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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

01.ROS control

ROS Control

ROS Control1. Course Content2. Preparations2.1 Content Description2.2 Start the Agent3. Launch Commands3.1 Function Description3.2 Program Start3.2.1 View Node Information3.2.2 Publish Control Commands3.2.3 Control the Car's Buzzer3.2.4 Control the Car's Light Strip3.2.5 Subscribe to LiDAR Data3.2.6 Subscribe to Battery Level Data3.2.7 Subscribe to IMU Data3.2.8 Subscribe to Odometry Data

 

1. Course Content

  1. Learn the basic method of controlling a robot via ROS.

This function allows for controlling the car's speed and buzzer using the ros2 topic tool, and also for reading low-level data such as LiDAR data, IMU data, odometry data, etc.

2. Preparations

2.1 Content Description

This lesson uses the Jetson Orin NX as an example. For Raspberry Pi and Jetson Nano boards, you need to open a terminal on the host machine, enter the command to get into the Docker container, and then input the commands mentioned in this lesson in the terminal within the Docker container. A tutorial on how to enter the Docker container from the host can be found in this product's guide [00.Configuration and Operation Guide]-[3.Enter the Docker (For RPI5)]. For Orin and RDKX5 boards, you can directly open a terminal and enter the commands mentioned in this lesson.

2.2 Start the Agent

Note: To test all examples, you must start the Docker agent first. If it is already running, you do not need to start it again. The agent starts by default on boot, so there is no need to restart it.

Enter the command in the vehicle's terminal:

start_agent

The terminal will print the following information, indicating a successful connection:

image-20250613104101795

 

3. Launch Commands

3.1 Function Description

This function allows for controlling the car's speed and buzzer using the ros2 topic tool, and also for reading low-level data such as LiDAR data, IMU data, odometry data, etc.

3.2 Program Start

3.2.1 View Node Information

After successfully connecting to the agent, you can enter ros2 node list in the terminal to see which nodes are available.

For Raspberry Pi 5 users, if the ROS container is not started, you need to start it first,

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bringup_m3

Open a terminal inside the container,

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exec_m3

Enter the command,

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ros2 node list

The execution screenshot is as follows,

image-20251205105559096

Here, /YB_Node indicates that the low-level control node has started. Enter the following command in the terminal to query information about this node,

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ros2 node info /YB_Node

The execution screenshot is as follows,

image-20251205105901217

A table has been compiled here to show which topics the node publishes and subscribes to, along with the message data type for each topic. The table is as follows,

Subscription Topics Table

Subscribed TopicTopic Message TypeTopic Attribute
/beepstd_msgs/msg/UInt16Control Buzzer
/cmd_velgeometry_msgs/msg/TwistControl Car Speed
/rgbstd_msgs/msg/ColorRGBAControl Light

Publication Topics Table

Published TopicTopic Message TypeTopic Attribute
/batterystd_msgs/msg/Float32Publish Battery Level Data
/imu/data_rawsensor_msgs/msg/ImuPublish IMU Data
/odom_rawnav_msgs/msg/OdometryPublish Odometry Data

LiDAR Topics for Dual-LiDAR Version

Published TopicTopic Message TypeTopic Attribute
/scan0sensor_msgs/msg/LaserScanLeft-Rear LiDAR Data of the Car
/scan1sensor_msgs/msg/LaserScanRight-Front LiDAR Data of the Car

LiDAR Topics for Single-LiDAR Version

Published TopicTopic Message TypeTopic Attribute
/scan2sensor_msgs/msg/LaserScanLiDAR First-Half Scan Data
/scan3sensor_msgs/msg/LaserScanLiDAR Second-Half Scan Data

 

 

3.2.2 Publish Control Commands

Based on the subscription topics table, use the command format ros2 topic pub <topic_name> <message_type> <data> --once to publish a single frame of control data.

 

For the first test, it is recommended to lift the car so that its wheels are off the ground. We will make the car move forward at a linear velocity of 0.1m/s. Enter the following command in the terminal,

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ros2 topic pub /cmd_vel geometry_msgs/msg/Twist "{linear: {x: 0.1, y: 0.0, z: 0.0}, angular: {x: 0.0, y: 0.0, z: 0.0}}" --once

After execution, the car will move forward at a speed of 0.1m/s. Similarly, to control the car to rotate at an angular velocity of 1.0rad/s, assign a value to the angular z component. The command is as follows,

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ros2 topic pub /cmd_vel geometry_msgs/msg/Twist "{linear: {x: 0.0, y: 0.0, z: 0.0}, angular: {x: 0.0, y: 0.0, z: 1.0}}" --once

After execution, the car will rotate. If we want to stop the car, we can publish a message with both linear and angular velocities set to 0. The command is as follows,

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ros2 topic pub /cmd_vel geometry_msgs/msg/Twist "{linear: {x: 0.0, y: 0.0, z: 0.0}, angular: {x: 0.0, y: 0.0, z: 0.0}}" --once

The --once flag means that only one frame of the message is sent. For other parameters of ros2 topic pub, please refer to this product's course [15. ROS2 Basic Course] under [19. Common ROS2 Command Tools].

 

 

3.2.3 Control the Car's Buzzer

To activate the buzzer, enter the following command in the terminal,

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ros2 topic pub /beep std_msgs/msg/UInt16 "data: 1" --once

To deactivate the buzzer, enter the following command in the terminal,

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ros2 topic pub /beep std_msgs/msg/UInt16 "data: 0" --once

3.2.4 Control the Car's Light Strip

To publish the color red, enter the following command in the terminal,

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ros2 topic pub /rgb std_msgs/msg/ColorRGBA "{r: 1.0, g: 0.0, b: 0.0, a: 1.0}" --once

Parameters related to a:

a: 101.0 Flowing light

a: 102.0 Marquee light

a: 103.0 Breathing light

a: 104.0 Gradient light

a: 105.0 Starlight effect

a: 106.0 Battery level display

 

Based on the publication table, use the command format ros2 topic echo <topic_name> to receive sensor data published by the car's node.

3.2.5 Subscribe to LiDAR Data

Dual-LiDAR version: The topic name for the left-rear LiDAR is /scan0, and for the right-front LiDAR is /scan1. Here we use the right-front LiDAR data as an example.

Single-LiDAR version: The complete data for one scan of the LiDAR is published on two topics, /scan2 and /scan3, with each topic representing half a scan.

  • Here is an example using the /scan1 topic from the dual-LiDAR version:
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ros2 topic echo /scan1

The subscribed data is shown in the figure below:

image-20250612185123429

 

3.2.6 Subscribe to Battery Level Data

The normal operating voltage of this product is theoretically between 9.6V and 12V. If the voltage drops below 10.3V, the buzzer will beep to indicate a low battery, and the battery needs to be charged. You can enter the following command in the terminal to check the battery voltage:

[!CAUTION]

Special Note!!!: For lithium batteries, the relationship between voltage and charge level is not linear. It is not always the case that a lower voltage means a lower charge. When the vehicle's alarm sounds, it means the battery level is already too low and needs to be charged promptly.

  • Over-discharging lithium batteries is prohibited, as long-term over-discharging can cause the battery to swell and become damaged.
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ros2 topic echo /battery

The subscribed data is shown in the figure below:

image-20250612185726290

This indicates that the battery voltage is 11.8V.

 

3.2.7 Subscribe to IMU Data

The control board is equipped with a 9-axis IMU sensor that can provide feedback on the car's attitude. Enter the following command in the terminal to get the IMU data,

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ros2 topic echo /imu/data_raw

The subscribed IMU data is shown in the figure below:

image-20250612190200979

 

3.2.8 Subscribe to Odometry Data

The four motors of this product are equipped with encoders. The ROS control board reads the encoder information and then publishes the calculated odometry data. Enter the following command in the terminal to read the odometry data,

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ros2 topic echo /odom_raw

The subscribed odometry data is shown in the figure below,

image-20250612190531906

Modify Control Board Firmware Parameters
Handle control

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