yahboom
HomeTutorialsProduct
Solution
Blog
Online Store
About Us
EN/中文
Store
ROSMASTER M1 PRO📖
0.Robot Configuration and Operation Guidelines
  • 0.Unboxing and Review
  • 1.Assembly Videos
  • 2.User Manual
  • 3.Enter Docker (For Raspberry Pi)
  • 4.FAQ
  • 5.RobotManager User Guide
1.Chassis Control Course
  • 1.Robot Information Publishing
  • 2.Robot Handle Control
  • 3.Robot Keyboard Control
  • 4.Linear Velocity Calibration
  • 5.Angular Velocity Calibration
  • 6.Robot URDF Model
  • 7.Robot EKF State Estimation
2.Lidar Course
  • 1.Lidar Introduction And Use
  • 2.Lidar Obstacle Avoidance
  • 3.Lidar Following
  • 4.Lidar Guard
  • 5.Lidar Patrol
  • 6.GMapping-SLAM Mapping
  • 7.Cartographer-SLAM Mapping
  • 8.Slam-Toolbox Mapping
  • 9.Lifelong Mode Mapping
  • 10.Navigation2 Single Point Navigation
  • 11.Navigation2 Multi Point Navigation
  • 12.Cartographer Relocalization Navigation
  • 13.Slam-Toolbox Localization Navigation
  • 14.Synchronous Mapping And Navigation
  • 15.Aruco Visual Relocalization Navigation
  • 16.Mobile App Mapping And Navigation
3.USB Camera-Depth Camera Course
  • 1.USB Camera Introduction
  • 2.USB Camera Intrinsic Calibration
  • 3.Nuwa-HP60C Camera Introduction (Depth Camera Only)
  • 4.Depth Pseudo Color Image (Depth Camera Only)
  • 5.Depth Camera Distance Measurement (Depth Camera Only)
  • 6.Block Volume Calculation (Depth Camera Only)
  • 7.Edge Detection (Depth Camera Only)
  • 8.RTAB-Map SLAM Mapping (Depth Camera Only)
  • 9.RTAB-Map Navigation (Depth Camera Only)
4.Robotic Arm and 3D Space Gripping Course
  • 1.Robotic Arm Basic Usage
  • 2.Robotic Arm Kinematics
  • 3.Robotic Arm Chassis Coordination Control
  • 4.AprilTag ID Sorting
  • 5.AprilTag Height Anomaly Sorting
  • 6.AprilTag Tracking and Grasping
  • 7.Color Block Sorting
  • 8.Color Block Height Anomaly Sorting
  • 9.Color Block Tracking and Grasping
  • 10.Wood Block Shape Sorting
  • 11.KCF Tracking and Grasping Objects
  • 12.Mediapipe Gesture ID Sorting AprilTag
  • 13.Mediapipe Gesture Height Sorting AprilTag
  • 14.Desktop Tracking and Grasping AprilTag
  • 15.3D Spatial Tracking AprilTag
  • 16.Transformer Model Tracking Algorithm
  • 17.Line Following Obstacle Clearing
5.Road network planning and navigation
  • 1.Introduction To Road Network Planning
  • 2.Road Network Annotation
  • 3.Planning And Navigation Between Waypoints
  • 4.Road Network Navigation In Any Pose
  • 5.Path Re-planning
6.OpenClaw Basic Course
  • 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 Method
  • 1.OpenClaw Webchat Interaction
  • 2.OpenClaw Tui Interaction
  • 3.OpenClaw Voice Dialogue Interaction
  • 4.OpenClaw WhatsApp Interaction
8.OpenClaw Development
  • 1.OpenClaw Connecting to Robot MCP Service
  • 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 Scheduled Tasks
  • 10.OpenClaw Multi-Agent Intelligent Agent
  • 11.OpenClaw Active Interaction
  • 12.OpenClaw Vector Memory
9.OpenClaw Embodied intelligent application
  • 1.OpenClaw Target Tracking
  • 2.OpenClaw Arm Tracking And Grasping
  • 3.OpenClaw Arm Fixed-point Placement
  • 4.OpenClaw Waste Sorting
  • 5.OpenClaw SLAM Mapping And Navigation
  • 6.OpenClaw Navigation Transport
  • 7.OpenClaw AprilTag Grasping
  • 8.OpenClaw Color Line Patrol
  • 9.OpenClaw Visual Distance Measurement
10.OpenClaw scenario sandbox
  • 1.Map Description and Props Placement
  • 2.Building the Sandbox Map
  • 3.Visual Relocalization Marker
  • 4.Map Mapping Marker
  • 5.Sandbox Scenario Comprehensive Case
11.AI Large 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 large Model Development
  • 1.Register a Model Service Provider Account
  • 2.Configuring API-KEY
  • 3.Introduction to Dify
  • 4.Basic Dify Features
  • 5.AI Large Model Development - Chatbot
  • 6.Deploy the RAG Knowledge Base
  • 7.RAG Knowledge Base + Chatbot
  • 8.AI Agent Workflow
  • 9.Custom Wake-up Response
13.AI Model - Voice Version
  • 1.Semantic Understanding and Command Following
  • 2.Multimodal Visual Understanding
  • 3.Multimodal Visual Understanding and Robotic Arm Gripping
  • 4.Multimodal Visual Understanding and SLAM Navigation
  • 5.Robotic arm gripping+Multimodal visual understand+SLAM navigation
  • 6.Intention Understanding
14.AI Model - Text Version
  • 1.Semantic Understanding and Command Following
  • 2.Multimodal Visual Understanding
  • 3.Multimodal Visual Understanding and Robotic Arm Gripping
  • 4.Multimodal Visual Understanding and SLAM Navigation
  • 5.Robotic arm gripping+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
  • 10.3D perception obstacle‑avoidance
17.Multi-vehicle Course
  • 1.Multi-vehicle Control
  • 2.Multi-vehicle Navigation
  • 3.Multi-vehicle Formation
18.YOLO26 Development
  • 1.CLI Use
  • 2.Object Detection
  • 3.Instance Segmentation
  • 4.Pose Estimation
  • 5.Image Classification
  • 6.Oriented Object Detection
  • 7.YOLOv26 + DeepSORT Tracking
  • 8.Monocular Depth Estimation
19.Control Board STM32 Driver Course
  • 0.Notes (Must Read)
  • 1.About Expansion Board
  • 2.STM32 Development Environment
  • 3.Button Control
  • 4.FreeRTOS Application
  • 5.Serial Communication
  • 6.SBUS Model Aircraft Remote Control
  • 7.CAN Bus Communication
  • 8.RGB Colorful Light Bar
  • 9.Timer Interrupt to Control PWM Servo
  • 10.Control Serial Port Servos
  • 11.Nine-axis Attitude Sensor to Obtain Data
  • 12.Control Motor
  • 13.Timer Captures the Encoder Data
  • 14.1 Kinematic Analysis of Mecanum Wheel
  • 14.2 Kinematic Analysis of Ackerman
  • 14.3 Kinematic Analysis of 4WD
  • 15.PID Control Robot Movement
20.Docker Course
  • 1.Docker Introduction and Installation
  • 2.Docker Basic Commands
  • 3.Docker Submission and Multi-Terminal Access
  • 4. Docker Startup Script
  • 5.Docker-Vscode Environment Construction
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
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
24.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
  • 18. Controlling RGB Cooling Fans
Download
  • System File
  • Code
  • 3D Model File
  • Software Tools
  • Hardware_Info
  • Deb function package
  • Dify AI Agent
Download ZIP

Welcome to ROSMASTER M1 PRO repository

1.Robotic Arm Basic Usage

Robotic Arm Basic Usage

Robotic Arm Basic Usage1. Course Content2. Preparation3.Basic Knowledge Definitions4. Case Demonstration4.1 Robotic Arm Joint Control and Torque Control4.2 End-effector Pose Control4.3 Real-time End-effector Pose Display5. Robotic Arm Middle Position Calibration6. Grasping Offset Calculation Standard7. Notes for DIY Robotic Arm Grasping

1. Course Content

This section is the introductory course for robotic arm related functions. It mainly leads users to understand the definitions of robotic arm related coordinate frames, master the basic control methods and debugging techniques of the robotic arm, and lay the foundation for subsequent cases such as robotic arm grasping, sorting, and tracking. The course content includes:

  • Basic Knowledge Definitions: introduces the definitions of the ROS robot coordinate frame, the robotic arm end-effector TCP coordinate frame, the robotic arm reference base coordinate frame (measure), and the camera optical axis coordinate frame, as well as how to determine them using the right-hand rule.

  • Robotic Arm Basic Control: demonstrates robotic arm joint angle control, torque enabling/disabling, and spatial position control based on the end-effector pose (x, y, z, roll, pitch, yaw, gripper), and explains how to add end-effector offsets via offset_x, offset_y, offset_z.

  • Real-time End-effector Pose Display: views the robotic arm joint angles and end-effector pose in real time through a visualization tool, and supports toggling the torque on/off with the spacebar to achieve drag teaching.

  • Robotic Arm Middle Position Calibration: explains how to perform middle position calibration when the robotic arm is bent or the servo angles are offset.

  • Grasping Offset Calculation Standard: introduces the adjustment principles and steps for the X/Y/Z axis offsets and the grasping angle, to compensate for systematic errors such as camera measurement errors and servo backlash.

  • Notes for DIY Robotic Arm Grasping: provides a correspondence table between gripper angles and object sizes, to avoid servo stall and burnout caused by unreasonable angle settings.

Important

  • When the robotic arm grasps objects, the gripper angle must be controlled reasonably. Never set it too large or too small, otherwise servo No. 6 may stall and burn out.

  • The poses and offsets involved in this section are all based on the measure reference base coordinate frame. Before learning, it is recommended to first master the method of determining the right-handed coordinate frame.

2. Preparation

ros2 launch yahboomcar_bringup car_base.launch.py odom_tf_source:=ekf

 

3.Basic Knowledge Definitions

  • ROS robot coordinate frame definition

    The ROS robot coordinate frame follows the right-hand rule, taking the robot body as the reference:

    • X axis (red): points to the robot's front

    • Y axis (green): points to the robot's left

    • Z axis (blue): points to the robot's top (perpendicular to the ground, upward)

image-20260915112519298

  • Method for determining the right-hand rule: point the right thumb in the positive X axis direction, the index finger in the positive Y axis direction, and the direction in which the middle finger naturally bends is the positive Z axis direction.

image-20260915113004629

 

  • Robotic arm end-effector TCP coordinate frame

TCP (Tool Center Point) is the working center point of the robotic arm end-effector (gripper), used to represent the position and pose of the robotic arm end-effector in 3D space. In kinematics calculations, the TCP is the reference point for forward and inverse kinematics solutions; all end-effector poses are expressed as the position and orientation of the TCP relative to the base coordinate frame, and this coordinate frame is defined in the robot's URDF model.

image-20260915111843131

 

  • Robotic arm reference base coordinate frame (measure)

The measure coordinate frame is the robotic arm's reference base coordinate frame, whose origin is located at the vertical projection point onto the ground of the center point of the car's front. This coordinate frame is the reference frame for the robotic arm's kinematics calculations; all robotic arm end-effector positions and target point coordinates are expressed relative to the measure coordinate frame. In the ROS TF transform tree, the measure coordinate frame serves as the parent coordinate frame of the robotic arm related coordinate frames.

image-20260915112001858

Tip

The reason for choosing this point as the robotic arm motion reference coordinate frame is that it facilitates observing and measuring the robotic arm end-effector position and the actual position of objects

image-20260915113737329

 

  • Camera optical axis coordinate frame

The camera optical axis coordinate frame takes the camera optical center (optical center) as the origin, with the Z axis along the camera optical axis pointing to the front of the camera (i.e., the shooting direction), the X axis pointing to the right of the image, and the Y axis pointing to the bottom of the image. This coordinate frame is used to project the position of objects in 3D space onto the 2D image plane, and is the basic coordinate frame for algorithms such as visual distance measurement and target localization.

  • Depth camera

image-20260915112052129

  • USB monocular camera

image-20260915112913596

 

4. Case Demonstration

4.1 Robotic Arm Joint Control and Torque Control

  • Refer to the prerequisite tutorial 【Chassis Control — ROS Control】

 

4.2 End-effector Pose Control

  • Example: end-effector to: front 0.15m / left 0.05m / height 0.10m, pitch 60° (downward pose), gripper 30° (the larger the gripper value, the smaller the opening)

xxxxxxxxxx
ros2 service call /move_to_pose interfaces/srv/MoveToPose "{x: 0.15, y: 0.05, z: 0.10, roll: 0.0, pitch: 60.0, yaw: 0.0, gripper:
30}"

 

  • Example: end-effector to: front 0.15m / left 0.05m / height 0.10m, pitch 0° (horizontal pose), roll 60.0 (gripper rotation), gripper 110°

xxxxxxxxxx
ros2 service call /move_to_pose interfaces/srv/MoveToPose "{x: 0.15, y: -0.07, z: 0.15, roll: 60.0, pitch: 0.0, yaw: 0.0, gripper: 110}"

 

  • The above examples control the end-effector to move to the theoretically calculated position of the robotic arm kinematics. However, in subsequent robotic arm grasping cases, it is often necessary to add an offset to the end-effector movement so that the gripper grasps different positions to achieve a better grasping effect. You can add the end-effector position offsets via offset_x, offset_y, offset_z. For example: based on the previous step, offset the end-effector by 0.02 meters along the x axis, 0.01 meters along the y axis, and 0.03 meters along the z axis

xxxxxxxxxx
ros2 service call /move_to_pose interfaces/srv/MoveToPose "{x: 0.15, y: -0.07, z: 0.15, roll: 60.0, pitch: 0.0, yaw: 0.0, offset_x: 0.02, offset_y: 0.01, offset_z: 0.03, gripper: 110}"

 

  • If the specified end-effector pose is kinematically unreachable, the program will give an approximate solution near the target point and indicate the error relative to the target value

image-20260915121714812

 

  • Restore the robotic arm to its initial pose

xxxxxxxxxx
ros2 service call /reset_arm_pose std_srvs/srv/Trigger

 

4.3 Real-time End-effector Pose Display

xxxxxxxxxx
ros2 run common_demo arm_visualization
  • arm1-arm6 are the corresponding angles of the robotic arm joints, and End-Effector Pose is the robotic arm end-effector pose

image-20260915122539632

 

  • Press the spacebar to enable/disable the robotic arm torque; when the robotic arm torque is disabled, you can slowly drag the robotic arm to move

image-20260915122708920

  • Press the spacebar again to enable the torque and fix the robotic arm's pose

image-20260915122831215

 

5. Robotic Arm Middle Position Calibration

  • Move the robotic arm to the middle position

xxxxxxxxxx
ros2 service call /set_arm_joints interfaces/srv/ArmPosition "{request_joints: [90, 90, 90, 90, 90, 180]}"
  • Check whether the robotic arm is straight. If the robotic arm is bent or a servo angle is obviously offset, follow the steps below to calibrate

image-20260915123034422

  • Disable the robotic arm torque

xxxxxxxxxx
ros2 service call /set_arm_torque std_srvs/srv/SetBool "{data: false}"
  • Manually straighten the robotic arm, making it form a straight line as much as possible, with the gripper fully closed, and then request the calibration service

xxxxxxxxxx
ros2 service call /calibrate_arm_offset std_srvs/srv/Trigger

image-20260915141053942

 

6. Grasping Offset Calculation Standard

  • When subsequent courses involve robotic arm grasping with systematic errors or the need to adjust the grasping point position, refer to the following offset calculation method to adjust and compensate the end-effector position

Important

  • Systematic errors mainly come from camera measurement errors, the different servo joint backlash of each robotic arm, and link rigidity degradation

Adjustment Principles:

  1. X-axis offset (front-back direction)

    • Positive value: the grasping point moves forward (away from the robot body); negative value: the grasping point moves backward (toward the robot body)

  1. Y-axis offset (left-right direction)

    • Positive value: the grasping point moves left (the left side of the robot's forward direction); negative value: the grasping point moves right (the right side of the robot's forward direction)

  2. Z-axis offset (up-down direction)

    • Positive value: the grasping point moves up; negative value: the grasping point moves down

    • Adjustment scenario: used when grasping is too high or too low, often to compensate for the height difference between the gripper and the object surface

  3. Grasping angle (gripper rotation angle)

    • Unit: degrees

    • Adjustment scenario: adjusted when the object needs to be grasped from a specific angle; adjustment is not recommended

Adjustment Steps:

  1. Perform a grasping operation once, and record the deviation direction between the actual grasping position and the ideal position

  2. According to the right-handed coordinate frame principle, determine the direction and sign that need to be adjusted

 

  • Example 1: the grasping point is too far back

    • You need to increase the offset in the x-axis direction (increase a positive value); conversely, if the grasping point is too far forward, you need to subtract the offset (increase a negative value)

image-20260915142427413

 

  • Example 2: the grasping point is too far left

    • You need to decrease the offset in the y-axis direction (increase a negative value); conversely, if it is too far right, you need to increase the offset in the y-axis direction (increase a positive value)

image-20260915144244551

 

  • Example 3: the grasping point is too low

  • You need to increase the offset in the z-axis direction (increase a positive value); conversely, if it is too high, you need to decrease the offset (increase a negative value)

image-20260915145005908

 

7. Notes for DIY Robotic Arm Grasping

Note: When the robotic arm grasps objects, the gripper angle needs to be controlled. An unreasonable angle setting may cause the servo to stall and burn out.

Here, a gripper angle correspondence table is compiled, recording approximately what servo angle needs to be set for grasping an object of every 0.5 cm size. You can reasonably adjust the angle set for your own grasping according to this table to avoid servo stall.

MVIMG_20250611_141934

Object length (unit: cm)Servo angle (unit: degrees)
0180
0.5176
1.0168
1.5160
2.0152
2.5143
3.0134
3.5125
4.0115
4.5105
5.095
5.580
6.057
6.0-6.40-57

For example, the block we provide is 3cm in length, width, and height, so setting the servo angle to 134° can grasp it. Never set the angle too large or too small.

MVIMG_20250611_180739

Note: This product provides four wooden blocks of different sizes and shapes. Grasping must be done strictly according to the block sizes marked in the tutorial. If the program is set to grasp a 3cm block but a 4cm block is actually placed for grasping, it will cause servo No. 6 to be damaged.

MVIMG_20250611_180739

RTAB-Map Navigation (Depth Camera Only)
Robotic Arm Kinematics

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

  • Tutorials
  • Edge AI hardware
  • Educational robots
  • Embedded intelligent hardware

WHERE TO BUY

  • Yahboom Online Store
  • Amazon
  • Alibaba
  • AliExpress
  • Mercado

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文