ROSOrin

Specification

  • 1. ROSOrin User Manual
    • 1.1 Introduction
      • 1.1.1 Packing List
    • 1.2 Accessories Installation and Startup Preparation
      • 1.2.1 Camera Installation
      • 1.2.2 Voice Module Installation
      • 1.2.3 Wiring Instruction
        • 1.2.3.1 Jetson Nano Wiring
        • 1.2.3.2 Jetson Orin Nano / Orin NX Wiring
        • 1.2.3.3 Raspberry Pi 5 Wiring
      • 1.2.4 Ackermann Chassis Switch
      • 1.2.5 Differential Drive Chassis Switch
    • 1.3 Initial Setup and Power-On
      • 1.3.1 Power-On Preparations
      • 1.3.2 Power-On Status
    • 1.4 Battery Usage and Charging Instructions
      • 1.4.1 Lithium Battery Care
      • 1.4.2 Charging Instructions
    • 1.5 App Installation and Connection
      • 1.5.1 App Installation
      • 1.5.2 Connection Modes
        • 1.5.2.1 AP Mode Connection (Must Read)
        • 1.5.2.2 LAN Mode Connection (Optional)
      • 1.5.3 App Control
        • 1.5.3.1 Preparation
        • 1.5.3.2 App Modes
        • 1.5.3.3 Robot Control
        • 1.5.3.4 LiDAR
        • 1.5.3.5 Target Tracking
        • 1.5.3.6 Line Following
        • 1.5.3.7 Driverless
    • 1.6 Wireless Controller Control
      • 1.6.1 Notes
      • 1.6.2 Device Connection
      • 1.6.3 Button Functions
    • 1.7 Development Environment Setup
      • 1.7.1 Remote Control Tool Introduction and Installation
        • 1.7.1.1 Tool Introduction
        • 1.7.1.2 Nomachine Installation
        • 1.7.1.3 VNC Installation
        • 1.7.1.4 MobaXterm Installation
      • 1.7.2 AP Mode Connection Steps
        • 1.7.2.1 Connecting via NoMachine
        • 1.7.2.2 Connecting via VNC
        • 1.7.2.3 Connecting via MobaXterm
      • 1.7.3 LAN Mode Connection
      • 1.7.4 Fixed IP Connection via USB Data Cable
      • 1.7.5 Changing Chassis Type
    • 1.8 Manual Mapping
      • 1.8.1 Preparation
      • 1.8.2 Operation Steps
        • 1.8.2.1 ROS1 Mapping
        • 1.8.2.2 ROS2 Mapping
    • 1.9 Autonomous Mapping
    • 1.10 Autonomous Navigation
      • 1.10.1 ROS1 Autonomous Navigation
      • 1.10.2 ROS2 Autonomous Navigation
    • 1.11 Hardware Introduction
      • 1.11.1 Hardware System
      • 1.11.2 Electronic Control System
        • 1.11.2.1 STM32 Controller
        • 1.11.2.2 Power Supply
        • 1.11.2.3 Hall Encoder DC Geared Motor
        • 1.11.2.4 PWM Servo
        • 1.11.2.5 OLED Display Module
        • 1.11.2.6 PS2 Wireless Controller
      • 1.11.3 ROS Controller
        • 1.11.3.1 Jetson Nano
        • 1.11.3.2 Jetson Orin Nano/Jetson Orin NX
        • 1.11.3.3 Raspberry Pi 5
      • 1.11.4 Deptrum Depth Camera
      • 1.11.5 LiDAR
      • 1.11.6 Microphone Array Module
    • 1.12. ROS Introduction
      • 1.12.1 ROS Controller Hardware Connection
      • 1.12.2 ROS Serial Communication Overview
    • 1.13 STM32 Source Code
      • 1.13.1 Introduction
      • 1.13.2 Control Process
      • 1.13.3 Program Framework
      • 1.13.4 Program Analysis
      • 1.13.5 Kinematics Models
      • 1.13.6 Project Compilation
      • 1.13.7 Program Download via USB
    • 1.14 System Software Architecture
      • 1.14.1 Introduction to ROS1 File Directory
      • 1.14.2 Introduction to ROS2 File Directory
    • 1.15 Image Flashing
      • 1.15.1 Preparation
      • 1.15.2 SD Card / SSD Formatting
      • 1.15.3 Image Flashing
  • 2. Chassis Motion Control Course
    • 2.1 Motion Control
      • 2.1.1 IMU Calibration
      • 2.1.2 Angular Velocity Calibration
      • 2.1.3 Linear Velocity Calibration
      • 2.1.4 IMU and Odometry Data Publishing
        • 2.1.4.1 Introduction to IMU and Odometry
        • 2.1.4.2 IMU Data Publishing
        • 2.1.4.3 Odometry Data Publishing
      • 2.1.5 Robot Speed Control
        • 2.1.5.1 Working Principle
        • 2.1.5.2 Disable the App Service and Enable Speed Control
        • 2.1.5.3 Modifying Forward Speed
        • 2.1.5.4 Program Outcome
        • 2.1.5.5 Program Analysis
    • 2.2 Kinematics Analysis
      • 2.2.1 Overview
        • 2.2.1.1 Wheel Types
        • 2.2.1.2 Typical Applications
      • 2.2.2 Mecanum Chassis
        • 2.2.2.1 Hardware Structure
        • 2.2.2.2 Physical Characteristics
        • 2.2.2.3 Kinematic Principles and Equations
        • 2.2.2.4 Program Implementation
      • 2.2.3 Ackermann Chassis
        • 2.2.3.1 Hardware Structure
        • 2.2.3.2 Physical Characteristics
        • 2.2.3.3 Kinematic Principles and Equations
        • 2.2.3.4 Program Implementation
  • 3. LiDAR Course
    • 3.1 Introduction
      • 3.1.1 Overview
      • 3.1.2 LiDAR Components and Classification
      • 3.1.3 Switch the LiDAR Version
    • 3.2 Working Principle and Distance Measurement Methods
      • 3.2.1 LiDAR Distance Measurement
      • 3.2.2 LiDAR Performance
    • 3.3 Radar obstacle avoidance
    • 3.4 LiDAR Following
    • 3.5 LiDAR Guarding
  • 4. Camera Basic Course
    • 4.1 Aurora Depth Camera
    • 4.2 Monocular Camera -USB
  • 5. Mapping & Navigation Course
    • 5.1 Mapping Tutorial
      • 5.1.1 Getting Started with URDF Model
        • 5.1.1.1 URDF Model Introduction
        • 5.1.1.2 Comparison Between Xacro Model and URDF Model
        • 5.1.1.3 Basic URDF Syntax
      • 5.1.2 ROS Robot URDF Model
        • 5.1.2.1 Getting Ready
        • 5.1.2.2 Access the Robot Model Code
      • 5.1.3 SLAM Map Construction Principle
        • 5.1.3.1 SLAM Introduction
        • 5.1.3.2 SLAM Mapping Principle
        • 5.1.3.3 Note on Map Construction
        • 5.1.3.4 Evaluate Map Construction Result
      • 5.1.4 slam_toolbox Mapping Algorithm
        • 5.1.4.1 Algorithm Concept
        • 5.1.4.2 Mapping Steps
        • 5.1.4.3 Save Map
        • 5.1.4.4 Exit Mapping
        • 5.1.4.5 Effect Optimization
        • 5.1.4.6 Parameter Description
        • 5.1.4.7 Launch File Analysis
      • 5.1.5 RTAB-VSLAM 3D Mapping
        • 5.1.5.1 RTAB-VSLAM Overview
        • 5.1.5.2 RTAB-VSLAM Working Principle
        • 5.1.5.3 3D Mapping Steps
        • 5.1.5.4 Save Map
        • 5.1.5.5 Exit Mapping
        • 5.1.5.6 Launch File Analysis
    • 5.2 Navigation Tutorial
      • 5.2.1 ROS Robot Autonomous Navigation
        • 5.2.1.1 Overview
        • 5.2.1.2 Package Details
      • 5.2.2 Adaptive Monte Carlo Localization(AMCL)
        • 5.2.2.1 AMCL Localization
        • 5.2.2.2 Particle Filtering
        • 5.2.2.3 Adaptive Monte Carlo Localization (AMCL)
        • 5.2.2.4 Costmap
        • 5.2.2.5 Global Path Planning
      • 5.2.3 Local Path Planning
        • 5.2.3.1 DWA Algorithm
        • 5.2.3.2 TEB Algorithm
      • 5.2.4 Single/Multi-Point Navigation and Obstacle Avoidance
        • 5.2.4.1 Single-Point Navigation
        • 5.2.4.2 Multi-Point Navigation
        • 5.2.4.3 Exit Navigation
        • 5.2.4.4 Launch Instruction
        • 5.2.4.5 Package Description
      • 5.2.5 RTAB-VSLAM 3D Navigation
        • 5.2.5.1 Algorithm Introduction and Principles
        • 5.2.5.2 Operating Steps
        • 5.2.5.3 Launch Instruction
  • 6. ROS+OpenCV Course
    • 6.1 Color Threshold Adjustment
      • 6.1.1 Launching and Closing LAB TOOL
      • 6.1.2 LAB TOOL Interface Introduction
      • 6.1.3 Adjust Color Threshold
      • 6.1.4 Add New Color for Detection
    • 6.2 Color Recognition
      • 6.2.1 Recognition Process
      • 6.2.2 Operation
      • 6.2.3 Project Outcome
      • 6.2.4 Program Analysis
    • 6.3 QR Code Creation and Recognition
      • 6.3.1 QR Code Generation
      • 6.3.2 QR Code Recognition
    • 6.4 Autonomous Patrolling
      • 6.4.1 Recognition Process
      • 6.4.2 Operation Steps
      • 6.4.3 Program Analysis
  • 7. ROS+Machine Learning Course
    • 7.1 MediaPipe Human-Robot Interaction
      • 7.1.1 MediaPipe Introduction and Getting Started
        • 7.1.1.1 Overview of MediaPipe
        • 7.1.1.2 Pros and Cons
        • 7.1.1.3 MediaPipe Usage Workflow
        • 7.1.1.4 Websites for MediaPipe Learning
      • 7.1.2 Background Segmentation
        • 7.1.2.1 Experiment Overview
        • 7.1.2.2 Operation Steps
        • 7.1.2.3 Project Outcome
        • 7.1.2.4 Program Analysis
      • 7.1.3 3D Object Detection
        • 7.1.3.1 Experiment Overview
        • 7.1.3.2 Operation Steps
        • 7.1.3.3 Project Outcome
        • 7.1.3.4 Program Analysis
      • 7.1.4 3D Face Detection
        • 7.1.4.1 Experiment Overview
        • 7.1.4.2 Operation Steps
        • 7.1.4.3 Project Outcome
        • 7.1.4.4 Program Analysis
      • 7.1.5 3D Face Detection
        • 7.1.5.1 Experiment Overview
        • 7.1.5.2 Operation Steps
        • 7.1.5.3 Project Outcome
        • 7.1.5.4 Program Analysis
      • 7.1.6 Hand Keypoint Detection
        • 7.1.6.1 Experiment Overview
        • 7.1.6.2 Operation Steps
        • 7.1.6.3 Project Outcome
        • 7.1.6.4 Program Analysis
      • 7.1.7 Body Keypoint Detection
        • 7.1.7.1 Experiment Overview
        • 7.1.7.2 Operation Steps
        • 7.1.7.3 Project Outcome
        • 7.1.7.4 Program Analysis
      • 7.1.8 Fingertip Trajectory Recognition
        • 7.1.8.1 Experiment Overview
        • 7.1.8.2 Operation Steps
        • 7.1.8.3 Project Outcome
        • 7.1.8.4 Program Analysis
      • 7.1.9 Body Gesture Control
        • 7.1.9.1 Experiment Overview
        • 7.1.9.2 Operation Steps
        • 7.1.9.3 Project Outcome
        • 7.1.9.4 Program Analysis
      • 7.1.10 Human Tracking
        • 7.1.10.1 Experiment Overview
        • 7.1.10.2 Operation Steps
        • 7.1.10.3 Project Outcome
        • 7.1.10.4 Program Analysis
        • 7.1.10.5 Feature Extension
      • 7.1.11 Body Gesture Control with RGB Fusion
        • 7.1.11.1 Experiment Overview
        • 7.1.11.2 Operation Steps
        • 7.1.11.3 Project Outcome
        • 7.1.11.4 Program Analysis
      • 7.1.12 Human Pose Detection
        • 7.1.12.1 Experiment Overview
        • 7.1.12.2 Operation Steps
        • 7.1.12.3 Project Outcome
        • 7.1.12.4 Program Analysis
    • 7.2 Machine Learning Basics
      • 7.2.1 Introduction to Machine Learning
        • 7.2.1.1 Overview
        • 7.2.1.2 What Is Machine Learning
        • 7.2.1.3 Types of Machine Learning
      • 7.2.2 Introduction to Machine Learning Libraries
        • 7.2.2.1 Common Machine Learning Frameworks
        • 7.2.2.2 PyTorch
        • 7.2.2.3 Tensorflow
        • 7.2.2.4 PaddlePaddle
        • 7.2.2.5 MXNet
    • 7.3 Machine Learning Application
      • 7.3.1 GPU Acceleration
        • 7.3.1.1 Introduction to GPU-Accelerated Computing
        • 7.3.1.2 Performance Comparison: GPU vs. CPU
        • 7.3.1.3 Advantages of GPU
      • 7.3.2 TensorRT Acceleration
        • 7.3.2.1 Introduction to TensorRT
        • 7.3.2.2 Optimization Methods
      • 7.3.3 YOLOv11 Model
        • 7.3.3.1 Overview of the YOLO Models
        • 7.3.3.2 YOLOv11 Model Structure
      • 7.3.4 YOLOv11 Workflow
        • 7.3.4.1 Prior Box
        • 7.3.4.2 Prediction Box
        • 7.3.4.3 Anchor Box
        • 7.3.4.4 Project Process
      • 7.3.5 Image Collection and Annotation
        • 7.3.5.1 Image Collection
        • 7.3.5.2 Image Annotation
      • 7.3.6 Data Format Conversion
        • 7.3.6.1 Preparation
        • 7.3.6.2 Format Conversion
      • 7.3.7 Model Training
        • 7.3.7.1 Preparation
        • 7.3.7.2 Training Process
        • 7.3.7.3 Importing Training Results (Optional)
      • 7.3.8 TensorRT Inference Acceleration
        • 7.3.8.1 Preparation
        • 7.3.8.2 Creating a TensorRT Model Engine
        • 7.3.8.3 Object Detection
      • 7.3.9 Traffic Sign Model Training
        • 7.3.9.1 Preparation
        • 7.3.9.2 Operation Steps
        • 7.3.9.3 Using the Model
      • 7.3.10 FAQ
    • 7.4 Autonomous Driving
      • 7.4.1 Lane Keeping
        • 7.4.1.1 Preparation
        • 7.4.1.2. Working Principle
        • 7.4.1.3 Operation Steps
        • 7.4.1.4 Program Outcome
      • 7.4.2 Traffic Sign Detection
        • 7.4.2.1 Preparation
        • 7.4.2.2 Working Principle
        • 7.4.2.3 Operation Steps
        • 7.4.2.4 Program Outcome
      • 7.4.3 Traffic Light Recognition
        • 7.4.3.1 Preparation
        • 7.4.3.2 Working Principle
        • 7.4.3.3 Operation Steps
        • 7.4.3.4 Program Outcome
      • 7.4.4 Turing Decision Making
        • 7.4.4.1 Preparation
        • 7.4.4.2 Working Principle
        • 7.4.4.3 Operation Steps
        • 7.4.4.4 Program Outcome
      • 7.4.5 Autonomous Parking
        • 7.4.5.1 Preparation
        • 7.4.5.2 Working Principle
        • 7.4.5.3 Operation Steps
        • 7.4.5.4 Program Outcome
      • 7.4.6 Comprehensive Application of Autonomous Driving
        • 7.4.6.1 Preparation
        • 7.4.6.2 Working Principle
        • 7.4.6.3 Operation Steps
        • 7.4.6.4 Program Outcome
        • 7.4.6.5 Program Analysis
  • 8. Voice Interaction Applications
    • 8.1 Voice Module Installation
      • 8.1.1 Install the WonderEcho Pro
      • 8.1.2 Install the 6-Microphone Array
    • 8.2 Switching Wake Words
    • 8.3 Six-Microphone Array Configuration (Must Read)
      • 8.3.1 Offline Speech Package & ID
      • 8.3.2 Replacing Offline Speech Resources and ID
    • 8.4 Voice-Controlled Robot Movement
      • 8.4.1 Program Overview
      • 8.4.2 Preparation
      • 8.4.3 Operation Steps
      • 8.4.4 Program Analysis
      • 8.4.5 Extensions
    • 8.5 Voice-Controlled Color Recognition
      • 8.5.1 Program Overview
      • 8.5.2 Preparation
      • 8.5.3 Operation Steps
      • 8.5.4 Program Analysis
    • 8.6 Voice-Controlled Multi-Point Navigation
      • 8.6.1 Program Overview
      • 8.6.2 Preparation
      • 8.6.3 Operation Steps
      • 8.6.4 Program Analysis
      • 8.6.5 Extensions
  • 9. Gazebo Simulation
    • 9.1 Virtual Machine Installation and Import
      • 9.1.1 Virtual Machine Software Installation
      • 9.1.2 Importing the Virtual Machine Image
      • 9.1.3 Virtual Machine Settings
    • 9.2 Configuration
      • 9.2.1 Importing the Feature Package
    • 9.3 Introduction to URDF Models
      • 9.3.1 Overview and Basics of URDF Models
      • 9.3.2 Robot URDF Model Description
    • 9.4 Gazebo Simulation
      • 9.4.1 Introduction to Gazebo
      • 9.4.2 Gazebo Xacro Model Visualization
      • 9.4.3 Gazebo Hardware Simulation
      • 9.4.4 Gazebo Mapping Simulation
      • 9.4.5 Gazebo Navigation Simulation
  • 10. Large AI Model Courses
    • 10.1 Large Models Basic Courses
      • 10.1.1 Large Language Model Courses
      • 10.1.2 Large Speech Model Courses
      • 10.1.3 Vision Language Model Courses
      • 10.1.4 Multimodal Model Basic Courses
    • 10.2 Multimodal Large Model Applications
      • 10.2.1 Large Model API Key Setup
        • 10.2.1.1 OpenAI Account Registration and Deployment
        • 10.2.1.2 OpenRouter Account Registration and Deployment
        • 10.2.1.3 API Configuration
      • 10.2.2 Version Confirmation
      • 10.2.3 Voice Control
        • 10.2.3.1 Program Overview
        • 10.2.3.2 Preparation
        • 10.2.3.3 Operation Steps
        • 10.2.3.4 Program Outcome
        • 10.2.3.5 Program Analysis
      • 10.2.4 Autonomous Patrolling
        • 10.2.4.1 Program Overview
        • 10.2.4.2 Preparation
        • 10.2.4.3 Operation Steps
        • 10.2.4.4 Program Outcome
        • 10.2.4.5 Program Analysis
      • 10.2.5 Color Tracking
        • 10.2.5.1 Program Overview
        • 10.2.5.2 Preparation
        • 10.2.5.3 Operation Steps
        • 10.2.5.4 Program Outcome
        • 10.2.5.5 Program Analysis
    • 10.3 Embodied AI Applications
      • 10.3.1 Large Model API Key Setup
        • 10.3.1.2 OpenRouter Account Registration and Deployment
        • 10.3.1.2 OpenRouter Account Registration and Deployment
        • 10.3.1.3 API Configuration
      • 10.3.2 Version Confirmation
      • 10.3.3 Real-Time Detection
        • 10.3.3.1 Program Overview
        • 10.3.3.2 Preparation
        • 10.3.3.3 Operation Steps
        • 10.3.3.4 Program Outcome
        • 10.3.4.5 Program Analysis
      • 10.3.4 Vision Tracking
        • 10.3.4.1 Program Overview
        • 10.3.4.2 Preparation
        • 10.3.4.3 Operation Steps
        • 10.3.4.4 Program Outcome
        • 10.3.4.5 Program Analysis
      • 10.3.4 Smart Home Assistant
        • 10.3.4.1 Program Overview
        • 10.3.4.2 Preparation
        • 10.3.4.3 Operation Steps
        • 10.3.4.4 Program Outcome
        • 10.3.4.5 Modifying Navigation Locations
        • 10.3.4.6 Program Analysis
    • 10.4 Comprehensive Application of Large AI Models
      • 10.4.1 Preparation
      • 10.4.2 Large Model API Key Setup
        • 10.4.2.1 OpenAI Account Registration and Deployment
        • 10.4.2.2 OpenRouter Account Registration and Deployment
        • 10.4.2.3 API Configuration
      • 10.4.3 Vision Application of Large AI Models
        • 10.4.3.1 Overview
        • 10.4.3.2 Preparation
        • 10.4.3.3 Operation Steps
        • 10.4.3.4 Project Outcome
        • 10.4.3.5 Program Analysis
      • 10.4.4 Smart Home Assistant
        • 10.4.4.1 Overview
        • 10.4.4.2 Preparation
        • 10.4.4.3 Operation Steps
        • 10.4.4.4 Project Outcome
    • 10.5 Offline Large AI Model Applications
      • 10.5.1 Preparation
      • 10.5.2 Offline Large AI Model Basic Course
        • 10.5.2.1 Speech-to-Text Test
        • 10.5.2.2 Text-to-Speech Test
        • 10.5.2.3 Large AI Model Invocation
        • 10.5.2.4 Semantic Understanding
        • 10.5.2.5 Emotion Perception
        • 10.5.2.6 Recording Test
      • 10.5.3 Offline Large AI Model for Voice Control
        • 10.5.3.1 Overview
        • 10.5.3.2 Preparation
        • 10.5.3.3 Operation Steps
        • 10.5.3.4 Project Outcome
        • 10.5.3.5 Program Analysis
      • 10.5.4 Offline Large AI Model for Autonomous Line Following
        • 10.5.4.1 Overview
        • 10.5.4.2 Preparation
        • 10.5.4.3 Operation Steps
        • 10.5.4.4 Project Outcome
        • 10.5.4.5 Program Analysis
      • 10.5.5 Offline Large AI Model for Color Tracking
        • 10.5.5.1 Overview
        • 10.5.5.2 Preparation
        • 10.5.5.3 Operation Steps
        • 10.5.5.4 Project Outcome
        • 10.5.5.5 Program Analysis
  • 11. Group Control
    • 11.1 Leader-Follower Configuration
      • 11.1.1 Preparation
      • 11.1.2 Working Principle
      • 11.1.3 Network Configuration
      • 11.1.4 Configuring Environment Variables
    • 11.2 Group Control Start-up Steps and Operations
      • 11.2.1 Synchronizing Time
      • 11.2.2 Program Execution
      • 11.2.3 Group Control Operation
  • Appendix
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