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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。

09. Customize service messages and usage

9.Custom Service Messages and Usage

9.1 Customized Service Messages

Switch to~/catkin_ ws/src/learning_ Under the server function package directory, create a new folder named srv to store custom service messages.

9.1.1 Define SRV files

Switch to the srv directory and create a new blank srv file, with srv as the suffix to indicate that it is an srv file. Here we use IntPlus.srv as an example to copy the following code into the just created srv file.

​x
uint8  a
uint8  b

---
uint8 result

Here is an explanation of the composition of the SRV file, which is divided into two parts by the symbol - - - - -. The upper side represents the request and the lower side is the response.

9.1.2 Add feature pack dependencies in package.xml

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<build_depend>message_generation</build_depend>
<exec_depend>message_runtime</exec_depend>

9.1.3. Add compilation options in CMakeLists.txt

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add_service_files(FILES IntPlus.srv)
generate_messages(DEPENDENCIES std_msgs)

9.1.4 Compile and generate language related files

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cd ~/catkin_ws
catkin_make

9.1.5 C++Language Implementation

  1. Switch to~/catkin_ ws/src/learning_ Under server/src, create two new cpp files named IntPlus_ Server.cpp and IntPlus_ Client.cpp, copy the following code into it separately,

IntPlus_server.cpp

x
#include <ros/ros.h>
#include "learning_server/IntPlus.h"

// Service callback function, input parameter req, output parameter res
bool IntPlusCallback(learning_server::IntPlus::Request  &req,
                    learning_server::IntPlus::Response &res)
{
    
    ROS_INFO("number 1 is:%d  ,number 2 is:%d  ", req.a, req.b);//Display request data

    res.result = req.a + req.b ;// The feedback result is the sum of two numbers

    return res.result;
}

int main(int argc, char **argv)
{
   
    ros::init(argc, argv, "IntPlus_server"); // ROS node initialization
    
    ros::NodeHandle n;// Create node handle

    // Create a server and register the callback function IntPlusCallback
    ros::ServiceServer Int_Plus_service = n.advertiseService("/Two_Int_Plus", IntPlusCallback);

    // Loop waiting callback function
    ROS_INFO("Ready to caculate.");
    ros::spin();

    return 0;
}

IntPlus_client.cpp

x
#include <ros/ros.h>
#include "learning_server/IntPlus.h"
#include <iostream>
using namespace std;
int main(int argc, char** argv)
{
    int i,k;
    cin>>i;
    cin>>k;

    ros::init(argc, argv, "IntPlus_client");// ROS node initialization

    
    ros::NodeHandle node;// Create node handle

    // Create a service client after discovering the/Two_int-Plus service
    ros::service::waitForService("/Two_Int_Plus");
    ros::ServiceClient IntPlus_client = node.serviceClient<learning_server::IntPlus>("/Two_Int_Plus");

    // Initialize learningservice:: IntPlus request data
    learning_server::IntPlus srv;
    srv.request.a = i;
    srv.request.b  = k;

    ROS_INFO("Call service to plus %d and %d", srv.request.a, srv.request.b);// Request service invocation

    IntPlus_client.call(srv);

    // Display service call results
    ROS_INFO("Show the result : %d", srv.response.result);//Display service call results

    return 0;
}
  1. Modify the CMakeLists.txt file
xxxxxxxxxx
add_executable(IntPlus_server src/IntPlus_server.cpp)
target_link_libraries(IntPlus_server ${catkin_LIBRARIES})
add_dependencies(IntPlus_server ${PROJECT_NAME}_generate_messages_cpp)

add_executable(IntPlus_client src/IntPlus_client.cpp)
target_link_libraries(IntPlus_client ${catkin_LIBRARIES})
add_dependencies(IntPlus_client ${PROJECT_NAME}_generate_messages_cpp)
  1. Core part

The implementation process here is the same as before, with the main difference being the introduction of header files and the use of custom service files:The import header file is

xxxxxxxxxx
#include "learning_server/IntPlus.h"

Front learning_ Server is the name of the feature pack, followed by IntPlus. h, which is the header file name generated by the previously created srv fileUsing custom service files is

x
client:
learning_server::IntPlus srv;
srv.request.a = i;
srv.request.b  = k;
#i. k is the addend input by the terminal
ros::ServiceClient IntPlus_client = node.serviceClient<learning_server::IntPlus>("/Two_Int_Plus");
IntPlus_client.call(srv);
server:
ros::ServiceServer Int_Plus_service = n.advertiseService("/Two_Int_Plus", IntPlusCallback);
bool IntPlusCallback(learning_server::IntPlus::Request  &req,
                    learning_server::IntPlus::Response &res)
  1. run a program
xxxxxxxxxx
roscore
rosrun learning_server IntPlus_client
rosrun learning_server IntPlus_server
  1. Run screenshot

  1. Program Description

Running IntPlus_ After the server, it will prompt to prepare for calculation; Running IntPlus_ After the client, the terminal inputs two integer numbers, followed by IntPlus_ The server accountant calculates the result and returns it to IntPlus_ Client, and then print out the results.

9.1.6 Python Language Implementation

  1. Switch to~/catkin_ ws/src/learning_ Under server/script, create two new py files and name them IntPlus_ Server.py and IntPlus_ Client.py, copy the following code into it separately,

IntPlus_server.py

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

from learning_server.srv import IntPlus, IntPlusResponse

def IntPlusCallback(req):
    
    rospy.loginfo("Ints: a:%d  b:%d", req.a, req.b)# Display request data
    
    return IntPlusResponse(req.a+req.b)# Feedback data

def IntPlus_server():
    
    rospy.init_node('IntPlus_server')# ROS node initialization

    # Create a server and register the callback function IntPlusCallback
    s = rospy.Service('/Two_Int_Plus', IntPlus, IntPlusCallback)

    
    print "Ready to caculate two ints."# Loop waiting callback function

    rospy.spin()

if __name__ == "__main__":
    IntPlus_server()

IntPlus_client.py

x
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import rospy
from learning_server.srv import IntPlus, IntPlusRequest

def Plus_client():
    # ROS node initialization
    rospy.init_node('IntPlus_client')

    rospy.wait_for_service('/Two_Int_Plus')
    try:
        Plus_client = rospy.ServiceProxy('/Two_Int_Plus', IntPlus)

        response = Plus_client(22, 20)# Request service call, input request data

        return response.result
    except rospy.ServiceException, e:
        print "failed to call service : %s"%e

if __name__ == "__main__":
    #Call the service and display the call result
    print "Show two_int_plus result : %s" %(Plus_client())
  1. Core part

Here is mainly an explanation of how to import a custom service message module and use it:Import

xxxxxxxxxx
server:
from learning_server.srv import IntPlus, IntPlusResponse
client:
from learning_server.srv import IntPlus, IntPlusRequest

use

x
server:
s = rospy.Service('/Two_Int_Plus', IntPlus, IntPlusCallback)
return IntPlusResponse(req.a+req.b)# Feedback data
client:
response = Plus_client(12, 20)#Request service call, input request data
return response.result
  1. Run program

Before running the program, add executable permissions to the py file

xxxxxxxxxx
sudo chmod a+x IntPlus_server.py
sudo chmod a+x IntPlus_client.py

run a program

xxxxxxxxxx
roscore
rosrun learning_server IntPlus_client.py
rosrun learning_server IntPlus_server.py
  1. Program operation instructions

What is inconsistent with the C++version here is that the addend is set in the program (12 and 20), so once the service is started, the result can be returned immediately.

Server
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