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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

15.ROS2 time related API

15.ROS2 time related API

 

1. Introduction to Time-Related APIs

ROS2's time-related APIs include Rate, Time, Duration, and operations on Time and Duration. These are explained below.

  • First, create a function package to store the relevant program files.
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ros2 pkg create learning_time --build-type ament_python --dependencies rclpy

2. create_rate

ROS2 also provides the create_rate function, a tool for controlling loop execution frequency. Its core function is to periodically execute a section of code at a fixed frequency. Rate ensures the stability of the loop execution frequency by controlling the "sleep time" of the loop. Remember that Rate should generally not be used directly in the main thread, as doing so will permanently block callback events. It is generally only used in programs with multi-threaded callbacks or in child threads.

How it works:

  1. Records the start time of each loop;
  2. Executes the code within the loop;
  3. Calculates the difference between the current loop's actual duration and the target interval;
  4. Automatically sleeps for the corresponding difference, ensuring that the interval from the start of one loop to the start of the next strictly equals the target interval (e.g., 100 milliseconds).

While both Rate and Timer can implement periodic execution, their application scenarios differ:

FeaturesRateTimer
ImplementationBased on active sleep within a loop (blocking the current thread)Based on callback functions (non-blocking, triggered by the ROS 2 event loop)
Applicable ScenariosSuitable for loops that need to execute at a fixed frequency within the same thread (such as main control logic)Suitable for periodic tasks that need to execute asynchronously (without blocking the main thread)
FlexibilityDirect control flow within the loop (such as break exit)Callback execution must be controlled using flags or other methods
  • The following is a basic usage of Rate to implement a loop that executes twice per second:
  • Create a new file in the function package called rate_demo.py
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import rclpy
from rclpy.node import Node
import threading
​
class RateExampleNode(Node):
    def __init__(self):
        super().__init__("rate_example_node")
        self.get_logger().info("Rate 示例节点启动")
​
    def run_loop(self):
        # Use the node's create_rate() to create a 2Hz Rate
        rate = self.create_rate(2)
        
        count = 0
        try:
            while rclpy.ok():
                self.get_logger().info(f"循环执行 {count} 次")
                count += 1
                rate.sleep()  # Sleep until the next cycle (0.5 seconds)
        except KeyboardInterrupt:
            self.get_logger().info("循环被中断")
​
def main(args=None):
    rclpy.init(args=args)
    node = RateExampleNode()
    
    # Create a thread run loop (to avoid blocking the main thread)
    loop_thread = threading.Thread(target=node.run_loop)
    loop_thread.start()
    
    # The main thread executes spin to keep the ROS 2 node running
    try:
        rclpy.spin(node)
    except KeyboardInterrupt:
        pass
    finally:
        loop_thread.join()  # Waiting for the thread to end
        node.destroy_node()
        rclpy.shutdown()
​
if __name__ == "__main__":
    main()
    

 

  • Configure the corresponding setup.py configuration file and add it in console_scripts
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'rate_demo=learning_time.rate_demo:main'

image-20250905155834618

 

  • Compile feature package
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colcon build --packages-select learning_time

image-20250905155916058

 

  • Refresh the workspace environment and run the node
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 source ./install/setup.bash
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ros2 run learning_time rate_demo

image-20250905155949678

 

3. Timer Application

  • Timer is used to create a timer that triggers periodic tasks.
  • Example: Create two timers: one that prints the execution count every 1 second, and one that prints the current time every 0.5 seconds.
  • Create a new program file called Timer_demo.py
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import rclpy
from rclpy.node import Node
​
class TimerDemoNode(Node):
    def __init__(self):
        super().__init__('timer_demo_node')
        
        # Counter, used to demonstrate the number of timer executions
        self.counter = 0
        
        # Create a timer: execute the callback function every 1 second
        self.timer = self.create_timer(1.0, self.timer_callback)
        
        # Create a faster timer: execute every 0.5 seconds
        self.fast_timer = self.create_timer(0.5, self.fast_timer_callback)
        
        self.get_logger().info("定时器节点已启动")
​
    def timer_callback(self):
        """1 second timer callback function"""
        self.counter += 1
        current_time = self.get_clock().now()
        
        # Print the current time and counter value
        self.get_logger().info(
            f"[1秒定时器] 第 {self.counter} 次执行,当前时间: {current_time.seconds_nanoseconds()}"
        )
​
    def fast_timer_callback(self):
        """0.5 second timer callback function"""
        # Print the current timestamp (nanoseconds)
        self.get_logger().info(
            f"[0.5秒定时器] 当前时间戳: {self.get_clock().now().nanoseconds}"
        )
​
def main(args=None):
    # Initializing ROS 2
    rclpy.init(args=args)
    
    # Creating a Node
    node = TimerDemoNode()
    
    # Running a Node
    rclpy.spin(node)
    
    # Shut down ROS 2
    node.destroy_node()
    rclpy.shutdown()
​
if __name__ == '__main__':
    main()
​
  • Configure the corresponding setup.py configuration file and add it in console_scripts
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'Timer_demo=learning_time.Timer_demo:main'

image-20250905160115901

 

  • Compile function package
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colcon build --packages-select learning_time

image-20250905155916058

 

  • Refresh the workspace environment and run the node
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source ./install/setup.bash
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ros2 run learning_time Timer_demo

image-20250905160239330

  • It can be seen that the timer is triggered according to the set timing, and the corresponding log information is printed in each callback function

 

4. Get the Current Time with get_clock

  • The get_clock function can be used to obtain a clock object and then use the () method to get the current time.
  • Create a new program file, get_clock_demo.py, and fill it with the following example program:
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import rclpy
from rclpy.node import Node
from rclpy.time import Time
​
class TimeExampleNode(Node):
    def __init__(self):
        super().__init__("time_example_node")
        
        # Get the node's clock object (the system clock is used by default)
        self.clock = self.get_clock()
        
        # Get the current time (return a Time object)
        current_time = self.clock.now()
        self.get_logger().info(f"当前时间:{current_time}")
​
def main(args=None):
    rclpy.init(args=args)
    node = TimeExampleNode()
    rclpy.spin_once(node)  # Run a node once
    node.destroy_node()
    rclpy.shutdown()
​
if __name__ == "__main__":
    main()
  • Configure the corresponding setup.py configuration file and add it in console_scripts
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'get_clock_demo=learning_time.get_clock_demo:main'

image-20250905160541502

 

  • Compile feature package
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colcon build --packages-select learning_time

image-20250905155916058

  • Refresh the workspace environment and run the node
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 source ./install/setup.bash
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ros2 run learning_time get_clock_demo

image-20250905160631573

 

5. Time and Duration

  • The Time class in ROS represents a specific time point (e.g., "2023-10-01 12:00:00"), typically used to mark the moment an event occurred.
  • The Duration class represents an interval between two time points (e.g., "5 seconds"), used to calculate time differences or delays.
  • Example: Time and Duration Application
  • Create a new program file, TimeDuration_demo.py.
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import rclpy
from rclpy.time import Time
from rclpy.duration import Duration
​
def main():
    rclpy.init()
    node = rclpy.create_node("time_opt_node")
   
    # How to use the time class to create 'time points and moments'
    time1 = Time(seconds=10)
    time2 = Time(seconds=4)
    # How to use the Duration class to create a 'duration, a period of time'
    duration1 = Duration(seconds=3)
    duration2 = Duration(seconds=5)
          
    # Moments can be compared
    node.get_logger().info("time1 >= time2 ? %d" % (time1 >= time2))
    node.get_logger().info("time1 < time2 ? %d" % (time1 < time2))
    
    # Time periods and times can be mathematically operated
    t3 = time1 + duration1
    t4 = time1 - time2    
    t5 = time1 - duration1
​
    node.get_logger().info("t3 = %d" % t3.nanoseconds)
    node.get_logger().info("t4 = %d" % t4.nanoseconds)
    node.get_logger().info("t5 = %d" % t5.nanoseconds)
​
    # Time periods can be compared
    node.get_logger().info("-" * 80)
    node.get_logger().info("duration1 >= duration2 ? %d" % (duration1 >= duration2))
    node.get_logger().info("duration1 < duration2 ? %d" % (duration1 < duration2))
​
    rclpy.shutdown()
​
if __name__ == "__main__":
    main()
  • Configure the corresponding setup.py configuration file and add it in console_scripts
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'TimeDuration_demo=learning_time.TimeDuration_demo:main'

image-20250905160751388

 

  • Compile feature package
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colcon build --packages-select learning_time

image-20250905155916058

 

  • Refresh the workspace environment and run the node
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 source ./install/setup.bash
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ros2 run learning_time TimeDuration_demo

image-20250905160840013

  • This example proves that mathematical operations can be performed on time points and time periods, allowing us to flexibly query and operate on data with different timestamps.

 

 

 

ROS2 DDS
ROS2 common command tools

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