yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
MicroROS-Pi5📖
01. Introduction
  • 0.Unpacking and reviewing video
  • 1. ReadMe - learning route
  • 2. Introduction to lidar
  • 3. Introduction to microROS control board
  • 4. FAQ
  • 5. About charging
02. Assembly course
  • Assembly steps
03. Preparation
  • First Trial
  • 1. Write firmware
  • 2. MicroROS control board parameter configuration
  • 3. Connect to MicroROS agent
  • 4. Connect to WIFI in hotspot mode
04. Quick start control course
  • 1. Self start handle control
  • 2. Close the self-starting handle control process
  • 3. Quick start keyboard control
  • 4. Map navigation and APP control
05. Robot basic course
  • 1. Robot information release
  • 2. Robot keyboard control
  • 3. Robot handle control
  • 4. Robot state estimation
  • 5. Linear speed calibration
  • 6. Angular velocity calibration
  • 7. Robot URDF model
06. ROS+OpenCV course
  • 1. Getting started with Opencv CV
  • 2. Geometric transformation
  • 3. Image processing and drawing
  • 4. Open Source CV image beautification
  • 5. QR code creation and recognition
  • 6. AR vision
  • 7. ROS+Opencv application
07. Mediapipe course
  • 1. Hand detection
  • 2. Posture detection
  • 3. Overall detection
  • 4. Face detection
  • 5. Face recognition
  • 6. Face effects
  • 7. 3D object recognition
  • 8. Brush
  • 9. Finger control
  • 10 .Gesture recognition
08. Robot visual interaction
  • 1. Monocular camera calibration
  • 2. Color tracking
  • 3. Object recognition and tracking
  • 4. Face recognition tracking
  • 5. Autonomous driving line patrol
  • 6. QR code motion control
  • 7. Gesture control of basic movement of the car
  • 8. Control the car movement with your palm
  • 9. Gesture control car action group
  • 10. Palm control of the two-dimensional gimbal servo
09. Lidar course
  • 1. Lidar avoid
  • 2. Lidar following
  • 3. Lidar guard
  • 4. Lidar patrol
  • 5. Gmapping mapping
  • 6. Cartographer mapping
  • 7. Navigation2 navigation avoid
  • 8. ROS Robot APP mapping
  • 9. ROS Robot APP navigation
10. Multi-machine course
  • 1. Multi-machine handle control
  • 2. Multi-machine keyboard control
  • 3. Multi-machine navigation
11. Linux basic 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
12. RPi basic setting course
  • 1. Update and upgrade operating system
  • 2. Introduction to raspi-config tool
  • 3. config.txt file description
  • 4. Network Configuration
  • 5. remote access
  • 6. Transfer files remotely
  • 7. Set display resolution and rotation
  • 8. Set screen to sleep
  • 9. Get real-time temperature of Raspberry Pi
  • 10. Play audio and video
  • 11. Using USB camera
  • 12. Using MIPI camera
13. Docker course
  • 1. Overview and installation
  • 2. Common commands
  • 3. Docker images deeply understand and publish images
  • 4. Docker hardware interaction and data processing
  • 5. Enter the robot's docker container
14. 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
15. microROS control board development environment
  • 1. Introduction to microROS control board
  • 2. Set up ESP32-IDF development environment
  • 3. ESP32-IDF configuration tool
  • 4. Install ESP32-microros components
  • 5. Install and start microROS agent
  • 6. Flash-tool burning firmware
16. ESP32 basic course
  • 1. Turn on the LED light
  • 2. Button function
  • 3. Drive the buzzer
  • 4. Serial communication
  • 5. Battery voltage detection
  • 6. Drive PWM servo
  • 7. Drive motor
  • 8. Read motor encoder data
  • 9. PID controls car speed
  • 10. Read IMU data
  • 11. Read radar data
  • 12. Flash access data
  • 13. Partition table and memory
  • 14. Bluetooth communication
  • 15. WiFi networking
  • 16. Robot kinematics analysis
  • 17.ROS-WiFi camera module
17. microROS basic course
  • 1. Publish topic
  • 2. Subscribe topic
  • 3. Multi-topic subscribe and publish
  • 4. Subscribe buzzer topics
  • 5. Subscribe PWM servo topics
  • 6. Subscribe speed control topics
  • 7. Release speed topic
  • 8. Release IMU data topic
  • 9. Publish lidar data topics
  • 10. Customized transmission method
Download
  • Mapping_APP(iOS_Android)
  • Source code
  • Hardware information
  • VM image file
  • RPi5 imgae file
  • Instruction Manual
  • 3D Model File
  • ROS2_Basic_VM
Download ZIP
FAQ
  • 1、1.USB camera abnormal termination

Welcome to MicroROS-Pi5 repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

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
xxxxxxxxxx
sudo apt install ros-${ROS_DISTRO}-gazebo-*
  • Run gazebo
  • Launch gazebo using the following command or directly from the desktop icon
xxxxxxxxxx
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
xxxxxxxxxx
ros2 node list

image-20250905185639464

Correct return: /gazebo

  1. View the external services provided by the node:
xxxxxxxxxx
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.
xxxxxxxxxx
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.

xxxxxxxxxx
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
xxxxxxxxxx
install(
    DIRECTORY urdf launch
    DESTINATION share/${PROJECT_NAME}
)

 

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

image-20250905190216658

 

  • Refresh the environment variables and run the launch startup file
xxxxxxxxxx
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

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

  • Tutorials
  • Edge AI hardware
  • Educational robots
  • Embedded intelligent hardware

WHERE TO BUY

  • Yahboom Online Store
  • Amazon
  • Alibaba
  • AliExpress
  • Mercado

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文