yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
Jetson Orin NX SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notes
  • 04.Disabling automatic updates and troubleshooting issues
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Write Jetson original system
  • 03.Installing Jetson environment
  • 04.Backup SSD system
  • 05.Restore initial image system
  • 06.Upgrade to SUPER version
  • 07.Write SUPER boot
  • 08.SSD expansion
  • 09.Jetpack old system upgraded to Yahboom SUPER factory system
  • 10.Write the system onto your own solid-state drive
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.Vision basic course
  • 01.CSI camera preview
  • 02.USB camera preview
  • 03.Use Jupyter Lab
  • 04.Use JetCam
06.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
  • 17.Preview USB camera image
  • 18.Preview CSI camera image
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 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
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.QR code recognition
06.YOLO26 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detectionnew
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS1-Melodic
01.ROS1 basic course
  • 01. Introduction to ROS
  • 02. Project file structure
  • 03. Common commands and tools
  • 04. Publisher
  • 05. Subscribers
  • 06. Customize topic messages and use
  • 07. Client
  • 08. Server
  • 09. Customize service messages and usage
  • 10. TF release and monitoring
02.ROS1 vision course
  • 00.Must read before running
  • 01.Camera preview
  • 02.QR code
  • 03.Human posture estimation
  • 04.Target detection
  • 05.AR vision
  • 06.Opencv application
  • 07.ROS+Opencv basics
  • 08.Mediapipe development
10.ROS2-Humble
01.ROS2 basic course
  • 01.Introduction to ROS2
  • 02.ROS2 install Humble
  • 03.ROS2 development environment
  • 04.ROS2 workspace
  • 05.ROS2 function package
  • 06.ROS2 node
  • 07.ROS2 topic communication
  • 08.ROS2 service communication
  • 09.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
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera preview (CSI)
  • 03.Camera calibration (USB)
  • 04.Camera calibration (CSI)
  • 05.AR vision (USB)
  • 06.AR vision (CSI)
11.Offline AI model coursenew
Offline large model (Text Interaction)
  • 1.AI large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v 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 image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
  • 10.AI large model offline voice assistant
12.Online AI model coursenew
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 proxy 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.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
13.NVIDIA lsaac ROS coursenew
  • 1.Build lsaac ROS environment
  • 2.Deep segmentation
  • 3.DNN stereoscopic depth
  • 4.Free space segmentation
  • 5.Image distortion processing
  • 6.Image segmentation
  • 7.3D scene reconstruction and mapping
  • 8.Object detection
  • 9.3D attitude estimation
  • 10.Visual SLAM
14.OpenClaw Deployment and Basic Usagenew
  • 01.OpenClaw Quick Start on Jetson Platform
  • 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.OpenClawHub 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
15.OpenClaw Advanced Developmentnew
1.Preparation Before Use
  • 1. Peripheral Hardware Configuration
  • 2. OpenClaw API Key Configuration
  • 3. OpenClaw Model Switching
  • 4. OpenClaw Prompts
  • 5. AI Voice Interaction Configuration
  • 6. 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
Download
  • System_File
  • VM_File
  • 3D_Model_File
  • Data_Sheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
Download ZIP

Welcome to Jetson Orin NX SUPER repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

4. Temperature and Humidity Sensor

OpenClaw Temperature and Humidity Reading Tutorial

Before starting, please complete the following in the Pre-use Preparation directory: [01-Peripheral Hardware Configuration], [02-OpenClaw Ajetson-KEY Configuration], [03-OpenClaw Model Switching], [04-AI Voice Interaction Configuration], [05-Three Dialogue Scheme Configuration Test]. If you are currently only using TUI / WhatsApp, you can skip 04 for now. This article assumes the basic environment is already prepared and only covers DHT-related content.

1. Tutorial Objective

This article demonstrates OpenClaw's natural language control capabilities through the DHT temperature and humidity sensor. After completing this tutorial, readers will be able to:

  • Understand the basic approach of OpenClaw reading temperature and humidity
  • Interact with OpenClaw through three methods: WhatsApp, TUI, and voice interaction
  • Complete four DHT cases: single reading, comfort judgment, threshold alert, and OLED linked display
  • Understand the complete chain from "user question" to "sensor reading" and "result feedback"

2. Hardware Preparation

  • OpenClaw main control board

  • DHT temperature and humidity sensor [Default DHT11]

  • [Optional] OLED display

  • [Optional] Microphone module

  • Wiring diagram

    image-20260427185102384

 

3. Software Preparation

Code entry points used in this tutorial

  • Low-level driver code: /home/jetson/hardware_hal_demo/
  • Temperature and humidity reading command: /home/jetson/hardware_hal_demo/dht
  • Temperature and humidity linked display: /home/jetson/hardware_hal_demo/dht_oled
  • Voice entry: /home/jetson/voice_to_openclaw/src/main.py

The dht tool supports the following capabilities:

  • Read current temperature and humidity
  • Output results in json or text format
  • Use mock fake data for local debugging
dht_oled  #Display temperature and humidity data on OLED
dht #Return `json` result

If you want to display temperature and humidity results directly on the OLED, you can also use dht_oled.

Note: If you want to write your own script to read temperature and humidity data using the ajetson Python driver library, it is recommended to read DHT data at 2-second intervals, as the DHT sensor works best with a 2-second sampling period.

4. Testing Temperature and Humidity Reading

Before starting this tutorial, please complete a basic openclaw tui dialogue self-check first; if you haven't done so, see [05-Three Dialogue Scheme Configuration Test]. After confirming that OpenClaw can reply normally, proceed to the DHT-specific test.

Enter the following command in the terminal to enter TUI:

xxxxxxxxxx
openclaw tui

image-20260529191301543

You can try entering the following content in the terminal:

  • Read the current temperature and humidity
  • What is the current ambient humidity
  • Help me judge whether the current environment is comfortable
  • Alert me if the temperature is too high

image-20260427185504616

5. Three Schemes for Communicating with OpenClaw

SchemeSuitable ScenariosAdvantagesRecommended Use
WhatsAppRemote control, classroom demonstrationEasy to connect, suitable for multi-person experienceDemonstrate "remote natural language control"
TUILocal debugging, command verificationMost direct, easiest to troubleshootFirst run through the control chain
Voice codeVoice interaction, complete AI experienceClosest to a real conversational experienceAs the final demonstration scheme

If you have the optional AI voice module + speaker, Feishu dialogue, TUI, and WhatsApp dialogue need to play back OpenClaw's replies via TTS. You can run the following program in the terminal. The AI voice module dialogue does not need to run this repeatedly.

  • Modify the TTS toggle parameter
xxxxxxxxxx
/home/jetson/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-20260529191327391

5.1 WhatsApp Scheme

For WhatsApp connection steps, please directly refer to [05-Three Dialogue Scheme Configuration Test]. This section only includes dialogue examples suitable for the DHT tutorial.

After creation, you can directly converse, for example:

  • Read the current temperature and humidity
  • Judge whether it's a bit hot right now
  • Alert me if the humidity is too low

image-20260427185928931

5.2 TUI Scheme

If you have completed the TUI self-check in Part 0, simply enter the following command in the terminal to continue the conversation:

xxxxxxxxxx
openclaw tui

image-20260529191306575

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 directly refer to [04-AI Voice Interaction Configuration].

You need to say "Hi, yahboom" to wake up for conversation.

After wake-up, you can directly speak control commands, for example:

  • What is the current temperature
  • Help me read the humidity
  • Tell me if it's too stuffy

image-20260427190043892

6. Comprehensive Cases

Note: The following are all demonstrated using the openclaw tui method. You can also choose WhatsApp or voice methods to complete them.

6.1 Case 1: Single Reading

Experimental Objective

Obtain a single reading of the current temperature and humidity to complete the most basic environmental sampling.

Effect Description

After OpenClaw receives the instruction, it will call the DHT driver to read the current temperature and humidity, and return the results to the user in natural language.

Example Instructions

  • Read the current temperature and humidity
  • What is the current indoor temperature
  • Help me check the current humidity

6.2 Case 2: Comfort Judgment

Experimental Objective

Have OpenClaw make a simple judgment about the current environment after reading the data.

Effect Description

In addition to just reporting numbers, OpenClaw can also roughly judge states like "too hot", "too cold", "too dry", or "too stuffy" based on temperature and humidity, making the output sound more like a natural conversation.

Example Instructions

  • Help me judge whether the current environment is comfortable
  • Is the current temperature and humidity suitable for long-time office work
  • Give me a brief evaluation based on the temperature and humidity

6.3 Case 3: Threshold Alert

Experimental Objective

Have OpenClaw alert the user when the temperature or humidity exceeds a set range.

Effect Description

When the temperature is too high or too low, or the humidity is too high or too low, OpenClaw can directly output alert messages, or link with RGB lights for more obvious feedback.

Example Instructions

  • Alert me if the temperature exceeds 30 degrees
  • Notify me if the humidity drops below 40%
  • Has the alarm condition been reached

Extensible Ideas

  • Link with RGB for red-yellow-blue three-color alerts
  • Link with OLED to display alarm text
  • Add a buzzer for stronger alerts

6.4 Case 4: OLED Linked Display

Experimental Objective

Display the just-read temperature and humidity results directly on the OLED, forming a complete data display chain.

Effect Description

After OpenClaw completes an environmental sampling, it can not only reply the results to the user but also simultaneously write the temperature and humidity to the OLED screen, forming a complete "reading + display" effect.

Example Instructions

  • Read the current temperature and humidity and display on OLED
  • Write the temperature and humidity to the screen
  • Create a simple temperature and humidity display page

Extensible Ideas

  • Periodically refresh OLED content
  • Create a temperature and humidity dashboard
  • Link with a weather station project to form a complete application
OLED Display
Camera Application

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

  • Tutorials
  • Edge AI hardware
  • Educational robots
  • Embedded intelligent hardware

WHERE TO BUY

  • Yahboom Online Store
  • Amazon
  • Alibaba
  • AliExpress
  • Mercado

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文