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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

2.Multi-vehicle Navigation

Multi-vehicle Navigation

Multi-vehicle Navigation1. Course Content1.1 Preparation Work1.2 Navigation Map1.3 Setting Vehicle Navigation Name2. Starting Example Programs2.1 Initialize Vehicle Pose3. Source Code Analysis3.1 View TF Coordinate Tree3.2 Launch Files

1. Course Content

  • Run example programs, give different navigation points to two vehicles in rviz. The two vehicles will plan their respective navigation paths based on their positions in the map, navigate to the specified locations, and avoid obstacles in real-time during navigation.

1.1 Preparation Work

Refer to the method in 【1. Multi-vehicle Control】 to set the vehicle's namespace and DOMAIN_ID

1.2 Navigation Map

Before starting multi-vehicle navigation, you need to place the map files in the ~/map/ path of the vehicle that starts the map service. The map files include .yaml format parameter files and .pgm format image files.

1.3 Setting Vehicle Navigation Name

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

bringup_m3

Open a terminal inside the container,

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exec_m3
  • Edit the MULTI_ROBOT environment variable in .bashrc
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nano .bashrc

image-20260106164620448

  • Modify the MULTI_ROBOT environment variable to be the same as the vehicle's namespace set earlier. Here we use robot1 as an example, and other vehicles follow this pattern.
  • After modification, press ctrl+x to save and exit
  • Refresh the terminal variables and verify if the setting is successful
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source .bashrc
echo $MULTI_ROBOT

image-20260106165751974

 

2. Starting Example Programs

  1. You need one vehicle to run the map service loading service and RVIZ visualization interface. This vehicle must place the map files in the ~/map/ path.
  2. Run commands on the vehicle terminal that starts visualization and map service:
  • (ROBOT1) (ORIN mainboard)
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ros2 launch m3_bringup multi_nav2.launch.py start_mapserve:=True gui:=True

(RDK & PI5 mainboard) run in steps, visualization starts on the paired virtual machine Rosmaster-M3-Ubuntu22.04,

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#Virtual machine side
rviz2 -d /home/yahboom/yahboomM3_ws/user_ws/src/m3_bringup/rviz/multi_nav2.rviz
#Host side
ros2 launch m3_bringup multi_nav2.launch.py start_mapserve:=True gui:=True use_rviz:=False
  • Other vehicles (ROBOT2/3) directly run multi-vehicle navigation nodes without startup parameters

(ORIN mainboard)

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ros2 launch m3_bringup multi_nav2.launch.py

(RDK & PI5 mainboard)

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ros2 launch m3_bringup multi_nav2.launch.py use_rviz:=False

[!NOTE]

Optional startup parameter descriptions:

  • start_mapserve: Start map server to load map, only one vehicle needs to start it, default False
  • use_rviz: Start rviz2 visualization interface, only one vehicle needs to start it, default False
  • gui: Start rtabmap visualization point cloud, default False
  • map: Name of the map file to be loaded, map must be saved in the system ~/map/ path, default is map.yaml

 

2.1 Initialize Vehicle Pose

image-20260106171845249

  • Use the 【2D Pose Estimate】 tool to give the vehicle an initial pose. There are two 【2D Pose Estimate】 tools in rviz. The first is the tool for giving robot1's initial pose.
  • The second is the tool for giving robot2's initial pose. Use these two tools respectively to give initial poses to the two vehicles.

As shown in the figure below, make the area scanned by the two radars overlap with the black areas on the map. Among them, the green point cloud is the radar scan of robot1 vehicle, and the red point cloud is the radar scan of robot2 vehicle.

image-20250619110527078

Next, in rviz2, use the 【2D Goal Pose】 tool to give the vehicle an initial pose. There are two 【2D Goal Pose】 tools in this rviz. The first is the tool for giving robot1's target point, and the second is the tool for giving robot2's target point. Use these two tools respectively to give target points to the two vehicles. As shown in the figure below, after the vehicle is given the target point, it will plan a path and navigate to the target point.

 

3. Source Code Analysis

3.1 View TF Coordinate Tree

  • Enter the following command in the vehicle terminal to view the TF tree,
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ros2 run rqt_tf_tree rqt_tf_tree

As shown in the figure below, this is the TF tree for multi-vehicle navigation

image-20250619112056253

 

3.2 Launch Files

  • Configuration file directory:
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~/yahboomM3_ws/user_ws/src/m3_bringup/config/multi_robot

Contains the following files:

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├── multi_amcl_param.yaml                 Navigation AMCL localization parameter file
├── multi_common_param.yaml              Sensor configuration parameter file
├── multi_nav_param.yaml                 Navigation Nav2 configuration parameter file
└── multi_robot_localization.yaml         Odometer fusion configuration parameter file

 

  • Launch source code path
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~/yahboomM3_ws/user_ws/src/m3_bringup/launch/multi_robot/multi_nav2.launch.py

 

  • Core part of the top-level launch file
  • This file will sequentially start the basic nodes of carbase robot sensors and odometer, map_serve map service node, nav2_stack navigation-related nodes, and rviz_node visualization display node, and distinguish which specific nodes to start through startup parameters.
​x
def generate_launch_description():
    MULTI_ROBOT = os.environ.get('MULTI_ROBOT', '')
​
    if MULTI_ROBOT == '':
        raise Exception(Fore.RED+'MULTI_ROBOT is not set!!!!'+Fore.RESET)
    m3_bringup_dir=os.path.join(get_package_share_directory('m3_bringup'))
    nav_rviz_config=os.path.join(m3_bringup_dir,'rviz','multi_nav2.rviz')
​
    #Declare arguments
    declared_arguments = []
    declared_arguments.append(
        DeclareLaunchArgument(
            "map",
            default_value="map.yaml",
            description="map name",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "gui",
            default_value='False',
            description="if rviz is used, set to True. If False, rviz will not be launched.",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "start_mapserve",
            default_value='False',
            description="if map server is used, set to True. If False, map server will not be launched.",
        )
    )
    declared_arguments.append(
        DeclareLaunchArgument(
            "use_sim_time",
            default_value="False",
            description="Use simulation (Gazebo) clock if true",
        )
    )
​
    #Initialize Arguments 
    map = LaunchConfiguration("map")
    map_path=PathJoinSubstitution([map_folder_dir, map])
    gui = LaunchConfiguration("gui")
    start_mapserve=LaunchConfiguration("start_mapserve")
​
    use_sim_time=LaunchConfiguration("use_sim_time",default="false")
​
    #navigation2 startup file
    nav2_launch_file = os.path.join(get_package_share_directory("m3_bringup"), 
                                    "launch",
                                    'multi_robot',
                                    'multi_bringup.launch.py')
    
    #Basic nodes such as robot models and radars
    carbase=ExecuteProcess(
                cmd=['ros2', 'launch', 'm3_bringup','multi_car_base.launch.py'],output='screen')
​
    # Navigation2 stack
    nav2_stack=TimerAction(
                period=5.0,
                actions=[
                    IncludeLaunchDescription(
                        PythonLaunchDescriptionSource(nav2_launch_file))   
                ]
            )
    
    amcl_localization=ExecuteProcess(
                cmd=['ros2', 'launch', 'm3_bringup','multi_amcl.launch.py'],output='screen')
​
    #map service
    map_serve = GroupAction(
        condition=IfCondition(PythonExpression(["'", start_mapserve, "' == 'True'" ])),
        actions=[
            Node(
                package='nav2_map_server',
                executable='map_server',
                name='map_server',
                output='screen',
                respawn_delay=2.0,
                parameters=[{'yaml_filename': map_path}],
                arguments=['--ros-args', '--log-level', "info"],
                remappings=[('/tf', 'tf'),('/tf_static', 'tf_static')]),
            Node(
                package='nav2_lifecycle_manager',
                executable='lifecycle_manager',
                name='lifecycle_manager_map',
                output='screen',
                arguments=['--ros-args', '--log-level', "info"],
                parameters=[{'use_sim_time': use_sim_time},
                            {'autostart': True},
                            {'node_names': ['map_server']}])
                ]
    )
​
    #rviz node
    rviz_node = Node(
        package = 'rviz2',
        executable = 'rviz2',
        name='rviz2',
        arguments=['-d', nav_rviz_config],
        parameters=[{'use_sim_time': use_sim_time}],
        output='screen',
        condition=IfCondition(PythonExpression(["'", gui, "' == 'True'"]))
    )
​

 

  • Other included underlying launch file directories:
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~/yahboomM3_ws/user_ws/src/m3_bringup/launch/multi_robot/
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├── multi_amcl.launch.py              AMCL localization node launch file
├── multi_bringup.launch.py          Nav2 navigation node launch file
├── multi_car_base.launch.py          Robot sensor and odometer launch file
├── multi_display.launch.py          Robot model launch file
├── multi_laser.launch.py            Laser radar sensor launch file
├── multi_merge_odom.launch.py        Odometer fusion launch file
└── multi_nav2.launch.py              Multi-vehicle navigation top-level launch file

 

 

 

 

 

Multi-vehicle Control
Multi-vehicle Platoon

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