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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。

04. Publisher

4.Publisher

4.1 Publisher

Publishers, as the name suggests, serve to publish messages. The message here can be sensor information transmitted from the lower computer to the upper computer, which is then packaged and sent to subscribers who have subscribed to the topic through the upper computer; It can also be the data from the upper computer that has been processed, packaged, and sent to subscribers who subscribe to the topic.

4.2. Create a workspace and topic feature pack

4.2.1 Creating a workspace

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
catkin_init_workspace

4.2.2 Compilation workspace

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cd ~/catkin_ws/
catkin_make

4.2.3. Updating Environmental Variables

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source devel/setup.bash

4.2.4. Checking Environmental Variables

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echo $ROS_PACKAGE_PATH

4.2.5. Create Function Package

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cd ~/catkin_ws/src
catkin_create_pkg learning_topic std_msgs rospy roscpp geometry_msgs turtlesim

4.2.6 Compilation Function Package

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cd ~/catkin_ws
catkin_make
source ~/catkin_ws/devel/setup.bash

4.3. Creating a Publisher

4.3.1 Creation steps

  1. Initialize ROS node
  2. Create handleRegister
  3. node information with ROS Master, including the published topic name, message types in the topic, and queue length
  4. Create and initialize message data
  5. Cycle sending messages at a certain frequency

4.3.2. C++Language Implementation

  1. In the src folder of the feature pack, create a c++file (with the suffix. cpp) and name it turtle_ velocity_ publisher.cpp
  2. Copy and paste the program code below into the title_ velocity_ In the publisher.cpp file
​x
#include <ros/ros.h>
#include <geometry_msgs/Twist.h>
int main(int argc, char **argv){
    
    ros::init(argc, argv, "turtle_velocity_publisher");
    
    ros::NodeHandle n;

   
    ros::Publisher turtle_vel_pub = n.advertise<geometry_msgs::Twist>("/turtle1/cmd_vel", 10);
    
    ros::Rate loop_rate(10);

    while (ros::ok()){
            
        geometry_msgs::Twist turtle_vel_msg;
        turtle_vel_msg.linear.x = 0.8;
        turtle_vel_msg.angular.z = 0.6;
        
        turtle_vel_pub.publish(turtle_vel_msg);/

      
        ROS_INFO("Publsh turtle velocity command[%0.2f m/s, %0.2f rad/s]", turtle_vel_msg.linear.x, turtle_vel_msg.angular.z);
        
        loop_rate.sleep();/
    }
    return 0;
}
  1. Program flowchart, corresponding to 1.3.1 content for viewing

  1. Configure in CMakelist.txt, under the build area, add the following content
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add_executable(turtle_velocity_publisher src/turtle_velocity_publisher.cpp)
target_link_libraries(turtle_velocity_publisher ${catkin_LIBRARIES})
  1. Compiling code under workspace directory
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cd ~/catkin_ws
catkin_make
source devel/setup.bash 
  1. run a program
  • Run score
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roscore
  • Running the Little Turtle Node
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rosrun turtlesim turtlesim_node
  • Run the publisher and continuously send speed to Little Turtle
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rosrun learning_topic turtle_velocity_publisher
  1. Screenshot of operation effect

  1. Program operation instructions
  • Entering the rostopic list on the terminal to view the topic list will reveal/tourle1/cmd_ The topic of vel
  • We use rostopic info/tourle1/cmd_ Looking at vel, you will find that

This indicates that Little Turtle is a subscription/tourle1/cmd_ The speed topic is vel, so the publisher keeps sending speed data. After receiving it, the little turtle starts to move according to the speed

4.3.3. Python Language Implementation

  1. In the feature pack directory, create a new folder called scripts, and then create a new Python file (with the suffix. py) under the scripts folder, named turtle_ velocity_ publisher.py
  2. Copy and paste the program code below into the title_ velocity_ In the publisher.py file
x
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import rospy
from geometry_msgs.msg import Twist

def turtle_velocity_publisher():
    
    rospy.init_node('turtle_velocity_publisher', anonymous=True) # ROS node initialization

    
    turtle_vel_pub = rospy.Publisher('/turtle1/cmd_vel', Twist, queue_size=8)

    
    rate = rospy.Rate(10) #Set the frequency of the loop

    while not rospy.is_shutdown():
        # Initializes a message of type geometry_msgs::Twist
        turtle_vel_msg = Twist()
        turtle_vel_msg.linear.x = 0.8
        turtle_vel_msg.angular.z = 0.6

        # Publish a message
        turtle_vel_pub.publish(turtle_vel_msg)
        rospy.loginfo("linear is :%0.2f m/s, angular is :%0.2f rad/s", 
                turtle_vel_msg.linear.x, turtle_vel_msg.angular.z)

        
        rate.sleep()# Delay according to the cycle frequency

if __name__ == '__main__':
    try:
        turtle_velocity_publisher()
    except rospy.ROSInterruptException:
        pass
  1. Process Flow Chart

  1. run a program
  • Run score
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roscore
  • Running the Little Turtle Node
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rosrun turtlesim turtlesim_node
  • Run the publisher and continuously send speed to Little Turtle
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rosrun learning_topic turtle_velocity_publisher

Note: Before running, it is necessary to give the title_ velocity_ Publisher.py adds executable permissions in the title_ velocity_ Open the terminal in the publisher.py folder,

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sudo chmod a+x turtle_velocity_publisher.py

All Python requires adding execute file permissions, otherwise an error will be reported!5) Refer to 1.3.2 for operational effects and program instructions.

Common commands and tools
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