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ROSMASTER M1📖
01.Robot Configuration and Operation Guide
  • 0. Assembly video
  • 1. Servo PTZ calibration
  • 2. Quick start guide handle control
  • 3. First trial
  • 4. Enter Docker (For JETSON Nano and RPi 5)
  • 1.5 Quick Start video
02.AI Model Preparation
  • AI Model Preparation
03.AI Model Basics course
  • 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
04.AI Model Developmentnew
  • 1.Register a model service provider account
  • 2. Configuring 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
  • 9. Custom wake-up response
  • 10. Core module testing tools
05.AI Model - Text Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand + PTZ tracking
  • 4.Multimodal visual understand + visual following
  • 5.Multimodal visual understand + visual line patroling
  • 6.Multimodal visual understand + Depth Camera Distance Question Answering
  • 7.Multimodal visual understand+SLAM navigation
  • 8.Multimodal visual understand+SLAM navigation + visual line patroling
  • 9.Intention estimation + Multimodal visual understand + SLAM navigation + visual functions
06.AI Model - Voice Version
  • 1.Semantic understand and command follow
  • 2.Multimodal visual understand
  • 3.Multimodal visual understand + PTZ tracking
  • 4.Multimodal visual understand + visual following
  • 5.Multimodal visual understand + visual line patroling
  • 6.Multimodal visual understand + Depth Camera Distance Question Answering
  • 7.Multimodal visual understand+SLAM navigation
  • 8.Multimodal visual understand+SLAM navigation + visual line patroling
  • 9.Intention estimation + Multimodal visual understand + SLAM navigation + visual functions
07.Chassis Control Course
  • 1. ROS control
  • 2. Handle control
  • 3. Keyboard control
  • 4. Linear velocity calibration
  • 5. Angular velocity calibration
  • 6. URDF model
08.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.Gesture recognition
  • 11.Palm target positioning
  • 12.Fingertip trajectory recognition
09.Advanced Vision Course
  • 1.QR code
  • 2.Human Pose Estimation
  • 3.Object Detection
  • 4.AR Vision
  • 5.ROS+Opencv Basics
  • 6.Yolov8 Object Detection (ORIN)
  • 7.Deep Learning Object Tracking (ORIN)
10.HD Camera Vision Course(Standard Version)
  • 01.Color tracking
  • 02.Face Tracking
  • 03.AprilTag machine code tracking
  • 04.KCF Object Tracking
  • 05.QR code tracking
  • 06.Mediapipe gesture tracking
  • 07.Meidpipe human posture tracking
  • 08.Color Follow
  • 09.Face Following
  • 10.AprilTag machine code following
  • 11.QR code follow
  • 12.Mediapipe gesture recognition and following
  • 13.Meidpipe human posture following
  • 14.Visual tracking autonomous driving
  • 15.QR code motion control
  • 16.Gesture motion control
11.Depth Camera Vision Course(Superior Version)
  • 1.Basic use of depth camera
  • 2.Deep pseudo-color image
  • 3.Depth camera ranging
  • 4.Color block volume measurement
  • 5.Color Following
  • 6.Face Following
  • 7.QR code follow
  • 8.Machine code following
  • 9.KCF Object Following
  • 10.Mediapipe gesture following
  • 11.Mediapipe Posture Following
  • 12.Visual tracking autonomous driving
  • 13.Edge Detection
12.LiDAR Course
  • 1.Basic use of Lidar
  • 2.Lidar obstacle avoidance
  • 3.Lidar following
  • 4.Lidar patrol
  • 5.Gmapping-SLAM mapping
  • 6.Cartographer-SLAM mapping
  • 7.slam_toolbox mapping
  • 8.Navigation2 single-point navigation avoid
  • 9.Navigation2 multi-point navigation avoid
  • 10.Cartographer Repositioning navigation
  • 11.RTAB-Map mapping
  • 12.RTAB-Map navigation
  • 13.APP mapping navigation
13.Multi-Vehicle Course
  • 1.Multi-vehicle control
  • 2.Multi-vehicle navigation
  • 3.Multi-vehicle platoon
14.Autonomous Driving expand(Need purchase track map)
【Track Map】Multimodal visual understand+SLAM navigation
  • 1. Multimodal visual understand+SLAM navigation
【Track Map】Autonomous driving (RDKX5 & Orin)
  • 1. Venue placement and precautions
  • 2. Start testing
  • 3. Identify and debug
  • 4. Lane keeping
  • 5. Road sign detection
  • 6. Traffic light recognition
  • 7. Fixed point parking
  • 8. Steering decision
  • 9. Horn sounding
  • 10. Deceleration
  • 11. Speed limit
  • 12. Autonomous parking
  • 13. Comprehensive application
  • 14. Unmanned driving integrated AI large model
【Track Map】Autonomous driving (Jetson Nano)
  • 1. Venue placement and precautions
  • 2. Start testing
  • 3. Identify and debug
  • 4. Lane keeping
  • 5. Road sign detection
  • 6. Traffic light recognition
  • 7. Fixed point parking
  • 8. Steering decision
  • 9. Horn sounding
  • 10. Deceleration
  • 11. Speed limit
  • 12. Autonomous parking
  • 13. Comprehensive application
【Track Map】Road network planning
  • 1. Introduction to Road Network Planning and Navigation
  • 2. Constructing a Pose Map
  • 3. Road Network Labelling
  • 4. Road Network Planning Combined with Sand Table Map
  • 5. Path Replanning
【Track Map】Model Training
  • 1. Data Collection
  • 2. Dataset Annotation
  • 3. YOLOv11 Model Training
  • 4. Model Format Conversion
15.YOLOV11 development course (ORIN)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Dataset annotation
  • 10.Model Training_Conversion
  • 11.Model prediction
16.YOLOV11 development course(RPi&Jetson NANO)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model conversion
  • 9.Model prediction
17.YOLOV11 development course(RDK X5)
  • 1.YOLO environment construction
  • 2.CLI use
  • 3.Object detection
  • 4.Instance segmentation
  • 5.Pose estimation
  • 6.Image classification
  • 7.Oriented object detection
  • 8.Model prediction
  • 9.Model Quantization
18.Main control board 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
  • 13. Third-party board + USB drive to start the car
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
RDK X5
  • 1.Introduction to RDK X5 board
  • 2.Write system file
  • 3.Hardware interface description
  • 4.Install case and other accessories
  • 5.Network configuration
  • 6.Remote login
  • 7.Update system
  • 8.srpi-config user guide
  • 9.config. txt configuration file
  • 10.Self-startup setting
  • 11.RDK exclusive command usage
  • 12.Linux command usage
  • 13.GPIO readwrite operation
  • 14.Image classification algorithm
  • 15.Use USB camera
  • 16.Use MIPI camera
  • 17.Binocular camera depth algorithm
19.AI large model course(ORIN-Pi-RDK X5)
Offline large model (Text Interaction)
  • 0. AI Large Model Mirroring Explanation
  • 1. Deployment of large-scale AI models
  • 2.Installation of the large-scale model dialogue platform
  • 3. Meta AI Llama 3.2 model
  • 4. Alibaba Cloud Qwen3 Model
  • 5. Microsoft Phi-4 mini model
  • 6. DeepSeek DeepSeek-R1 model
  • 7. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 8. Google Gemma3 Visual Multimodal Large Model
  • 9. Llava Visual Multimodal Large Model
  • 10.MiniCPM-V Visual Multimodal Large Model
  • 11. Multimodal Visual Understanding Applications
  • 12. Multimodal Text-Based Image Application
  • 13. Multimodal Video Analysis Application
  • 14. Multimodal visual positioning applications
  • 15. Multimodal table scanning application
  • 16. 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
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
20.AI large model course(Jetson NANO)
Offline large model (Text Interaction)
  • 4.Multimodal visual understand speech interaction
  • 0.AI large model system image instructions
  • 1.AI large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta AI llama 3.2 model
  • 4.Alibaba Cloud Qwen2 model
  • 5.Alibaba Cloud Qwen3 model
  • 6.SUTD TinyLlama
  • 7.DeepSeek DeepSeek-R1 model
  • 8.Microsoft Phi-3
  • 9.Microsoft Orca Mini
  • 10.NVIDIA StarCoder2
  • 11.Google Gemma3 Visual Multimodal Large Model
  • 12.Offline Text to Speech (TTS)
  • 13.Offline Speech to Text (ASR)
Online large model (Text Interaction)
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech-to-text (ASR)
  • 2.Online Text-to-Speech (TTS)
  • 3.AI large model voice interaction
  • 5.Multimodal visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
21.Linux operating 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
22.Docker course
  • 1.Docker Introduction and Installation
  • 2.Docker basic commands
  • 3.Docker Submission and Multi-Terminal Access
  • 4.Docker Startup Script
  • 5.Docker-Vscode environment construction
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
24.Image processing 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
25.STM32 driver course
  • 0. Notes (must read)
  • 1. About expansion board
  • 2. STM32 Development environment
  • 3. Button control
  • 4. FreeRTOS application
  • 5. Serial communication
  • 6. SBUS model aircraft remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt to control PWM servo
  • 10. Control serial port servos
  • 11. Nine-axis attitude sensor to obtain data
  • 12. Control motor
  • 13. Timer captures the encoder data
  • 14. Robot kinematics analysis theory
  • 15. PID control robot movement
26.Main control communication course
  • 1. Update the expansion board firmware
  • 2. Install Rosmaster driver library
  • 3. Jupyter lab basic usage tutorial
  • 4. Buzzer whistle
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects display
  • 7. Control motor
  • 8. Control robot movement
  • 9. Control Serial Servo
Download
  • Mapping_APP(iOS_Android)
  • Robot_Image_System_File
  • Code-Firmware
  • Instruction_Manual
  • MainBoard_AI_Pure_System
  • VM_File
  • Software_Tools
  • Hardware_Info
  • 3D_Model_File
Download ZIP

Welcome to ROSMASTER M1 repository

19.ROS2 Launch startup file configuration

19. ROS2 Launch startup file configuration

19. ROS2 Launch startup file configuration

 

1. Introduction to Launch

Until now, every time we launched a ROS node, we had to open a new terminal and run a command. With so many nodes in a robotic system, doing this every time is cumbersome. Is there a way to launch all nodes at once? The answer is, of course, a launch file, a script that launches and configures multiple nodes in the ROS system.

In ROS2, launch is used to launch multiple nodes and configure program parameters. ROS2 launch files are available in XML, YAML, and Python formats. This lesson uses a Python launch file as an example. Compared to the other two formats, the Python format is more flexible:

  • Python has numerous function libraries that can be used in launch files;
  • ROS2 general and specific launch features are written in Python, allowing access to launch features that may not be exposed in XML and YAML;

The key to writing ROS2 launch files in Python is to abstract each node, file, script, etc. into an action, launching them using a unified interface.

References:

  • Launch System Design Document: ROS 2 Launch System
  • Official Launch API Documentation: Architecture of launch — launch 0.4.0 documentation
  • Preparation: Create a package to store program files
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ros2 pkg create learn_launch --build-type ament_python

2. Writing a Single Node Launch Program

2.1. Creating a Launch File

Create a launch folder under the package, then create a file called [single_node_launch.py] within the launch folder. Copy the following content into the file:

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from launch import LaunchDescription
from launch_ros.actions import Node
​
def generate_launch_description():
    node = Node(
        package='pkg_helloworld_py',
        executable='helloworld',
        output='screen'
    )
    return LaunchDescription([node])

image-20250905174633450

 

2.2 Configuring the setup.py File

The launch file is often named "LaunchName_launch.py." LaunchName is customizable, while _launch.py is considered fixed. You need to modify the setup.py file in the package to add the files in the launch path and compile to generate the executable .py file.

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#1. Import related header files
import os
from glob import glob
​
#2. In the data_files list, add the launch path and the launch.py ••file under the path
(os.path.join('share',package_name,'launch'),glob(os.path.join('launch','*launch.py')))

image-20231031114303641

 

2.3. Compile the package

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colcon build --packages-select learn_launch

image-20250905174942260

2.4. Run the program

  • Refresh the environment variables and run the launch file
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ros2 launch learn_launch single_node_launch.py

image-20250905175036003

2.5. Source Code Analysis

  1. Import related libraries
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from launch import LaunchDescription
from launch_ros.actions import Node
  1. Define a function called generate_launch_description and return a launch_description.
xxxxxxxxxx
def generate_launch_description():
    node = Node(
        package='pkg_helloworld_py',
        executable='helloworld',
    )
    return LaunchDescription([node])

We define a variable called node as the return value of a node startup. We then call the Node function with two important parameters: package and executable.

  • package: represents the package name.
  • executable: represents the executable program name.

Finally, we call the LaunchDescription function, passing in the node parameter, and execute the function.

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return LaunchDescription([node])

 

3. Writing a Launch Program for Multiple Nodes

3.1. Creating a Launch File

Create a new file called [multi_node_launch.py] and add the following content:

xxxxxxxxxx
from launch import LaunchDescription
from launch_ros.actions import Node
​
def generate_launch_description():
    publisher_node = Node(
        package='pkg_topic',
        executable='publisher_demo',
        output='screen'
    )
    subscriber_node = Node(
        package='pkg_topic',
        executable='subscriber_demo',
        output='screen'
    )
    return LaunchDescription([
        publisher_node,
        subscriber_node
    ])

3.2. Compile the package

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colcon build --packages-select learn_launch

image-20250905174942260

3.3. Run the program

  • Refresh the environment variables and run the launch file.
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ros2 launch learn_launch multi_node_launch.py

image-20250905175250026

If the terminal does not print anything, we can verify that the nodes have started successfully by checking which nodes have started. In the terminal, enter:

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ros2 node list

image-20231031120049494

3.4 Source Code Analysis

Similar to simple_node_launch.py, except for one more node.

4 Topic Remapping Example

4.1 Creating a New Launch File

Create a new file called [remap_name_launch.py] in the same directory as multi_node_launch.py and add the following content:

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from launch import LaunchDescription
from launch_ros.actions import Node
​
def generate_launch_description():
    publisher_node = Node(
        package='pkg_topic',
        executable='publisher_demo',
        output='screen',
        remappings=[("/topic_demo", "/topic_update")]
    )
    return LaunchDescription([
        publisher_node
    ])

4.2. Compile the package

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colcon build --packages-select learn_launch

image-20250905174942260

4.3. Run the program

Let's first see what topics the publisher_demo node publishes before remapping topics:

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ros2 launch learn_launch multi_node_launch.py
ros2 topic list

image-20231031121230604

The topic here is [/topic_demo]

  • Refresh the environment variables again and run the program after remapping the topic to see the changes:
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ros2 launch learn_launch remap_name_launch.py
ros2 topic list

image-20250905175613834

As shown above, the topic name has been remapped to [/topic_update]

4.4 Source Code Analysis

The following sections have been added:

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remappings=[("/topic_demo", "/topic_update")]

Here, the original /topic_demo topic is remapped to /topic_update

5. Example of Launching Another Launch File from a Nested Launch File

5.1. Creating a New Launch File

Create a new file, [include_launch.py], in the same directory as multi_node_launch.py ​​and add the following content:

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from launch import LaunchDescription
from launch_ros.actions import Node 
import os
from launch.actions import IncludeLaunchDescription
from launch.launch_description_sources import PythonLaunchDescriptionSource
from ament_index_python.packages import get_package_share_directory
​
def generate_launch_description():
    hello_launch = IncludeLaunchDescription(PythonLaunchDescriptionSource(
        [os.path.join(get_package_share_directory('learn_launch'), 'launch'),
        '/multi_node_launch.py']),
    )
    return LaunchDescription([
        hello_launch
    ])

 

5.2. Compiling the Function Package

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colcon build --packages-select learn_launch

image-20250905174942260

5.3. Running the Program

  • Refresh the environment variables and run the launch file
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ros2 launch learn_launch include_launch.py

image-20250905180404738

5.4. Source Code Analysis

  • Nested launch files require the use of the launch system's IncludeLaunchDescription and PythonLaunchDescriptionSource classes.
  • os.path.join(get_package_share_directory('learn_launch')): Gets the package location, where 'learn_launch' is the package name;
  • launch): Specifies the folder where the launch file is stored within the package;
  • /multi_node_launch.py: Specifies the /multi_node_launch.py ​​file within the launch folder of the package.

6. Comprehensive Launch File Example

This example primarily demonstrates how to write a complex launch file; the program's functionality is ignored.

6.1. Creating a New Launch File

Create a new file, [complex_launch.py], in the same directory as [multi_node_launch.py] and add the following content:

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import os
from ament_index_python import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.actions import IncludeLaunchDescription
from launch.actions import GroupAction
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration
from launch.substitutions import TextSubstitution
from launch_ros.actions import Node
from launch_ros.actions import PushRosNamespace
​
​
def generate_launch_description():
​
    # args that can be set from the command line or a default will be used
    background_r_launch_arg = DeclareLaunchArgument(
        "background_r", default_value=TextSubstitution(text="0")
    )
    background_g_launch_arg = DeclareLaunchArgument(
        "background_g", default_value=TextSubstitution(text="255")
    )
    background_b_launch_arg = DeclareLaunchArgument(
        "background_b", default_value=TextSubstitution(text="0")
    )
    chatter_ns_launch_arg = DeclareLaunchArgument(
        "chatter_ns", default_value=TextSubstitution(text="my/chatter/ns")
    )
​
    # include another launch file
    launch_include = IncludeLaunchDescription(
        PythonLaunchDescriptionSource(
            os.path.join(
                get_package_share_directory('demo_nodes_cpp'),
                'launch/topics/talker_listener.launch.py'))
    )
    # include another launch file in the chatter_ns namespace
    launch_include_with_namespace = GroupAction(
        actions=[
            # push-ros-namespace to set namespace of included nodes
            PushRosNamespace(LaunchConfiguration('chatter_ns')),
            IncludeLaunchDescription(
                PythonLaunchDescriptionSource(
                    os.path.join(
                        get_package_share_directory('demo_nodes_cpp'),
                        'launch/topics/talker_listener.launch.py'))
            ),
        ]
    )
​
    # start a turtlesim_node in the turtlesim1 namespace
    turtlesim_node = Node(
            package='turtlesim',
            namespace='turtlesim1',
            executable='turtlesim_node',
            name='sim'
        )
​
    # start another turtlesim_node in the turtlesim2 namespace
    # and use args to set parameters
    turtlesim_node_with_parameters = Node(
            package='turtlesim',
            namespace='turtlesim2',
            executable='turtlesim_node',
            name='sim',
            parameters=[{
                "background_r": LaunchConfiguration('background_r'),
                "background_g": LaunchConfiguration('background_g'),
                "background_b": LaunchConfiguration('background_b'),
            }]
        )
​
    # perform remap so both turtles listen to the same command topic
    forward_turtlesim_commands_to_second_turtlesim_node = Node(
            package='turtlesim',
            executable='mimic',
            name='mimic',
            remappings=[
                ('/input/pose', '/turtlesim1/turtle1/pose'),
                ('/output/cmd_vel', '/turtlesim2/turtle1/cmd_vel'),
            ]
        )
​
    return LaunchDescription([
        background_r_launch_arg,
        background_g_launch_arg,
        background_b_launch_arg,
        chatter_ns_launch_arg,
        launch_include,
        launch_include_with_namespace,
        turtlesim_node,
        turtlesim_node_with_parameters,
        forward_turtlesim_commands_to_second_turtlesim_node,
    ])

6.2. Compile the Workspace

xxxxxxxxxx
colcon build --packages-select learn_launch

image-20250905174942260

6.3. Run the Program

  • Refresh the environment variables in the terminal and run the launch file.
xxxxxxxxxx
ros2 launch learn_launch complex_launch.py

Two turtles will appear on the host machine's VNC.

image-20250905181104164

  • Start the keyboard control node and add the namespace (because we added the namespace when starting the node in the launch file).
xxxxxxxxxx
ros2 run turtlesim turtle_teleop_key --ros-args -r __ns:=/turtlesim1
  • Use the up, down, left, and right keys to control the movement of turtle 1. Turtle 2 will completely imitate the behavior of turtle 1.

image-20250902150215135

6.4. Program Description

The program mainly starts:

  1. The talker_listener node in demo_nodes_cpp
  2. The namespaced talker_listener node
  3. Turtle 1, which has been namespaced as turtlesim1
  4. Turtle 2, which has been namespaced as turtlesim2
  5. Perform remapping so that both turtles can hear the same command topic

7. XML Implementation

7.1. Creating a Launch File

Create a file called [complex_launch.xml] in the same directory as complex_launch.py ​​and add the following content:

xxxxxxxxxx
<launch>
​
    <!-- args that can be set from the command line or a default will be used -->
    <arg name="background_r" default="0"/>
    <arg name="background_g" default="255"/>
    <arg name="background_b" default="0"/>
    <arg name="chatter_ns" default="my/chatter/ns"/>
  
    <!-- include another launch file -->
    <include file="$(find-pkg-share demo_nodes_cpp)/launch/topics/talker_listener.launch.py"/>
    <!-- include another launch file in the chatter_ns namespace-->
    <group>
      <!-- push-ros-namespace to set namespace of included nodes -->
      <push-ros-namespace namespace="$(var chatter_ns)"/>
      <include file="$(find-pkg-share demo_nodes_cpp)/launch/topics/talker_listener.launch.py"/>
    </group>
  
    <!-- start a turtlesim_node in the turtlesim1 namespace -->
    <node pkg="turtlesim" exec="turtlesim_node" name="sim" namespace="turtlesim1"/>
    <!-- start another turtlesim_node in the turtlesim2 namespace
        and use args to set parameters -->
    <node pkg="turtlesim" exec="turtlesim_node" name="sim" namespace="turtlesim2">
      <param name="background_r" value="$(var background_r)"/>
      <param name="background_g" value="$(var background_g)"/>
      <param name="background_b" value="$(var background_b)"/>
    </node>
    <!-- perform remap so both turtles listen to the same command topic -->
    <node pkg="turtlesim" exec="mimic" name="mimic">
      <remap from="/input/pose" to="/turtlesim1/turtle1/pose"/>
      <remap from="/output/cmd_vel" to="/turtlesim2/turtle1/cmd_vel"/>
    </node>
</launch> 

7.2. setup.py File Configuration

  • You need to configure the compilation file. During compilation, copy our .xml launch file to the install directory so that the ROS system can find it.

image-20250905182956976

7.3. Compile the Function Package

xxxxxxxxxx
colcon build --packages-select learn_launch

image-20250905174942260

7.4. Run the Program

Enter the terminal:

xxxxxxxxxx
ros2 launch learn_launch complex_launch.xml
  • As expected, two turtles will appear, and the terminal will print log information.

image-20250905181104164

  • Start the keyboard control node and add a namespace.
xxxxxxxxxx
ros2 run turtlesim turtle_teleop_key --ros-args -r __ns:=/turtlesim1

Use keyboard control to start Turtle 1. Turtle 2 will completely mimic Turtle 1's behavior.

8. YAML Implementation

8.1. Create a New Launch File

Create a new file, [complex_launch.yaml], in the same directory as complex_launch.py ​​and add the following content:

xxxxxxxxxx
launch:
​
# args that can be set from the command line or a default will be used
- arg:
    name: "background_r"
    default: "0"
- arg:
    name: "background_g"
    default: "255"
- arg:
    name: "background_b"
    default: "0"
- arg:
    name: "chatter_ns"
    default: "my/chatter/ns"
​
​
# include another launch file
- include:
    file: "$(find-pkg-share demo_nodes_cpp)/launch/topics/talker_listener.launch.py"
​
# include another launch file in the chatter_ns namespace
- group:
    - push-ros-namespace:
        namespace: "$(var chatter_ns)"
    - include:
        file: "$(find-pkg-share demo_nodes_cpp)/launch/topics/talker_listener.launch.py"
​
# start a turtlesim_node in the turtlesim1 namespace
- node:
    pkg: "turtlesim"
    exec: "turtlesim_node"
    name: "sim"
    namespace: "turtlesim1"
​
# start another turtlesim_node in the turtlesim2 namespace and use args to set parameters
- node:
    pkg: "turtlesim"
    exec: "turtlesim_node"
    name: "sim"
    namespace: "turtlesim2"
    param:
    -
      name: "background_r"
      value: "$(var background_r)"
    -
      name: "background_g"
      value: "$(var background_g)"
    -
      name: "background_b"
      value: "$(var background_b)"
​
# perform remap so both turtles listen to the same command topic
- node:
    pkg: "turtlesim"
    exec: "mimic"
    name: "mimic"
    remap:
    -
        from: "/input/pose"
        to: "/turtlesim1/turtle1/pose"
    -
        from: "/output/cmd_vel"
        to: "/turtlesim2/turtle1/cmd_vel" 

8.2. Configuration

  • You need to configure the compilation file. During compilation, copy the .yaml launch file to the install directory so that the ROS system can find it.

image-20250905183832452

8.3. Compile the package

ROS2 rqt toolbox
ROS2 recording and playback tool

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