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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。

7. Scheduled Tasks

OpenClaw Scheduled Tasks 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 "fixed-time automatic script execution" part of the content.

1. Tutorial Objectives

This article demonstrates through "AI-generated script + Linux scheduled execution" how OpenClaw upgrades from immediate dialogue to automated peripheral applications. After completing this tutorial, readers can:

  • Understand the basic implementation approach of OpenClaw scheduled tasks

  • Distinguish the scenarios suitable for HEARTBEAT and cron respectively

  • Describe automation requirements through three methods: whatsapp, TUI, and voice code

2. Hardware Preparation

  • OpenClaw mainboard

  • DHT temperature and humidity sensor

  • OLED display

  • RGB light strip / RGB light bead module

  • [Optional] CSI camera

  • [Optional] AI voice module + speaker

Description:

  • The core of this tutorial is "scheduled execution", not requiring all peripherals to be connected simultaneously

  • Connect the corresponding hardware first for the type of automation task you want to do

3. Test Scheduled Task Basic Capability

Before starting this project, please complete one openclaw tui basic dialogue self-check; if you haven't done it yet, see [05-Three Dialogue Solution Configuration Tests]. After passing, enter scheduled task specific testing.

It is recommended to test in the following order:

  1. First enter OpenClaw TUI

  2. Let OpenClaw generate a simple script

  3. Run the script manually in terminal, confirm functionality is normal

Terminal input:

openclaw tui

It is recommended to first try the following type of requirements:

  • Help me write a script to display current temperature and humidity on OLED, save to desktop

  • Generate a script that reads temperature and humidity and prints results every time it runs

  • Write a script to make RGB light green for 3 seconds then turn off

After the script is generated, first run it manually, for example:

xxxxxxxxxx
python3 /home/jetson/Desktop/your_script.py

4. Three Solutions for Communicating with OpenClaw

SolutionSuitable ScenariosAdvantagesRecommended Positioning
whatsappRemote description of automation requirementsSuitable for first clarifying "what to do on schedule"Used to generate scripts and confirm requirements
TUILocal creation and debugging of scheduled tasksEasiest to jointly debug scripts and cronPreferred implementation solution
Voice codeVoice description of automation scenariosCloser to desktop assistant experienceSuitable for demonstrating "one sentence generates automation"

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-20260529194211404

4.1 whatsapp Solution

For whatsapp connection steps, please directly refer to [05-Three Dialogue Solution Configuration Tests]. This section is more suitable for describing requirements, for example:

  • Help me create a scheduled task: make RGB lights up red every minute as a remind, then turn off the light.

image-20260428191042352

4.2 TUI Solution

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

xxxxxxxxxx
openclaw tui

Then you can directly enter project-related commands, for example:

  • Help me create a scheduled task: read temperature and humidity every minute and display on OLED

image-20260428191030929

4.3 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:

  • Help me create a scheduled task: read temperature and humidity every minute and display on OLED

image-20260529194236118

  • If you want to stop the scheduled task, you can say in the current dialogue session: stop the current scheduled task for me

    image-20260511194439572

  • You can use the command cron list to view current scheduled tasks

image-20260511194516325

5. Project Implementation

Note: The following are all dialogue demonstrations using openclaw tui method. You can also choose whatsapp or voice method to complete.

5.1 Scenario One: Hourly Temperature and Humidity Sampling

Experimental Objective

Let the device automatically read temperature and humidity once per hour, and record results or synchronize to screen.

Effect Description

This is the most basic and common scheduled task scenario. It can later be further expanded into logs, dashboards, environmental alerts, or plant care projects.

Example Instructions

  • Help me make a scheduled task: read temperature and humidity every minute and display on OLED

5.2 Scenario Two: Timed Status Reminder

Experimental Objective

At a fixed time, use RGB or OLED to make a clear reminder.

Effect Description

This type of task is suitable for water drinking reminders, class reminders, patrol reminders, status switching reminders. It does not emphasize continuous running, but emphasizes triggering a clear action at a certain time point.

Example Instructions

  • Help me make a scheduled task: make RGB light red every minute to remind, then turn off

5.3 Scenario Three: Timed Patrol Script

Experimental Objective

String multiple peripheral actions together to form a periodically executable small automation task.

Effect Description

For example, read temperature and humidity once per hour. If status is abnormal, light alert, and display results to OLED. This type of task is already close to a complete small project, and is also the most obvious place where "AI-generated script" combines with "scheduled execution".

Example Instructions

  • Help me make a scheduled task: read temperature and humidity once per minute, light red when abnormal, and prompt on OLED

     

 

Temperature and Humidity Dashboard

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