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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。

07. Client

7.Client

In ROS communication, in addition to topic communication, there is also a type of service communication. Services include both client and server, where the client requests the service and the server provides the service. This section focuses on the client and explains how C++and Python can implement the client.

7.1 Preparation work

7.1.1 Establishing a Function Package

  1. Switch to~/catkin_ In the ws/src directory,
catkin_create_pkg learning_server std_msgs rospy roscpp geometry_msgs turtlesim
  1. Switch to~/catkin_ Under the ws directory,
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catkin_make

7.2. C++Language Implementation

7.2.1 Implementation steps

  1. Initialize ROS node
  2. Create handle
  3. Create a Client instance
  4. Initialize and publish service request data
  5. Wait for the response result after the server processes it

7.2.2. Switch to~/catkin_ ws/src/learning_ Create a new. cpp file under the server/src directory and name it a_ new_ Turtle, paste the following code inside

a_new_turtle.cpp

x
#include <ros/ros.h>
#include <turtlesim/Spawn.h>

int main(int argc, char** argv)
{
    
    ros::init(argc, argv, "a_nes_turtle");// Initialize ROS nodes

    ros::NodeHandle node;

    ros::service::waitForService("/spawn"); // Waiting for/sawn service
    
    ros::ServiceClient new_turtle = node.serviceClient<turtlesim::Spawn>("/spawn");//Create a service client to connect to a service named/sawn

    //Initialize the request data for turtlesim:: Spawn
    turtlesim::Spawn new_turtle_srv;
    new_turtle_srv.request.x = 6.0;
    new_turtle_srv.request.y = 8.0;
    new_turtle_srv.request.name = "turtle2";

    // Request the service to pass in xy location parameters and name parameters
    ROS_INFO("Call service to create a new turtle name is %s,at the x:%.1f,y:%.1f", new_turtle_srv.request.name.c_str(),
        new_turtle_srv.request.x, 
        new_turtle_srv.request.y);

    new_turtle.call(new_turtle_srv);

    
    ROS_INFO("Spwan turtle successfully [name:%s]", new_turtle_srv.response.name.c_str());// Display service call results

    return 0;
};
  1. Process Flow Chart

 

  1. Configure in CMakelist.txt, under the build area, add the following content
xxxxxxxxxx
add_executable(a_new_turtle src/a_new_turtle.cpp)
target_link_libraries(a_new_turtle ${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
xxxxxxxxxx
roscore
rosrun turtlesim turtlesim_node
rosrun learning_server a_new_turtle
  1. Running effect screenshot

  1. Program Description

After starting the node of Little Turtle, run a_ new_ The Turtle program will find that there will be another small turtle appearing in the screen, because the turtle's node provides a service/spa wn, which will generate another small turtle Turtle2. To view the services provided by the turtle, you can use the rossservice list command, as shown in the following figure

You can view the parameters required for this service through rossservice info/spa, as shown in the following figure

It can be seen that there are four parameters required: x, y, theta, and name, which are in a_ new_ There is initialization in turtle.cpp

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srv.request.x = 6.0;
srv.request.y = 8.0;
srv.request.name = "turtle2";

7.3. Python Language Implementation

7.3.1. Switch to~/catkin_ ws/src/learning_ Under the server directory, create a new script folder, cut it in, and create a new py file named a_ new_ Turtle, paste the following code inside

a_new_turtle.py

x
#!/usr/bin/env python
# -*- coding: utf-8 -*-


import sys
import rospy
from turtlesim.srv import Spawn

def turtle_spawn():
    
    rospy.init_node('new_turtle')# Initialize ROS nodes

    rospy.wait_for_service('/spawn')# Waiting for/sawn service

    try:
        new_turtle = rospy.ServiceProxy('/spawn', Spawn)

        
        response = new_turtle(2.0, 2.0, 0.0, "turtle2")# Input request data
        return response.name
    except rospy.ServiceException, e:
        print "failed to call service : %s"%e

if __name__ == "__main__":
    #Call the service and display the call result
    print "a new turtle named %s." %(turtle_spawn())
  1. Program flowchart

  1. run a program
xxxxxxxxxx
roscore
rosrun turtlesim turtlesim_node
rosrun learning_server a_new_turtle.py
  1. The program operation effect and program description are consistent with the implementation effect of C++. Here, we mainly discuss how Python provides the parameters required for the service,
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 response = add_turtle(2.0, 2.0, 0.0, "turtle2")

The corresponding parameters are x, y, theta, and name.

Customize topic messages and use
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