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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

1.Robotic arm solution

Robotic arm solution

Robotic arm solution1. Content Description2. Program startup2.1、Call fk2.2, call ik3. Core code analysis

 

1. Content Description

This course implements the forward kinematics and inverse kinematics calculations of the robotic arm. Forward kinematics is the calculation of the end-point pose from the angle value of each servo of the robotic arm, while inverse kinematics is the calculation of the angle value of each servo from the end-point pose. Both play a vital role in three-dimensional space clamping. The forward kinematics algorithm can be used to determine the current pose of the end of the robotic arm. We need to know this value when performing coordinate system conversion. The inverse kinematics algorithm can be used to calculate the angle of each servo of the robotic arm in order for the end of the robotic arm to reach the target pose. Before clamping, this algorithm needs to be called to calculate the servo value and then control the servo to move to the clamping posture.

This section requires entering commands in the terminal. The terminal you open depends on your motherboard type. This lesson uses the Raspberry Pi 5 as an example. For Raspberry Pi and Jetson-Nano boards, you need to open a terminal on the host computer and enter the command to enter the Docker container. Once inside the Docker container, enter the commands mentioned in this section in the terminal. For instructions on entering the Docker container from the host computer, refer to this product tutorial [Configuration and Operation Guide]--[Enter the Docker (Jetson Nano and Raspberry Pi 5 users, see here)].

Simply open the terminal on the Orin motherboard and enter the commands mentioned in this section.

2. Program startup

In the terminal, enter the following command to start,

ros2 run arm_kin kin_srv

After startup, enter the terminal ros2 node listto view the node list,

image-20250623151725420

/kinemarics_arm is the node that starts the forward and inverse solution. Enter ros2 node info /kinemarics_arm in the terminal to query the node information.

image-20250623151909071

As shown in the figure above, the /kinemarics_arm node provides a service /get_kinemarics. The service type is arm_interface/srv/ArmKinemarics. Enter the terminal ros2 interface show arm_interface/srv/ArmKinemaricsto view the content of this service data.

xxxxxxxxxx
float64 tar_x
float64 tar_y
float64 tar_z
float64 roll
float64 pitch
float64 yaw
float64 cur_joint1
float64 cur_joint2
float64 cur_joint3
float64 cur_joint4
float64 cur_joint5
float64 cur_joint6
string kin_name
---
float64 joint1
float64 joint2
float64 joint3
float64 joint4
float64 joint5
float64 joint6
float64 x
float64 y
float64 z
float64 roll
float64 pitch
float64 yaw

---Divide the data into two parts, the upper part is the request and the lower part is the response. The request part is as follows,

xxxxxxxxxx
#The x coordinate of the end position of the robotic arm, in meters
float64 tar_x
#The y coordinate of the end position of the robotic arm, in meters
float64 tar_y
#The z coordinate of the end position of the robotic arm, in meters
float64 tar_z
#Rotation value of the end-arm posture roll around the X-axis, in radians
float64 roll
#Rotation value of the pitch value of the end arm around the y-axis, in radians
float64 pitch
#The yaw value of the end position of the robot arm is the rotation value around the z axis, in radians
float64 yaw
#Current value of Servo No. 1, in degrees
float64 cur_joint1
#Current value of Servo 2, in degrees
float64 cur_joint2
#Current value of Servo 3, in degrees
float64 cur_joint3
#Current value of Servo 4, in degrees
float64 cur_joint4
#Current value of servo No. 5, unit is degree
float64 cur_joint5
#Current value of servo No. 6, unit is degree
float64 cur_joint6
#Solution type: ik represents inverse kinematics solution, fk represents forward kinematics solution
string kin_name

The response part is as follows,

xxxxxxxxxx
#1 Servo Angle
float64 joint1
#2 Servo Angle
float64 joint2
#3 Servo Angle
float64 joint3
#4 Servo Angle
float64 joint4
#5 Servo Angle
float64 joint5
#6 Servo Angle
float64 joint6
#x coordinate of the end pose of the robotic arm
float64 x
#The end pose coordinates of the robotic arm
float64 y
#z coordinate of the end position of the robotic arm
float64 z
#Rotation value of the end-arm posture roll around the X-axis, in radians
float64 roll
#Rotation value of the pitch value of the end arm around the Y axis, in radians
float64 pitch
#The yaw value of the end-arm position is the rotation value around the Z axis, in radians
float64 yaw

2.1、Call fk

We call fk to calculate: when the robot arm is straightened upward, what is the position of the end of the robot arm? First, we enter the following command to straighten the robot arm upward. After successfully connecting to the agent, enter the following command in the terminal,

xxxxxxxxxx
ros2 topic pub /arm6_joints arm_msgs/msg/ArmJoints { "joint1: 90,joint2: 90,joint3: 90,joint4: 90,joint5: 90,joint6: 90,time: 1500" } --once

After running, the robotic arm will straighten upwards. Then, we enter the following command in the terminal to call the fk service.

xxxxxxxxxx
ros2 service call /get_kinemarics arm_interface/srv/ArmKinemarics   "{tar_x: 0.0, tar_y: 0.0, tar_z: 0.0, roll: 0.0, pitch: 0.0, yaw: 0.0, cur_joint1: 90.0, cur_joint2: 90.0, cur_joint3: 90.0, cur_joint4: 90.0, cur_joint5: 90.0, cur_joint6: 90.0, kin_name: 'fk'}"

The values to be entered here are cur_joint1 to cur_joint6. We enter 90.0 for each. For the value of kin_name, we enter 'fk' to call the fk-positive solution service. The terminal will respond with the following content as shown below.

image-20250623160905881

Check out the response section:

xxxxxxxxxx
response:
arm_interface.srv.ArmKinemarics_Response (joint1 = 0 .0, joint2 = 0 .0, joint3 = 0 .0, joint4 = 0 .0, joint5 = 0 .0, joint6 = 0 .0, x = 0 .03141308752246765, y = 0 .00020942581836905875, z = 0 .5517500187814817, roll = 1 .5728637148906415, pitch = -1 .5707324948694676, yaw = -1 .5728927150075942)

We only need to care about the following x, y, z, roll, pitch and yaw values. Here we represent the pose coordinates of the end of the robot arm, which means the position of the end of the robot arm in the world coordinate system, with base_link (0, 0, 0) as the reference point. When the robot arm is straightened upward, the values of xyz and rpy are x=0.03141308752246765, y=0.00020942581836905875, z=0.5517500187814817, roll=1.5728637148906415, pitch=-1.5707324948694676, yaw=-1.5728927150075942. Here, start the urdf display in the virtual machine and enter the following command in the virtual machine terminal to start the urdf display.

xxxxxxxxxx
ros2 launch yahboom_M3Pro_description display_launch.py

As shown in the figure below, the TF plug-in is used to view the Gripping pose. The xyz coordinate values are almost the same as the response values, while the rpy value needs to be obtained by converting the quaternion to rpy.

image-20250623163746353

2.2, call ik

We call fk to calculate: when the robot arm, the end of the robot arm pose is x=0.03141308752246765, y=0.00020942581836905875, z=0.5517500187814817, roll=1.5728637148906415, pitch=-1.5707324948694676, yaw=-1.5728927150075942, what is the value of each servo. In fact, this is reverse calculation. Theoretically, the result should be that the value of all six servos is 90.0. Enter the following command in the terminal,

xxxxxxxxxx
ros2 service call /get_kinemarics arm_interface/srv/ArmKinemarics   "{tar_x: 0.03141308752246765, tar_y: 0.00020942581836905875, tar_z: 0.5517500187814817, roll: 1.5728637148906415, pitch: -1.5707324948694676, yaw: -1.5728927150075942, cur_joint1: 0.0, cur_joint2: 0.0, cur_joint3: 0.0, cur_joint4: 0.0, cur_joint5: 0.0, cur_joint6: 0.0, kin_name: 'ik'}"

The values entered here are xyz and rpy. For cur_joint1-cur_joint6, we use the default values. The result is shown in the figure below.

image-20250623165932686

The final response value returned is as follows:

xxxxxxxxxx
arm_interface.srv.ArmKinemarics_Response(joint1=90.0, joint2=90.0, joint3=90.0, joint4=90.0, joint5=90.0, joint6=0.0, x=0.0, y=0.0, z=0.0, roll=0.0, pitch=0.0, yaw=0.0)

Here, we only need to focus on the values of joint1-joint5. Because the end of the robot arm is gripping and connected to servo No. 5, the value of servo No. 6 is not within the range of the inverse solution. Therefore, the value obtained here [90.0, 90.0, 90.0, 90.0, 90.0] is the same as the value of each servo in the current posture of the robot arm. Therefore, the result of the inverse solution can be considered correct.

3. Core code analysis

Program code path:

  • Raspberry Pi 5 and Jetson-Nano board

    The program code is in the running docker. The path in docker is/root/yahboomcar_ws/src/arm_kin/src/kin_srv.cpp

  • Orin Motherboard

    The program code path is /home/jetson/yahboomcar_ws/src/arm_kin/src/kin_srv.cpp

Main function main,

xxxxxxxxxx
int  main ( int  argc , char  ** argv )
{
    rclcpp::init ( argc , argv );
    rcutils_logging_set_logger_level ( "kdl_parser" , RCUTILS_LOG_SEVERITY_ERROR );
    auto  node  =  rclcpp::Node::make_shared ( "kinemarics_arm" );
    //Create a service with the service name get_kinemarics and the service callback function handle_service
    auto  service  =  node -> create_service < arm_interface::srv::ArmKinemarics > ( "get_kinemarics" , handle_service );
    rclcpp::spin ( node );
    rclcpp::shutdown ();
    return  0 ;   
}

Service callback function handle_service,

​x
void  handle_service (
  const  std::shared_ptr < arm_interface::srv::ArmKinemarics::Request >  request ,
  std::shared_ptr < arm_interface::srv::ArmKinemarics::Response >  response )
{
  cout << "-----------------" << endl ;
  cout << request -> kin_name << endl ;
​
  
      if ( request -> kin_name  ==  "fk" ) {
        double  joints []{ request -> cur_joint1 , request -> cur_joint2 , request -> cur_joint3 , request -> cur_joint4 ,
                        request -> cur_joint5 , request -> cur_joint6 };
        // Define the target joint angle container
        vector < double >  initjoints ;
        // Define pose container
        vector < double >  initpos ;
        // Target joint angle unit conversion, from degrees to radians
        for ( int  i  =  0 ; i  <  6 ; ++ i ) initjoints . push_back (( joints [ i ] -  90 ) *  DE2RA );
        //Call fk to get the target pose initpos
        arm_getFK ( urdf_file , initjoints , initpos );
        response -> x  =  initpos . at ( 0 );
        response -> y  =  initpos . at ( 1 );
        response -> z  =  initpos . at ( 2 );
        response -> roll  =  initpos . at ( 3 );
        response -> pitch  =  initpos . at ( 4 );
        response -> yaw  =  initpos . at ( 5 );
        cout << "-----------------" << endl ;
    }
    
    if ( request -> kin_name  ==  "ik" ) {
        // Grasping pose
        double  Roll  =  request -> roll ;
        double  Pitch  =  request -> pitch ;
        double  Yaw  =  request- > yaw ;
        double  x = request -> tar_x ;
        double  y = request- > tar_y ;
        double  z = request -> tar_z ;
        // End position (unit: m)
        double  xyz []{ x , y , z };
        cout  <<  x  <<  y  <<  z  <<  endl ;
        // End attitude (unit: radians)
        //double rpy[]{Roll * DE2RA, Pitch * DE2RA, Yaw * DE2RA};
        double  rpy []{ Roll , Pitch , Yaw };
        // Create output angle container
        vector < double >  outjoints ;
        // Create the end position container
        vector < double >  targetXYZ ;
        // Create an end gesture container
        vector < double >  targetRPY ;
        for ( int  k  =  0 ; k  <  3 ; ++ k ) targetXYZ . push_back ( xyz [ k ]);
        for ( int  l  =  0 ; l  <  3 ; ++ l ) targetRPY . push_back ( rpy [ l ]);
        // //Call fk to get the target servo angle outjoints
        arm_getIK ( urdf_file , targetXYZ , targetRPY , outjoints );
        // Print the inverse solution
        for ( int  i  =  0 ; i  <  5 ; i ++ ) cout  << ( outjoints . at ( i ) *  RA2DE ) +  90  <<  "," ;
        cout  <<  endl ;
        a ++ ;
        response -> joint1  = ( outjoints . at ( 0 ) *  RA2DE ) +  90 ;
        response -> joint2  = ( outjoints . at ( 1 ) *  RA2DE ) +  90 ;
        response -> joint3  = ( outjoints . at ( 2 ) *  RA2DE ) +  90 ;
        response -> joint4  = ( outjoints . at ( 3 ) *  RA2DE ) +  90 ;
        response -> joint5  = ( outjoints . at ( 4 ) *  RA2DE ) +  90 ;
        cout << "-----------------" << endl ;
    }
}

Transformer Model Tracking Algorithm
Robotic arm chassis linkage control

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