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Raspberry Pi 5📖
00.Quick Start (Must See)
  • 1.Quick Start (Must See)
  • 2.TF card expand capacity
  • 3.Raspberry Pi 5 - 1GB Version Must - See Before Use
01.Introduction to Raspberry PI 5
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Installation and introduction of shell and active radiator
  • 6. GPIO and 40-pin pins
  • 7. Tips for using Raspberry PI 5
  • 8. Raspberry PI PCIe connector
  • 9. MIPI CSI DSI connector
  • 10. UART connector
  • 11. Real-time clock
  • 12. Power over Ethernet (PoE) connector
02.Introduction to common mirror systems
  • 1. Official Raspberry PI system
  • 2. Ubuntu
03.Basic settings of Raspberry Pi
  • 1. Update and upgrade operating system
  • 2.Introduction to raspi-config tool
  • 3.config.txt file description
  • 4.Network Configuration
  • 5.remote access
  • 6.Transfer files remotely
  • 7.Set display resolution and rotation
  • 8.Set screen to sleep
  • 9.Get real-time temperature of Raspberry Pi
  • 10.Play audio and video
  • 11.Using USB camera
  • 12.Using MIPI camera
04.Basic programming course
1. C language programming
  • 1. C language environment construction
  • 2. Print Hello World
2. Python programming
  • 1.Python usage
  • 2.Use APT to install Python packages
  • 3.Python virtual environment
  • 4.GPIO Zero
  • 5.Pin output high and low levels
  • 6.Pin reads high and low levels
  • 7.Pin output PWM
3.Jupyter lab programming
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Lab
  • 3.Jupyter Lab plug-in installation
  • 4.Basic use of Jupyter Widgets
05.Advanced AI course
5.Advanced AI gameplay
  • 1. Human pose estimation
  • 2. target detection
  • 3.yolov5
  • 4. Camera display
  • 5. QR code
  • 6. face detection
  • 7. Color recognition
2.Getting started with Open Source CV
  • 1. Introduction to Open Source CV
  • 2. Read and display Open Source CV images
  • 3. Open Source CV image writing
  • 4. Open Source CV image quality
  • 5.Open Source CV pixel operation
3.Open Source CV Geometric Transformations
  • 1. Image resizing
  • 2. Cut the image
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine transformation
  • 6. Rotate the image
  • 7. Perspective transformation
4.Text Picture Drawing Open Source CV image processing and drawing text line segments
  • 1、Grey Processing
  • 2、image binaryzation
  • 3、edge detection
  • 4、Line segment drawing
  • 5、Rectangle and circle drawing
  • 6、Text image drawing
1.Open Source CV environment setup
  • Open Source CV environment setup
6.YOLOv11 development
  • 1. YOLOv11 environment construction
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model conversion
  • 9. Dataset Annotation
  • 10. Model training_conversion
  • 11. Model prediction
7.YOLO26 development
  • 01.YOLO26 environment
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented bounding box object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training - conversion
  • 11.Model prediction
06.Mediapipe course
  • 1. Face special effects
  • 2. Face detection
  • 3. Three-dimensional object recognition
  • 4. Paintbrush
  • 5. Finger control
  • 6. Gesture recognition
07.Docker Tutorial
  • 1.Overview and installation
  • 2.Common commands
  • 3.Understand and publish images
  • 4.Hardware interaction data processing
  • 5.Enter docker container
  • 6.Update docker images
  • 7. docker container to install ROS using accessories
08.ROS1 advanced tutorial
1.ROS1 basic course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
2.ROS1 vision application
  • 0.Precautions
  • 1.AR vision
  • 2.AR QR code
  • 3.ROS+Opencv basics
  • 4.Face recognition
  • 5.harris corner detection
  • 6.Target tracking algorithm
  • 7.Contour moment
  • 8.Polygon outline
  • 9.Discrete fourier transform algorithm
  • 10.Edge detection algorithm
  • 11.Face detection algorithm
  • 12.Optical flow detection algorithm
  • 13.Contour detection
  • 14.General contour detection
  • 15.Feature point tracking
  • 16.HLS color filtering
  • 17.Hough circle detection
  • 18.Hough linear detection
  • 19.HSV color filtering
  • 20.LK optical flow algorithm
  • 21.detection algorithm
  • 22.Phase dependent displacement detection
  • 23.Image pyramid sampling algorithm
  • 24.RGB color filtering
  • 25.Clear background detection algorithm
  • 26.Simplified optical flow algorithm
  • 27.Simple filter
  • 28.Threshold image processing
  • 29.Watershed segmentation algorithm
09.ROS2 advanced tutorial
1.ROS2 basic course
  • 1. Introduction to ROS2
  • 2.ROS2 install Humble
  • 3.ROS2 development environment
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.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
2.ROS2 vision application
  • 0.Precautions
  • 1.Opencv basic course
  • 2.ROS+opencv application
  • 3.QR code creation and recognition
  • 4.AR vision
  • 5.Interesting gameplay of mediapipe
10.Offline AI model coursenew
Offline large model (Text Interaction)
  • 0. AI large model image description
  • 1. Deployment of large AI model environments
  • 2. Switch Chinese input method
  • 3. Installation of large model dialogue platform
  • 4. Meta AI Llama 3.2 model
  • 5. Alibaba Cloud Qwen3 Model
  • 6. Microsoft Phi-4-mini model
  • 7. DeepSeek DeepSeek-R1 model
  • 8. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 9. Google Gemma3 Visual Multimodal Large Model
  • 10. Llava Visual Multimodal Large Model
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Visual Understanding Applications
  • 13. Multimodal Text-Based Image Application
  • 14. Multimodal Video Analysis Application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal table scanning application
  • 17. Multimodal Autonomous Agent Application
Offline large model (Voice Interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Offline speech-to-text (ASR)
  • 2. Offline Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual understanding and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
11.Online AI model coursenew
Online large model (text interaction)
  • 1.OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal Visual Understanding Applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (voice interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
12.OpenClaw Deployment and Basic Usagenew
  • 01. OpenClaw Deployment
  • 02. OpenClaw WhatsApp Plugin Application
  • 03. OpenClaw WebChat Interaction
  • 04. OpenClaw TUI Interaction
  • 05. OpenClaw Tools Introduction
  • 06. OpenClawGate Gateway User Manual
  • 07. OpenClaw Features Overview
  • 08. OpenClawHub Introduction (Skill Installation)
  • 09. OpenClaw Application - File Management
  • 10. OpenClaw Application - Camera
  • 11. OpenClaw Application - Browser Control
  • 12. OpenClaw Application - Script Execution
  • 13. OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 14. OpenClaw Application - Dedicated AI Assistant
13.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. 3D 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
  • SystemFile
  • Code
  • SoftWare
  • 3D_Model_File
  • GuideManual
Download ZIP

Welcome to Raspberry Pi 5 repository

10.ROS service server

10.ROS service server

In the previous tutorial, we talked about how to write a client node program, so this program will explain how to write a server node. The server has a service callback function, which can be compared to the callback function of a topic subscriber. After receiving the request for service, describe the specific service content in the service callback function, and then return a response value.

10.1 Create a server

The general creation steps are as follows:

  • Initialize ROS nodes
  • Create server instance
  • Loop waiting for service requests and enter the callback function
  • Complete the function processing of the service in the callback function and feed back the response data

10.2 C++ version

10.2.1 Writing source code

In the src folder of the function package learn_server, create a C++ file (the file suffix is .cpp), name it turtle_vel_command_server.cpp, and paste the following content into turtle_vel_command_server.cpp.

/**
* This routine will execute the /turtle_vel_command service, service data type std_srvs/Trigger
*/
#include <ros/ros.h>
#include <geometry_msgs/Twist.h>
#include <std_srvs/Trigger.h>
ros::Publisher turtle_vel_pub;
bool pubvel = false;
// service callback function, input parameter req, output parameter res
bool pubvelCallback(std_srvs::Trigger::Request &req,
std_srvs::Trigger::Response &res)
{
    pubvel = !pubvel;
    ROS_INFO("Do you want to publish the vel?: [%s]",
    pubvel==true?"Yes":"No");// Print client request data
    // Set feedback data
    res.success = true;
    res.message = "The status is changed!";
    return true;
}
int main(int argc, char **argv)
{
    ros::init(argc, argv, "turtle_vel_command_server");
    ros::NodeHandle n;
    // Create a server called /turtle_vel_command and register the callback function pubvelCallback
    ros::ServiceServer command_service =
    n.advertiseService("/turtle_vel_command", pubvelCallback);
    // Create a Publisher, publish a topic named /turtle1/cmd_vel, the message type is geometry_msgs::Twist, and the queue length is 8
    turtle_vel_pub = n.advertise<geometry_msgs::Twist>("/turtle1/cmd_vel", 8);
    ros::Rate loop_rate(10);// Set the frequency of the loop
    while(ros::ok())
    {
        ros::spinOnce();// View a callback function queue
    // If pubvel is judged to be True, the turtle speed command will be issued.
        if(pubvel)
        {
            geometry_msgs::Twist vel_msg;
            vel_msg.linear.x = 0.6;
            vel_msg.angular.z = 0.8;
            turtle_vel_pub.publish(vel_msg);
        }
        loop_rate.sleep();//Delay according to cycle frequency
    }
    return 0;
}

10.2.2 Modify CMakeLists.txt file

Configure in CMakelist.txt, under the build area, add the following content,

xxxxxxxxxx
add_executable(turtle_vel_command_server src/turtle_vel_command_server.cpp)
target_link_libraries(turtle_vel_command_server ${catkin_LIBRARIES})

10.2.3 Compile

Terminal input,

xxxxxxxxxx
cd ~/ros_ws
catkin_make

image-20231024094607120

After the compilation is passed, you need to re-source the current environment variables to find or update the program. Enter in the terminal.

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cd ~/ros_ws
source devel/setup.bash

10.2.4 Running the program

Open roscore,

xxxxxxxxxx
roscore

Run the little turtle node program,

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rosrun turtlesim turtlesim_node

Run the server node,

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rosrun learn_service turtle_vel_command_server

Calling a service,

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rosservice call /turtle_vel_command

Program running instructions:

First, after running the Little Turtle node, you can enter rosservice list in the terminal to see what services are currently available. The results are as follows:

image-20231024094707528

Then, we run the turtle_vel_command_server program and enter rosservice list, and we will find that there is an additional turtle_vel_command, as shown in the figure below,

image-20231024094824003

Then, we call this service by entering rosservice call /turtle_vel_command in the terminal, and we will find that the little turtle makes a circular motion. If we call the service again, the little turtle stops moving. This is because in the service callback function, we invert the value of pubvel and then feed it back. The main function will judge the value of pubvel. If it is True, the speed command will be issued. If it is False, no command will be issued.

image-20231024095011574

 

10.3 Python version

10.3.1 Writing source code

Create a new scripts folder under the function package learn_service, then create a new python file (file suffix .py) in this scripts folder, name it turtle_vel_command_server.py, copy and paste the following program code into the turtle_vel_command_server.py file,

xxxxxxxxxx
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This routine will execute the /turtle_command service, service data type std_srvs/Trigger
import rospy
import _thread,time
from geometry_msgs.msg import Twist
from std_srvs.srv import Trigger, TriggerResponse
pubvel = False;
turtle_vel_pub = rospy.Publisher('/turtle1/cmd_vel', Twist, queue_size=8)
def pubvel_thread():
    while True:
        if pubvel:
            vel_msg = Twist()
            vel_msg.linear.x = 0.6
            vel_msg.angular.z = 0.8
            turtle_vel_pub.publish(vel_msg)
        time.sleep(0.1)
def pubvelCallback(req):
    global pubvel
    pubvel = bool(1-pubvel)
    rospy.loginfo("Do you want to publish the vel?[%s]", pubvel)# Show request data
    return TriggerResponse(1, "Change state!")# Feedback data
def turtle_pubvel_command_server():
    rospy.init_node('turtle_vel_command_server')# ROS node initialization
    # Create a server named /turtle_command and register the callback function pubvelCallback
    s = rospy.Service('/turtle_vel_command', Trigger, pubvelCallback)
    # Loop waiting for callback function
    print ("Ready to receive turtle_pub_vel_command.")
    _thread.start_new_thread(pubvel_thread, ())
    rospy.spin()
if __name__ == "__main__":
    turtle_pubvel_command_server()

10.3.2 Running the program

Open roscore,

xxxxxxxxxx
roscore

Run the little turtle node program,

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rosrun turtlesim turtlesim_node

Run the server node,

xxxxxxxxxx
rosrun learn_service turtle_vel_command_server.py

Calling a service,

xxxxxxxxxx
rosservice call /turtle_vel_command

Enter rosservice call /turtle_vel_command in the terminal to call this service. You will find that the little turtle moves in a circle. If you call the service again, the little turtle stops moving.

image-20231024100444523

 

 

ROS service client
ROS action client

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