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DOFBOT SE📖
1.First Trial
  • 1.First Trial
  • 2.Analysis of common problems
  • 3.Calibration offset
  • 4.Precautions for DIY Robotic Arm
  • 5.Robotic arm gripping anomaly solutions
2.Assembly Course
  • 1.Installation video
  • 2.Calibrate the Robotic Arm
  • 3.Install the map
3.Remote Control Course
  • 1.Mobile APP control
  • 2.USB handle control
  • 3.PC software control
4.Preparation
  • 1.Manage APP control service
  • 2.Manage JupyterLab service
  • 3.Write STM32 firmware
  • 4.Introduction to expansion board
5.Basic Control Course
  • 1.Control RGB light
  • 2.Control buzzer
  • 3.Control single servo
  • 4.Read servo angle
  • 5.Control all servo
  • 6.DOFBOT up down left right
  • 7.DOFBOT dancing
  • 8.DOFBOT memory action
  • 9.DOFBOT grab block
  • 10.Porter
  • 11.Stack block
  • 12.USB camera display
  • 13.Synchronous action
6.AI Vision Basic Course
  • 1.Gesture Recognition
  • 2.Color threshold adjustment Color calibration
  • 3.Color recognition
  • 4.Face recognition
  • 5.Garbage recognition
  • 6.Yolo object recognition
  • 7.Real-time web view
7.2D Visual Tracking Course
  • 1.PID algorithm basics
  • 2.Color block positioning
  • 3.Color block tracking
  • 4.Face positioning
  • 5.Face tracking
  • 6.Gesture recognition fixed action
8.2D Visual Grasping Course
  • 0.Visual positioning
  • 1.Gesture recognition stack blocks
  • 2.Color recognition grab blocks
  • 3.Catch game
  • 4.Color sorting and stacking
  • 5.Garbage sorting
  • 6.Color interaction
9.2D Mediapipe Course
  • 1.Hand detection
  • 2.Posture detection
  • 3.Overall inspection
  • 4.Face detection
  • 5.Face recognition
  • 6.Face effects
  • 7.3D object recognition
  • 8.Paintbrush
  • 9.Finger control
  • 10.Gesture recognition
  • 11.Gesture control robotic arm action group
  • 12.Arm posture control robotic arm
  • 13.Palm Targeting
  • 14.Robotic arm tracks the palm
  • 15.Fingertip trajectory recognition
  • 16.Fingertip trajectory control robotic arm
  • 17.Gesture Grasp and Release Objects
  • 18.Finger Control Robot Arm
10.AI Large Model Voice Module (Must Read)
  • 1. Introduction to AI Large Model Voice Module
  • 2. Flash the firmware (Must read)
  • 3. Modify the wake-up word and command word
11.2D Voice Control Course
  • 1.Simple Voice Control
  • 2.Smart Voice Control
  • 3.Voice Action Control
  • 4.Memory Learning Mode
  • 5.Color Recognition with Voice Broadcast
  • 6.Color Recognition and Grasping with Voice Broadcast
  • 7.Color Recognition and Sorting with Voice Broadcast
  • 8.Voice Recognition Target Tracking
  • 9.Voice Control Stacking
  • 10.Voice Control Color Sorting
  • 11.Garbage Recognition with Voice Broadcast
  • 12.Garbage Sorting with Voice Broadcast
12.Robotic Arm Control and Inverse Kinematics
  • 1. Robotic arm ROS control
  • 2. Inverse solution
13.3D Sorting and Grasping Course
  • 1.AprilTag ID sorting
  • 2.MediaPipe Gesture-AprilTag ID sorting
  • 3.MediaPipe Gesture-AprilTag distance sorting
  • 4.Block color sorting
  • 5.Block different shape sorting
  • 6.Yolov11 garbage sorting
14.3D Tracking Course
  • 1.AprilTag Tracking
  • 2.Color Block Tracking
  • 3.KCF Object Tracking
15.3D Voice Control for Sorting & Tracking Course
  • 1.Voice Control AprilTag ID Sorting
  • 2.Voice Control Color Block Sorting
  • 3.Voice Control Yolov11 Garbage Recognition Sorting
  • 4.Voice Control AprilTag Tracking
  • 5.Voice Control Color Block Tracking
  • 6.Voice Control Object Tracking
16.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
17.AI Model Development
  • 1. Register a model service provider account
  • 2. Configure API-KEY
  • 3. Introduction to Dify
  • 4. Basic Dify Features
  • 5. AI Large Model Development - chatbot
  • 6. Deploy the RAG knowledge base
  • 7. RAG knowledge base + chatbot
  • 8. AI agent workflow
18.AI Model – Text Version
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Text Large Model + robotic arm control
  • 4.Multimodal Large Model+video understanding
  • 5.Multimodal Large Model+robotic arm gripping
  • 6.Multimodal Large Model+robotic arm tracking
  • 7.Multimodal Large Model+robotic arm sorting
  • 8.Multimodal Large Model+robotic arm handling
  • 9.Multimodal Large Model+Color Blocks Back In Place
  • 10.Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11.Text Large Model + YOLOv11 + Intelligent Garbage Classification
19.AI Model – Voice Version
  • 1.Semantic understand instruction following
  • 2.Multimodal visual understand
  • 3.Text Large Model + robotic arm control
  • 4.Multimodal Large Model+video understanding
  • 5.Multimodal Large Model+robotic arm gripping
  • 6.Multimodal Large Model+robotic arm tracking
  • 7.Multimodal Large Model+robotic arm sorting
  • 8.Multimodal Large Model+robotic arm handling
  • 9.Multimodal Large Model+Color Blocks Back In Place
  • 10.Multimodal Large Model + Intent Prediction + Robotic Arm Gripping
  • 11.Text Large Model + YOLOv11 + Intelligent Garbage Classification
20.AI Model – Fun Applications
  • 1.AI Butler-Intent Inference
  • 2.AI Encyclopedia-Arithmetic Master
  • 3.AI Encyclopedia-You asked I Pointed
  • 4.AI Encyclopedia-Nutritional Analysis
  • 5.AI Encyclopedia-Problem Solving Expert
  • 6.AI Encyclopedia-English Spelling
  • 7.AI Butler-Garbage Sorting
  • 8.AI Top Chef
21.MoveIt2 Course
  • 1. MoveIt Configuration
  • 2. MoveIt Usage
  • 3. MoveIt Drive Real Robot
  • 4. MoveIt Random Movement
  • 5. MoveIt Forward Kinematics Design
  • 6. MoveIt Inverse Kinematics Design
  • 7. MoveIt Cartesian Path
  • 8. MoveIt Scene Design
  • 9. MoveIt Trajectory Planning
  • 10. MoveIt Collision Detection
22.YOLOv11 Development Course
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Conversion
  • 9. Model Prediction
23. Linux Operating System
  • 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
24.OpenCV Course
  • 1.Introduction to Open Source CV
  • 2.Image reading and display
  • 3.Picture writing
  • 4.Image Quality
  • 5.Pixel operations
  • 6.Image zoom
  • 7.Picture cut
  • 8.Picture pan
  • 9.Picture mirroring
  • 10.Affine transformation
  • 11.Picture rotation
  • 12.perspective transformation
  • 13.Grayscale processing
  • 14.Binary image
  • 15.Edge green detection
  • 16.Line segment drawing
  • 17.Rectangular circle drawing
  • 18.Text and picture drawing
25.Advanced Visuals Course
  • 1. QR Code
  • 2. Human Pose Estimation
  • 3. Object Detection
  • 4. AR Vision
  • 5. ROS + OpenCV Basics
26.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
27.Jupyter Lab Usage Course
  • 0.Read the document before operation
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Widgets
  • 3.Launch JupyterLab
  • 4.How to Use JupyterLab
Download
  • APP(iOS/Android)
  • VM File
  • Instruction manual
  • Hardware_3DModelFile
  • PC_Software
  • Code
  • Communication Protocol-Python Library
  • Tools
Download ZIP

Welcome to DOFBOT SE 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.
xxxxxxxxxx
<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:

xxxxxxxxxx
 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:

xxxxxxxxxx
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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