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Raspberry Pi 5📖
00.Quick Start (Must See)
  • 1.Quick Start (Must See)
  • 2.TF card expand capacity
  • 3.Raspberry Pi 5 - 1GB Version Must - See Before Use
01.Introduction to Raspberry PI 5
  • 1. Introduction to Raspberry PI 5
  • 2. Raspberry PI system installation and backup
  • 3. Powering the Raspberry PI 5
  • 4. Startup of Raspberry PI 5
  • 5. Installation and introduction of shell and active radiator
  • 6. GPIO and 40-pin pins
  • 7. Tips for using Raspberry PI 5
  • 8. Raspberry PI PCIe connector
  • 9. MIPI CSI DSI connector
  • 10. UART connector
  • 11. Real-time clock
  • 12. Power over Ethernet (PoE) connector
02.Introduction to common mirror systems
  • 1. Official Raspberry PI system
  • 2. Ubuntu
03.Basic settings of Raspberry Pi
  • 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
04.Basic programming course
1. C language programming
  • 1. C language environment construction
  • 2. Print Hello World
2. Python programming
  • 1.Python usage
  • 2.Use APT to install Python packages
  • 3.Python virtual environment
  • 4.GPIO Zero
  • 5.Pin output high and low levels
  • 6.Pin reads high and low levels
  • 7.Pin output PWM
3.Jupyter lab programming
  • 1.Jupyter Lab environment construction
  • 2.Basic use of Jupyter Lab
  • 3.Jupyter Lab plug-in installation
  • 4.Basic use of Jupyter Widgets
05.Advanced AI course
5.Advanced AI gameplay
  • 1. Human pose estimation
  • 2. target detection
  • 3.yolov5
  • 4. Camera display
  • 5. QR code
  • 6. face detection
  • 7. Color recognition
2.Getting started with Open Source CV
  • 1. Introduction to Open Source CV
  • 2. Read and display Open Source CV images
  • 3. Open Source CV image writing
  • 4. Open Source CV image quality
  • 5.Open Source CV pixel operation
3.Open Source CV Geometric Transformations
  • 1. Image resizing
  • 2. Cut the image
  • 3. Image translation
  • 4. Image mirroring
  • 5. Affine transformation
  • 6. Rotate the image
  • 7. Perspective transformation
4.Text Picture Drawing Open Source CV image processing and drawing text line segments
  • 1、Grey Processing
  • 2、image binaryzation
  • 3、edge detection
  • 4、Line segment drawing
  • 5、Rectangle and circle drawing
  • 6、Text image drawing
1.Open Source CV environment setup
  • Open Source CV environment setup
6.YOLOv11 development
  • 1. YOLOv11 environment construction
  • 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. Dataset Annotation
  • 10. Model training_conversion
  • 11. Model prediction
7.YOLO26 development
  • 01.YOLO26 environment
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented bounding box object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training - conversion
  • 11.Model prediction
06.Mediapipe course
  • 1. Face special effects
  • 2. Face detection
  • 3. Three-dimensional object recognition
  • 4. Paintbrush
  • 5. Finger control
  • 6. Gesture recognition
07.Docker Tutorial
  • 1.Overview and installation
  • 2.Common commands
  • 3.Understand and publish images
  • 4.Hardware interaction data processing
  • 5.Enter docker container
  • 6.Update docker images
  • 7. docker container to install ROS using accessories
08.ROS1 advanced tutorial
1.ROS1 basic course
  • 1.ROS introduction
  • 2.ROS installation
  • 3.ROS common command tools
  • 4.ROS workspace
  • 5.ROS function package
  • 6.ROS node
  • 7.ROS topic publisher
  • 8.ROS topic subscribers
  • 9.ROS service client
  • 10.ROS service server
  • 11.ROS action client
  • 12.ROS action server
  • 13.ROS custom message reception
  • 14.ROS-launch file
  • 15.ROS-TF transformation
  • 16.ROS parameter service
  • 17.ROS-rviz use
  • 18.ROS-rqt tool usage
  • 19.Topic message recording and playback
  • 20.urdf model introduction
  • 21.gazebo introduction
  • 22.ROS distributed communication
2.ROS1 vision application
  • 0.Precautions
  • 1.AR vision
  • 2.AR QR code
  • 3.ROS+Opencv basics
  • 4.Face recognition
  • 5.harris corner detection
  • 6.Target tracking algorithm
  • 7.Contour moment
  • 8.Polygon outline
  • 9.Discrete fourier transform algorithm
  • 10.Edge detection algorithm
  • 11.Face detection algorithm
  • 12.Optical flow detection algorithm
  • 13.Contour detection
  • 14.General contour detection
  • 15.Feature point tracking
  • 16.HLS color filtering
  • 17.Hough circle detection
  • 18.Hough linear detection
  • 19.HSV color filtering
  • 20.LK optical flow algorithm
  • 21.detection algorithm
  • 22.Phase dependent displacement detection
  • 23.Image pyramid sampling algorithm
  • 24.RGB color filtering
  • 25.Clear background detection algorithm
  • 26.Simplified optical flow algorithm
  • 27.Simple filter
  • 28.Threshold image processing
  • 29.Watershed segmentation algorithm
09.ROS2 advanced tutorial
1.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
2.ROS2 vision application
  • 0.Precautions
  • 1.Opencv basic course
  • 2.ROS+opencv application
  • 3.QR code creation and recognition
  • 4.AR vision
  • 5.Interesting gameplay of mediapipe
10.Offline AI model coursenew
Offline large model (Text Interaction)
  • 0. AI large model image description
  • 1. Deployment of large AI model environments
  • 2. Switch Chinese input method
  • 3. Installation of large model dialogue platform
  • 4. Meta AI Llama 3.2 model
  • 5. Alibaba Cloud Qwen3 Model
  • 6. Microsoft Phi-4-mini model
  • 7. DeepSeek DeepSeek-R1 model
  • 8. Alibaba Cloud Qwen2.5vl Visual Multimodal Large Model
  • 9. Google Gemma3 Visual Multimodal Large Model
  • 10. Llava Visual Multimodal Large Model
  • 11.MiniCPM-V Visual Multimodal Large Model
  • 12. Multimodal Visual Understanding Applications
  • 13. Multimodal Text-Based Image Application
  • 14. Multimodal Video Analysis Application
  • 15. Multimodal visual positioning applications
  • 16. Multimodal table scanning application
  • 17. Multimodal Autonomous Agent Application
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 and voice interaction
  • 5. Multimodal Text-Based Image Application
  • 6. Multimodal Video Analysis Application
  • 7. Multimodal visual positioning applications
  • 8. Multimodal table scanning application
  • 9. Multimodal Autonomous Agent Applications
  • 10. AI large model offline voice assistant
11.Online AI model coursenew
Online large model (text interaction)
  • 1.OpenRouter Large Model API Aggregation Platform
  • 2. Multimodal Visual Understanding Applications
  • 3. Multimodal visual positioning applications
  • 4. Multimodal table scanning application
  • 5. Multimodal Autonomous Agent Applications
Online large model (voice interaction)
  • 0. Voice Interaction Hardware Connection (Must Read)
  • 1. Online speech-to-text (ASR)
  • 2. Online Text-to-Speech (TTS)
  • 3. AI large model voice interaction
  • 4. Multimodal visual and voice interaction
  • 5. Multimodal visual positioning applications
  • 6. Multimodal table scanning application
  • 7. Multimodal Autonomous Agent Applications
  • 8. AI large model online voice assistant
12.OpenClaw Deployment and Basic Usagenew
  • 01. OpenClaw Deployment
  • 02. OpenClaw WhatsApp Plugin Application
  • 03. OpenClaw WebChat Interaction
  • 04. OpenClaw TUI Interaction
  • 05. OpenClaw Tools Introduction
  • 06. OpenClawGate Gateway User Manual
  • 07. OpenClaw Features Overview
  • 08. OpenClawHub Introduction (Skill Installation)
  • 09. 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
13.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. 3D 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
  • SystemFile
  • Code
  • SoftWare
  • 3D_Model_File
  • GuideManual
Download ZIP

Welcome to Raspberry Pi 5 repository

21.gazebo introduction

21.gazebo introduction

In the previous section, we talked about the URDF model to describe the robot. We also loaded the URDF model in rviz and used some plug-ins to make it move in rviz to achieve simulation. But often this is not enough to meet our simulation needs. We hope that the robot simulation environment can be as similar as possible to the actual environment. Then, the gazebo described in this course can meet the above needs.

21.1 Overview

Gazebo is a free robot simulation software that provides high-fidelity physical simulation, a complete set of sensor models, and a very user- and program-friendly interaction method. Functions that can accurately and efficiently simulate robot work in complex indoor and outdoor environments. By loading the model, an operating environment similar to the actual operating environment is constructed, the robot is loaded into it, and the program is run to realize simulation.

21.1.1 Install gazebo

According to the installed ROS version, here is noetic as an example, input in the terminal,

sudo apt install ros-noetic-gazebo-* -y
sudo apt install gazebo11* -y
sudo apt install libgazebo11-dev -y

21.1.2 Run gazebo

After the installation is complete, enter in the terminal,

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gazebo

image-20231027143156082

21.1.3 GUI interface introduction

  • Scenes

    The scene is the main part of the simulator, where the simulation model is displayed.

    image-20231027143527874

  • Left and right panels

    • Left panel

      • world:Display the model currently in the scene, view and modify model parameters;
      • Insert: Add a new object (model) to the simulation. To view a list of models click the arrow to expand the folder. Click (and release) on the model you want to insert, then click again in the scene to add;
      • Layer:Organizes and displays the different visualization groups available in the simulation. Layer generally contains one or more models. Multiple models can display the model of this layer by turning it on or off.

    image-20231027144418127

    • Right panel

      By default, the right panel is hidden. Click and drag the bar to open it. The right panel can be used to interact with the moving parts (joints) of the selected model, including pose, velocity, etc. If no model is selected in the scene, the panel will display no information.

    image-20231027144635994

  • Top toolbar

    Starting from the arrow on the left to the cube on the right, they are:

    • Select model: mark in the scene

    • Translate mode: select the model to be moved;

    • Rotate mode: select the model to be rotated;

    • Scale mode: select the model to be scaled;

      image-20231027145747784

    • Undo/Redo: Undo/redo operations in the scene;

      image-20231027145731829

    • Create simple shapes (cube, sphere, cylinder)

      image-20231027145921512

    • Lights: Add lights to the scene, including the positions illuminated by light sources in all directions

      image-20231027145941783

    • Copy/Paste: Copy/paste the model in the scene

      image-20231027150012847

    • Align, snap models: Align models to each other, snap one model to another

      image-20231027150156326

    • Change view: View the scene from different angles

      image-20231027150238473

21.2 Load urdf model in gazebo

To use urdf files with gazebo, you must add some tags specific for simulation to work properly with gazebo. For each robot model, each needs to add a tag. Let's take Yahboom's rosmaster-X3 as an example to explain how gazebo loads the urdf model.

Here are two function packages:

  • yahboomcar_description:stores urdf file of rosmaster-X3
  • yahboomcar_gazebo:stores files related to the started gazebo

In addition, two function packages need to be installed. One is the function package for keyboard control nodes and terminal input.

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sudo apt install ros-noetic-teleop-twist-keyboard -y

The other is the turtlebot3-gazebo function package, which contains a set gazebo environment and terminal input.

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sudo apt install ros-noetic-turtlebot3-gazebo -y

Let’s start the program first to see the effect, enter in the terminal,

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roslaunch yahboomcar_gazebo yahboom.launch

After starting, the following screen appears,

image-20231027171215162

Click [Camera] above and select [Orthographic].

image-20231027171355928

Then you can see the built environment, as shown below,

image-20231027171504425

Then start the keyboard control node,

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rosrun teleop_twist_keyboard teleop_twist_keyboard.py

image-20231027171632053

Press [i], [u], [o], [j], [l], [m], [,], [.] to control the movement of the car, and press the space bar to stop. Since the car is equipped with a camera and radar, we can also see the image data and radar data in rviz when it is started.

21.2.1 Launch file analysis

First look at the launched launch file,

​x
<?xml version="1.0"?>
<launch>
  <arg name="ns" default="robot1"/>
  <arg name="format" default="xarco" doc="xacro ; urdf"/>
  <arg name="robot_type" value="X3" />

  <arg name="gui" default="true" />
  <arg name="run_camera" default="false"/>

  <arg name="x_pos" default="-2.0"/>
  <arg name="y_pos" default="-0.5"/>
  <arg name="z_pos" default="0.0"/>
    
  <node name="tf_footprint_base" pkg="tf"   type="static_transform_publisher"   args="0 0 0 0 0 0 base_link base_footprint 40" />

  <include file="$(find gazebo_ros)/launch/empty_world.launch">
    <arg name="world_name" value="$(find turtlebot3_gazebo)/worlds/turtlebot3_world.world"/>
    <arg name="paused" value="false"/>
    <arg name="use_sim_time" value="true"/>
    <arg name="gui" value="true"/>
    <arg name="headless" value="false"/>
    <arg name="debug" value="false"/>
  </include>


<!-- urdf xml robot description loaded on the Parameter Server, converting the xacro into a proper urdf file-->
 <param name="robot_description" command="$(find xacro)/xacro --inorder $(find yahboomcar_description)/urdf/yahboomcar_X3.gazebo.urdf.xacro ns:=$(arg ns)" />
 

    <node pkg="gazebo_ros" type="spawn_model" name="spawn_urdf"  args="-urdf -model yahboomcar_$(arg robot_type)  -x $(arg x_pos) -y $(arg y_pos) -z $(arg z_pos) -param robot_description" />
      <!-- load the controllers -->
    <!--include file="$(find yahboomcar_gazebo)/launch/control.launch"></include-->
    <node   name="joint_state_publisher"   pkg="joint_state_publisher"    type="joint_state_publisher" />
  <node  name="robot_state_publisher" pkg="robot_state_publisher"  type="robot_state_publisher" />
 <node name="rviz" pkg="rviz" type="rviz" args="-d $(find yahboomcar_gazebo)/config/rviz/yahboomcar_X3.rviz"/> 
  
</launch>

Mainly focus on the following points:

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<include file="$(find gazebo_ros)/launch/empty_world.launch">
    <arg name="world_name" value="$(find turtlebot3_gazebo)/worlds/turtlebot3_world.world"/>
    <arg name="paused" value="false"/>
    <arg name="use_sim_time" value="true"/>
    <arg name="gui" value="true"/>
    <arg name="headless" value="false"/>
    <arg name="debug" value="false"/>
  </include>

Here is to start gazebo and then load the turtlebot3_world.world file, followed by some parameter settings, such as whether to use simulation time, etc. Generally, simulation time is used in gazebo, that is, use_sim_time is true.

xxxxxxxxxx
 <param name="robot_description" command="$(find xacro)/xacro --inorder $(find yahboomcar_description)/urdf/yahboomcar_X3.gazebo.urdf.xacro ns:=$(arg ns)" />

Describe the urdf model that needs to be loaded. We will analyze the model here later.

xxxxxxxxxx
<node pkg="gazebo_ros" type="spawn_model" name="spawn_urdf"  args="-urdf -model yahboomcar_$(arg robot_type)  -x $(arg x_pos) -y $(arg y_pos) -z $(arg z_pos) -param robot_description" />

Load the robot model in gazebo, the parameter is robot_description.

21.2.2 Model file analysis

File directory::~/ros_ws/src/yahboomcar_description/urdf/yahboomcar_X3.gazebo.urdf.xacro

Most of the tags of the file were mentioned in the previous lesson, so I won’t go into details here. The main ones are the following parts.

xxxxxxxxxx
 <xacro:include filename="$(find yahboomcar_description)/urdf/yahboomcar_X3.gazebo.xacro"/>

The yahboomcar_X3.gazebo.xacro file is loaded here. The directory of this file is,

xxxxxxxxxx
~/ros_ws/src/yahboomcar_description/urdf

Take a look at the contents of this file. This involves adding the tag and some plug-ins to each .

xxxxxxxxxx
<?xml version="1.0"?>
<robot name="yahboomcar_X3" xmlns:xacro="http://ros.org/wiki/xacro">
    <gazebo reference="back_left_wheel">
        <mu1 value="2.0"/>
        <mu2 value="2.0"/>
        <kp  value="10000000.0" />
        <kd  value="1.0" />
        <fdir1 value="1 0 0"/>
        <material>Gazebo/Black</material>
    </gazebo>

    <gazebo reference="back_right_wheel">
        <mu1 value="2.0"/>
        <mu2 value="2.0"/>
        <kp  value="10000000.0" />
        <kd  value="1.0" />
        <fdir1 value="1 0 0"/>
        <material>Gazebo/Black</material>
    </gazebo>
    <gazebo reference="front_left_wheel">
        <mu1 value="2.0"/>
        <mu2 value="2.0"/>
        <kp  value="10000000.0" />
        <kd  value="1.0" />
        <fdir1 value="0 0 1"/>
        <material>Gazebo/Black</material>
    </gazebo>
        <gazebo reference="front_right_wheel">
        <mu1 value="2.0"/>
        <mu2 value="2.0"/>
        <kp  value="10000000.0" />
        <kd  value="1.0" />
        <fdir1 value="0 0 1"/>
        <material>Gazebo/Black</material>
    </gazebo>

    <gazebo>
        <plugin name="gazebo_ros_control" filename="libgazebo_ros_control.so">
            <robotNamespace>/</robotNamespace>
            <!--robotParam>robot_description</robotParam-->
            <robotSimType>gazebo_ros_control/DefaultRobotHWSim</robotSimType>
            <legacyModeNS>true</legacyModeNS>
        </plugin>
    </gazebo>
 
    <gazebo>
        <plugin name="mecanum_controller" filename="libgazebo_ros_planar_move.so">
            <robotNamespace>/</robotNamespace>
            <commandTopic>cmd_vel</commandTopic>
            <odometryTopic>odom</odometryTopic>
            <odometryFrame>odom</odometryFrame>
            <leftFrontJoint>front_left_joint</leftFrontJoint>
            <rightFrontJoint>front_right_joint</rightFrontJoint>
            <leftRearJoint>back_left_joint</leftRearJoint>
            <rightRearJoint>back_right_joint</rightRearJoint>
            <odometryRate>60.0</odometryRate>
            <robotBaseFrame>base_link</robotBaseFrame>
        </plugin>
    </gazebo>

<xacro:property name="camera_name"  value  = "camera"/>
<xacro:property name="frame_name"  value  = "camera_link"/>
<gazebo reference="camera_link">
    <!--arg name="camera_name"  default  = "mycamera"/-->
    <sensor name="abzg_camera" type="depth">
        <update_rate>30</update_rate>
        <camera>
            <horizontal_fov>1.047198</horizontal_fov>
            <image>
                <width>640</width>
                <height>480</height>
                <format>R8G8B8</format>
            </image>
            <clip>
                <near>0.05</near>
                <far>3</far>
            </clip>
        </camera>
        <plugin name="camera_link_controller" filename="libgazebo_ros_openni_kinect.so">
            <baseline>0.2</baseline>
            <alwaysOn>true</alwaysOn>
            <updateRate>1.0</updateRate>
            <cameraName>${camera_name}_ir</cameraName>
            <imageTopicName>/${camera_name}/color/image_raw</imageTopicName>
            <cameraInfoTopicName>/${camera_name}/color/camera_info</cameraInfoTopicName>
            <depthImageTopicName>/${camera_name}/depth/image_raw</depthImageTopicName>
            <depthImageInfoTopicName>/${camera_name}/depth/camera_info</depthImageInfoTopicName>
            <pointCloudTopicName>/${camera_name}/depth/points</pointCloudTopicName>
            <frameName>${frame_name}</frameName>
            <pointCloudCutoff>0.5</pointCloudCutoff>
            <pointCloudCutoffMax>3.0</pointCloudCutoffMax>
            <distortionK1>0.00000001</distortionK1>
            <distortionK2>0.00000001</distortionK2>
            <distortionK3>0.00000001</distortionK3>
            <distortionT1>0.00000001</distortionT1>
            <distortionT2>0.00000001</distortionT2>
            <CxPrime>0</CxPrime>
            <Cx>0</Cx>
            <Cy>0</Cy>
            <focalLength>0</focalLength>
            <hackBaseline>0</hackBaseline>
        </plugin>
    </sensor>
</gazebo>


    <gazebo reference="laser_link">
        <sensor type="ray" name="head_hokuyo_sensor">
            <pose>0 0 0 0 0 0</pose>
            <visualize>false</visualize>
            <update_rate>40</update_rate>
            <ray>
                <scan>
                    <horizontal>
                        <samples>720</samples>
                        <resolution>1</resolution>
                        <min_angle>-1.570796</min_angle>
                        <max_angle>1.570796</max_angle>
                    </horizontal>
                </scan>
                <range>
                    <min>0.10</min>
                    <max>30.0</max>
                    <resolution>0.01</resolution>
                </range>
                <noise>
                    <type>gaussian</type>
                    <!-- Noise parameters based on published spec for Hokuyo laser
                    achieving "+-30mm" accuracy at range < 10m.  A mean of 0.0m and
                    stddev of 0.01m will put 99.7% of samples within 0.03m of the true
                    reading. -->
                    <mean>0.0</mean>
                    <stddev>0.01</stddev>
                </noise>
            </ray>
            <plugin name="gazebo_ros_head_hokuyo_controller" filename="libgazebo_ros_laser.so">
                <topicName>/scan</topicName>
                <frameName>laser_link</frameName>
            </plugin>
        </sensor>
    </gazebo>

    <gazebo reference="imu_link">
            <material>Gazebo/Black</material>
        </gazebo>

        <gazebo reference="imu_link">
            <gravity>true</gravity>
            <sensor name="imu_sensor" type="imu">
                <always_on>true</always_on>
                <update_rate>100</update_rate>
                <visualize>true</visualize>
                <topic>/imu/imu_data</topic>
                <plugin filename="libgazebo_ros_imu_sensor.so" name="imu_plugin">
                    <topicName>/imu/imu_data</topicName>
                    <bodyName>imu_link</bodyName>
                    <updateRateHZ>100.0</updateRateHZ>
                    <gaussianNoise>0.0</gaussianNoise>
                    <xyzOffset>0 0 0</xyzOffset>
                    <rpyOffset>0 0 0</rpyOffset>
                    <frameName>imu_link</frameName>
                </plugin>
                <pose>0 0 0 0 0 0</pose>
            </sensor>
        </gazebo>
</robot>

First look at the link of one of the wheels.

xxxxxxxxxx
<gazebo reference="back_left_wheel">
        <mu1 value="2.0"/>
        <mu2 value="2.0"/>
        <kp  value="10000000.0" />
        <kd  value="1.0" />
        <fdir1 value="1 0 0"/>
        <material>Gazebo/Black</material>
    </gazebo>

A necessary tag here is , which is used to set appearance parameters. The color set here is black, which is the color of the left rear wheel. The other parameters represent:

  • mu1、mu1:friction coefficient
  • kp、kd:stiffness coefficient, damping coefficient
  • fdir1:direction corresponding to friction coefficient mu1

Let’s take a look at one of the plug-ins:

xxxxxxxxxx
<gazebo>
        <plugin name="mecanum_controller" filename="libgazebo_ros_planar_move.so">
            <robotNamespace>/</robotNamespace>
            <commandTopic>cmd_vel</commandTopic>
            <odometryTopic>odom</odometryTopic>
            <odometryFrame>odom</odometryFrame>
            <leftFrontJoint>front_left_joint</leftFrontJoint>
            <rightFrontJoint>front_right_joint</rightFrontJoint>
            <leftRearJoint>back_left_joint</leftRearJoint>
            <rightRearJoint>back_right_joint</rightRearJoint>
            <odometryRate>60.0</odometryRate>
            <robotBaseFrame>base_link</robotBaseFrame>
        </plugin>
</gazebo>

Add a plug-in here. The name of the plug-in file is libgazebo_ros_planar_move.so, which is included with a pair of . There are some parameter settings inside, such as this commandTopic, which is set to cmd_vel here. Plug-in files are saved in, /opt/ros/noetic/lib

image-20231027181110760

For additional information about the plug-in, please refer to the following URL:

gazebo_plugins - ROS Wiki

Let's take a look at some of the added sensors,

xxxxxxxxxx
<gazebo reference="laser_link">
        <sensor type="ray" name="head_hokuyo_sensor">
            <pose>0 0 0 0 0 0</pose>
            <visualize>false</visualize>
            <update_rate>40</update_rate>
            <ray>
                <scan>
                    <horizontal>
                        <samples>720</samples>
                        <resolution>1</resolution>
                        <min_angle>-1.570796</min_angle>
                        <max_angle>1.570796</max_angle>
                    </horizontal>
                </scan>
                <range>
                    <min>0.10</min>
                    <max>30.0</max>
                    <resolution>0.01</resolution>
                </range>
                <noise>
                    <type>gaussian</type>
                    <!-- Noise parameters based on published spec for Hokuyo laser
                    achieving "+-30mm" accuracy at range < 10m.  A mean of 0.0m and
                    stddev of 0.01m will put 99.7% of samples within 0.03m of the true
                    reading. -->
                    <mean>0.0</mean>
                    <stddev>0.01</stddev>
                </noise>
            </ray>
            <plugin name="gazebo_ros_head_hokuyo_controller" filename="libgazebo_ros_laser.so">
                <topicName>/scan</topicName>
                <frameName>laser_link</frameName>
            </plugin>
        </sensor>
    </gazebo>

This is a sensor that adds a radar, including the radar-related parameters set in the tag, including the scanning angle, scanning depth, etc.;A plug-in libgazebo_ros_laser.so was also added later to set the radar topic and frameid.

urdf model introduction
ROS distributed communication

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