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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

6. Temperature and Humidity Dashboard

OpenClaw Temperature and Humidity Dashboard 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 temperature and humidity dashboard project itself will be expanded.

1. Tutorial Objectives

This article demonstrates OpenClaw's multi-peripheral collaboration capabilities in a desktop dashboard scenario through a combination of "sensor + display + indication". After completing this tutorial, readers will be able to:

  • Learn how to combine DHT, OLED, RGB and servo into a temperature and humidity dashboard
  • Converse with OpenClaw through WhatsApp, TUI, and voice code
  • Complete four project scenarios: real-time reading, OLED indicator panel, RGB status light, and servo pointer mapping

2. Hardware preparation

  • OpenClaw main control board
  • DHT temperature and humidity sensor
  • OLED display
  • RGB light strip/RGB lamp bead module
  • [Optional] AI voice module + speaker
  • Wiring diagram

image-20260528201034862

3. Software preparation

Code entry that will be used in the tutorial

  • Temperature and humidity reading command: /home/sunrise/hardware_hal_demo/dht
  • OLED control command: /home/sunrise/hardware_hal_demo/oled
  • RGB control command: /home/sunrise/hardware_hal_demo/rgb
  • Servo control command: /home/sunrise/hardware_hal_demo/cli/servo
  • Voice entrance: /home/sunrise/voice_to_openclaw/src/main.py

This project is very close to the AI weather station, but the focus is different:

  • AI weather station places more emphasis on “broadcast” and “explanation”
  • The temperature and humidity dashboard places more emphasis on "display" and "status mapping"

4. Test the basic capabilities of the project

Before starting the current project, please complete a basic dialogue self-test of openclaw tui; if you have not done so yet, please read [05-Three dialogue scheme configuration tests] first. After passing, enter the special test of temperature and humidity instrument panel.

It is recommended to enter OpenClaw TUI first:

openclaw tui

image-20260527164855215

Then test the following types of instructions in sequence:

  • Read current temperature and humidity
  • Display current temperature and humidity on OLED
  • If the environment is normal, the green light will turn on
  • Turn the servo to 90 degrees

5. Three options for talking to OpenClaw

SolutionSuitable scenarioAdvantagesSuggested positioning
WhatsAppRemote status viewing, classroom demonstrationConvenient remote reading of current panel dataDisplay of "Remote Monitoring Panel"
TUILocal debugging, mapping verificationThe easiest way to check the relationship between readings and actionsRun through the visual link first
Voice codeVoice broadcast, desktop assistantCloser to daily useAs a complete demonstration solution

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

5.1 WhatsApp solution

For WhatsApp access and robot creation steps, please refer directly to [06-Configuration Test of Three Dialogue Schemes]. After creating it, you can remotely view the status of the dashboard in WhatsApp, for example:

  • Temperature and humidity dashboard

image-20260428184244000

5.2 TUI scheme

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

xxxxxxxxxx
openclaw tui

Then enter project-related instructions, such as:

  • Temperature and humidity dashboard

image-20260428184318072

5.3 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:

  • Temperature and humidity dashboard

image-20260428184406620

6. Project implementation

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

6.1 Scenario 1: Real-time reading

Experimental goals

Read the current temperature and humidity and complete the basic data input of the dashboard.

Effect description

OpenClaw will first get the temperature and humidity readings, and these values are the basis for subsequent OLED, RGB and servo mapping.

Example instructions

  • Read current temperature and humidity
  • What is the current temperature and humidity?
  • Help me refresh the current readings

6.2 Scenario 2: OLED indicator disk

Experimental goals

Display the current temperature and humidity results into a concise OLED dashboard content.

Effect description

It is suitable to use the metrics or dht_oled method to combine Temp and Hum into a compact screen area to form a small data panel.

Example instructions

  • Display Temp and Hum on OLED
  • Make a simple temperature and humidity panel
  • Write the current temperature and humidity on the screen

 

6.3 Scene 3: RGB status light

Experimental goals

Use RGB colors to express whether the current environment is normal, hot, cold, or dry.

Effect description

This link can make the dashboard more intuitive. For example, normal bright green, hot bright yellow, abnormal bright red, cold bright blue, the status can be seen at a glance.

Example instructions

  • If the environment is normal, the green light will turn on
  • If the temperature is high, the yellow light will turn on
  • If the humidity is abnormal, the red light will turn on

6.4 Scenario 4: Servo pointer mapping

Experimental goals

Map temperature or humidity to servo angle so that the servo points to a position like a dashboard needle.

Effect description

For example, if the temperature range is mapped to 0 to 180 degrees, the higher the current temperature, the further to the right the servo pointer will be. This effect is very suitable for classroom demonstrations, because "numbers turn into physical actions" will be very intuitive.

Example instructions

  • Use the servo to indicate the current temperature like a pointer
  • Map current temperature to servo angle
  • Let the pointer point to the current state
AI Weather Station
Scheduled Tasks

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