, all clients are affected.
  • The second, the Broker model, optimizes the peer-to-peer model. The broker centrally processes all requests and then identifies the roles that can truly respond to the service. This eliminates the need for clients to worry about the server's specific address. However, the problem is obvious: as the core, the broker's processing speed affects the efficiency of all nodes. Once the system scales to a certain level, the broker can become a performance bottleneck for the entire system. Furthermore, if the broker fails, the entire system can cease to function properly. The previous ROS1 system used a similar architecture.
  • The third model is the Broadcast model, in which all nodes can broadcast messages on a channel and receive them. This model solves the server address problem and eliminates the need for separate connections between communicating parties. However, the sheer number of messages on the broadcast channel forces all nodes to pay attention to every message, many of which are irrelevant to them.
  • The fourth model is the data-centric DDS model. This model is somewhat similar to the broadcast model, in which all nodes can publish and subscribe to messages on the DataBus. However, its advantage lies in the fact that communication involves multiple parallel pathways, allowing each node to focus only on messages of interest and ignore those it doesn't. It's a bit like a rotating hot pot, with all the delicious food being transferred across the DataBus. Each node only needs to take what it wants; the rest is irrelevant to it.

Of these communication models, DDS has a clear advantage.

3. Application of DDS in ROS2

DDS plays a crucial role in the ROS2 system. All upper-layer infrastructure is built on DDS. In this ROS2 architecture diagram, the blue and red components represent DDS.

image-20220528020937717

Among the four major components of ROS, the addition of DDS greatly enhances the comprehensive capabilities of the distributed communication system. This allows us to avoid worrying about communication issues during robot development and focus more time on other application development components.

4. Quality of Service (QoS)

The basic structure in DDS is the domain. A domain binds applications together for communication. Recall that when we configured communication between the Raspberry Pi and the computer, the domain ID we configured earlier defines the grouping of the global data space. Only nodes within the same domain group can communicate with each other. This prevents useless data from occupying resources.

Another important feature of DDS is the quality of service (QoS).

QoS is a network transmission policy. Applications specify the desired network transmission quality behavior, and the QoS service implements this behavior to meet the client's communication quality requirements as much as possible. It can be thought of as a contract between data providers and receivers.

The policies are as follows:

  • DEADLINE policy: This policy requires data to be transmitted within a certain deadline.
  • HISTORY policy: This policy specifies the cache size for historical data.
  • RELIABILITY policy: This policy specifies the data communication mode. Configuring BEST_EFFORT ensures smooth data transmission even when network conditions are poor, which may result in data loss. Configuring RELIABLE ensures reliable communication and maximizes image integrity. We can choose the appropriate communication mode based on the application scenario.
  • DURABILITY policy: This policy ensures that a certain amount of historical data is sent to late-joining nodes, allowing new nodes to quickly adapt to the system.

5. Test Cases

5.1 Case 1—Configuring DDS via the Command Line

  • Open the first terminal and publish the topic using the following command:
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ros2 topic pub /chatter std_msgs/msg/Int32 "data: 66" --qos-reliability best_effort

image-20250827115245194

  • Open another terminal and print the topic using a different QoS. If the QoS policy used differs from the publisher's, a warning message will appear indicating that the topic data cannot be received properly:
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ros2 topic echo /chatter --qos-reliability reliable

image-20250827115358309

  • We can receive topic data using the same QoS policy as the topic publisher.
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ros2 topic echo /chatter --qos-reliability best_effort

image-20250827120250112

5.2 Example 2—Configuring QoS Service Policies for Topic Nodes

  • Create a new function package using the following command
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ros2 pkg create learning_dds --build-type ament_python --dependencies rclpy std_msgs
  • Create a new file, dds_controller_pub.py, as the publisher for topic communication. Fill in the following content:
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import rclpy
from rclpy.node import Node
from std_msgs.msg import String
# Import QoS related classes
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy
​
class ControllerPublisher(Node):
    def __init__(self, name):
        super().__init__(name)
        #1. Configure QoS policy: reliable transmission, retain the last historical data
        self.qos_profile = QoSProfile(
            reliability=QoSReliabilityPolicy.RELIABLE,  # Reliable transmission (retransmission of lost data)
            history=QoSHistoryPolicy.KEEP_LAST,         # Keep the last N pieces of data
            depth=1                                     # Keep 1 historical data
        )
        # 2. Create a publisher: topic name /robot_cmd, message type String, QoS policy
        self.publisher = self.create_publisher(
            String, 
            "/robot_cmd", 
            self.qos_profile
        )
        # 3. Create a timer: send a command once per second
        self.timer = self.create_timer(1.0, self.timer_callback)
        self.cmd_list = ["forward", "backward", "stop"]  # Instruction List
        self.cmd_index = 0  #Instruction index, loop switching
​
​
    def timer_callback(self):
        # Cycle switching instructions (forward → backward → stop → forward...)
        current_cmd = self.cmd_list[self.cmd_index % 3]
        # Create a message and fill it with data
        msg = String()
        msg.data = current_cmd
        # Publish a message
        self.publisher.publish(msg)
        # Print log (shows issued commands)
        self.get_logger().info(f"发布控制指令:{msg.data}")
        # Update command index
        self.cmd_index += 1
​
​
def main(args=None):
    # Initializing ROS2
    rclpy.init(args=args)
    # Create a publisher node
    node = ControllerPublisher("robot_controller_pub")
    # Looping nodes
    rclpy.spin(node)
    # Destroy the node and shut down ROS2
    node.destroy_node()
    rclpy.shutdown()
​
​
if __name__ == "__main__":
    main()

 

  • To implement the subscriber code, create a new dds_robot_sub.py file and fill in the following subscriber implementation code
xxxxxxxxxx
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
from rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy
​
class RobotSubscriber(Node):
    def __init__(self, name):
        super().__init__(name)
        # 1. Configure a QoS policy compatible with the publisher
        self.qos_profile = QoSProfile(
            reliability=QoSReliabilityPolicy.BEST_EFFORT,
            history=QoSHistoryPolicy.KEEP_LAST,
            depth=1
        )
        # 2. Create a subscriber: topic name/robot_cmd, callback function, QoS policy
        self.subscription = self.create_subscription(
            String,
            "/robot_cmd",
            self.cmd_callback,  # Callback function executed after receiving data
            self.qos_profile
        )
​
    def cmd_callback(self, msg):
        # Callback function: Processes received instructions
        self.get_logger().info(f"接收控制指令:{msg.data} → 执行对应动作")
​
​
def main(args=None):
    rclpy.init(args=args)
    node = RobotSubscriber("robot_subscriber")
    rclpy.spin(node)
    node.destroy_node()
    rclpy.shutdown()
​
​
if __name__ == "__main__":
    main()
  • Configure the compilation file (setup.py)
xxxxxxxxxx
entry_points={
    'console_scripts': [
        # Publisher node: command name = package name. file name: main function
        'dds_controller_pub = learning_dds.dds_controller_pub:main',
        # Subscriber Node
        'dds_robot_sub = learning_dds.dds_robot_sub:main',
    ],
},

image-20250905153513257

 

  • Open the terminal in the workspace directory and compile the function package
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colcon build --packages-select learning_dds

image-20250905153611086

 

  • Set environment variables. You need to set environment variables every time you recompile.
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source install/setup.bash
  • Running publisher and subscriber nodes
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ros2 run learning_dds dds_controller_pub
xxxxxxxxxx
ros2 run learning_dds dds_robot_sub

image-20250905153734043

 

Advanced Robot Development:

  • Typically, publishers and subscribers of topic communications should maintain the same QoS communication strategy to avoid potential communication layer issues.
  • QoS communication strategy settings should be selected based on the specific application scenario.
  • If the application scenario involves drone communication, encrypted communication, real-time communication, or other scenarios with special communication layer requirements, the official Fast-DDS manual is included in the tutorial introduction for a more detailed description of DDS features.

image-20250905155146210

ROS2 distributed communication
ROS2 time related API

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