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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
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  • 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
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Welcome to ROSMASTER M1 PRO repository

4.Forward kinematics design

Forward Kinematics Design

Forward Kinematics Design1. Course Content2. Preparation3. Case Demonstration4. Core Source Code Analysis

1. Course Content

This course explains how to call the functions in the MoveIt library to implement forward kinematics design of the robot arm. Forward kinematics design refers to computing the pose of the robot arm's end effector based on the joint angles. Here, in RViz, we specify the angle of each joint, and then let MoveIt2 plan and move to this pose.

2. Preparation

ros2 launch yahboomcar_bringup moveit_simulation.launch.py sim2real:=true

Once the program starts, when You can start planning now! appears in the terminal, it means the startup was successful, as shown in the figure below:

image-20260914145339789

 

3. Case Demonstration

  • Enter the following command in the terminal to launch the forward kinematics design program:

xxxxxxxxxx
ros2 run moveit_demo set_target_joints --ros-args -p target_joints:="[90.0,-24.0,44.0,146.0,90.0]"

Note

  • target_joints is the angle of robot arm joints 1-5, as floating-point numbers. The above command is a reference example; the angles can be specified by yourself through parameters.

 

After the program runs, the robot arm in RViz will plan and move to the pose of each joint we set, as shown in the figure below:

image-20260914154540616

image-20260914154528855

4. Core Source Code Analysis

  • Source code path

xxxxxxxxxx
~/yahboomcar_ros2_ws/yahboomcar_ws/src/moveit_demo/src/set_target_joints.cpp

initialize function description:

  • declare_parameter<std::vector<double>>("target_joints", ...): Declares the target joint angle parameter, with a default value of [86.0, 47.0, -26.0, 89.0, 90.0], which can be overridden via the -p command line.

  • declare_parameter<bool>("ignore_obstacles", false): Declares the obstacle ignore switch, off by default.

  • target_joints_deg.size() != 5: Verifies that the number of joint angles must be 5; if it does not match, an error is reported and the node is shut down.

  • deg * M_PI / 180.0: Converts the angle values into radians one by one; MoveIt2 internally uses radians for motion planning.

  • MoveGroupInterface(shared_from_this(), "arm_group"): Creates a MoveGroup control interface, bound to the arm_group planning group.

  • setNumPlanningAttempts(10): Sets the number of planning attempts per call to 10.

  • setPlanningTime(5.0): Sets the maximum time for a single planning attempt to 5 seconds.

  • setMaxVelocityScalingFactor(1.0) / setMaxAccelerationScalingFactor(1.0): Both velocity and acceleration scaling factors are 1.0, using the full speed of the planned motion.

  • clearSceneObstacles(): Called when ignore_obstacles:=true, clears all collision objects and the octomap in the scene.

  • setStartStateToCurrentState(): Sets the planning start state to the robot arm's current state.

  • setJointValueTarget(target_joints_rad): Sets the target joint angles (radians), i.e., the input to forward kinematics.

  • plan(my_plan): Plans a trajectory for the target joint angles.

  • execute(my_plan): If planning succeeds, executes the trajectory and drives the robot arm to the target pose.

  • printEndEffectorPose(target_joints_rad): After execution, computes and prints the pose of the end effector in the measure coordinate frame.

clearSceneObstacles function description:

  • PlanningSceneInterface: Creates a planning scene interface, used to manage the collision objects in the scene.

  • getKnownObjectNames(): Gets the list of names of all known collision objects in the scene.

  • removeCollisionObjects(object_ids): Removes all collision objects in the scene in batches.

  • create_publisher<PlanningScene>("/planning_scene", 10): Creates a planning scene publisher, used to send scene update messages.

  • ps_msg.is_diff = true: Marks it as an incremental update message, modifying only part of the scene content.

  • ps_msg.world.octomap.header.frame_id = "measure": Sets the reference frame of the octomap; publishing an octomap with empty data indicates clearing the existing map data.

  • sleep_for(300ms) / sleep_for(500ms): Waits for the message publication to take effect, ensuring the scene update is complete.

printEndEffectorPose function description:

  • getRobotModel(): Gets the robot model, which contains information about all joints and links.

  • RobotState(robot_model): Creates a robot state object, used for kinematics computation.

  • getJointModelGroup("arm_group"): Gets the joint model group corresponding to the arm_group planning group.

  • setJointGroupPositions(jmg, joint_positions): Sets the given joint angles into the robot state.

  • robot_state.update(): Updates the robot state, triggering the forward kinematics solution.

  • getGlobalLinkTransform("gripper_tcp"): Gets the pose transformation matrix of the gripper end link in the global coordinate frame.

  • getGlobalLinkTransform("measure"): Gets the pose transformation matrix of the measure-frame link in the global coordinate frame.

  • measure_pose.inverse() * gripper_tcp_pose: Transforms the gripper pose into the measure coordinate frame to obtain the relative pose.

  • std::atan2(rot(2,1), rot(2,2)) / std::asin(-rot(2,0)) / std::atan2(rot(1,0), rot(0,0)): Extracts the RPY Euler angles from the rotation matrix (ROS fixed-axis XYZ convention) and converts them to degrees.

​x
class SetTargetJoints : public rclcpp::Node
{
public:
  SetTargetJoints()
    : Node("set_target_joints")
  {
    RCLCPP_INFO(this->get_logger(), "Initializing SetTargetJoints.");
  }

  void initialize()
  {
    // Declare ROS parameters: target joint angles in degrees and obstacle ignore flag
    this->declare_parameter<std::vector<double>>(
      "target_joints",
      std::vector<double>{86.0, 47.0, -26.0, 89.0, 90.0});
    this->declare_parameter<bool>("ignore_obstacles", false);

    bool ignore_obstacles = this->get_parameter("ignore_obstacles").as_bool();
    std::vector<double> target_joints_deg = this->get_parameter("target_joints").as_double_array();

    if (target_joints_deg.size() != 5)
    {
      RCLCPP_ERROR(this->get_logger(),
        "target_joints must have exactly 5 elements, got %zu.", target_joints_deg.size());
      rclcpp::shutdown();
      return;
    }

    // Convert degrees to radians
    std::vector<double> target_joints_rad;
    target_joints_rad.reserve(5);
    for (double deg : target_joints_deg)
    {
      target_joints_rad.push_back(deg * M_PI / 180.0);
    }

    // Initialize MoveGroupInterface after shared_ptr is fully constructed
    move_group_interface_ = std::make_shared<moveit::planning_interface::MoveGroupInterface>(
      shared_from_this(), "arm_group");

    move_group_interface_->setNumPlanningAttempts(10);
    move_group_interface_->setPlanningTime(5.0);
    move_group_interface_->setMaxVelocityScalingFactor(1.0);
    move_group_interface_->setMaxAccelerationScalingFactor(1.0);

    // Optionally clear all scene obstacles (collision objects + octomap)
    // so that the target object is not treated as an obstacle during planning
    if (ignore_obstacles)
    {
      RCLCPP_INFO(this->get_logger(), "ignore_obstacles:=true, clearing scene obstacles...");
      clearSceneObstacles();
    }

    RCLCPP_INFO(this->get_logger(),
      "Target joints (deg): [%.2f, %.2f, %.2f, %.2f, %.2f]",
      target_joints_deg[0], target_joints_deg[1], target_joints_deg[2],
      target_joints_deg[3], target_joints_deg[4]);
    RCLCPP_INFO(this->get_logger(),
      "Target joints (rad): [%.4f, %.4f, %.4f, %.4f, %.4f]",
      target_joints_rad[0], target_joints_rad[1], target_joints_rad[2],
      target_joints_rad[3], target_joints_rad[4]);

    move_group_interface_->setStartStateToCurrentState();
    move_group_interface_->setJointValueTarget(target_joints_rad);
    moveit::planning_interface::MoveGroupInterface::Plan my_plan;
    if (move_group_interface_->plan(my_plan) == moveit::core::MoveItErrorCode::SUCCESS)
    {
      RCLCPP_INFO(this->get_logger(), "Planning succeeded, executing.");
      move_group_interface_->execute(my_plan);
      printEndEffectorPose(target_joints_rad);
    }
    else
    {
      RCLCPP_ERROR(this->get_logger(), "Planning failed!");
    }
  }

private:
  void clearSceneObstacles()
  {
    // 1. Remove all known collision objects via PlanningSceneInterface
    planning_scene_interface_ =
      std::make_shared<moveit::planning_interface::PlanningSceneInterface>();

    std::vector<std::string> object_ids = planning_scene_interface_->getKnownObjectNames();
    if (!object_ids.empty())
    {
      RCLCPP_INFO(this->get_logger(),
        "Removing %zu collision objects from scene.", object_ids.size());
      planning_scene_interface_->removeCollisionObjects(object_ids);
    }

    // 2. Clear the octomap by publishing a PlanningScene diff message
    //    Setting frame_id with empty octomap.data signals the PlanningSceneMonitor
    //    to remove the existing octomap
    auto scene_pub = this->create_publisher<moveit_msgs::msg::PlanningScene>(
      "/planning_scene", 10);
    rclcpp::sleep_for(std::chrono::milliseconds(300));

    moveit_msgs::msg::PlanningScene ps_msg;
    ps_msg.is_diff = true;
    ps_msg.world.octomap.header.frame_id = "measure";
    ps_msg.world.octomap.header.stamp = this->now();

    scene_pub->publish(ps_msg);
    rclcpp::sleep_for(std::chrono::milliseconds(500));

    RCLCPP_INFO(this->get_logger(),
      "Scene obstacles cleared (collision objects removed, octomap cleared).");
  }

  void printEndEffectorPose(const std::vector<double>& joint_positions)
  {

    moveit::core::RobotModelConstPtr robot_model = move_group_interface_->getRobotModel();
    moveit::core::RobotState robot_state(robot_model);
    const moveit::core::JointModelGroup* jmg = robot_model->getJointModelGroup("arm_group");

    robot_state.setJointGroupPositions(jmg, joint_positions);
    robot_state.update();

    // Get link transforms in the global (model root) frame
    const Eigen::Isometry3d& gripper_tcp_pose =
      robot_state.getGlobalLinkTransform("gripper_tcp");
    const Eigen::Isometry3d& measure_pose =
      robot_state.getGlobalLinkTransform("measure");

    // Compute gripper_tcp pose relative to measure frame
    Eigen::Isometry3d gripper_in_measure = measure_pose.inverse() * gripper_tcp_pose;

    double x = gripper_in_measure.translation().x();
    double y = gripper_in_measure.translation().y();
    double z = gripper_in_measure.translation().z();

    // Extract RPY from rotation matrix (ROS convention: fixed-axis XYZ)
    Eigen::Matrix3d rot = gripper_in_measure.rotation();
    double roll = std::atan2(rot(2, 1), rot(2, 2)) * 180.0 / M_PI;
    double pitch = std::asin(-rot(2, 0)) * 180.0 / M_PI;
    double yaw = std::atan2(rot(1, 0), rot(0, 0)) * 180.0 / M_PI;

    RCLCPP_INFO(this->get_logger(),
      "End-effector pose in measure frame: "
      "x=%.4f, y=%.4f, z=%.4f, roll=%.4f deg, pitch=%.4f deg, yaw=%.4f deg",
      x, y, z, roll, pitch, yaw);
  }

  std::shared_ptr<moveit::planning_interface::MoveGroupInterface> move_group_interface_;
  std::shared_ptr<moveit::planning_interface::PlanningSceneInterface> planning_scene_interface_;
};

 

Random movement
Inverse kinematics design

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