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RDK X5 Board📖
1.Introduction to RDK X5 board
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Use 7-inch display
2.Basic settings course
  • 1.Network configuration
  • 2.Remote login
  • 3.Update system
  • 4.srpi-config user guide
  • 5.config. txt configuration file
  • 6.Self-startup setting
  • 7.RDK exclusive command usage
  • 8.Linux command usage
  • 9.GPIO readwrite operation
  • 10.Image classification algorithm
  • 11.Use USB camera
  • 12.Use MIPI camera
  • 13.Binocular camera depth algorithm
3.Basic development course
C language
  • 1.Camera image display
  • 2.Local save camera images
  • 3.Camera image encoding
  • 4.Video file decoding
  • 5.RTSP streaming decoding
  • 6.VPS scaling example
  • 7.Target detection algorithm-fcos
  • 8.Target detection algorithm-YOLOv5s
  • 9.Using CAN
Python
  • 0.ReadMe
  • 1.Using GPIO
  • 2.Using PWM
  • 3.Using Serial port
  • 4.Using I2C
  • 5.Using SPI
4.ROS expansion board
Python basic control
  • 0.Bind PCB port devices
  • 1.Install the Rosmaster driver library
  • 2.Buzzer whistle
  • 3.Control PWM servo
  • 4.RGB colorful light bar special effects display
  • 5.Control the forward and reverse rotation of the motor
  • 6.Control the movement of the robot
  • 7.Control Serial Servo
  • 8.Update firmware of expansion board
  • 9.Drive OLED
ROS2 robot control
  • 1.Function package usage process
  • 2.Robot information release
  • 3.Robot keyboard control
  • 4.Robot state estimation
  • 5.Robot calibration
  • 6.URDF model
5.T-MINI PLUS Lidar
  • 0.Preparation before use(Must read)
  • 1.Environment setup
  • 2.T-MINI PLUS LiDAR drive
  • 3.Handheld LiDAR mapping
6.AI VIEW Depth camera
AI VIEW Depth camera use
  • 1.AI VIEW Depth camera use
  • 2.Camera internal parameter calibration
  • 3.Color tracking
  • 4.KCF object tracking
Mediapipe functions
  • 1.Hand detection
  • 2.Attitude detection
  • 3.Overall inspection
  • 4.Facial detection
  • 5.Facial recognition
OpenCV series courses
  • 1.ROS+opencv application
  • 2.QR code creation and recognition
  • 3.AR vision
7.Offline AI model course
Offline-LLM-Text-Interaction
  • 0.AI-Large-Model-Image-Description
  • 1.AI-Large-Model-Environment-Deployment
  • 2.LLM-Conversation-Platform-Installation
  • 3.Meta-AI-Llama3.2-Model
  • 4.Alibaba-Cloud-Qwen3-Model
  • 5.Microsoft-Phi-4-mini-Model
  • 6.DeepSeek-DeepSeek-R1-Model
  • 7.Alibaba-Cloud-Qwen2.5vl-Vision-Multimodal-Model
  • 8.Google-Gemma3-Vision-Multimodal-Model
  • 9.Llava-Vision-Multimodal-Model
  • 10.MiniCPM-V-Vision-Multimodal-Model
  • 11.Multimodal-Text-to-Image-Application
  • 12.Multimodal-Video-Analysis-Application
  • 13.Multimodal-Visual-Understanding-Application
  • 14.Multimodal-Visual-Localization-Application
  • 15.Multimodal-Table-Scanning-Application
  • 16.Multimodal-Autonomous-Agent-Application
Offline-LLM-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-Analysis-Application
  • 7.Multimodal-Visual-Localization-Application
  • 8.Multimodal-Table-Scanning-Application
  • 9.Multimodal-Autonomous-Agent-Application
  • 10.AI-Large-Model-Offline-Voice-Assistant
8.Online AI model course
Online-LLM-Text-Interaction
  • 1.OpenRouter-Large-Model-API-Aggregation-Platform
  • 2.Multimodal-Visual-Understanding-Application
  • 3.Multimodal-Visual-Localization-Application
  • 4.Multimodal-Table-Scanning-Application
  • 5.Multimodal-Autonomous-Agent-Application
Online-LLM-Voice-Interaction
  • 0.Voice-Interaction-Hardware-Connection-Must-Read
  • 1.Online-Speech-to-Text-ASR
  • 2.Online-Text-to-Speech-TTS
  • 3.AI-Large-Model-Voice-Interaction
  • 4.Multimodal-Visual-Voice-Interaction
  • 5.Multimodal-Visual-Localization-Application
  • 6.Multimodal-Table-Scanning-Application
  • 7.Multimodal-Autonomous-Agent-Application
  • 8.AI-Large-Model-Online-Voice-Assistant
9.YOLO26 tutorials
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented bounding box object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
10.OpenClaw Deployment and Basic Usagenew
  • 01.OpenClaw Quick Start
  • 02.OpenClaw Deployment
  • 03.OpenClaw WhatsApp Plugin Application
  • 04.OpenClaw WebChat Interaction
  • 05.OpenClaw TUI Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawClawHub Introduction (Skill Installation)
  • 10.OpenClaw Application - File Management
  • 11.OpenClaw Application - Camera
  • 12.OpenClaw Application - Browser Control
  • 13.OpenClaw Application - Script Execution
  • 14.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 15.OpenClaw Application - Dedicated AI Assistant
  • 16.OpenClaw Web Search Capability Configuration
11.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 01. Peripheral Hardware Configuration
  • 02. OpenClaw API-KEY Configuration
  • 03. OpenClaw Model Switching
  • 04. OpenClaw Prompts
  • 05. AI Voice Interaction Configuration
  • 06. Three Dialogue Configuration Tests
2.OpenClaw AI Programming Peripheral Control
  • 1. Servo Control
  • 2. RGB Strip
  • 3. OLED Display
  • 4. Temperature and Humidity Sensor
  • 5. Camera Application
  • 6. AI Script Peripheral Control
3.OpenClaw Peripheral Integration Applications
  • 1. Plant Care Assistant
  • 2. AI Calorie Estimation
  • 3. AI Rock Paper Scissors Game
  • 4. AI Pet
  • 5. AI Weather Station
  • 6. Temperature and Humidity Dashboard
  • 7. Scheduled Tasks
12.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
  • System_File
  • Code
  • Tools
Download ZIP

Welcome to RDK X5 Board repository

1. Servo Control

OpenClaw Servo Control Tutorial

Before starting, please complete [01-Peripheral Hardware Configuration], [02-OpenClaw API-KEY Configuration], [03-OpenClaw Model Switching], [04-OpenClaw Prompts], [05-AI Voice Interaction Configuration], and [06-Three Conversation Scheme Configuration and Testing] in the Preparation for Use directory. If you are currently only using TUI / WhatsApp, you can skip 05 for now. This document assumes the basic environment is already prepared and only expands on servo-related content.

1. Tutorial Objectives

This tutorial demonstrates OpenClaw's natural language control capabilities through servo peripherals. After completing this tutorial, readers will be able to:

  • Understand the basic idea of OpenClaw controlling servos

  • Interact with OpenClaw via three methods: WhatsApp, TUI, and voice code

  • Complete four servo cases: single servo control, servo dancing, yes/no Q&A, simulated ping pong, simulated pointer

     

2. Hardware Preparation

  • OpenClaw main board
  • Servo [1 unit / 4 units]
  • [Optional] Microphone module
  • Wiring diagram

3. Software Preparation

Code entry points used in the tutorial

  • Low-level driver code: /home/sunrise/hardware_hal_demo/
  • Python driver library: /home/sunrise/openclaw-fw/python/
  • Voice entry point: /home/sunrise/voice_to_openclaw/src/main.py
  • Terminal text entry point: /home/sunrise/voice_to_openclaw/src/text_main.py

4. Testing the Servo

Before starting this tutorial, please complete a basic openclaw tui conversation self-check first; if you haven't done so, see [06-Three Conversation Scheme Configuration and Testing]. After confirming that OpenClaw can respond normally, proceed to the servo-specific test.

Enter the following command in the terminal to enter TUI:

openclaw tui

image-20260527164855215

After ensuring the hardware is connected, you can test servo control by entering the following content in the terminal:

  • Turn servo 1 to 0 degrees
  • Turn the servo to 90 degrees
  • Turn the servo to 180 degrees

Note: You can specify which servo to control individually.

image-20260424183345752

If you can see the servo driving normally, it means the following points are normal.

  • OpenClaw can receive instructions
  • Servo power supply is normal
  • Servo angle control range is correct
  • Clear feedback is visible after instruction execution

5. Three Schemes for Conversing with OpenClaw

SchemeSuitable ScenariosAdvantagesSuggested Positioning
WhatsAppRemote control, classroom demonstrationEasy access, suitable for multi-user experienceDemonstrate "remote natural language control"
TUILocal debugging, command verificationMost direct, easiest to troubleshootGet the control pipeline running first
Voice CodeVoice interaction, complete AI experienceClosest to real conversation experienceAs the final demonstration scheme

If you have selected the AI voice module + speaker, Feishu dialogue, tui, and WhatsApp dialogue need to broadcast the content of openclaw's reply. You can run the following program on the terminal. The AI voice module dialogue does not need to be run repeatedly

  • Modify whether to enable broadcast parameter
xxxxxxxxxx
/home/sunrise/voice_to_openclaw/.env
xxxxxxxxxx
ENABLE_TTS=true  # This parameter controls whether to broadcast. After modifying and saving, run the following program in the terminal.
  • Enter in terminal:
xxxxxxxxxx
cd ~/voice_to_openclaw
source .venv/bin/activate
python -m src.openclaw_session_tts

image-20260527162942497

5.1 WhatsApp Scheme

For WhatsApp access, QR code creation, and gateway restart steps, please refer directly to [06-Three Conversation Scheme Configuration and Testing]. This section only retains conversation examples suitable for the servo tutorial.

You can converse directly, for example:

  • Turn servo 1 to 0 degrees
  • Turn the servo to 90 degrees
  • Make the servo swing left and right once

107ff2282b6e022cdd999b244f6ff6d4

5.2 TUI Scheme

If you have completed the TUI self-check in Part 0, you can continue the conversation by entering the following command in the terminal:

xxxxxxxxxx
openclaw tui

image-20260527164855215

5.3 Voice Code Scheme

xxxxxxxxxx
Requires selection of AI voice interaction module + speaker

For voice module installation, environment configuration, and basic wake-up process, please refer directly to [05-AI Voice Interaction Configuration]. Here it is more suitable to directly demonstrate servo-type oral instructions, for example:

  • Turn the servo to 90 degrees
  • Make the servo nod
  • Make the servo sway left and right

image-20260424193902498

6. Comprehensive Cases

Note: The following conversations are conducted via openclaw tui (you can choose your own conversation method).

6.1 Case 1: Servo Dancing

Experiment Objective

Make the servo move continuously according to a set of preset angles, forming a rhythmic effect similar to "dancing."

Effect Description

The servo can switch between multiple angles, such as swinging left, returning to center, swinging right, shaking quickly, and then returning to the initial position. Although there is only one servo, a clear "sense of dance" can still be expressed through changes in movement rhythm.

Example Instructions

  • Make the servo dance for 10 seconds
  • Make the servo sway left and right with a faster rhythm
  • Arrange a three-beat dance step for the servo

6.2 Case 2: Yes/No Q&A

Experiment Objective

Let the servo express "yes" or "no" with actions, for example, nodding for "yes" and shaking head for "no."

Effect Description

When OpenClaw judges the answer as affirmative, the servo performs a nodding action; when the answer is negative, the servo performs a head-shaking action. This case is very suitable for highlighting the combination of "natural language understanding + action expression."

Example Instructions

  • If the answer is "yes", make the servo nod
  • If the answer is "no", make the servo shake its head
  • Help me judge this question and answer with servo actions

6.3 Case 3: Simulated Ping Pong

Experiment Objective

Make the servo perform continuous left-and-right往返 movements, simulating the feeling of a ping pong ball bouncing back and forth between two sides of a table.

Effect Description

The servo swings uniformly or variably between two boundary angles on the left and right. If accompanied by voice description or screen prompts, the audience will easily understand that this is a simulation of "ping pong moving back and forth."

Example Instructions

  • Make the servo simulate ping pong moving back and forth
  • Swing left and right 20 times, faster speed
  • Start from the left side and do a round-trip movement demonstration

6.4 Case 4: Simulated Pointer

Experiment Objective

Use the servo as a rotatable pointer to indicate values, directions, or states.

Effect Description

This case can very intuitively demonstrate the mapping relationship of "abstract value -> actual angle." For example, mapping values from 0 to 100 to angles from 0 to 180 degrees, the servo can point to the target position like a dashboard pointer.

Example Instructions

  • Turn the pointer to 30%
  • Make the servo point to the 90-degree position
  • Simulate a dashboard, current value is 75

 

Three Dialogue Configuration Tests
RGB Strip

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