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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

7.ROS2 topic communication

7. ROS2 Topic Communication

1. Introduction to Topic Communication

Topic communication is the most frequently used communication method in ROS2. A publisher publishes data on a specified topic, and subscribers who subscribe to that topic receive the data.

Topic communication is based on the publish/subscribe model, as shown in the figure:

image8

Topic data transmission is a process where the data is transmitted from one node to another. The object sending data is called a publisher, and the object receiving data is called a subscriber. Each topic must have a name, and the data transmitted must have a fixed data type.

Next, we will explain how to implement topic communication between nodes using Python.

2. Create a New Package

  • Switch to the src directory of the workspace
  • Create a new pkg_topic package
xxxxxxxxxx
ros2 pkg create pkg_topic --build-type ament_python --dependencies rclpy --node-name publisher_demo

After executing the above command, the pkg_topic package will be created, along with a publisher_demo node and the relevant configuration files.

image-20231023170921392

3. Publisher Implementation

3.1 Create a Publisher

Next, edit [publisher_demo.py] to implement the publisher functionality and add the following code:

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#Import the rclpy library
import rclpy
from rclpy.node import Node
#Import String messages
from std_msgs.msg import String 
#Create a Topic_Pub node subclass that inherits from the Node base
class Topic_Pub(Node):
    def __init__(self,name):
        super().__init__(name)
        #Create a publisher using the create_publisher function. The passed parameters are:
        #Topic data type, topic name, and queue length for storing messages
        self.pub = self.create_publisher(String,"/topic_demo",1) 
        #Create a timer that enters the interrupt handler every 1 second. The passed parameters are:
        #Interval between interrupt executions, interrupt handler function
        self.timer = self.create_timer(1,self.pub_msg)
    #Define the interrupt handler function
    def pub_msg(self):
        msg = String()  #Create a String variable, msg
        msg.data = "Hi,I send a message." #Assign data to msg
        self.pub.publish(msg) #Publish topic data
        
#Main function
def main():
    rclpy.init() #Initialization
    pub_demo = Topic_Pub("publisher_node") #Create a Topic_Pub class object, passing in the node name as a parameter
    rclpy.spin(pub_demo)     #Execute the rclpy.spin function, passing in the Topic_Pub class object just created as a parameter
    pub_demo.destroy_node()  #Destroy the node object
    rclpy.shutdown()         #Shut down the ROS2 Python interface

 

3.2 Editing the Configuration File

image-20231023173312172

 

3.3 Compiling the Package

  • Compiling the Package
xxxxxxxxxx
colcon build --packages-select pkg_topic
  • Refresh the environment variables in the workspace

image-20250905115606561

3.4 Running the Program

  • After refreshing the environment variables, run the command
xxxxxxxxxx
ros2 run pkg_topic publisher_demo

After the program successfully runs, nothing is printed. We can use the ros2 topic tool to view the data. First, check if there are any topics being published. Open another terminal and enter:

xxxxxxxxxx
ros2 topic list

image-20231023173728019

This topic_demo is the topic data defined in the program. Next, we'll use ros2 topic echo to print this data. In the terminal, enter:

xxxxxxxxxx
ros2 topic echo /topic_demo

image-20231023173821223

As you can see, the output "Hi, I send a message." from the terminal matches the line msg.data = "Hi, I send a message." in our code.

4. Subscriber Implementation

4.1 Creating a Subscriber

Create a new file, [subscriber_demo.py], in the same directory as [publisher_demo.py].

image-20231023174819565

Next, edit [subscriber_demo.py] to implement the subscriber functionality and add the following code:

xxxxxxxxxx
#Import related libraries
import rclpy
from rclpy.node import Node
from std_msgs.msg import String
​
class Topic_Sub(Node):
    def __init__(self,name):
        super().__init__(name)  
        #Create a subscriber using create_subscription. The passed parameters are: topic data type, topic name, callback function name, and queue length.
        self.sub = self.create_subscription(String,"/topic_demo",self.sub_callback,1) 
    #Callback function executes the program: prints the received message.
    def sub_callback(self,msg):
        # print(msg.data,flush=True)
        self.get_logger().info(msg.data)
​
​
​
def main():
    rclpy.init() #Initialize the ROS2 Python interface.
    sub_demo = Topic_Sub("subscriber_node")#Create the object and initialize it.
    rclpy.spin(sub_demo)
    sub_demo.destroy_node()  #Destroy the node object
    rclpy.shutdown()         #Shut down the ROS2 Python interface

 

4.2 Editing the Configuration File

image-20231023175338754

 

4.3 Compile the Workspace

  • Compile the package
xxxxxxxxxx
colcon build --packages-select pkg_topic
  • Refresh the environment variables in the workspace

image-20250905115606561

4.4 Run the Program

Execute the following command in a separate terminal:

xxxxxxxxxx
# Start the publisher node
ros2 run pkg_topic publisher_demo
# Start the subscriber node
ros2 run pkg_topic subscriber_demo

image-20250905120356726

As shown in the figure above, the terminal running the subscriber node will print the information published by the publisher, /topic_demo.

ROS2 node
ROS2 service communication

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