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ROSMASTER M3 PRO📖
0.Configuration and Operation Guide
  • 1. Quick start handle to control the car
  • 2. Log in to the car and view the code
  • 3. Robotic Arm Calibration
  • 4. Enter the Docker (Jetson-Nano and Raspberry Pi 5 users, see here)
  • 5. Assembly video
  • 6. Modify the firmware parameters of the car control board
  • 7. Frequently Asked Questions and Precautions
  • 8. Precautions for DIY Robotic Arm
1.Chassis Control Course
  • 1.ROS control
  • 2.Handle control
  • 3.Keyboard control
  • 4.Angular velocity calibration
  • 5.Line velocity calibration
  • 6.Line patrol automatic driving
  • 7.Patrol
2.Lidar Course
  • 1.Lidar introduction and use
  • 2.Dual Lidar fusion
  • 3.Lidar obstacle avoidance
  • 4.Lidar tracking
  • 5.Lidar guard
  • 6.Gmapping-SLAM mapping
  • 7.Cartographer-SLAM mapping
  • 8.slam_toolbox mapping
  • 9.Navigation2 single-point navigation avoid
  • 10.Navigation2 multi-point navigation avoid
  • 11.Repositioning navigation
  • 12.RTAB-Map mapping
  • 13.RTAB-Map navigation
  • 14.APP mapping navigation
  • 15.Machine code handling
  • 16.Color block transport
  • 17.Navigation via Pose-Constrained Visual Relocalizationnew
3.Depth Camera Course
  • 1.Dabai_DCW2 camera introduction
  • 2.Depth pseudo-color image
  • 3.Depth camera distance measurement
  • 4.Wood block volume measurement
  • 5.Mediapipe gesture recognition
  • 6.Edge detection
  • 7.YOLOv8 object detection
  • 8.Deep learning object tracking
  • 9. Transformer Model Tracking Algorithmnew
4.Robotic Arm and 3D Space Gripping Course
  • 1.Robotic arm solution
  • 2.Robotic arm chassis linkage control
  • 3.Machine code ID sorting
  • 4.Sorting height abnormality machine code
  • 5.Tracking and gripping machine code
  • 6.Color block color sorting
  • 7.Sorting height abnormality color block
  • 8.Tracking and gripping color block
  • 9.Wood block shape sorting
  • 10.KCF tracking and gripping objects
  • 11.Medipipe gesture ID sorting machine code
  • 12.Mediapipe gesture height sorting machine code
  • 13.Desktop tracking and gripping machine code
  • 14.3D tracking machine code
  • 15.Line patrol and obstacle removal
5.Road network planning and navigationnew
  • 1. Introduction To Road Network Planning
  • 2. Build A Pose Map
  • 3. Road Network Annotation
  • 4. Planning And Navigation Between Waypoints
  • 5. Road Network Navigation In Any Pose
  • 6. Path Re-planning
6.OpenClaw Basic Coursenew
  • 1. Introduction to OpenClaw
  • 2. OpenClaw Core Concepts
  • 3. OpenClaw API-KEY Configuration
  • 4. OpenClaw Switch Models
  • 5. OpenClaw System Prompt Words
  • 6. OpenClaw Common CLI Commands
7.OpenClaw Interaction Methodnew
  • 1. OpenClaw Webchat Interaction
  • 2. OpenClaw Tui Interaction
  • 3. OpenClaw Voice Dialogue Interaction
  • 4. OpenClaw WhatsApp Interaction
8.OpenClaw Developmentnew
  • 1. OpenClaw connects to the MCP interface of the robot
  • 2. Robot CLI Command Tool
  • 3. Robot control plugin tool
  • 4. Reply bridge plugin tool
  • 5. OpenClaw multimodal vision
  • 6. OpenClaw chassis control
  • 7. OpenClaw robotic arm control
  • 8. OpenClaw skills development
  • 9. OpenClaw timed tasks
  • 10. OpenClaw multi-agent intelligent agent
  • 11. OpenClaw active interaction
  • 12. OpenClaw vector memory
9.OpenClaw Embodied intelligent applicationnew
  • 1. OpenClaw target tracking
  • 2. OpenClaw robotic arm tracking and grasping
  • 3. OpenClaw robotic arm fixed-point placement
  • 4. OpenClaw garbage sorting
  • 5. OpenClaw slam mapping and navigation
  • 6. OpenClaw navigation transfer
  • 7. OpenClaw machine code clip
  • 8. OpenClaw color line patrol
  • 9. OpenClaw visual distance measurement
10.OpenClaw scenario sandboxnew
  • 1. Map description and prop placement
  • 2. Build sand table map
  • 3. Visual relocation markers
  • 4. Map mapping markers
  • 5. OpenClaw scenario sandbox comprehensive case
11.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
12.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
13.AI Large Model (Text Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
14.AI Large Model(Voice Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
15.Mediapipe Visual Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Facial Landmark Detection
  • 5.Face detection
  • 6.Face special effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Palm target positioning
  • 11.Fingertip trajectory recognition
  • 12.Fingertip gesture control robotic arm
  • 13.Gesture grabbing and releasing objects
  • 14.Finger control robotic arm
  • 15.Medipipe gesture control robotic arm action group
16.MoveIt2 Simulation Course
  • 1.MovelT2 configuration
  • 2.MovelT2 simulation-reality linkage
  • 3.Random movement
  • 4.Forward kinematics design
  • 5.Inverse kinematics design
  • 6.Cartesian path
  • 7.Trajectory planning
  • 8.Collision detection
  • 9.Scene design
17.Multi-vehicle Course
  • 1.Multi-vehicle chassis control
  • 2.Multi-vehicle robotic arm control
  • 3.Multi-vehicle navigation
  • 4.Multi-vehicle formation
18.Control Board Course
  • 1.Development environment setup
  • 2.Control board STM32 basic routine
  • 3.Control board micro_ros basic routine
19.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
20.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
21.Main Control Course
Jetson Nano B01
  • 1. Introduction to the Jetson Nano B01 SUB Board
  • 2. Burn the system image
  • 3. Re-read the memory card or USB drive that has been burned
  • 4. Startup system
  • 5. Jetson Nano B01 System and Desktop Introduction
  • 6. Memory card or USB flash drive expansion
  • 7. Network Configuration
  • 8. SSH remote login & file transfer
  • 9. VNC Remote Login
  • 10. Jetson Nano B01 system backup
  • 11. Jetson Nano Swap space increased
  • 12. Installation and use of Jtop
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
22.Linux System Course
  • 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
23.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
Download
  • Mapping_APP(iOS_Android)
  • 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 PRO 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.

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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