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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

4.Lidar Guard

LiDAR Guard

LiDAR Guard1. Course Content2. Preparation3. Case Demonstration4. Core Source Code Analysis

1. Course Content

  • Run the example program. It will then detect in real time the object closest to the car body and control the chassis to rotate in place so that the front of the car aligns with that object. At the same time, if the distance between that object and the car is smaller than the alarm threshold, the buzzer on the car will sound an alarm.

2. Preparation

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

image-20260827175403851

3. Case Demonstration

  • Start tracking

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ros2 param set /laser_Warning enable true
  • After the program starts, the LiDAR scans for the object closest to the car within the detection range. Slowly move that object left and right; the program will control the chassis to rotate in place and track that object so that the front of the car aligns with it. If the distance between the car's base_link and that object is less than 0.37 meters, the buzzer on the car will sound.

image-20260827181143207

  • Stop tracking

xxxxxxxxxx
ros2 param set /laser_Warning enable false

4. Core Source Code Analysis

  • Program code path:

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

 

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

  • Checks the is_enabled parameter; when not enabled, it returns directly

  • 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, and finds the closest obstacle

  • When no obstacle is detected, stops the car and turns off the buzzer

  • Buzzer alarm: turns on the buzzer when the distance to the closest obstacle ≤ ResponseDist, and turns it off when the distance > ResponseDist; the buzzer_active state flag avoids repeated sending

  • Angular velocity PID: uses 0 (aligned) as the setpoint and the obstacle's 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

xxxxxxxxxx
    def registerScan(self, scan_data):
        if not isinstance(scan_data, LaserScan): return
        
        # Auto-detect laser frame_id
        if not self.laser_frame and scan_data.header.frame_id:
            self.laser_frame = scan_data.header.frame_id
            self.get_logger().info(f'Detected laser frame: {self.laser_frame}')
        
        # Check if TF is ready
        if not self.tf_ready or not self.laser_frame:
            return
        
        # Check if transform is still available
        try:
            trans = self.tf_buffer.lookup_transform(
                'base_link',
                self.laser_frame,
                rclpy.time.Time()
            )
        except TransformException as ex:
            current_time = self.get_clock().now().nanoseconds / 1e9
            if (current_time - self.last_log_time) >= 3.0:
                self.get_logger().warn(f'TF transform error: {ex}')
                self.last_log_time = current_time
            return
        
        # Check if tracking is enabled
        if not self.is_enabled:
            return
        
        # Get laser data
        ranges = np.array(scan_data.ranges)
        
        # Find the nearest obstacle in front using TF2
        min_dist = float('inf')
        nearest_x = 0.0
        nearest_y = 0.0
        nearest_angle = 0.0
        found_obstacle = False
        
        for i in range(len(ranges)):
            # Skip invalid measurements
            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
                
                # Check if point is within LaserAngle in front
                dist = math.sqrt(x**2 + y**2)
                angle_to_base = math.atan2(y, x) * RAD2DEG
                
                if abs(angle_to_base) < self.LaserAngle:
                    # Find nearest obstacle
                    if dist < min_dist:
                        min_dist = dist
                        nearest_x = x
                        nearest_y = y
                        nearest_angle = angle_to_base
                        found_obstacle = True
            except Exception as e:
                continue
        
        if not found_obstacle:
            # No obstacle found, stop robot
            velocity = Twist()
            self.pub_vel.publish(velocity)
            # Turn off buzzer
            if self.buzzer_active:
                buzzer_msg = Bool()
                buzzer_msg.data = False
                self.pub_Buzzer.publish(buzzer_msg)
                self.buzzer_active = False
            return
        
        # Log nearest obstacle info (throttled)
        current_time = self.get_clock().now().nanoseconds / 1e9
        if (current_time - self.last_log_time) >= self.log_interval:
            self.get_logger().info(
                f'Nearest: dist={min_dist:.2f}m, '
                f'x={nearest_x:.2f}m, y={nearest_y:.2f}m, '
                f'angle={nearest_angle:.1f}°'
            )
            self.last_log_time = current_time
        
        # Control buzzer based on distance
        if min_dist <= self.ResponseDist:
            if not self.buzzer_active:
                buzzer_msg = Bool()
                buzzer_msg.data = True
                self.pub_Buzzer.publish(buzzer_msg)
                self.buzzer_active = True
        else:
            if self.buzzer_active:
                buzzer_msg = Bool()
                buzzer_msg.data = False
                self.pub_Buzzer.publish(buzzer_msg)
                self.buzzer_active = False
        
        # Calculate angular velocity to track the nearest obstacle
        # Target angle is 0 (aligned with x-axis)
        angle_rad = math.radians(nearest_angle)
        ang_pid_compute = self.ang_pid.pid_compute(0, angle_rad)
        
        velocity = Twist()
        
        # Reverse angular velocity direction
        velocity.angular.z = -ang_pid_compute
        
        # Apply deadzone
        if abs(ang_pid_compute) < self.angular_deadzone:
            velocity.angular.z = 0.0
        
        # Clamp angular velocity
        if abs(velocity.angular.z) > self.max_angular:
            velocity.angular.z = self.max_angular * (1.0 if velocity.angular.z > 0 else -1.0)
        
        # Publish velocity
        self.pub_vel.publish(velocity)

 

  • Parameter file path

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

Parameter Description for the laser_Warning node in common.yaml:

ParameterDefault ValueMeaningDebugging Suggestion
LaserAngle45.0°Half-angle of the target search sectorThe larger the angle, the wider the search range, but it may introduce lateral interference
ResponseDist0.37 mBuzzer alarm distance thresholdThe buzzer alarms when the distance is less than this value; adjust according to the scene
enablefalseWhether to enable tracking and alarming at startupDisabled by default; needs to be enabled manually via ros2 param set
ang_pid_P/I/D3.0/0.0/5.0Angular velocity PID parametersP determines the turning response, D suppresses overshoot; it is recommended to start with P < D
angular_deadzone0.5 rad/sAngular velocity deadzoneBelow this value no turning is output, avoiding jitter caused by tiny deviations
max_angular1.2 rad/sAngular velocity upper limitLimits the maximum turning speed to prevent overly aggressive turning
laser_Warning:
  ros__parameters:
    # Basic warning parameters
    linear: 0.15                  # Default linear velocity (m/s) - not used for warning
    angular: 1.0                  # Default angular velocity (rad/s) - not used for warning
    LaserAngle: 45.0              # Laser detection angle range (degrees)
    ResponseDist: 0.37            # Warning distance threshold (m) - buzzer triggers when closer
    enable: false                 # Enable tracking and warning (true/false)
      
    # Angular PID parameters [P, I, D]
    ang_pid_P: 3.0                # Angular PID proportional gain
    ang_pid_I: 0.0                # Angular PID integral gain
    ang_pid_D: 5.0                # Angular PID derivative gain
    # Angular velocity control
    angular_deadzone: 0.5         # Angular velocity deadzone threshold
    max_angular: 1.2              # Maximum angular velocity limit (rad/s)
Lidar Following
Lidar Patrol

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