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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

13.ROS2 distributed communication

13. ROS2 Distributed Communication

1. Concept

Multi-machine communication, or distributed communication, refers to a communication strategy that enables data exchange between different hosts over a network.

ROS2 itself is a distributed communication framework that facilitates communication between different devices. The middleware underlying ROS2 is DDS. When running on the same network, distributed communication is achieved through the DDS domain ID mechanism (ROS_DOMAIN_ID). The general process is as follows: Before starting a node, you can set the domain ID value. Nodes with the same domain ID can freely discover and communicate with each other. Conversely, if the domain ID values ​​are different, this communication is impossible. By default, all nodes start with domain ID 0. In other words, as long as they are on the same network, distributed communication can be achieved between different nodes on different ROS2 devices without any configuration.

Distributed communication has a wide range of application scenarios, including unmanned vehicle platooning, drone platooning, and remote control. These data exchanges all rely on distributed communication.

2. Implementation

2.1. Default Implementation

Distributed communication is achieved by simply placing the master and slave devices (you can have multiple devices) on the same network. For example, the master and slave devices can be connected to the same WiFi or router.

In Windows, setting the virtual machine to "bridge mode" will place them on the same network as the host.

Test:

Here, we assume we have two hosts, A and B. These can be any network-capable host, such as a virtual machine, Raspberry Pi, Jetson, x86/ARM host, or card motherboard. They only need to have the same version of the ROS2 environment installed.

  1. Execute on host A:

This demonstration shows the car running in Docker. Docker uses host mode. Simply put, host mode means the car shares the same network, so executing the command is identical to running the command on the car.

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ros2 run demo_nodes_py talker
  1. Host B executes:
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ros2 run demo_nodes_py listener

If the following display appears: The slaves can subscribe to the topics published by the host, multi-machine communication has been achieved.

2.2. Distributed Network Grouping

If you are currently using other robots in your network, you can also set up a group for your robot to prevent interference from other robots.

ROS2 provides a DOMAIN mechanism, similar to grouping. Only computers in the same DOMAIN can communicate. We can assign both the host (car) and the slave (virtual machine) to the same group by adding the following line to their .bashrc files:

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$ export ROS_DOMAIN_ID=<your_domain_id>

If the host (car) and the slave (virtual machine) are assigned different IDs, they will not be able to communicate, thus achieving the purpose of grouping.

2.2.1 Example 1

  1. Execute on the host (car):

This demonstrates the car running in Docker, which uses host network mode. Simply put, host mode means the car shares the same network with the car, so the execution is identical to running on the car.

echo "export ROS_DOMAIN_ID=6" >> ~/.bashrc # The value 6 here refers to the ROS_DOMAIN_ID. It doesn't have to be 6, as long as it conforms to the ROS_DOMAIN_ID rules.
source ~/.bashrc
ros2 run demo_nodes_py talker
  1. Simultaneously, execute the following command on the slave machine [virtual machine]:
echo "export ROS_DOMAIN_ID=6" >> ~/.bashrc # This value matches the value on the master machine.
source ~/.bashrc
ros2 run demo_nodes_py listener

If the following message appears: The slave machine can subscribe to the topic published by the master machine in a timely manner, indicating that grouped multi-machine communication has been achieved.

2.2.2 Case 2

Controlling turtle movement through distributed communication

  • Host A run command
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ros2 run turtlesim turtlesim_node
  • Host B run command
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ros2 run turtlesim turtle_teleop_key

3. Notes

Setting the ROS_DOMAIN_ID value is not arbitrary and is subject to certain constraints:

  1. It is recommended that the ROS_DOMAIN_ID value be between [0, 101], inclusive;
  2. The total number of nodes within each domain ID is limited and must be less than or equal to 120;
  3. If the domain ID is 101, the total number of nodes in that domain must be less than or equal to 54.

4. DDS Domain ID Calculation Rules (Advanced Knowledge)

The calculation rules for domain ID values ​​are as follows:

  1. DDS is based on the TCP/IP or UDP/IP network communication protocol. Network communication requires a specified port number, which is represented by a 2-byte unsigned integer and has a value range of [0, 65535].
  2. Port number assignment is also based on specific rules and cannot be used arbitrarily. According to the DDS protocol, port 7400 is used as the starting port, meaning the available ports are [7400, 65535]. Furthermore, according to the DDS protocol, each domain ID occupies 250 ports by default. Therefore, the number of domain IDs is: (65535 - 7400) / 250 = 232, corresponding to a value range of [0, 231].
  3. The operating system also sets some reserved ports. When using ports in DDS, you need to avoid these reserved ports to avoid conflicts. Different operating systems have different reserved ports. As a result, in Linux, the available domain IDs are [0, 101] and [215-231]. In Windows and Mac, the available domain IDs are [0, 166]. In summary, for compatibility across multiple platforms, it is recommended that domain IDs be within the range of [0, 101].
  4. Each domain ID occupies 250 ports by default, and each ROS2 node requires two ports. Furthermore, according to the DDS protocol, ports 1 and 2 within each domain ID's port range are the Discovery Multicast port and the User Multicast port. Ports 11 and 12 onwards are the Discovery Unicast port and the User Unicast port for the first node in the domain, and subsequent nodes occupy these ports in descending order. Therefore, the maximum number of nodes in a domain ID is: (250 - 10) / 2 = 120.
  5. Special case: For domain ID 101, the second half of the ports are reserved by the operating system, and the maximum number of nodes is 54.

The above calculation rules are sufficient for understanding.

ROS2 meta-function package
ROS2 DDS

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