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。

6. Temperature and Humidity Dashboard

OpenClaw Temperature and Humidity Dashboard Tutorial

Before starting, please complete the following from the Pre-use Preparation directory: [01-Peripheral Hardware Configuration], [02-OpenClaw Jetson-KEY Configuration], [03-OpenClaw Model Switching], [04-AI Voice Interaction Configuration], [05-Three Dialogue Solutions Configuration Test]. If you are currently only using TUI / WhatsApp, you can skip 04. This article assumes the basic environment is already prepared and only covers the Temperature and Humidity Dashboard project itself.

1. Tutorial Objectives

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

  • Understand how to combine DHT, OLED, RGB, and servo into a temperature and humidity dashboard
  • Interact with OpenClaw through three methods: WhatsApp, TUI, and voice code
  • Complete four project scenarios: real-time readings, 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 LED module
  • [Optional] AI voice module + speaker
  • Wiring diagram

image-20260601095549027

3. Software Preparation

Code entry points used in this tutorial

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

This project is very similar to the AI Weather Station, but with a different focus:

  • The AI Weather Station emphasizes "broadcasting" and "interpretation"
  • The Temperature and Humidity Dashboard emphasizes "display" and "status mapping"

4. Testing Project Basic Capabilities

Before starting this project, please complete a basic openclaw tui dialogue self-check; if you haven't done so yet, see [05-Three Dialogue Solutions Configuration Test] first. After passing, proceed to the Temperature and Humidity Dashboard specific test.

It is recommended to first enter OpenClaw TUI:

openclaw tui

image-20260529194358126

Then test the following types of commands in sequence:

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

5. Three Solutions for Communicating with OpenClaw

SolutionSuitable ScenariosAdvantagesRecommended Use
WhatsAppRemote status viewing, classroom demonstrationsConvenient for remotely reading current panel dataDemonstrate "remote monitoring panel"
TUILocal debugging, mapping verificationEasiest for troubleshooting data flow between sensors and actuatorsRun through the visualization pipeline first
Voice codeVoice broadcast, desktop assistantCloser to daily usage methodsUse as the complete demonstration solution

If the optional AI voice module + speaker is selected, Feishu dialogue, TUI, and WhatsApp dialogue need to read aloud OpenClaw's reply content. You can run the following program in the terminal. The AI voice module dialogue does not need to run this program repeatedly.

  • Enable the broadcast parameter
xxxxxxxxxx
/home/jetson/voice_to_openclaw/.env
xxxxxxxxxx
ENABLE_TTS=true  #This parameter controls whether audio readback is enabled. After modification, save and run the following program in the terminal
  • Run the following in the terminal:
xxxxxxxxxx
cd ~/voice_to_openclaw
source .venv/bin/activate
python -m src.openclaw_session_tts

image-20260529194326500

5.1 WhatsApp Solution

For WhatsApp integration and bot creation steps, please refer directly to [05-Three Dialogue Solutions Configuration Test]. After creation, you can remotely view dashboard status in WhatsApp, for example:

  • Temperature and humidity dashboard

image-20260428184244000

5.2 TUI Solution

If you have completed the TUI self-check in article 0, directly enter terminal text dialogue:

xxxxxxxxxx
openclaw tui

Then enter project-related commands, for example:

  • Temperature and humidity dashboard

image-20260428184318072

5.3 Voice Code Solution

xxxxxxxxxx
Requires optional AI voice interaction module + speaker

For voice module installation, environment configuration, and basic wake-up procedure, please refer to [04-AI Voice Interaction Configuration]. After wake-up, you can directly speak control commands, for example:

  • Temperature and humidity dashboard

image-20260529194336084

6. Project Implementation

Note: The following demonstrations are all conducted using openclaw tui dialogue. You can also choose WhatsApp or voice methods to complete them.

6.1 Scenario 1: Real-time Readings

Experiment Objective

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

Effect Description

OpenClaw will first retrieve the temperature and humidity readings, which serve as the basis for subsequent OLED, RGB, and servo mapping.

Example Commands

  • Read current temperature and humidity
  • What's the current temperature and humidity
  • Help me refresh the current readings

6.2 Scenario 2: OLED Indicator Panel

Experiment Objective

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

Effect Description

This is suitable for using metrics or dht_oled methods to combine Temp and Hum into a compact screen area, creating a small data panel.

Example Commands

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

6.3 Scenario 3: RGB Status Light

Experiment Objective

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

Effect Description

This step can make the dashboard more intuitive. For example, green for normal, yellow for hot, red for abnormal, blue for cold — you can see the status at a glance.

Example Commands

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

6.4 Scenario 4: Servo Pointer Mapping

Experiment Objective

Map temperature or humidity to servo angle, making the servo point to a position like a gauge pointer.

Effect Description

For example, map the temperature range 0 - 50 to 0 - 180 degrees. The higher the current temperature, the farther right the servo pointer moves. This effect is very suitable for classroom demonstrations, because "turning numbers into physical actions" is very intuitive.

Example Commands

  • Use the servo to represent current temperature like a pointer
  • Map the current temperature to servo angle
  • Make the pointer point to the current status
AI Weather Station
Scheduled Tasks

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