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Jetson ORIN NANO SUPER📖
01.Get started
  • 01. Learning route
  • 02. Get started quickly
  • 03. System Update Notesnew
  • 04. Disable automatic updates and troubleshooting issues
  • 05. Notes on Jetson Orin Nano Official Kitnew
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Write Jetson original system
  • 03.Installing Jetson environment
  • 04.Backup SSD system
  • 05.Restore initial image system
  • 06.Write SUPER original system(optional)
  • 07.Write SUPER boot (optional)
  • 08. SSD expansion
  • 09.Jetpack old system upgraded to Yahboom SUPER factory system
  • 10.Write the system onto your own solid-state drive
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
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.Vision basic course
  • 01.CSI camera preview
  • 02.USB camera preview
  • 03.Use Jupyter Lab
  • 04.Use JetCam
06.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
  • 17.Preview USB camera image
  • 18.Preview CSI camera image
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
  • QR code recognition
06.YOLO26 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detectionnew
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented 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.ROS1-Melodic
01.ROS1 basic course
  • 01. Introduction to ROS
  • 02. Project file structure
  • 03. Common commands and tools
  • 04. Publisher
  • 05. Subscribers
  • 06. Customize topic messages and use
  • 07. Client
  • 08. Server
  • 09. Customize service messages and usage
  • 10. TF release and monitoring
02.ROS1 vision course
  • 00.Must read before running
  • 01.Camera preview
  • 02.QR code
  • 03.Human posture estimation
  • 04.Target detection
  • 05.AR vision
  • 06.Opencv application
  • 07.ROS+Opencv basics
  • 08.Mediapipe development
10.ROS2-Humble
01.ROS2 basic course
  • 01.Introduction to ROS2
  • 02.ROS2 install Humble
  • 03.ROS2 development environment
  • 04.ROS2 workspace
  • 05.ROS2 function package
  • 06.ROS2 node
  • 07.ROS2 topic communication
  • 08.ROS2 service communication
  • 09.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 preview (CSI)
  • 03.Camera calibration (USB)
  • 04.Camera calibration (CSI)
  • 05.AR vision (USB)
  • 06.AR vision (CSI)
11.Offline AI model coursenew
Offline large model (Text Interaction)
  • 1.AI 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
  • 10.AI large model offline voice assistant
12.Online AI model coursenew
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
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
13.NVIDIA lsaac ROS coursenew
  • 1.Build lsaac ROS environment
  • 2.Deep segmentation
  • 3.DNN stereoscopic depth
  • 4.Free space segmentation
  • 5.Image distortion processing
  • 6.Image segmentation
  • 7.3D scene reconstruction and mapping
  • 8.Object detection
  • 9.3D attitude estimation
  • 10.Visual SLAM
14.OpenClaw Deployment and Basic Usagenew
  • 01.OpenClaw Quick Start on Jetson Platform
  • 02.OpenClaw Deployment
  • 03.OpenClaw WhatsApp Plugin Application
  • 04.OpenClaw WebChat Interaction
  • 05.OpenClaw TUI Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawHub Introduction (Skill Installation)
  • 10.OpenClaw Application - File Management
  • 11.OpenClaw Application - Camera
  • 12.OpenClaw Application - Browser Control
  • 13.OpenClaw Application - Script Execution
  • 14.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 15.OpenClaw Application - Dedicated AI Assistant
  • 16.OpenClaw Web Search Capability Configuration
15.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. Three Dialogue 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
  • System_File
  • VM_File
  • 3D_Model_File
  • DataSheet_File
  • Software_Tools
  • Code
  • Instruction_Manual
  • ROS2_Basic_VM
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11.ROS2 parameter service case

11. ROS2 parameter service case

1. Introduction to parameters

Similar to global variables in C++ programming, they facilitate sharing data across multiple programs. Parameters are global dictionaries in the ROS robot system, allowing data to be shared across multiple nodes.

In the ROS system, parameters exist in the form of a global dictionary. What is a dictionary? Just like a real dictionary, they consist of a name and a value, also known as a key and a value. Alternatively, we can think of them as being like parameters in programming: there's a parameter name, followed by an equals sign, and then the parameter value. To use it, you simply access the parameter name.

Parameters have a rich set of features. For example, if a node shares a parameter, other nodes can access it. If a node modifies a parameter, other nodes can immediately be notified and obtain the latest value.

2. Parameters in the Little Turtle Example

In the Little Turtle example, the emulator provides a number of parameters. Let's use this example to familiarize ourselves with the meaning of parameters and command line usage.

Since a GUI is displayed here, the following examples are demonstrated in the virtual machine that comes with the tutorial for ease of operation.

  1. Launch two terminals in the virtual machine, running the turtle simulator and the keyboard control node respectively:
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ros2 run turtlesim turtlesim_node
ros2 run turtlesim turtle_teleop_key

image-20231030182608893

  1. Launch a terminal in the virtual machine and use the following command to view the parameter list:
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ros2 param list

image-20231030182640039

  1. Querying and Modifying Parameters

To query or modify the value of a parameter, follow the param command with a get or set subcommand:

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ros2 param describe turtlesim background_b # View the description of a parameter
ros2 param get turtlesim background_b # Query the value of a parameter
ros2 param set turtlesim background_b 10 # Modify the value of a parameter
  1. Saving and Loading Parameter Files

Querying/modifying parameters one by one is too cumbersome. Why not try using a parameter file? Parameter files in ROS use the YAML format. You can follow the param command with the dump subcommand to save all the parameters for a node to a file, or use the load command to load all the contents of a parameter file at once:

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ros2 param dump turtlesim >> turtlesim.yaml # Save the parameters of a node to a parameter file
ros2 param load turtlesim turtlesim.yaml # Load all parameters from a file at once

3. Parameter Examples

3.1. Creating a New Function Package

  • Create a new function package in the src directory of the workspace
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ros2 pkg create pkg_param --build-type ament_python --dependencies rclpy --node-name param_demo

After executing the above command, the pkg_param package will be created, a param_demo node will be created, and the relevant configuration files will be configured.

image-20231030185328553

3.2 Code Implementation

Next, edit [param_demo.py] to implement the publisher functionality and add the following code:

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import rclpy                                     # ROS2 Python interface library
from rclpy.node import Node                    # ROS2 Node Class
​
class ParameterNode(Node):
    def __init__(self, name):
        super().__init__(name)                                    # ROS2 node parent class initialization
        self.timer = self.create_timer(2, self.timer_callback)    # Create a timer (a period in seconds, a callback function that is executed at a fixed time)
        self.declare_parameter('robot_name', 'muto')              # Create a parameter and set its default value
​
    def timer_callback(self):                                      # Create a callback function that is executed periodically by the timer
        robot_name_param = self.get_parameter('robot_name').get_parameter_value().string_value   # Read parameter values from the ROS2 system
        self.get_logger().info('Hello %s!' % robot_name_param)     # Output log information and print the parameter values read
​
def main(args=None):                                 # ROS2 node main entry main function
    rclpy.init(args=args)                            # ROS2 Python interface initialization
    node = ParameterNode("param_declare")            # Create a ROS2 node object and initialize it
    rclpy.spin(node)                                 # Loop waiting for ROS2 to exit
    node.destroy_node()                              # Destroy node object
    rclpy.shutdown()                                 # Close the ROS2 Python interface

 

3.3. Compile the package

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colcon build --packages-select pkg_param

image-20250905150404041

3.4. Run the program

  • Refresh the environment variables first, then run the node.
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ros2 run pkg_param param_demo

image-20250905150438440

Open another terminal and set robot_name to robot:

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ros2 param set param_declare robot_name Robot

You can see the log information being printed in a loop in the terminal. "muto" represents the default parameter value for "robot_name." Changing this parameter via the command line will also change the value in the terminal.

image-20231030190533095

ROS2 custom interface message
ROS2 meta-function package

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