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Jetson AGX Orin📖
01.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flash new system to the onboard eMMC
  • 03.Flash new system to an NVMe SSD
  • 04.Install Jetpack environment
  • 05.Backup SSD system
  • 06.Restore factory image system
  • 07.SSD expansion
  • 08.Solid State Drive Speed Test
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
  • 10. Change screen resolution
  • 11. Virtual Desktop
  • 12. Set root user password
  • 13. Configure sudo passwordless operation
  • 14. Managing custom services
  • 15. View system version information
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
06.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.Barcode Recognition
  • 02.QR Code Recognition
  • 03.Machine Code Recognition
  • 04.Handwritten Digit Recognition
06.YOLO26 development
  • 01.YOLO Environment Setupnew
  • 02.CLI Usagenew
  • 03.Object Detectionnew
  • 04.Instance Segmentationnew
  • 05.Pose Estimationnew
  • 06.Image Classificationnew
  • 07.Oriented Bounding Boxes Object Detectionnew
  • 08.Model Conversionnew
  • 09.Dataset Annotationnew
  • 10.Model Training_Conversionnew
  • 11.Model Predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS2-Humble
01.ROS2 basic course
  • 3.ROS2 development environment
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 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
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
10.Offline AI model course
Offline large model (Text Interaction)
  • 10.AI large model offline voice assistant
  • 1.Al large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
11.Online AI model course
Online large model (Text Interaction)
  • 8.AI large model offline voice assistant
  • 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
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
12.Offline Generative AI Applications
  • 01.Children's Storytelling Session
  • 02.Cooking Assistant
  • 03.Homework tutor
  • 04.Image to animation
13.OpenClaw Advanced Development
  • 00.OpenClaw Quick Start on Jetson Platform
  • 01.OpenClaw Installation
  • 02.OpenClaw Online Platform Deployment
  • 03.OpenClaw Local Model Deployment
  • 04.OpenClaw TUI Interaction
  • 05.OpenClaw WebChat Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawClawHub Introduction (Skill Installation)
  • 10. OpenClaw Switching Models
  • 11.OpenClaw Application - File Management
  • 12.OpenClaw Application - Camera
  • 13.OpenClaw Application - Browser Control
  • 14.OpenClaw Application - Script Execution
  • 15.OpenClaw Application - AI Programming
  • 16.OpenClaw Application - Dedicated AI Assistant
  • 17.OpenClaw WhatsApp Plugin Application
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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
xxxxxxxxxx
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:

xxxxxxxxxx
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.

xxxxxxxxxx
#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

xxxxxxxxxx
colcon build --packages-select learn_launch

image-20250905174942260

2.4. Run the program

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

image-20250905175036003

2.5. Source Code Analysis

  1. Import related libraries
xxxxxxxxxx
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.

xxxxxxxxxx
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

xxxxxxxxxx
colcon build --packages-select learn_launch

image-20250905174942260

3.3. Run the program

  • Refresh the environment variables and run the launch file.
xxxxxxxxxx
ros2 launch learn_launch multi_nod
ROS2 rqt toolbox
ROS2 recording and playback tool

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