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DOFBOT-Pi📖
00.Must Read Before Use!!
  • 00.Must Read Before Use!!
01.First Trial
  • 1.First Trial
  • 2.Analysis of common problems
  • 3.Calibration offset
  • 4.Precautions for DIY Robotic Arm
  • 5.Jetson Nano Version – Must Read
  • 6.Robotic arm gripping anomaly solutions
02.Assemble Course
  • 1.Installation video
  • 2.Calibrate the Robotic Arm
  • 3.Install the map
03.Remote control course
  • 1.Mobile APP control
  • 2.USB handle control
  • 3.PC web page programming
04.Preparation
  • 1.Manage APP control service
  • 2.Manage JupyterLab service
  • 3.Manage OLED service
  • 4.Modifying ROS_DOMAIN_ID for Multi-Machine
  • 5.Jetpack old system upgraded to SUPER system
05.Basic control course
  • 1.Control RGB light
  • 2.Control buzzer
  • 3.Control single servo
  • 4.Read servo angle
  • 5.Control all servo
  • 6.DOFBOT up down left right
  • 7.DOFBOT dancing
  • 8.DOFBOT memory action
  • 9.DOFBOT grab block
  • 10.Porter
  • 11.Stacked Arhat
  • 12.Synchronous action
  • 13.USB camera display
06.AI vision basic course
  • 1.Gesture Recognition
  • 2.Color threshold adjustment&Color calibration
  • 3.Color recognition
  • 4.Face recognition
  • 5.YOLOv11 garbage recognition
  • 6.YOLOv11 object recognition
07.2D Visual Tracking Course
  • 1.PID algorithm basics
  • 2.Color block positioning
  • 3.Color block tracking
  • 4.Face positioning
  • 5.Face tracking
  • 6.Gesture recognition fixed action
08.2D Visual Grasping Course
  • 1.Gesture recognition stack blocks
  • 2.Color recognition grab blocks
  • 3.Catch game
  • 4.Color sorting and stacking
  • 5.Garbage sorting
  • 6.Color interaction
09.Mediapipe course
  • 1.Hand detection
  • 02.Posture detection
  • 3.Overall detection
  • 4.Facial Detection
  • 5.Face Detection
  • 6.Face effects
  • 7.3D object recognition
  • 8.Paintbrush
  • 9.Finger control
  • 10.Gesture Recognition
  • 11.Gesture control robotic arm action group
  • 12.Arm posture control robotic arm
  • 13.Palm Targeting
  • 14.Robotic arm tracks the palm
  • 15.Fingertip trajectory recognition
  • 16.Fingertip trajectory control robotic arm
  • 17.Gesture Grasp and Release Objects
  • 18.Finger Control Robot Arm
10.AI Large Model Voice Module (Must Read)new
  • 1. Introduction to AI Large Model Voice Module
  • 2. Flash the firmware (Must read)
  • 3. Modify the wake-up word and command word
11.2D Voice Control Coursenew
  • 01.Simple Voice Control
  • 02.Smart Voice Control
  • 03.Voice Action Control
  • 04.Memory Learning Mode
  • 05.Color Recognition with Voice Broadcast
  • 06.Color Recognition and Grasping with Voice Broadcast
  • 07.Color Recognition and Sorting with Voice Broadcast
  • 08.Voice Recognition Target Tracking
  • 09.Voice Control Stacking
  • 10.Voice Control Color Sorting and Stacking
  • 11.Garbage Recognition with Voice Broadcast
  • 12.Garbage Sorting with Voice Broadcast
12.Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
13.3D Sorting and Grasping Coursenew
  • 1.AprilTag ID sorting
  • 2.MediaPipe Gesture-AprilTag ID sorting
  • 3.MediaPipe Gesture-AprilTag distance sorting
  • 4.Block color sorting
  • 5.Block different shape sorting
  • 6.Yolov11 garbage sorting
14.3D Tracking Coursenew
  • 1.AprilTag Tracking
  • 2.Color Block Tracking
  • 3.KCF Object Tracking
15.3D Voice Control for Sorting & Tracking Coursenew
  • 1.Voice Control AprilTag ID Sorting
  • 2.Voice Control Color Block Sorting
  • 3.Voice Control Yolov11 Garbage Recognition Sorting
  • 4.Voice Control AprilTag Tracking
  • 5.Voice Control Color Block Tracking
  • 6.Voice Control Object Tracking
16.AI Model Basicsnew
  • 1.AI large model types and principles
  • 2.RAG retrieval enhancement and model training samples
  • 3.Embodied intelligent robot system architecture
  • 4.Embodied intelligent functions core source code
17.AI Model Developmentnew
  • 1.Register a model service provider account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. Deploy the RAG knowledge base
  • 6. AI Large Model Development - chatbot
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
18.AI Model – Text Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
19.AI Model – Voice Versionnew
  • 1. Semantic understand instruction following
  • 2. Multimodal visual understand
  • 3. Text Large Model + robotic arm control
  • 4. Multimodal Large Model+video understanding
  • 5. Multimodal Large Model+robotic arm gripping
  • 6. Multimodal Large Model+robotic arm tracking
  • 7. Multimodal Large Model+robotic arm sorting
  • 8. Multimodal Large Model+robotic arm handling
  • 9. Multimodal Large Model+Color Blocks Back In Place
  • 10. Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11. Text Large Model + YOLOv11 + Intelligent Garbage Classification
20.AI Model – Fun Applicationsnew
  • 1.AI Butler-Intent Inference
  • 2.AI Encyclopedia-Arithmetic Master
  • 3.AI Encyclopedia-You asked I Pointed
  • 4.AI Encyclopedia-Nutritional Analysis
  • 5.AI Encyclopedia-Problem Solving Expert
  • 6.AI Encyclopedia-English Spelling
  • 7.AI Butler-Garbage Sorting
  • 8.AI Top Chef
21.MoveIt2 Course
  • 1.MoveIt configuration
  • 2. MoveIt Usage
  • 3. MoveIt Drive Real Robot
  • 4. MoveIt Random Movement
  • 5. MoveIt Forward Kinematics Design
  • 6. MoveIt Inverse Kinematics Design
  • 7. MoveIt Cartesian Path
  • 8. MoveIt Scene Design
  • 9. MoveIt Trajectory Planning
  • 10. MoveIt Collision Detection
22.YOLOv11 Development Course
  • 1. YOLO Environment Setup
  • 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. Model Prediction
23.Linux Operating System
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Capacity expansion and resource allocation
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
24.GPIO Hardware Control Course
  • 1.GPIO description
  • 2.GPIO library installation
  • 3.GPIO reading
  • 4.GPIO output
  • 5.Serial communication
  • 6.I2C communication
25.OpenCV course
  • 0. Must Read!
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6.Image zoom
  • 7.Picture cut
  • 8.Picture pan
  • 9.Picture mirroring
  • 10.Affine transformation
  • 11.Picture rotation
  • 12.perspective transformation
  • 13.Grayscale processing
  • 14.Binary image
  • 15.Edge green detection
  • 16.Line segment drawing
  • 17.Rectangular circle drawing
  • 18.Text and picture drawing
26.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
27. Advanced Visuals Course
  • 0. Must Read! Porting and Compilation
  • 1. QR Code
  • 2. Human Pose Estimation
  • 3. Object Detection
  • 4. AR Vision
  • 5. ROS + OpenCV Basics
28.Jupyter Lab Usage Course
  • 0.Read the document before operation
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Lab
  • 3.Jupyter Lab plug-in installation
  • 4.Basic use of Jupyter Widgets
29.Docker Course
  • 1.Docker overview and docker installation
  • 2.Common commands for docker image containers
  • 3.Docker images deeply understand and publish images
  • 4.Docker hardware interaction and data processing
  • 5.Start the Dofbot container
30.Python Programming Basics
  • 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
Download
  • APP(iOS/Android)
  • Code
  • System Image
  • VM File
  • PC software
  • Instruction manual
  • More Annex
  • Tools
Download ZIP
FAQ
  • 1、Precautions for DIY Robotic Arm
  • 2、Where i can to check Raspberry Pi 4 Version tutorials?
  • 3、When you control the No.1 servo, all servos will turn.
  • 4、Dofbot start up process video
  • 5、Gesture recognition cannot work normally
  • 6、How to write image file into SD card
  • 7、How open VNC service on DOFBOT system
  • 8、Where can I get the URDF file?

Welcome to DOFBOT-Pi 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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