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ROSMASTER M3 PRO📖
0.Configuration and Operation Guide
  • 1. Quick start handle to control the car
  • 2. Log in to the car and view the code
  • 3. Robotic Arm Calibration
  • 4. Enter the Docker (Jetson-Nano and Raspberry Pi 5 users, see here)
  • 5. Assembly video
  • 6. Modify the firmware parameters of the car control board
  • 7. Frequently Asked Questions and Precautions
  • 8. Precautions for DIY Robotic Arm
1.Chassis Control Course
  • 1.ROS control
  • 2.Handle control
  • 3.Keyboard control
  • 4.Angular velocity calibration
  • 5.Line velocity calibration
  • 6.Line patrol automatic driving
  • 7.Patrol
2.Lidar Course
  • 1.Lidar introduction and use
  • 2.Dual Lidar fusion
  • 3.Lidar obstacle avoidance
  • 4.Lidar tracking
  • 5.Lidar guard
  • 6.Gmapping-SLAM mapping
  • 7.Cartographer-SLAM mapping
  • 8.slam_toolbox mapping
  • 9.Navigation2 single-point navigation avoid
  • 10.Navigation2 multi-point navigation avoid
  • 11.Repositioning navigation
  • 12.RTAB-Map mapping
  • 13.RTAB-Map navigation
  • 14.APP mapping navigation
  • 15.Machine code handling
  • 16.Color block transport
  • 17.Navigation via Pose-Constrained Visual Relocalizationnew
3.Depth Camera Course
  • 1.Dabai_DCW2 camera introduction
  • 2.Depth pseudo-color image
  • 3.Depth camera distance measurement
  • 4.Wood block volume measurement
  • 5.Mediapipe gesture recognition
  • 6.Edge detection
  • 7.YOLOv8 object detection
  • 8.Deep learning object tracking
  • 9. Transformer Model Tracking Algorithmnew
4.Robotic Arm and 3D Space Gripping Course
  • 1.Robotic arm solution
  • 2.Robotic arm chassis linkage control
  • 3.Machine code ID sorting
  • 4.Sorting height abnormality machine code
  • 5.Tracking and gripping machine code
  • 6.Color block color sorting
  • 7.Sorting height abnormality color block
  • 8.Tracking and gripping color block
  • 9.Wood block shape sorting
  • 10.KCF tracking and gripping objects
  • 11.Medipipe gesture ID sorting machine code
  • 12.Mediapipe gesture height sorting machine code
  • 13.Desktop tracking and gripping machine code
  • 14.3D tracking machine code
  • 15.Line patrol and obstacle removal
5.Road network planning and navigationnew
  • 1. Introduction To Road Network Planning
  • 2. Build A Pose Map
  • 3. Road Network Annotation
  • 4. Planning And Navigation Between Waypoints
  • 5. Road Network Navigation In Any Pose
  • 6. Path Re-planning
6.OpenClaw Basic Coursenew
  • 1. Introduction to OpenClaw
  • 2. OpenClaw Core Concepts
  • 3. OpenClaw API-KEY Configuration
  • 4. OpenClaw Switch Models
  • 5. OpenClaw System Prompt Words
  • 6. OpenClaw Common CLI Commands
7.OpenClaw Interaction Methodnew
  • 1. OpenClaw Webchat Interaction
  • 2. OpenClaw Tui Interaction
  • 3. OpenClaw Voice Dialogue Interaction
  • 4. OpenClaw WhatsApp Interaction
8.OpenClaw Developmentnew
  • 1. OpenClaw connects to the MCP interface of the robot
  • 2. Robot CLI Command Tool
  • 3. Robot control plugin tool
  • 4. Reply bridge plugin tool
  • 5. OpenClaw multimodal vision
  • 6. OpenClaw chassis control
  • 7. OpenClaw robotic arm control
  • 8. OpenClaw skills development
  • 9. OpenClaw timed tasks
  • 10. OpenClaw multi-agent intelligent agent
  • 11. OpenClaw active interaction
  • 12. OpenClaw vector memory
9.OpenClaw Embodied intelligent applicationnew
  • 1. OpenClaw target tracking
  • 2. OpenClaw robotic arm tracking and grasping
  • 3. OpenClaw robotic arm fixed-point placement
  • 4. OpenClaw garbage sorting
  • 5. OpenClaw slam mapping and navigation
  • 6. OpenClaw navigation transfer
  • 7. OpenClaw machine code clip
  • 8. OpenClaw color line patrol
  • 9. OpenClaw visual distance measurement
10.OpenClaw scenario sandboxnew
  • 1. Map description and prop placement
  • 2. Build sand table map
  • 3. Visual relocation markers
  • 4. Map mapping markers
  • 5. OpenClaw scenario sandbox comprehensive case
11.AI Model Basics
  • 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
12.AI Model Development
  • 01.Register a model service provider account
  • 02. Configuring API-KEY
  • 03. Introduction to Dify
  • 04. Basic Dify Features
  • 05. AI Large Model Development - chatbot
  • 06. Deploy the RAG knowledge base
  • 07. RAG knowledge base + chatbot
  • 08. AI agent workflow
  • 09. Custom wake-up response
  • 10. Core module testing tools
13.AI Large Model (Text Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
14.AI Large Model(Voice Version)
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand+robotic arm grab
  • 4.Multimodal visual understand+SLAM navigation
  • 5.Robotic arm grab+Multimodal visual understand+SLAM navigation
  • 6.Intention estimation
15.Mediapipe Visual Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall detection
  • 4.Facial Landmark Detection
  • 5.Face detection
  • 6.Face special effects
  • 7.3D object recognition
  • 8.Brush
  • 9.Finger control
  • 10.Palm target positioning
  • 11.Fingertip trajectory recognition
  • 12.Fingertip gesture control robotic arm
  • 13.Gesture grabbing and releasing objects
  • 14.Finger control robotic arm
  • 15.Medipipe gesture control robotic arm action group
16.MoveIt2 Simulation Course
  • 1.MovelT2 configuration
  • 2.MovelT2 simulation-reality linkage
  • 3.Random movement
  • 4.Forward kinematics design
  • 5.Inverse kinematics design
  • 6.Cartesian path
  • 7.Trajectory planning
  • 8.Collision detection
  • 9.Scene design
17.Multi-vehicle Course
  • 1.Multi-vehicle chassis control
  • 2.Multi-vehicle robotic arm control
  • 3.Multi-vehicle navigation
  • 4.Multi-vehicle formation
18.Control Board Course
  • 1.Development environment setup
  • 2.Control board STM32 basic routine
  • 3.Control board micro_ros basic routine
19.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、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
20.Image Processing Basics Course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 6. Image zoom
  • 7. Image cutting
  • 8. Image translation
  • 9. Image Mirroring
  • 10. Affine Transformation
  • 11. Image Rotation
  • 12. Perspective Transformation
  • 13. Grayscale processing
  • 14. Image Binarization
  • 15. Edge Detection
  • 16. Line drawing
  • 17. Rectangle and circle drawing
  • 18. Drawing text and pictures
21.Main Control Course
Jetson Nano B01
  • 1. Introduction to the Jetson Nano B01 SUB Board
  • 2. Burn the system image
  • 3. Re-read the memory card or USB drive that has been burned
  • 4. Startup system
  • 5. Jetson Nano B01 System and Desktop Introduction
  • 6. Memory card or USB flash drive expansion
  • 7. Network Configuration
  • 8. SSH remote login & file transfer
  • 9. VNC Remote Login
  • 10. Jetson Nano B01 system backup
  • 11. Jetson Nano Swap space increased
  • 12. Installation and use of Jtop
Jetson Orin Nano_NX
  • 1.Jetson Orin board introduction
  • 2.Restore the factory image system
  • 3.Backup SSD system
  • 4.SSD expansion
  • 5.Network configuration
  • 6.SSH remote login
  • 7.VNC remote control
  • 8.Remote file transfer
  • 9.Jtop tool
  • 10.Exchange space expansion
Raspberry Pi
  • 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. Update and upgrade operating system
  • 6. Introduction to raspi-config tool
  • 7. config.txt file description
  • 8. Network Configuration
  • 9. remote access
  • 10. Transfer files remotely
  • 11. Set display resolution and rotation
  • 12. Set screen to sleep
  • 13. Play audio and video
  • 14. Using USB camera
  • 15. Using MIPI camera
  • 16. Get real-time temperature of Raspberry Pi
22.Linux System Course
  • 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.Static IP and hotspot mode
  • 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
23.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
Download
  • Mapping_APP(iOS_Android)
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • VM_File
  • Software_Tools
  • Firmware_DriverFile
  • 3D_Model_File
  • ROS2_Basic_VM
  • Other_Annex
Download ZIP

Welcome to ROSMASTER M3 PRO 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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