yahboom
HomeTutorialsProduct
Solution
Blog
Online Store
About Us
EN/中文
Store
ROSMASTER A1 PRO📖
00.Configuration and Operation Guide
  • 0. Unboxing and Review
  • 1.Assembly Videos
  • 2.User Manual
  • 3.Enter Docker (For Raspberry Pi)
  • 4.A1Pro Front Wheel Servo Calibration
  • 5.USB Camera Calibration
  • 6.Depth Camera Calibration
  • 7.FAQ
  • 8.Desktop Configuration Center and Remote Control Guide
01.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
02.Lidar Course
  • 1.Lidar Introduction and Use
  • 2.Lidar Obstacle Avoidance
  • 3.Lidar Following
  • 4.Lidar Patrol
  • 5.GMapping-SLAM Mapping
  • 6.Cartographer-SLAM Mapping
  • 7.Slam-Toolbox Mapping
  • 8.Lifelong Mode Mapping
  • 9.Navigation2 Single Point Navigation
  • 10.Navigation2 Multi Point Navigation
  • 11.Cartographer Relocalization Navigation
  • 12.Slam-Toolbox Localization Navigation
  • 13.Synchronous Mapping And Navigation
  • 14.Aruco Visual Relocalization Navigation
  • 15.Mobile App Mapping And Navigation
  • 16.AprilTag Transport
  • 17.Color Block Transport
03.USB Camera-Depth Camera Course
  • 1.USB Camera Introduction
  • 2.USB Camera Intrinsic Calibration
  • 3.Nuwa-HP60C Camera Introduction (Depth Camera Only)
  • 4.Depth Camera Distance Measurement (Depth Camera Only)
  • 5.Depth Pseudo Color Image (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)
04.Robotic Arm and 3D Space Gripping Course
  • 0.Robotic Arm Basic Usage
  • 1.Robotic Arm Kinematics
  • 2.Robotic Arm Chassis Coordination Control
  • 3.AprilTag ID Sorting
  • 4.AprilTag Height Anomaly Sorting
  • 5.AprilTag Tracking and Grasping
  • 6.Color Block Sorting
  • 7.Color Block Height Anomaly Sorting
  • 8.Color Block Tracking and Grasping
  • 9.Wood Block Shape Sorting
  • 10.KCF Tracking and Grasping Objects
  • 11.Mediapipe Gesture ID Sorting AprilTag
  • 12.Mediapipe Gesture Height Sorting AprilTag
  • 13.Desktop Tracking and Grasping AprilTag
  • 14.3D Spatial Tracking AprilTag
  • 15.Line Following Obstacle Clearing
  • 16.Transformer Model Tracking Algorithm
05.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
06.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
07.OpenClaw Interaction Method
  • 1.OpenClaw Webchat Interaction
  • 2.OpenClaw Tui Interaction
  • 3.OpenClaw Voice Dialogue Interaction
  • 4.OpenClaw WhatsApp Interaction
08.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
09.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 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
  • 1.Register a Model Service Provider Aaccount
  • 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 - Text Version
  • 1.Semantic Understand and Command Follow
  • 2.Multimodal Visual Understand
  • 3.Multimodal Visual Understand + Robotic Arm Gripping
  • 4.Multimodal Visual Understand+SLAM Navigation
  • 5.Robotic Arm Gripping+Multimodal Visual Understand+SLAM Navigation
  • 6.Intention Estimation
14.AI Model - Voice Version
  • 1.Semantic Understand and Command Follow
  • 2.Multimodal Visual Understand
  • 3. Multimodal Large Model+Robotic Arm Gripping
  • 4.Multimodal Visual Understand+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 Cobotic Arm
  • 13.Gesture Grabbing and Releasing Objects
  • 14.Finger Control Robotic Arm
  • 15.Medipipe Gesture Control Robotic arm Cction 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.Scene Design
  • 8.Trajectory Planning
  • 9.Collision Detection
  • 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
  • SystemFile
  • Code
  • 3D_Model_File
  • Software_Tools
  • Hardware_Info
  • Introduction_Manual
  • Script_File
  • Dify AI Agent
Download ZIP

Welcome to ROSMASTER A1 PRO repository

1.Robotic Arm Kinematics

🦾 Robotic Arm Forward and Inverse Kinematics

1. Course Overview

This course introduces the forward kinematics (FK) and inverse kinematics (IK) computation principles for the DOFBOT Pro 6-DOF robotic arm, C++ KDL solver implementation, and ROS 2 service invocation methods:

  • Forward Kinematics (FK): Input servo joint angles to calculate the pose of the end-effector gripper reference frame relative to the robotic arm base frame in 3D space

  • Inverse Kinematics (IK): Input the target position and orientation of the end-effector, optionally with current joint angles, to solve for the target angles of servos 1-5


2. Preparation and Running

This case suggests opening 3 terminals for step-by-step startup and debugging:

  • Terminal 1: Start the kinematics solver server node (kinemarics_arm)

  • Terminal 2: Start the robot URDF model, RViz visualization, and driver

  • Terminal 3: Initiate ROS 2 service requests, publish control commands, and compare TF coordinate transforms

2.1 Start the Kinematics Solver Service

Run in Terminal 1:

ros2 run dofbot_pro_info kinemarics_dofbot

This node listens for /get_kinemarics service requests in the background after startup, with the terminal remaining on standby. Kinematics Service Startup

2.2 Start the Theoretical Model and RViz Visualization Environment

Run in Terminal 2 (enable GUI joint slider mode):

xxxxxxxxxx
ros2 launch dofbot_pro_description display_A1pro.launch.py gui:=true 

In gui:=true mode, /joint_states is generated by the graphical sliders, and RViz renders the manually set theoretical model pose, facilitating comparison between KDL algorithm computed values and model TF. image-20260902093002849 RViz Theoretical Model

2.3 Check Service and Node Status

Run in Terminal 3:

xxxxxxxxxx
ros2 service list | grep get_kinemarics
ros2 node info /kinemarics_arm

image-20260902093251739


3. Service Interface and Data Structure

The kinematics node provides the /get_kinemarics service with type arm_interface/srv/ArmKinemarics:

xxxxxxxxxx
ros2 interface show arm_interface/srv/ArmKinemarics

Kinematics Service Interface

3.1 Field Definitions and Mode Description

xxxxxxxxxx
# ====== Request ======
float64 tar_x       # Target X coordinate (meters, used in IK mode)
float64 tar_y       # Target Y coordinate (meters, used in IK mode)
float64 tar_z       # Target Z coordinate (meters, used in IK mode)
float64 roll        # Target Roll angle (radians, used in IK mode)
float64 pitch       # Target Pitch angle (radians, used in IK mode)
float64 yaw         # Target Yaw angle (radians, used in IK mode)
float64 cur_joint1  # Current servo 1 angle (degrees, used in FK; used as IK seed value)
float64 cur_joint2  # Current servo 2 angle (degrees, used in FK; used as IK seed value)
float64 cur_joint3  # Current servo 3 angle (degrees, used in FK; used as IK seed value)
float64 cur_joint4  # Current servo 4 angle (degrees, used in FK; used as IK seed value)
float64 cur_joint5  # Current servo 5 angle (degrees, used in FK; used as IK seed value)
float64 cur_joint6  # Current gripper servo angle (degrees, not used in computation)
string  kin_name    # "fk" for forward mode, "ik" for inverse mode
---
# ====== Response ======
float64 joint1      # Servo 1 target angle computed by IK (degrees)
float64 joint2      # Servo 2 target angle computed by IK (degrees)
float64 joint3      # Servo 3 target angle computed by IK (degrees)
float64 joint4      # Servo 4 target angle computed by IK (degrees)
float64 joint5      # Servo 5 target angle computed by IK (degrees)
float64 joint6      # Gripper servo reserved (default 0)
float64 x           # End-effector X coordinate computed by FK (meters)
float64 y           # End-effector Y coordinate computed by FK (meters)
float64 z           # End-effector Z coordinate computed by FK (meters)
float64 roll        # End-effector Roll angle computed by FK (radians)
float64 pitch       # End-effector Pitch angle computed by FK (radians)
float64 yaw         # End-effector Yaw angle computed by FK (radians)
  • kin_name: 'fk': Reads cur_joint1 ~ cur_joint5, computes and returns x, y, z, roll, pitch, yaw;

  • kin_name: 'ik': Reads target pose (tar_x, tar_y, tar_z, roll, pitch, yaw) and current joint angles as seed values, computes and returns joint1 ~ joint5.


4. Theoretical Model Verification and TF Comparison

4.1 Forward Kinematics (FK) Call Verification

Keep all joints centered in the GUI (servo angles corresponding to 90 degrees for each joint), and initiate the forward kinematics service request in Terminal 3:

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'}"

FK Call Result

The service returns the current end-effector pose approximately as:

xxxxxxxxxx
x     = -0.0026619 m
y     =  0.0002654 m
z     =  0.5263000 m
roll  =  2.5984269 rad
pitch = -1.5707962 rad
yaw   = -2.5984559 rad

4.2 Inverse Kinematics (IK) Closed-Loop Verification

Fill the spatial pose obtained from forward kinematics into the inverse kinematics request to verify if it accurately back-calculates the initial servo angles [90, 90, 90, 90, 90]:

xxxxxxxxxx
ros2 service call /get_kinemarics arm_interface/srv/ArmKinemarics "{tar_x: -0.00266, tar_y: 0.000265, tar_z: 0.5263, roll: 2.598, pitch: -1.5707, yaw: -2.598, 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'}"

IK Call Result

4.3 TF Transform Ground Truth Comparison

FK computes the relative transform between arm_base_Link -> Gripping coordinate frames. Use the TF tool to print and compare values:

xxxxxxxxxx
ros2 run tf2_ros tf2_echo arm_base_Link Gripping

Theoretical Model TF Comparison

The Translation values in TF are completely consistent with FK's (x, y, z).


5. Physical Robotic Arm Motion and Physical Closed-Loop Verification

After completing theoretical verification, stop the GUI simulation in Terminal 2 with Ctrl+C and switch to physical robot real feedback mode:

xxxxxxxxxx
ros2 launch dofbot_pro_description display_A1pro.launch.py gui:=false 

At this point, the driver node reads physical servo angles in real-time and publishes /arm_status/arm_joint, and RViz and the TF tree faithfully reflect the real robotic arm pose.

image-20260902094352124

5.1 Drive the Robotic Arm to a Specified Working Pose

Publish multi-joint control commands in Terminal 3:

xxxxxxxxxx
ros2 topic pub /arm6_joints dofbot_pro_interface/msg/ArmJoints "{joint1: 90, joint2: 120, joint3: 0, joint4: 0, joint5: 90, joint6: 90, time: 1500}" --once

image-20260902094259494

5.2 Get End-Effector Pose Based on Current Actual Joint Angles (FK)

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: 120.0, cur_joint3: 0.0, cur_joint4: 0.0, cur_joint5: 90.0, cur_joint6: 90.0, kin_name: 'fk'}"

Obtain the end-effector pose: image-20260902094442421

5.3 Back-Calculate Servo Angles Based on Obtained Pose (IK)

xxxxxxxxxx
ros2 service call /get_kinemarics arm_interface/srv/ArmKinemarics "{tar_x: 0.11178, tar_y: 0.000262, tar_z: 0.16021, roll: -3.61e-06, pitch: 1.04719, yaw: -2.74e-05, cur_joint1: 90.0, cur_joint2: 120.0, cur_joint3: 0.0, cur_joint4: 0.0, cur_joint5: 90.0, cur_joint6: 90.0, kin_name: 'ik'}"

Inverse solved joint angles: image-20260902094715070

5.4 View Physical Robot TF Transform

xxxxxxxxxx
ros2 run tf2_ros tf2_echo arm_base_Link Gripping

Verify the real transform correspondence: image-20260902094910565

Robotic Arm Basic Usage
Robotic Arm Chassis Coordination Control

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