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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
  • 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

3.Lidar Following

LiDAR Tracking

LiDAR Tracking1. Course Content2. Preparation3. Case Demonstration4. Source Code Analysis

1. Course Content

  • Run the example program. The robot scans the surrounding environment with the LiDAR, detects in real time the object closest to the car body, and controls the chassis to track that object.

 

2. Preparation

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ros2 launch yahboomcar_bringup lidar_demo.launch.py demo:=laser_Tracker

image-20260827173423882

 

3. Case Demonstration

Place an object that can be scanned in front of the car's LiDAR, then start/stop tracking via commands

  • Start tracking

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ros2 param set /laser_Tracker enable true

After starting tracking, the LiDAR scans for the object closest to the car within the detection range. Slowly move that object; the program will control the chassis to move, maintaining a distance of 0.5 meters from it and aligning the front of the car with that object.

  • Stop tracking

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ros2 param set /laser_Tracker enable false

 

4. Source Code Analysis

  • Program code path:

xxxxxxxxxx
~/yahboomcar_ros2_ws/yahboomcar_ws/src/common_demo/common_demo/lidar/laser_Tracker.py

 

  • Checks the is_enabled parameter; when not enabled, it returns directly and the car stays stationary

  • Automatically detects the LiDAR frame_id and establishes the base_link ← laser_frame coordinate transform chain via TF

  • Iterates through all laser points, filters out invalid values, and transforms each point from the LiDAR coordinate frame to the base_link coordinate frame

  • Keeps only the front points whose angle is within the ±LaserAngle range, recording the distance and coordinates

  • Selects the closest obstacle among the front points, obtaining its distance minDist and angle min_angle

  • Linear velocity PID: uses ResponseDist as the setpoint and minDist as the feedback, computing the forward velocity via lin_pid; the farther the distance, the faster the velocity

  • Angular velocity PID: uses 0 (aligned) as the setpoint and min_angle as the feedback, computing the turning velocity via ang_pid

  • Angular velocity deadzone: when the output is less than angular_deadzone, it is set to zero to avoid wheel jitter caused by tiny deviations

  • Angular velocity clamping: when it exceeds max_angular, it is clamped to ±max_angular to prevent overly aggressive turning

  • Outputs tracking status logs throttled by log_interval, and finally publishes the Twist velocity command to /cmd_vel

xxxxxxxxxx
    def registerScan(self, scan_data):
        if not isinstance(scan_data, LaserScan): return
        
        # Skip processing if not enabled
        if not self.is_enabled:
            return
        
        # Auto-detect laser frame_id
        if not self.laser_frame:
            self.laser_frame = scan_data.header.frame_id
            self.get_logger().info(f'Detected laser frame: {self.laser_frame}')
        
        # If TF is not ready, try to establish connection
        if not self.tf_ready:
            try:
                self.tf_buffer.lookup_transform(
                    'base_link',
                    scan_data.header.frame_id,
                    rclpy.time.Time(),
                    timeout=rclpy.duration.Duration(seconds=0.1)
                )
                self.tf_ready = True
                self.get_logger().info(f'TF ready: base_link <- {scan_data.header.frame_id}')
            except TransformException:
                # TF not ready, skip this scan
                return
        
        # Get transform from laser to base_link
        try:
            trans = self.tf_buffer.lookup_transform(
                'base_link',
                scan_data.header.frame_id,
                rclpy.time.Time(),
                timeout=rclpy.duration.Duration(seconds=0.1)
            )
        except TransformException:
            # Throttle TF error logs
            current_time = self.get_clock().now().nanoseconds / 1e9
            if (current_time - self.last_tf_log_time) >= self.tf_log_interval:
                self.get_logger().warn('TF not available, skipping scan')
                self.last_tf_log_time = current_time
            return
        
        # Iterate through all laser points and find closest obstacle in front
        ranges = np.array(scan_data.ranges)
        minDistList = []
        minDistBaseList = []  # Store positions in base_link frame
        
        for i in range(len(ranges)):
            if ranges[i] == 0.0 or ranges[i] > scan_data.range_max:
                continue
                
            # Calculate laser point position in laser coordinate frame
            angle = scan_data.angle_min + scan_data.angle_increment * i
            point_in_laser = PointStamped()
            point_in_laser.header = scan_data.header
            point_in_laser.point.x = ranges[i] * math.cos(angle)
            point_in_laser.point.y = ranges[i] * math.sin(angle)
            point_in_laser.point.z = 0.0
            
            # Transform to base_link coordinate frame
            try:
                point_in_base = do_transform_point(point_in_laser, trans)
                x = point_in_base.point.x
                y = point_in_base.point.y
                
                # Only consider obstacles in front (within LaserAngle)
                dist = math.sqrt(x**2 + y**2)
                angle_to_base = math.atan2(y, x) * RAD2DEG
                
                if abs(angle_to_base) < self.LaserAngle:
                    minDistList.append(dist)
                    minDistBaseList.append((x, y, angle_to_base))
            except TransformException:
                continue
        
        if len(minDistList) == 0:
            return
        
        # Find the closest obstacle
        minDist = min(minDistList)
        minIdx = minDistList.index(minDist)
        min_x, min_y, min_angle = minDistBaseList[minIdx]
        
        # PID control for tracking
        velocity = Twist()
        
        # Distance deadzone
        if abs(minDist - self.ResponseDist) < 0.1:
            minDist = self.ResponseDist
        
        # Linear velocity PID (maintain target distance)
        velocity.linear.x = -self.lin_pid.pid_compute(self.ResponseDist, minDist)
        
        # Angular velocity PID (align with obstacle)
        # Use angle directly in radians for better sensitivity
        angle_rad = math.radians(min_angle)
        ang_pid_compute = self.ang_pid.pid_compute(0, angle_rad)  # Target is 0 (aligned), current is angle_rad
        
        velocity.angular.z = -ang_pid_compute
        
        # Reduced deadzone for better responsiveness
        if abs(ang_pid_compute) < self.angular_deadzone:
            velocity.angular.z = 0.0
        
        # Clamp angular velocity to safe limits
        if abs(velocity.angular.z) > self.max_angular:
            velocity.angular.z = self.max_angular * (1.0 if velocity.angular.z > 0 else -1.0)
        
        # Throttle logging
        current_time = self.get_clock().now().nanoseconds / 1e9
        if (current_time - self.last_log_time) >= self.log_interval:
            self.get_logger().info(
                f'Tracking: dist={minDist:.2f}m, angle={min_angle:.1f}deg, '
                f'linear={velocity.linear.x:.2f}, angular={velocity.angular.z:.2f}'
            )
            self.last_log_time = current_time
        
        self.pub_vel.publish(velocity)

 

  • Parameter file path

xxxxxxxxxx
~/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_bringup/param/common.yaml

Parameter Description:

ParameterDefault ValueMeaningDebugging Suggestion
linear0.15 m/sMaximum forward linear velocityUpper limit for tracking velocity; increasing it speeds up tracking but easily causes overshoot
angular1.0 rad/sMaximum turning angular velocityUpper limit for turning response speed; too large will cause swaying
LaserAngle45.0°Half-angle of the target search sectorThe larger the angle, the wider the search range, but it may introduce lateral interference
ResponseDist0.45 mTracking holding distanceThe desired distance to maintain from the target; adjust according to the scene
enablefalseWhether to enable following at startupDisabled by default; needs to be enabled manually via ros2 param set
lin_pid_P/I/D1.0/0.0/1.0Linear velocity PID parametersP determines the response speed, D suppresses oscillation, I eliminates steady-state error
ang_pid_P/I/D2.0/0.0/2.0Angular velocity PID parametersP determines the turning response, D suppresses overshoot; it is recommended to start with P=D
angular_deadzone0.1 rad/sAngular velocity deadzoneBelow this value no turning is output, avoiding jitter caused by tiny deviations
max_angular2.0 rad/sAngular velocity upper limitLimits the maximum turning speed to prevent overly aggressive turning
laser_Tracker:
  ros__parameters:
    # Basic tracking parameters
    linear: 0.15                  # Default linear velocity (m/s)
    angular: 1.0                  # Default angular velocity (rad/s)
    LaserAngle: 45.0              # Laser detection angle range (degrees)
    ResponseDist: 0.45             # Target tracking distance (m)
    enable: false                 # Enable tracking (true/false)
      
    # Linear PID parameters [P, I, D]
    lin_pid_P: 1.0                # Linear PID proportional gain
    lin_pid_I: 0.0                # Linear PID integral gain
    lin_pid_D: 1.0                # Linear PID derivative gain
      
    # Angular PID parameters [P, I, D]
    ang_pid_P: 2.0                # Angular PID proportional gain
    ang_pid_I: 0.0                # Angular PID integral gain
    ang_pid_D: 2.0                # Angular PID derivative gain
    # Angular velocity control
    angular_deadzone: 0.1        # Angular velocity deadzone threshold
    max_angular: 2.0              # Maximum angular velocity limit (rad/s)
Lidar Obstacle Avoidance
Lidar Guard

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