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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

7. Scheduled Tasks

OpenClaw scheduled task tutorial

Before starting, please complete [01-Peripheral hardware configuration], [02-openclaw API-KEY configuration], [03-openclaw switching model], [04-OpenClaw prompt words], [05-AI voice interaction configuration], [06-Three dialogue scheme configuration tests] in the Preparation before use directory. If you are currently only using TUI/WhatsApp, you can skip 05 first. By default, the basic environment of this article has been prepared and only the "automatic execution of scripts at fixed time" section will be expanded.

1. Tutorial Objectives

This article uses the method of "AI generated script + Linux scheduled execution" to show how OpenClaw can be further upgraded from real-time dialogue to automated peripheral applications. After completing this tutorial, readers will be able to:

  • Understand the basic implementation ideas of OpenClaw scheduled tasks
  • Distinguish the appropriate scenarios for HEARTBEAT and cron
  • Describe automation requirements through three methods: WhatsApp, TUI, and voice code

2. Hardware preparation

  • OpenClaw main control board

  • DHT temperature and humidity sensor

  • OLED display

  • RGB light strip/RGB lamp bead module

  • [optional] CSI camera

  • [Optional] AI voice module + speaker

    image-20260528201034862

Description:

  • The core of this tutorial is "scheduled execution", it is not mandatory for all peripherals to be connected at the same time
  • Which type of automation tasks you want to do, just connect the corresponding hardware first

3. Test basic capabilities of scheduled tasks

Before starting the current project, please complete a basic dialogue self-test of openclaw tui; if you have not done so yet, please read [06-Three dialogue scheme configuration tests] first. After passing, enter the timed task special test.

It is recommended to test in the following order:

  1. First enter OpenClaw TUI
  2. Let OpenClaw generate a simple script
  3. Run the script manually in the terminal to confirm that the function is normal
  4. Then write the script into crontab

Terminal input:

openclaw tui

image-20260527164855215

It is recommended to try the following requirements first:

  • Help me write a script to display the current temperature and humidity on OLED and save it to the desktop
  • Generate a script that reads temperature and humidity and prints the results each time it is run. -Write a script to make the RGB light turn green for 3 seconds and then turn off

After the script is generated, run it manually first, for example:

xxxxxxxxxx
python3 /home/sunrise/Desktop/your_script.py

4. Three options for talking to OpenClaw

SolutionSuitable scenarioAdvantagesSuggested positioning
WhatsAppRemotely describe automation requirementsSuitable for clarifying "what you want to do on a regular basis" firstUsed to generate scripts and confirm requirements
TUICreate and debug scheduled tasks locallyThe easiest way to jointly debug scripts and cronThe preferred implementation solution
Voice codeVoice description automation sceneCloser to desktop assistant experienceSuitable for demonstrating "speak a sentence to generate automation"

If the AI voice module + speaker is optional, 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 open broadcast parameters
xxxxxxxxxx
/home/sunrise/voice_to_openclaw/.env
xxxxxxxxxx
ENABLE_TTS=true #This parameter is to control whether to broadcast. After modification, save it and run the following program on the terminal.
  • Terminal input
xxxxxxxxxx
cd ~/voice_to_openclaw
source .venv/bin/activate
python -m src.openclaw_session_tts

image-20260527162942497

4.1 WhatsApp solution

For WhatsApp access steps, please refer directly to [06-Configuration Test of Three Dialogue Schemes]. This section is more suitable for describing requirements, such as:

  • Help me do a scheduled task: make the RGB light red every minute as a reminder and then turn off the light

image-20260428191042352

4.2 TUI scheme

If you have completed the TUI self-test in Part 6, enter the terminal text dialogue directly:

xxxxxxxxxx
openclaw tui

Then you can directly enter project-related instructions, such as:

  • Help me do a scheduled task: read the temperature and humidity every minute and display it on the OLED

image-20260428191030929

4.3 Voice code scheme

xxxxxxxxxx
Requires optional 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]. After waking up, you can directly say the control content, such as:

  • Help me do a scheduled task: read the temperature and humidity every minute and display it on the OLED

image-20260428191253233

  • If you want to close the scheduled task, you can ask Help me stop the current scheduled task in the current conversation.

    image-20260511194439572

  • You can use the command cron list to view the current scheduled tasks

image-20260511194516325

5. Project implementation

Note: The following are all dialogue demonstrations in openclaw tui mode. You can also choose WhatsApp or voice mode to complete it.

5.2 Scenario 1: Hourly temperature and humidity sampling

Experimental goals

Let the device automatically read temperature and humidity every hour and record the results or sync to the screen.

Effect description

This is the most basic and common scheduled task scenario, and can be expanded into logs, dashboards, environmental alarms, or plant care projects later.

Example instructions

  • Help me do a scheduled task: read the temperature and humidity every minute and display it on the OLED

5.3 Scenario 2: Scheduled status reminder

Experimental goals

Use RGB or OLED to make a clear reminder at a fixed time.

Effect description

This type of task is suitable for drinking water reminders, class reminders, inspection reminders, and status switching reminders. It does not emphasize continuous operation, but emphasizes triggering a clear action at a certain point in time.

Example instructions

  • Help me do a scheduled task: make the RGB light red every minute as a reminder and then turn off the light

5.4 Scenario 3: Regular inspection script

Experimental goals

String together multiple peripheral actions to form a small automation task that can be executed periodically.

Effect description

For example, the temperature and humidity are read once every hour, and if the status is abnormal, a light will be lit to remind, and the results will be displayed on the OLED. This type of task is close to a complete small project, and it is also the most obvious place where "AI generation script" and "timed execution" are combined.

Example instructions

  • Help me do a scheduled task: read the temperature and humidity once every minute, light up the red light when abnormal, and prompt on the OLED
Temperature and Humidity Dashboard
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