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RDK S100(P) Board📖
0. RDK S100(P) Developer Kit
  • 1. RDK S100(P) Official Tutorial
1. Introduction to RDK S100(P) Development Board
  • 1.1 Introduction to the RDK S100(P) Development Board
  • 1.2 Install Operating System
  • 1.3 Interface Descriptions
  • 1.4 Accessory installation
  • 1.5 Camera Expansion Board
  • 1.6 MCU Port Expansion Board
  • 1.7 Precautions for using 15.6-inch screens
2. Basic Settings Course
  • 2.1 Network Configuration
  • 2.2 Remote Login
  • 2.3 System Update
  • 2.4 Boot Auto-Start Configuration
  • 2.5 RDK Exclusive Command Usage
  • 2.6 Linux Command Usage
  • 2.7 Example of GPIO read and write operations
  • 2.8 Using USB Camera
  • 2.9 Using MIPI Camera
  • 2.10 SSD mounting
  • 2.11 Binocular camera depth algorithm
3. TogetheROS.Bot Vision Applications
  • 1. About TogetheROS. Bot
  • 2. System Installation
  • 3. Object Detection(YOLO)
  • 4. Image Classification(mobilenetv2)
  • 5. Image Segmentation(mobilenet_unet)
  • 6. Perception Algorithm(BEV)
  • 7. Human Detection and Tracking (Ultralytics YOLO Pose)
4.Offline AI model course
Offline large model (Text Interaction)
  • 1. AI large model environment deployment
  • 2. Install large 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. Lava Visual Multimodal Large Model
  • 10. MiniCPM-V Visual Multimodal Large Model
  • 11. Multimodal visual understand application
  • 12. Multimodal text to image application
  • 13. Multimodal visual localization application
  • 14. Multimodal table scanning application
  • 15. Multimodal Autonomous Agent Applications
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 voice interaction
  • 5. Multimodal Text-to-Image application
  • 6. Multimodal video analytics application
  • 7. Multimodal visual localization application
  • 8. Multimodal table scanning application
  • 9. Multimodal autonomous agent application
  • 10. AI Large Model Offline Voice Assistant
5.Online AI model course
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 agent 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.Multimodal visual localization application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
  • 8.AI large model online voice assistant
6.YOLO26 tutorials
  • 01.YOLO26 environment
  • 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
7.OpenClaw Advanced Development
  • 1.OpenClaw Deployment
  • 2.OpenClaw WhatsApp Plugin Application
  • 3.OpenClaw WebChat Interaction
  • 4.OpenClaw TUI Interaction
  • 5.OpenClaw Tools Introduction
  • 6.OpenClawGate Gateway User Manual
  • 7.OpenClaw Features Overview
  • 8.OpenClawClawHub Introduction (Skill Installation)
  • 9.OpenClaw Application - File Management
  • 10.OpenClaw Application - Camera
  • 11.OpenClaw Application - Browser Control
  • 12.OpenClaw Application - Script Execution
  • 13.OpenClaw Application - AI Programming (Peripheral & GPIO Control)
  • 14.OpenClaw Application - Dedicated AI Assistant
  • 15.OpenClaw Web Search Capability Configuration
8.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
  • Annex_File
  • Code
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Welcome to RDK S100(P) Board repository

22.ROS2 Gazebo simulation platform

22. ROS2 Gazebo Simulation Platform

1. Introduction to Gazebo

Gazebo is the most commonly used 3D physics simulation platform in the ROS system. It supports a dynamics engine and enables high-quality graphics rendering. It not only simulates the robot and its surrounding environment, but also incorporates physical properties such as friction and elasticity.

For example, if we want to develop a Mars rover, we can simulate the Martian surface environment in Gazebo. Or, if we're developing a drone, battery life and flight restrictions prevent us from frequently experimenting with the actual drone. In these cases, we can use Gazebo to simulate first, then deploy to the actual drone once the algorithm is fully developed.

Simulation platforms like Gazebo can help us verify robotic algorithms, optimize robot designs, and test robot applications, providing more possibilities for robotics development.

Note: This section is for learning purposes only. The tutorial does not configure the environment because we are using real-device debugging

2. Installation and Operation

  • Install gazebo using the apt command
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sudo apt install ros-${ROS_DISTRO}-gazebo-*
  • Run gazebo
  • Launch gazebo using the following command or directly from the desktop icon
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gazebo --verbose -s libgazebo_ros_init.so -s libgazebo_ros_factory.so

image-20250905185527569

image-20250905185455271

After running, you should see the following page:

img

  • Optional: To ensure smooth model loading, you can download the offline model and place it in the ~/.gazebo/models directory. The download link is as follows: https://github.com/osrf/gazebo_models

3. Start the Gazebo Node and Service

  1. View the Node
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ros2 node list

image-20250905185639464

Correct return: /gazebo

  1. View the external services provided by the node:
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ros2 service list

You can see the following results:

image-20250905185658045

Excluding the last few regular services, we will only focus on the first three special services:

  • /spawn_entity, used to load models into gazebo
  • /get_model_list, used to obtain a model list
  • /delete_entity, used to delete loaded models in gazebo

4. Create a function package

  • Create a myrobot package to store our URDF model and launch files.
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ros2 pkg create myrobot --build-type ament_cmake
  • Go to the myrobot directory and create folders launch and urdf. Within the urdf folder, create a file called demo01_base.urdf. This file is a simple demonstration file containing only a basic cube.
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<robot name="myrobot">
    <link name="base_link">
        <visual>
            <geometry>
                <box size="0.2 0.2 0.2"/>
            </geometry>
            <origin xyz="0.0 0.0 0.0"/>
        </visual>
        <collision>
            <geometry>
                <box size="0.2 0.2 0.2"/>
            </geometry>
            <origin xyz="0.0 0.0 0.0"/>
        </collision>
        <inertial>
            <mass value="0.1"/>
            <inertia ixx="0.000190416666667" ixy="0" ixz="0" iyy="0.0001904" iyz="0" izz="0.00036"/>
        </inertial>
    </link>
    <gazebo reference="base_link">
        <material>Gazebo/Red</material>
    </gazebo>
</robot>

 

5. Writing the launch file

Writing a launch file consists of two main parts: launching the Gazebo file and then loading the robot model into Gazebo.

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start_gazebo_cmd =  ExecuteProcess(
        cmd=['gazebo', '--verbose','-s', 'libgazebo_ros_init.so', '-s', 'libgazebo_ros_factory.so'],
        output='screen')

This command starts Gazebo. It is a simple startup command and is not particularly complicated. Here is the command to load the model:

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 spawn_entity_cmd = Node(
        package='gazebo_ros', 
        executable='spawn_entity.py',
        arguments=['-entity', robot_name_in_model,  '-file', urdf_model_path ], output='screen')

Note the following two parameters in this command: -entity is the name of the model file, and -file is the parameter loaded through the urdf file. Later we can also see how the model is loaded through the topic. Create a bringup_model.launch.py ​​file in the launch directory. The complete startup file is as follows:

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import os
from launch import LaunchDescription
from launch.actions import ExecuteProcess
from launch_ros.actions import Node
from launch_ros.substitutions import FindPackageShare
from launch_ros.parameter_descriptions import ParameterValue
from launch.substitutions import Command
​
def generate_launch_description():
    robot_name_in_model = 'myrobot'
    package_name = 'myrobot'
    urdf_name = "demo01_base.urdf"
   
    ld = LaunchDescription()
    pkg_share = FindPackageShare(package=package_name).find(package_name) 
    urdf_model_path = os.path.join(pkg_share, f'urdf/{urdf_name}')
   
    # Start Gazebo server
    start_gazebo_cmd =  ExecuteProcess(
        cmd=['gazebo', '--verbose','-s', 'libgazebo_ros_init.so', '-s', 'libgazebo_ros_factory.so'],
        output='screen')
​
    # Launch the robot
    spawn_entity_cmd = Node(
        package='gazebo_ros', 
        executable='spawn_entity.py',
        arguments=['-entity', robot_name_in_model,  '-file', urdf_model_path ], output='screen')
​
    ld.add_action(start_gazebo_cmd)
    ld.add_action(spawn_entity_cmd)
​
    return ld
​
  • Fill in the following content in Cmakelist to install our urdf and launch folders into the install directory
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install(
    DIRECTORY urdf launch
    DESTINATION share/${PROJECT_NAME}
)

 

  • Then compile and run the function package
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colcon build --packages-select myrobot

image-20250905190216658

 

  • Refresh the environment variables and run the launch startup file
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ros2 launch myrobot bringup_model.launch.py

image-20250905190401459

 

After launching, you should see the following Gazebo model:

image-20250905190451228

You can see the red model because you added the Gazebo tag settings.

 

 

ROS2 URDF model
ROS2 TF2 coordinate transformation

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