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ROSMASTER M1 PRO📖
0.Robot Configuration and Operation Guidelines
  • 0.Unboxing and Review
  • 1.Assembly Videos
  • 2.User Manual
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  • 4.FAQ
  • 5.RobotManager User Guide
1.Chassis Control Course
  • 1.Robot Information Publishing
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  • 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
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3.USB Camera-Depth Camera Course
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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
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  • 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
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8.OpenClaw Development
  • 1.OpenClaw Connecting to Robot MCP Service
  • 2.Robot CLI Command Tool
  • 3.robot-control Plugin Tool
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  • 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
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  • 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
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  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
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  • 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
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  • 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
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  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
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  • 11. Set display resolution and rotation
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  • 14. Using USB camera
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  • 16. Get real-time temperature of Raspberry Pi
RDKX5
  • 1.Introduction to RDK X5 board
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Welcome to ROSMASTER M1 PRO repository

2.Robotic Arm Kinematics

Robotic Arm Forward and Inverse Kinematics Solution

Robotic Arm Forward and Inverse Kinematics Solution1. Course Content2. Kinematics CLI Command Format3. Case Demonstration3.1 View Robotic Arm Kinematics Information3.2 Robotic Arm Forward Kinematics Solution3.3 Robotic Arm Inverse Kinematics Solution4. Core Source Code Analysis

1. Course Content

  • This course implements the calculation of robotic arm forward kinematics and inverse kinematics through CLI commands.

  • Forward kinematics calculates the pose of the end-effector from the angle value of each servo of the robotic arm

  • Inverse kinematics, on the other hand, calculates the angle value of each servo from the end-effector pose. Both play a crucial role in 3D spatial grasping. The forward kinematics algorithm can tell the current pose of the robotic arm end-effector

  • The inverse kinematics algorithm can calculate, for the robotic arm end-effector to reach a target pose, what the angle of each servo of the robotic arm should be. Before grasping, this algorithm needs to be called to calculate the servo values, and then the servos are controlled to move to the grasping pose.

2. Kinematics CLI Command Format

  • Forward kinematics solution: joint1—joint5 are the values of robotic arm joints 1-5

kin_measure_cal fk joint1 joint2 joint3 joint4 joint5
  • Inverse kinematics solution: --x --y --z --roll --pitch --yaw are the pose of the robotic arm end-effector TCP coordinate frame

xxxxxxxxxx
kin_measure_cal ik --x  --y  --z  --roll --pitch --yaw

 

3. Case Demonstration

3.1 View Robotic Arm Kinematics Information

xxxxxxxxxx
kin_measure_cal info

Here joint(5) is the number of joints that contribute degrees of freedom to the end-effector pose, i.e., joint 1-joint 5; after arm1-arm5 are the joint limits used when calculating kinematics (slightly larger than the actual joint limits, ensuring that the kinematics is solvable)

image-20260918125435059

 

3.2 Robotic Arm Forward Kinematics Solution

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kin_measure_cal fk 68 37 -10 149 90

image-20260915161823288

 

3.3 Robotic Arm Inverse Kinematics Solution

Important

  • The end-effector of this robotic arm configuration has only 5 degrees of freedom (lacking yaw), and the end-effector pose roll is determined only by joint 5 (servo No. 5), so the actual inverse solution is simplified to a 4-degree-of-freedom solution: only the end-effector position x, y, z and pitch need to be specified, while the weights of roll and yaw are ignored during the inverse solution

  • The robotic arm's low-level firmware can only use integers when receiving robotic arm angles, so when actually applying the inverse solution result (a floating-point number), it is rounded to an integer for use

 

  • Perform the inverse solution using the pose from the forward solution result

xxxxxxxxxx
kin_measure_cal ik --x 0.1632  --y 0.1005  --z 0.1940 --pitch 4.00
  • You can see that the angles are basically consistent with the forward solution

image-20260915185635867

 

4. Core Source Code Analysis

  • Program source code path:

xxxxxxxxxx
~/yahboomcar_ros2_ws/yahboomcar_ws/src/robot_util/cli/kin_measure_cal
  • Robotic arm low-level forward and inverse solution library

xxxxxxxxxx
~/yahboomcar_ros2_ws/yahboomcar_ws/src/robot_util/robot_util/arm_kinemarics.py

 

Global variables and helper functions:

  • DEFAULT_URDF: default URDF model file path, pointing to rosmaster_m1pro.urdf.xacro, used to parse kinematic parameters such as the link lengths and joint limits of the robotic arm

  • _build_kin(args): creates an ArmKinematics kinematics solver instance, specifying the base coordinate frame as measure and the end-effector coordinate frame as gripper_tcp

cmd_fk function (forward kinematics):

  • kin.forward_kinematics(joints): takes 5 joint angle values (degrees) as input and calculates the pose of the end-effector TCP in the measure coordinate frame

  • result.success: determines whether the solution succeeded; if it failed, outputs an error message and exits

  • pose.x / pose.y / pose.z: end-effector position coordinates (unit: meters), relative to the measure coordinate frame

  • pose.roll / pose.pitch / pose.yaw: end-effector pose Euler angles (unit: degrees), describing the spatial orientation of the gripper

cmd_ik function (inverse kinematics):

  • kin.inverse_kinematics(...): takes the target pose (x/y/z position and roll/pitch/yaw orientation) as input and solves for each joint angle

  • seed_angles_deg: optional seed joint angles, providing an initial guess for the solver, which helps select the solution closest to the current pose among multiple solutions

  • allow_approximate=True: allows approximate solutions; when the target pose is kinematically unreachable, returns the closest feasible solution

  • pitch_search_space=20.0: pitch angle search space (degrees), searching for a feasible pitch value within this range

  • result.approximate: marks whether the solution is approximate; if so, outputs the position error

  • result.adjusted_pitch: if the solver automatically adjusted the pitch angle, outputs the values before and after adjustment

  • result.data: the 5 joint angle values obtained from the solution (degrees)

cmd_info function (joint information):

  • kin.NUM_JOINTS: number of robotic arm joints

  • kin._joint_names: list of each joint name

  • kin._joint_limits_deg: angle limit range of each joint (degrees), parsed from the URDF file

main function (CLI entry):

  • argparse.ArgumentParser: creates the command-line argument parser

  • sub.add_subparsers: adds a subcommand parser, supporting three subcommands: fk (forward solution), ik (inverse solution), and info (joint information)

  • fk subcommand: the joints argument receives space-separated joint angle values (degrees)

  • ik subcommand: --x/--y/--z are required target position arguments (meters), --roll/--pitch/--yaw are optional target orientation arguments (degrees), and --seed is an optional seed angle

  • {'fk': cmd_fk, 'ik': cmd_ik, 'info': cmd_info}[args.command](args): calls the corresponding handler function according to the subcommand name

​x
DEFAULT_URDF =os.path.join(get_package_share_directory("yahboomcar_description"),"urdf","rosmaster_m1pro.urdf.xacro")


def _build_kin(args) -> ArmKinematics:
    return ArmKinematics(urdf_path=args.urdf, base_link="measure", end_link="gripper_tcp")

def cmd_fk(args):
    """Forward kinematics: joint angles -> end-effector pose."""
    kin = _build_kin(args)
    joints = [float(x) for x in args.joints]
    result = kin.forward_kinematics(joints)
    if result.success:
        pose = result.data
        print(f"Position:    x={pose.x:.4f}  y={pose.y:.4f}  z={pose.z:.4f} (m)")
        print(f"Orientation: roll={pose.roll:.2f}  pitch={pose.pitch:.2f}  yaw={pose.yaw:.2f} (deg)")
    else:
        print(f"FK failed: {result.message}", file=sys.stderr)
        sys.exit(1)


def cmd_ik(args):
    """Inverse kinematics: target pose -> joint angles."""
    kin = _build_kin(args)
    result = kin.inverse_kinematics(
        target_x=args.x,
        target_y=args.y,
        target_z=args.z,
        target_roll=args.roll,
        target_pitch=args.pitch,
        target_yaw=args.yaw,
        seed_angles_deg=[float(x) for x in args.seed] if args.seed else None,
        allow_approximate=True,
        pitch_search_space=20.0
    )
    if result.success:
        joints_str = ', '.join(f'{j:.2f}' for j in result.data)
        print(f"Joint angles (deg): [{joints_str}]")
        if result.approximate:
            print(f"NOTE: approximate solution, position error = {result.position_error:.4f} m")
        if result.adjusted_pitch:
            print(f"NOTE: adjusted pitch angle from {args.pitch:.2f} to {result.adjusted_pitch:.2f} deg")
    else:
        print(f"IK failed: {result.message}", file=sys.stderr)
        sys.exit(1)


def cmd_info(args):
    """Show joint names and limits from URDF."""
    kin = _build_kin(args)
    print(f"Joints ({kin.NUM_JOINTS}):")
    for name, (lo, hi) in zip(kin._joint_names, kin._joint_limits_deg):
        print(f"  {name:20s}  [{lo:7.2f}, {hi:7.2f}] deg")


def main():
    parser = argparse.ArgumentParser(
        description="Robot arm kinematics calculator")
    parser.add_argument(
        '--urdf', default=DEFAULT_URDF,
        help='Path to URDF/xacro file (default: rosmaster_m1pro)')

    sub = parser.add_subparsers(dest='command', required=True)

    # --- fk ---
    p_fk = sub.add_parser('fk', help='Forward kinematics: joints -> pose')
    p_fk.add_argument(
        'joints', nargs='+', metavar='ANGLE',
        help='Joint angles in degrees (space separated)')

    # --- ik ---
    p_ik = sub.add_parser('ik', help='Inverse kinematics: pose -> joints')
    p_ik.add_argument('--x', type=float, required=True, help='Target x (m)')
    p_ik.add_argument('--y', type=float, required=True, help='Target y (m)')
    p_ik.add_argument('--z', type=float, required=True, help='Target z (m)')
    p_ik.add_argument('--roll', type=float, default=0.0, help='Target roll (deg, default 0)')
    p_ik.add_argument('--pitch', type=float, default=0.0, help='Target pitch (deg, default 0)')
    p_ik.add_argument('--yaw', type=float, default=0.0, help='Target yaw (deg, default 0)')
    p_ik.add_argument('--seed', nargs='+', metavar='ANGLE',
                      help='Seed joint angles in degrees (optional)')

    # --- info ---
    sub.add_parser('info', help='Show joint names and limits')

    args = parser.parse_args()
    {'fk': cmd_fk, 'ik': cmd_ik, 'info': cmd_info}[args.command](args)

 

Robotic Arm Basic Usage
Robotic Arm Chassis Coordination Control

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