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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

9.Scene design

Scene design

Preface: Raspberry Pi 5 and Jetson Nano run ROS in Docker, so the performance of running MoveIt2 is generally poor. Users of Raspberry Pi 5 and Jetson Nano boards are advised to run MoveIt2 examples in a virtual machine. Orin motherboards run ROS directly on the motherboard, so users of Orin boards can run MoveIt2 examples directly on the motherboard, using the same instructions as running in a virtual machine. This section uses running in a virtual machine as an example.

1. Content Description

This course implements a scenario: in rviz, there is a three-layer shelf. After the robotic arm grabs the cylindrical object, it plans to move it to the upper and lower layers in the middle to simulate the action of placing the object.

2. Program startup

Open a terminal in the virtual machine and enter the following command to start MoveIt2.

xxxxxxxxxx
ros2 launch test_moveit_config demo.launch.py

After the program is started, when the terminal displays "You can start planning now!" , it indicates that the program has been successfully started, as shown in the figure below.

image-20250621145255762

Then, we add the scene in rviz as shown below.

image-20250621145513185

After selecting the scene file, click [Open] to complete the addition. The addition is as shown below.

image-20250621145656116

Click [Publish] to publish the obstacle information. The robot arm will then avoid obstacles during planning.

Then, enter the following command in another terminal to start the upper and lower layer planning program,

xxxxxxxxxx
ros2 run MoveIt_demo set_scene

After the program runs, the robot arm will plan to the set init posture, then a purple cylindrical object will be added to the gripper, and then the simulation action of placing the object will be planned and executed on the middle two layers.

image-20250621150155933

3. Core code analysis

The code path in the virtual machine is:/home/yahboom/moveit2_ws/src/MoveIt_demo/src/set_scene.cpp

#include <rclcpp/rclcpp.hpp>
#include <moveit/move_group_interface/move_group_interface.h>
#include <moveit/planning_scene_interface/planning_scene_interface.h>
#include <geometry_msgs/msg/pose.hpp>
​
class  Scene : public  rclcpp::Node
{
public :
  Scene ()
    : Node ( "set_scene_node" )
  {
    // Initialize other content
    RCLCPP_INFO ( this -> get_logger (), "Initializing SceneMoveIt2Control." );
  }
​
  void  initialize ()
  {
    int  max_attempts  =  5 ;   // Maximum number of planning attempts
    int  attempt_count  =  0 ;   // Current number of attempts
    // Initialize move_group_interface_ in this function and create a planning group named arm_group
    move_group_interface_  =  std::make_shared < moveit::planning_interface::MoveGroupInterface > ( shared_from_this (), "arm_group" );
    //Create an interface for managing planning scenes to add collision objects (obstacles) to the scene
    planning_scene_interface_  =  std::make_shared < moveit::planning_interface::PlanningSceneInterface > ();
​
    move_group_interface _-> setNumPlanningAttempts ( 10 );   // Set the maximum number of planning attempts to 10
    move_group_interface _-> setPlanningTime ( 5.0 );          // Set the maximum time for each planning to 5 seconds
    //Define a collision object
    moveit_msgs::msg::CollisionObject  object_to_attach ;
    object_to_attach . id  =  "cylinder1" ;
    // Create a simple collision body or visualization object to describe the robot's environment
    shape_msgs::msg::SolidPrimitive  cylinder_primitive ;
    //Set the type of the geometric object to cylinder
    cylinder_primitive . type  =  cylinder_primitive . CYLINDER ;
    cylinder_primitive . dimensions . resize ( 2 );
    //Set the type of the geometric object to the size of the cylinder, in meters
    cylinder_primitive . dimensions [ cylinder_primitive . CYLINDER_HEIGHT ] =  0.03 ;
    cylinder_primitive . dimensions [ cylinder_primitive . CYLINDER_RADIUS ] =  0.02 ;
    //Get the coordinate system of the end effector of the current planning group as the collision object
    object_to_attach . header . frame_id  =  move_group_interface_ -> getEndEffectorLink ();
    //Create an object that describes the cylinder pose of the collection object and assign the data in the object
    geometry_msgs::msg::Pose  grab_pose ;
    grab_pose . orientation . w  =  1.0 ;
    grab_pose . position . z  =  0.10 ;
    grab_pose . position . x  =  0.00 ;        
    //Add the shape of the collision object, the cylinder_primitive just defined
    object_to_attach . primitives . push_back ( cylinder_primitive );
    //Add the pose of the collision object, the box_pose just defined
    object_to_attach . primitive_poses . push_back ( grab_pose );
    //Add collision objects to the environment
    object_to_attach . operation  =  object_to_attach . ADD ;
    planning_scene_interface_ -> applyCollisionObject ( object_to_attach );  
    
    RCLCPP_INFO ( this -> get_logger (), "Attach the object to the robot" );
    std::vector < std::string >  touch_links ;
    touch_links . push_back ( "llink2" );
    touch_links . push_back ( "rlink2" );
    //Attach the object in the scene to the robotic arm so that it becomes part of the robotic arm model and moves with the robotic arm. The parameters passed in are the ID of the collision object, the link of the end effector, and the list of links that are allowed to contact the object (to avoid collision detection)
    move_group_interface_ -> attachObject ( object_to_attach . id , "Gripping" , touch_links );
​
​
    // Create a plan
    moveit::planning_interface::MoveGroupInterface::Plan  my_plan ;
    // Set the predefined target position   
    move_group_interface_ -> setNamedTarget ( "init" );
    //Start planning the robot arm to the init position
    bool  success  = ( move_group_interface_ -> plan ( my_plan ) ==  moveit::core::MoveItErrorCode::SUCCESS );
    //If the plan is successful, then execute the plan
    if ( success )
    {
        RCLCPP_INFO ( this -> get_logger (), "Arm execute successful." );
        move_group_interface_ -> execute ( my_plan );
    }
    else
    {
        RCLCPP_ERROR ( this -> get_logger (), "Arm down failed!" );
    }
    // Set the predefined target position
    move_group_interface_ -> setNamedTarget ( "down" );
     //Start planning the robot arm to the down position
    success = ( move_group_interface_ -> plan ( my_plan ) ==  moveit::core::MoveItErrorCode::SUCCESS );
    //If the plan is successful, then execute the plan
    if ( success )
    {
        RCLCPP_INFO ( this -> get_logger (), "Init arm successful." );
        move_group_interface_ -> execute ( my_plan );
    }
    else
    {
        RCLCPP_ERROR ( this -> get_logger (), "Init arm failed!" );
    }
​
    while ( attempt_count  <  max_attempts )
    {
        attempt_count ++ ;
        // Target joint angle (unit: radians)
        std::vector < double >  target_joints  = { 0.0 , - 1.57 , - 0.5 , 0.15 , 0 };
        // Set the target joint space value
        move_group_interface_ -> setJointValueTarget ( target_joints );
        // Create a plan
        moveit::planning_interface::MoveGroupInterface::Plan  my_plan ;
        //Start planning to the posture of the target joint angle
        bool  success  = ( move_group_interface_ -> plan ( my_plan ) ==  moveit::core::MoveItErrorCode::SUCCESS );
         //If the plan is successful, then execute the plan
        if ( success )
        {
            RCLCPP_INFO ( this -> get_logger (), "Planning succeeded, moving the arm." );
            move_group_interface_ -> execute ( my_plan );
            return ;
        }
        else
        {
            RCLCPP_INFO ( this -> get_logger (), "Planning failed!" );
        }
    }
    
    RCLCPP_ERROR ( this -> get_logger (), "Exit!" );
​
  }
​
private :
  std::shared_ptr < moveit::planning_interface::MoveGroupInterface >  move_group_interface_ ;
  std::shared_ptr < moveit::planning_interface::PlanningSceneInterface >  planning_scene_interface_ ;
};
​
int  main ( int  argc , char  ** argv )
{
  rclcpp::init ( argc , argv );
  auto  node  =  std::make_shared < Scene > ();
  
  // Initialization
  node- > initialize ();
​
  rclcpp::spin ( node );
  rclcpp::shutdown ();
  return  0 ;
}

Collision detection
Multi-vehicle chassis control

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