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Jetson ORIN NANO SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notesnew
  • 04. Disable automatic updates and troubleshooting issues
  • 05. Notes on Jetson Orin Nano Official Kitnew
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.Write SUPER original system(optional)
  • 07.Write SUPER boot (optional)
  • 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
  • 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
  • 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
Download
  • System_File
  • VM_File
  • 3D_Model_File
  • DataSheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
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Welcome to Jetson ORIN NANO 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 Acquisition Tutorial

Before starting, please complete the [01-Hardware Configuration], [02-openclaw API-KEY Configuration], [03-openclaw Model Switching], [04-AI Voice Interaction Configuration], [05-Three Dialogue Solution Configuration Tests] under the Prerequisites directory. If you are currently only using TUI / whatsapp, you can skip 04 for now. This article assumes the basic environment has been prepared and only expands on DHT-related content.

1. Tutorial Objectives

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

  • Understand the basic approach to reading temperature and humidity with OpenClaw

  • Communicate with OpenClaw through three methods: whatsapp, TUI, and voice code

  • Complete four DHT cases: single reading, comfort judgment, threshold alert, OLED linked display

  • Understand the complete chain from "user question" to "sensor reading" and "result feedback"

2. Hardware Preparation

  • OpenClaw mainboard

  • DHT temperature and humidity sensor [Default DHT11]

  • [Optional] OLED display

  • [Optional] Microphone module

  • Wiring diagram

    image-20260427185102384

 

3. Software Preparation

Code entry points that will be used in the 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 already 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 to OLED
dht #Feedback `json` results

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

Note: If you want to write the tutorial yourself to read temperature and humidity data from the ajetson in the Python driver library, it is recommended to read DHT data at 2-second intervals, because DHT sensor itself is more suitable for 2s sampling

4. Test Temperature and Humidity Acquisition

Before starting this tutorial, please complete one openclaw tui basic dialogue self-check; if you haven't done it yet, see [05-Three Dialogue Solution Configuration Tests]. After confirming OpenClaw can respond normally, enter DHT-specific testing.

Terminal input to enter TUI:

xxxxxxxxxx
openclaw tui

image-20260529191301543

You can try entering the following content in the terminal:

  • Read current temperature and humidity

  • What is the current environmental humidity

  • Help me judge if the current environment is comfortable

  • Remind me if the temperature is too high

image-20260427185504616

5. Three Solutions for Communicating with OpenClaw

SolutionSuitable ScenariosAdvantagesRecommended Positioning
whatsappRemote control, classroom demonstrationsEasy 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 real conversation experienceAs final demonstration solution

If optional AI voice module + speaker are selected, whatsapp dialogue, tui, whatsapp dialogue need to broadcast OpenClaw's reply content. You can run the following program in terminal. AI voice module dialogue does not need to run repeatedly

  • Modify whether to enable broadcast parameter

xxxxxxxxxx
/home/jetson/voice_to_openclaw/.env
xxxxxxxxxx
ENABLE_TTS=true  #This parameter controls whether to broadcast. After modification and saving, run the following program in terminal
  • Terminal input

xxxxxxxxxx
cd ~/voice_to_openclaw
source .venv/bin/activate
python -m src.openclaw_session_tts

image-20260529191327391

5.1 whatsapp Solution

For whatsapp connection steps, please directly refer to [05-Three Dialogue Solution Configuration Tests]. This section only retains dialogue examples suitable for the DHT tutorial.

After creation, you can directly dialogue, for example:

  • Read current temperature and humidity

  • Judge if it's currently too hot

  • Remind me if humidity is too low

image-20260427185928931

5.2 TUI Solution

If you have completed the TUI self-check in Chapter 0, directly enter the following command in the terminal to continue the dialogue:

xxxxxxxxxx
openclaw tui

image-20260529191306575

5.3 Voice Code Solution

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 dialogue.

After waking up, you can directly say control content, 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 dialogue demonstrations using openclaw tui method. You can also choose whatsapp or voice method to complete.

6.1 Case One: Single Reading

Experimental Objective

Get current temperature and humidity once to complete the most basic environmental sampling.

Effect Description

After receiving the command, OpenClaw 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 current temperature and humidity

  • What is the current indoor temperature

  • Help me check the current humidity

6.2 Case Two: Comfort Judgment

Experimental Objective

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

Effect Description

Besides directly reporting numbers, OpenClaw can also roughly judge states like "too hot", "too cold", "too dry", "too stuffy" based on temperature and humidity, making results closer to natural dialogue.

Example Instructions

  • Help me judge if the current environment is comfortable

  • Is the current temperature and humidity suitable for long-term office work

  • Give me a brief evaluation based on temperature and humidity

6.3 Case Three: Threshold Alert

Experimental Objective

Let OpenClaw give alerts when temperature or humidity exceeds the set range.

Effect Description

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

Example Instructions

  • Remind me if temperature exceeds 30 degrees

  • Prompt if humidity is below 40% that air is too dry

  • Are there any alarm conditions now

Extensible Gameplay

  • Link with RGB for red, yellow, blue three-color alerts

  • Link with OLED to display alarm text

  • Add buzzer for stronger alerts

6.4 Case Four: OLED Linked Display

Experimental Objective

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

Effect Description

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

Example Instructions

  • Read current temperature and humidity and display to OLED

  • Write temperature and humidity to the screen

  • Make a simple temperature and humidity display page

Extensible Gameplay

  • Periodically refresh OLED content

  • Make into a temperature and humidity dashboard

  • Link with weather station project to form a complete application

OLED Display
Camera Application

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