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RDK S100(P) Board📖
0. RDK S100(P) Developer Kit
  • 1. RDK S100(P) Official Tutorial
1. Introduction to RDK S100(P) Development Board
  • 1.1 Introduction to the RDK S100(P) Development Board
  • 1.2 Install Operating System
  • 1.3 Interface Descriptions
  • 1.4 Accessory installation
  • 1.5 Camera Expansion Board
  • 1.6 MCU Port Expansion Board
  • 1.7 Precautions for using 15.6-inch screens
2. Basic Settings Course
  • 2.1 Network Configuration
  • 2.2 Remote Login
  • 2.3 System Update
  • 2.4 Boot Auto-Start Configuration
  • 2.5 RDK Exclusive Command Usage
  • 2.6 Linux Command Usage
  • 2.7 Example of GPIO read and write operations
  • 2.8 Using USB Camera
  • 2.9 Using MIPI Camera
  • 2.10 SSD mounting
  • 2.11 Binocular camera depth algorithm
3. TogetheROS.Bot Vision Applications
  • 1. About TogetheROS. Bot
  • 2. System Installation
  • 3. Object Detection(YOLO)
  • 4. Image Classification(mobilenetv2)
  • 5. Image Segmentation(mobilenet_unet)
  • 6. Perception Algorithm(BEV)
  • 7. Human Detection and Tracking (Ultralytics YOLO Pose)
4.Offline AI model course
Offline large model (Text Interaction)
  • 1. AI large model environment deployment
  • 2. Install large model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 model
  • 5. Microsoft Phi-4 mini model
  • 6. DeepSeek - DeepSeek-R1 model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Lava Visual Multimodal Large Model
  • 10. MiniCPM-V Visual Multimodal Large Model
  • 11. Multimodal visual understand application
  • 12. Multimodal text to image application
  • 13. Multimodal visual localization application
  • 14. Multimodal table scanning application
  • 15. Multimodal Autonomous Agent Applications
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3.AI large model voice interaction
  • 4. Multimodal visual understanding voice interaction
  • 5. Multimodal Text-to-Image application
  • 6. Multimodal video analytics application
  • 7. Multimodal visual localization application
  • 8. Multimodal table scanning application
  • 9. Multimodal autonomous agent application
  • 10. AI Large Model Offline Voice Assistant
5.Online AI model course
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous agent application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal visual localization application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
6.YOLO26 tutorials
  • 01.YOLO26 environment
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training - conversion
  • 11.Model prediction
7.OpenClaw Advanced Development
  • 1.OpenClaw Deployment
  • 2.OpenClaw WhatsApp Plugin Application
  • 3.OpenClaw WebChat Interaction
  • 4.OpenClaw TUI Interaction
  • 5.OpenClaw Tools Introduction
  • 6.OpenClawGate Gateway User Manual
  • 7.OpenClaw Features Overview
  • 8.OpenClawClawHub Introduction (Skill Installation)
  • 9.OpenClaw Application - File Management
  • 10.OpenClaw Application - Camera
  • 11.OpenClaw Application - Browser Control
  • 12.OpenClaw Application - Script Execution
  • 13.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 14.OpenClaw Application - Dedicated AI Assistant
  • 15.OpenClaw Web Search Capability Configuration
8.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
Download
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  • Code
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Welcome to RDK S100(P) Board repository

5.ROS2 function package

5. ROS2 Function Packages

1. Introduction to Function Packages

Each robot may have many functions, such as motion control, visual perception, and autonomous navigation. While it's possible to lump the source code for all of these functions together, when we want to share some of these functions with others, we often find the code all mixed together, making it difficult to separate them.

Function packages work in this way. We separate the code for different functions into separate packages, minimizing the coupling between them. When sharing with others in the ROS community, we only need to explain how to use the package, and others can quickly start using it.

Thus, the function package mechanism is one of the key methods for increasing software reuse in ROS.

2. Creating Function Packages

How do I create a function package in ROS2? We can use this command:

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ros2 pkg create <package_name> --build-type <build-type> --dependencies <dependencies> --node-name <node-name>

In the ros2 command:

  • pkg: Indicates the functions associated with the package;
  • create: Creates the package;
  • package_name: Required: The name of the new package;
  • build-type: Required: Indicates whether the newly created package is C++ or Python. If using C++ or C, use ament_cmake; if using Python, use ament_python;
  • dependencies: Optional: Indicates the package's dependencies. A C++ package must include rclcpp; a Python package must include rclpy, as well as other required dependencies;
  • node-name: Optional: The name of the executable program. The corresponding source files and configuration files will be automatically generated.

For example, to create C++ and Python versions of the package in the terminal:

  • Switch to the src directory of the workspace.
  • Replace "workspace" with your actual folder path.
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cd workspace/src
  • Create a C++ package example
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ros2 pkg create pkg_helloworld_cpp --build-type ament_cmake --dependencies rclcpp --node-name helloworld
  • Create a Python package example
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ros2 pkg create pkg_helloworld_py --build-type ament_python --dependencies rclpy --node-name helloworld

3. Compile the package

In the created package, we can continue writing code. We will then need to compile and configure environment variables for proper operation:

  • Switch to the workspace directory
  • Compile all packages
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colcon build
  • Compile a specific package
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colcon build --packages-select pkg1 pkg2

4. Complete Workspace Structure with Feature Packages

The directory structure of a ROS2 workspace is as follows:

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WorkSpace --- Customized workspace.
    |--- build: Directory for storing intermediate files. A separate subdirectory is created within this directory for each feature package.
    |--- install: Installation directory. A separate subdirectory is created within this directory for each feature package.
    |--- log: Log directory for storing log files.
    |--- src: Directory for storing feature package source code.
        |-- C++ Feature Package
            |-- package.xml: Package information, such as package name, version, author, and dependencies.
            |-- CMakeLists.txt: Configuration of compilation rules, such as source files, dependencies, and target files.
            |-- src: Directory for C++ source files.
            |-- include: Directory for header files.
            |-- msg: Directory for message interface files.
            |-- srv: Directory for service interface files.
            |-- action: Action interface file directory.
        |-- Python package
            |-- package.xml: Package information, such as package name, version, author, and dependencies.
            |-- setup.py: Similar to the CMakeLists.txt file for a C++ package.
            |-- setup.cfg: Basic package configuration file.
            |-- resource: Resource directory.
            |-- test: Stores test-related files.
            |-- Package directory with the same name: Python source file directory.

In addition, both Python and C++ packages can customize directories related to configuration files.

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|-- C++ or Python package
    |-- launch: Stores launch files.
    |-- rviz: Stores rviz2 configuration files.
    |-- urdf: Stores robot model files.
    |-- params: Stores parameter files.
    |-- world: Stores simulation environment-related files.
    |-- map: Stores map files required for navigation.
    |-- ......

These directories can also be defined with other names, or additional directories can be created as needed.

 

ROS2 workspace
ROS2 node

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