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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
Download ZIP

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。

2. RGB Strip

OpenClaw RGB Light Control 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 RGB-related content.

1. Tutorial Objectives

This article demonstrates OpenClaw's natural language control capabilities through RGB light peripherals. After completing this tutorial, readers can:

  • Understand the basic approach to controlling RGB lights with OpenClaw

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

  • Complete four RGB cases: single color lighting, breathing light, warning flash, rainbow marquee

  • Understand the complete chain from "user input" to "light execution"

2. Hardware Preparation

  • OpenClaw mainboard

  • RGB light strip / RGB light bead module [Default 14 WS2812]

  • [Optional] Microphone module

  • Wiring diagram

image-20260610120706531

You can add RGB wiring diagram and hardware physical diagram here.

3. Software Preparation

Code entry points that will be used in the tutorial:

  • Low-level driver code: /home/jetson/hardware_hal_demo/

  • RGB control command: /home/jetson/hardware_hal_demo/rgb

  • Voice entry: /home/jetson/voice_to_openclaw/src/main.py

The rgb tool already supports the following capabilities:

  • Set fixed color color

  • Set light effect effect

  • Turn off lights off

Current common light effects include:

  • breathe Breathing light

  • blink Flash

  • flow Flowing light

  • marquee Marquee

  • rainbow Rainbow light

If you want to verify underlying RGB commands individually, you can also input the following in the terminal:

rgb color --color red  #All turn red
rgb effect --effect breathe --color blue --speed 5   #Breathing light
rgb off   #Turn off lights

More commands can be found in the md document in this folder,

xxxxxxxxxx
/home/jetson/hardware_hal_demo/README.md

If underlying commands can execute normally, it indicates hardware and Jetson mainboard connection is normal and communication is normal.

4. Test RGB Light

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 RGB-specific testing.

Terminal input to enter TUI:

xxxxxxxxxx
openclaw tui

image-20260529190932175

You can try inputting the following content in the terminal:

  • Set all RGB lights to red

  • Change RGB lights to blue

  • Lower RGB light brightness

  • Turn off RGB lights

image-20260427171923502

If you want to test a specific light bead, you can also try inputting content like:

  • Set the 1st light bead to green

  • Set the 5th light bead to purple

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-20260529190948629

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 RGB tutorial.

After creation, you can directly dialogue, for example:

  • Make the light strip blue

  • Turn on rainbow light effect

  • Make the lights flash faster

image-20260427172818119

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-20260529190959032

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]. After waking up, you can directly say control content, for example:

  • Take the lights red

  • Turn on blue breathing light

  • Make the light flow like a rainbow

image-20260427173006031

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 Color Lighting

Experimental Objective

Make RGB lights stably display a certain fixed color to complete the most basic light control.

Effect Description

After receiving commands, all RGB light beads will uniformly display the specified color, such as red, green, blue, white, or warm white. This case is suitable for verifying whether color mapping is normal.

Example Instructions

  • Set all RGB lights to red

  • Change the light strip to blue

  • Adjust the light to warm white

6.2 Case Two: Breathing Light

Experimental Objective

Make RGB lights perform brightness gradients on a certain color to form a breathing light effect.

Effect Description

RGB lights will cycle from dark to bright, then from bright to dark. The overall effect is relatively soft, suitable for standby prompts, ambient lights, or status indicator lights.

Example Instructions

  • Turn on blue breathing light

  • Change the light to green breathing effect

  • Slow down the breathing light speed

6.3 Case Three: Warning Flash

Experimental Objective

Make RGB lights simulate warning lights or alarm prompt effects through rapid flashing.

Effect Description

Lights can flash rapidly using red, yellow, or alternating red and blue, suitable for expressing information like "attention", "warning", "abnormal status", etc. The visual feedback will be more obvious.

Example Instructions

  • Make the lights flash red

  • Turn on yellow warning light

  • Make RGB lights flash three times quickly

6.4 Case Four: Rainbow Marquee

Experimental Objective

Make RGB lights form continuous dynamic flowing effects to display richer light performance.

Effect Description

Light bead colors will change sequentially and flow forward, looking like rainbow flowing lights or marquee. This effect is very suitable for boot animations, display animations, or interactive light effects.

Example Instructions

  • Turn on rainbow light effect

  • Make the light flow like a marquee

  • Turn on blue flowing light, faster speed

Servo Control
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