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Jetson ORIN NANO SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notesnew
  • 04. Disable automatic updates and troubleshooting issues
  • 05. Notes on Jetson Orin Nano Official Kitnew
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Write Jetson original system
  • 03.Installing Jetson environment
  • 04.Backup SSD system
  • 05.Restore initial image system
  • 06.Write SUPER original system(optional)
  • 07.Write SUPER boot (optional)
  • 08. SSD expansion
  • 09.Jetpack old system upgraded to Yahboom SUPER factory system
  • 10.Write the system onto your own solid-state drive
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.Vision basic course
  • 01.CSI camera preview
  • 02.USB camera preview
  • 03.Use Jupyter Lab
  • 04.Use JetCam
06.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
  • 17.Preview USB camera image
  • 18.Preview CSI camera image
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • QR code recognition
06.YOLO26 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detectionnew
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS1-Melodic
01.ROS1 basic course
  • 01. Introduction to ROS
  • 02. Project file structure
  • 03. Common commands and tools
  • 04. Publisher
  • 05. Subscribers
  • 06. Customize topic messages and use
  • 07. Client
  • 08. Server
  • 09. Customize service messages and usage
  • 10. TF release and monitoring
02.ROS1 vision course
  • 00.Must read before running
  • 01.Camera preview
  • 02.QR code
  • 03.Human posture estimation
  • 04.Target detection
  • 05.AR vision
  • 06.Opencv application
  • 07.ROS+Opencv basics
  • 08.Mediapipe development
10.ROS2-Humble
01.ROS2 basic course
  • 01.Introduction to ROS2
  • 02.ROS2 install Humble
  • 03.ROS2 development environment
  • 04.ROS2 workspace
  • 05.ROS2 function package
  • 06.ROS2 node
  • 07.ROS2 topic communication
  • 08.ROS2 service communication
  • 09.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
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera preview (CSI)
  • 03.Camera calibration (USB)
  • 04.Camera calibration (CSI)
  • 05.AR vision (USB)
  • 06.AR vision (CSI)
11.Offline AI model coursenew
Offline large model (Text Interaction)
  • 1.AI large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
  • 10.AI large model offline voice assistant
12.Online AI model coursenew
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
13.NVIDIA lsaac ROS coursenew
  • 1.Build lsaac ROS environment
  • 2.Deep segmentation
  • 3.DNN stereoscopic depth
  • 4.Free space segmentation
  • 5.Image distortion processing
  • 6.Image segmentation
  • 7.3D scene reconstruction and mapping
  • 8.Object detection
  • 9.3D attitude estimation
  • 10.Visual SLAM
14.OpenClaw Deployment and Basic Usagenew
  • 01.OpenClaw Quick Start on Jetson Platform
  • 02.OpenClaw Deployment
  • 03.OpenClaw WhatsApp Plugin Application
  • 04.OpenClaw WebChat Interaction
  • 05.OpenClaw TUI Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawHub Introduction (Skill Installation)
  • 10.OpenClaw Application - File Management
  • 11.OpenClaw Application - Camera
  • 12.OpenClaw Application - Browser Control
  • 13.OpenClaw Application - Script Execution
  • 14.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 15.OpenClaw Application - Dedicated AI Assistant
  • 16.OpenClaw Web Search Capability Configuration
15.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 01. Peripheral Hardware Configuration
  • 02. OpenClaw API-KEY Configuration
  • 03. OpenClaw Model Switching
  • 04. OpenClaw Prompts
  • 05. AI Voice Interaction Configuration
  • 06. Three Dialogue Configuration Tests
2.OpenClaw AI Programming Peripheral Control
  • 1. Servo Control
  • 2. RGB Strip
  • 3. OLED Display
  • 4. Temperature and Humidity Sensor
  • 5. Camera Application
  • 6. AI Script Peripheral Control
3.OpenClaw Peripheral Integration Applications
  • 1. Plant Care Assistant
  • 2. AI Calorie Estimation
  • 3. AI Rock Paper Scissors Game
  • 4. AI Pet
  • 5. AI Weather Station
  • 6. Temperature and Humidity Dashboard
  • 7. Scheduled Tasks
Download
  • System_File
  • VM_File
  • 3D_Model_File
  • DataSheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
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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.

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

xxxxxxxxxx
$ 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
xxxxxxxxxx
ros2 run turtlesim turtlesim_node
  • Host B run command
xxxxxxxxxx
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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