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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

7.Release Speed Topic

Release speed topic

Release speed topic1. Experimental Purpose2. Hardware Connection3. Core code analysis4. Compile, download and burn firmware5. Experimental Results

 

1. Experimental Purpose

Learn about STM32-microROS components, access the ROS2 environment, and publish a topic on the robot car's odom speed.

2. Hardware Connection

As shown in the figure below, the STM32 control board integrates four encoder motor drivers and interfaces, connecting the four motors to the motor interfaces. The corresponding names of the four motor interfaces are: left front wheel -> M1, left rear wheel -> M2, right front wheel -> M3, and right rear wheel -> M4.

Since the encoder motor requires high voltage and high current, it must be powered by a battery.

Use a Type-C data cable to connect the USB port of the main control board and the USB Connect port of the STM32 control board.

image-20250618162614850

Note: There are many types of main control boards. Here we take the Jetson Orin series main control board as an example, with the default factory image burned.

3. Core code analysis

The virtual machine path corresponding to the program source code is:

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Board_Samples/Microros_Samples/Publisher_odom

Create the publisher "odom_raw" and specify the publisher's information type as nav_msgs/msg/Odometry.

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RCCHECK(rclc_publisher_init_default(
        &odom_publisher,
        &node,
        ROSIDL_GET_MSG_TYPE_SUPPORT(nav_msgs, msg, Odometry),
        "odom_raw"));

Create a publisher timer with a publishing frequency of 11HZ.

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#define ODOM_PUBLISHER_TIMEOUT (50)
RCCHECK(rclc_timer_init_default(
        &odom_publisher_timer,
        &support,
        RCL_MS_TO_NS(ODOM_PUBLISHER_TIMEOUT),
        odom_publisher_callback));

Adds the publisher's timer to the executor.

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RCCHECK(rclc_executor_add_timer(&executor, &odom_publisher_timer));

The main function of the odom timer callback function is to update the odom data and send the data out.

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void odom_publisher_callback(rcl_timer_t *timer, int64_t last_call_time)
{
    RCLC_UNUSED(last_call_time);
    if (timer != NULL)
    {
        publish_odom_data();
    }
}
void publish_odom_data()
{
    timespec_t time_stamp = get_ros2_timestamp();
    int64_t now = get_millisecond();
    float vel_dt = (now - prev_odom_update) / 1000.0;
    prev_odom_update = now;
    
    odom_update(
        vel_dt,
        car_data.v_x,
        car_data.v_y,
        car_data.v_z);
    odom_msg.header.stamp.sec = time_stamp.tv_sec;
    odom_msg.header.stamp.nanosec = time_stamp.tv_nsec;
    RCSOFTCHECK(rcl_publish(&odom_publisher, &odom_msg, NULL));
}

Read the speed from the robot car and update the odom information according to the car speed.

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void odom_update(float vel_dt, float linear_vel_x, float linear_vel_y, float angular_vel_z)
{
    float delta_heading = angular_vel_z * vel_dt; // radians
    float cos_h = cos(heading_);
    float sin_h = sin(heading_);
    float delta_x = (linear_vel_x * cos_h - linear_vel_y * sin_h) * vel_dt; // m
    float delta_y = (linear_vel_x * sin_h + linear_vel_y * cos_h) * vel_dt; // m
​
    // calculate current position of the robot
    x_pos_ += delta_x;
    y_pos_ += delta_y;
    heading_ += delta_heading;
​
    // calculate robot's heading in quaternion angle
    // ROS has a function to calculate yaw in quaternion angle
    float q[4];
    odom_euler_to_quat(0, 0, heading_, q);
​
    // robot's position in x,y, and z
    odom_msg.pose.pose.position.x = x_pos_;
    odom_msg.pose.pose.position.y = y_pos_;
    odom_msg.pose.pose.position.z = 0.0;
​
    // robot's heading in quaternion
    odom_msg.pose.pose.orientation.x = (double)q[1];
    odom_msg.pose.pose.orientation.y = (double)q[2];
    odom_msg.pose.pose.orientation.z = (double)q[3];
    odom_msg.pose.pose.orientation.w = (double)q[0];
​
    odom_msg.pose.covariance[0] = 0.001;
    odom_msg.pose.covariance[7] = 0.001;
    odom_msg.pose.covariance[35] = 0.001;
​
    // linear speed from encoders
    odom_msg.twist.twist.linear.x = linear_vel_x;
    odom_msg.twist.twist.linear.y = linear_vel_y;
    odom_msg.twist.twist.linear.z = 0.0;
​
    // angular speed from encoders
    odom_msg.twist.twist.angular.x = 0.0;
    odom_msg.twist.twist.angular.y = 0.0;
    odom_msg.twist.twist.angular.z = angular_vel_z;
​
    odom_msg.twist.covariance[0] = 0.0001;
    odom_msg.twist.covariance[7] = 0.0001;
    odom_msg.twist.covariance[35] = 0.0001;
}

Call rclc_executor_spin_some in a loop to make microros work properly.

xxxxxxxxxx
while (ros_error < 3)
    {
        rclc_executor_spin_some(&executor, RCL_MS_TO_NS(ROS2_SPIN_TIMEOUT_MS));
        vTaskDelayUntil(&lastWakeTime, 10);
        // vTaskDelay(pdMS_TO_TICKS(100));
    }

4. Compile, download and burn firmware

Select the project to be compiled in the file management interface of STM32CUBEIDE and click the compile button on the toolbar to start compiling.

image-20250609174806368

If there are no errors or warnings, the compilation is complete.

image-20250609175148035

Since the Type-C communication serial port used by the microros agent is multiplexed with the burning serial port, it is recommended to use the STlink tool to burn the firmware.

If you are using the serial port to burn, you need to first plug the Type-C data cable into the computer's USB port, enter the serial port download mode, burn the firmware, and then plug it back into the USB port of the main control board.

5. Experimental Results

Note: When using ROS2 to control the car's motor, it will rotate. Please place the car in the air first to prevent it from moving around on the table.

The MCU_LED light flashes every 200 milliseconds.

If the proxy is not enabled on the main control board terminal, enter the following command to enable it. If the proxy is already enabled, disable it and then re-enable it.

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sh ~/start_agent.sh

After the connection is successful, a node, a publisher and a subscriber are created.

image-20250714104602753

Open another terminal and view the /YB_Example_Node node.

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ros2 node list
ros2 node info /YB_Example_Node

Publish data to the /cmd_vel topic to control the robot car to move forward at 0.5m/s.

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ros2 topic pub --once /cmd_vel geometry_msgs/msg/Twist "{linear: {x: 0.5, y: 0.0, z: 0.0}, angular: {x: 0.0, y: 0.0, z: 0.0}}"

Subscribe to the data of the /odom_raw topic,

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ros2 topic echo /odom_raw

Press Ctrl+C to end the command.

image-20240118112009255

image-20240118113344508

Check the frequency of the /odom_raw topic. A frequency of around 20 Hz is normal.

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ros2 topic hz /odom_raw

Press Ctrl+C to end the command.

image-20240118112158523

Publish data to the /cmd_vel topic to control the robot car to stop.

xxxxxxxxxx
ros2 topic pub --once /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}}"

Subscribe to the rate control topic
Publish IMU data topic

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