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ROSMASTER X3📖
1.About ROSMASTER X3
  • 1.1 Wiring Introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
2.Assembly steps
  • Installation Video(1-10)
  • Installation Video(11-18)
  • Installation Video(20-28)
  • Installation Video(29-30)
3.ROS1-Remote control course
  • 3.1 APP remote control
  • 3.2 Model aircraft remote control
  • 3.3 Handle remote control
4.ROS1-Hardware course
  • 4.1 About expansion board_V3.0
  • 4.1 About expansion board_V1.0
  • 4.2 STM32 Development environment
  • 4.3 Button to control the buzzer to whistle
  • 4.4 FreeRTOS application
  • 4.5 Serial communication
  • 4.6 SBUS model aircraft remote control
  • 4.7 CAN bus communication
  • 4.8 RGB colorful light bar
  • 4.9 Timer interrupt to control PWM servo
  • 4.10 Control serial port servos
  • 4.11 Nine-axis attitude sensor to obtain data
  • 4.12 Control motor
  • 4.13 Timer captures the encoder data
  • 4.14.1 Kinematic Analysis of Mecanum Wheel
  • 4.14.2 Kinematics Analysis of Ackermann Car
  • 4.15 PID control robot movement
5.ROS1-Basic course
  • 5.1 Update the firmware
  • 5.2 Turn off the self-starting process at startup
  • 5.3 Install the Rosmaster driver library
  • 5.4 Buzzer whistle
  • 5.5 Control PWM servo
  • 5.6 RGB colorful light bar special effects display
  • 5.7 Control motor
  • 5.8 Control robot movement
  • 5.9 Control Serial Servo
6.ROS1-Linux operating system
  • 6.1 Virtual machine installation and use
  • 6.2 Linux basics
  • 6.3 Remote control
  • 6.4 Multi-machine communication configuration
  • 6.5 Static IP and hotspot mode
  • 6.6 Bind the device ID
  • 6.7 Web page real-time monitoring
  • 6.8 Expansion tutorial
  • 6.9.1 Jetson Nano write sytem
  • 6.9.2 Jetson Xavier NX write system
  • 6.9.3 Jetson TX2 NX write system
  • 6.9.4 Raspberry Pi write system
7.ROS1-Docker-pi5
  • 1、Docker overview and docker installation
  • 2、Common commands for docker image containers
  • 3、Docker images deeply understand and publish images
  • 4、Docker hardware interaction and data processing
  • 5、Enter the bot's docker container
  • 6、Robot development environment construction in Docker
  • 7、How customers can update docker images in the future
8.ROS1-ROS 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
9.ROS1-OpenCV courses
  • 9.1 Getting started with Open Source CV
  • 9.2 Geometric transformation
  • 9.3 Image processing and drawing text line segments
  • 9.4 Image beautification
  • 9.5 Opencv application
  • 9.6 AR vision
  • 9.7 AR QR code
  • 9.8 ROS+Opencv foundation
  • 9.9 ROS+Opencv application
  • 9.10 Data transformation and point cloud
  • 9.11 MediaPipe development
10.ROS1-Robot control course
  • 10.1 PID algorithm theory
  • 10.2 Robot PID debugging
  • 10.3 Robot information release
  • 10.4 Robot keyboard control
  • 10.5 Robot handle control
  • 10.6 Robot state estimation
  • 10.7 Robot calibration
  • 10.8 Robot URDF model
11.ROS1-Depth camera course
  • 11.1 How to use the Astra camera
  • 11.2 Astra camera calibration
  • 11.3 Astra Color Tracking
  • 11.4 Astra Object Tracking
  • 11.5 Visual tracking autopilot
  • 11.6 Pure visual 2D mapping and navigation
  • 11.7 RTAB-Map 3D mapping navigation
  • 11.8 ORB_SLAM2 basics
  • 11.9 ORB_SLAM2_Octomap
12.ROS1-Lidar course
  • 12.1 Lidar basic-SLAM
  • 12.1 Lidar basic-4ROS
  • 12.2 Lidar avoiding
  • 12.3 Lidar guard
  • 12.4 Lidar follow
  • 12.5 Patrol game
  • 12.6 gmapping mapping algorithm
  • 12.7 hector mapping algorithm
  • 12.8 karto mapping algorithm
  • 12.9 cartographer mapping algorithm
  • 12.10 rrt_exploration mapping algorithm
  • 12.11 AMCL
  • 12.12 Navigation and avoiding
  • 12.13 APP mapping and navigation
13.ROS1-Multi-robot control course
  • 13.1 Multi-machine handle control
  • 13.2 Multi-machine navigation
  • 13.3 Multi-machine formation
  • 13.4 Multi-machine surround
14.ROS1-Deep learning courses
  • 14.1 KNN recognizes handwritten digits
  • 14.2 Basic use of TensorFlow
  • 14.3 Basic use of PyTorch(jetson)
  • 14.4 yolov5 model training(jetson)
  • 14.5 yolov5+tensorrt acceleration(jetson)
  • 14.6 yolov4-tiny
15.ROS1-Voice control courses
  • 15.0 How to use old voice module in new image
  • 15.1 Introduction of voice control module-NEW
  • 15.2 Voice control module port binding-NEW
  • 15.1 Introduction of voice control module-Old
  • 15.2 Voice control module port binding-Old
  • 15.3 Voice control car movement
  • 15.4 Voice control Autopilot
  • 14.5 Voice control color recognition
  • 15.6 Voice control color tracking
  • 15.7 Voice control multi-point navigation
16.ROS1-Model training Autopilot
  • 16.0 ReadMe
  • 16.1 yolov5 to train traffic signs
  • 16.2 TensorRt acceleration identification mark
  • 16.3 Road sign indication function description
  • 16.4 Collect data
  • 16.5 Training model
  • 16.6 Model transformation and application
  • 16.7 AI Autopilot
17.ROS2- Remote control course
  • 1. APP control
  • 2. Model aircraft control
  • 3. Wireless Handle Control Tutorial
18.ROS2- Hardware course
  • 1. About expansion board_V1.0
  • 1. About expansion board_V3.0
  • 2. STM32 Development environment
  • 3. Button control
  • 4. FreeRTOS application
  • 5. Serial communication
  • 6. SBUS model aircraft remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt to control PWM servo
  • 10. Control serial port servos
  • 11. Nine-axis attitude sensor to obtain data
  • 12. Control motor
  • 13. Timer captures the encoder data
  • 14. Robot kinematics analysis theory
  • 15. PID control robot movement
19.ROS2- ROSMASTER basic course
  • 1. Update the firmware
  • 2. Turn off the self-starting process at startup
  • 3. Install the Rosmaster driver library
  • 4. Buzzer whistle
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects display
  • 7. Control motor
  • 8. Control robot movement
  • 9. Control Serial Servo
20.ROS2- Linux operating systemnew
  • 1. Virtual machine installation and use
  • 2. Linux basics
  • 3. Remote control
  • 4. Multi-machine communication configuration
  • 5. Static IP and hotspot mode
  • 6. Bind the device ID
  • 7. Web page real-time monitoring
  • 8. Expansion tutorial
  • 9.1 Jetson Nano write sytem
  • 9.2 Raspberry Pi write system
  • 9.3 Orin series write system
  • 10.Jetpack old system upgraded to SUPER systemnew
21.ROS2- Docker(Nano Pi)
  • 1. Docker Overview and Docker Installation
  • 2.Common commands for docker image containers
  • 3.Docker images deeply understand and publish images
  • 4. Docker hardware interaction and data processing
  • 5. Entering the Docker Container of the Robot
  • 6. Building a Robot Development Environment in Docker
  • 7. Method for customers to update Docker images in the future
22.ROS2- ROS2 Basic Tutorial
  • 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
23.ROS2- Robot control course
  • 1. PID Algorithm Theory
  • 2. Robot information release
  • 3. Robot control
  • 4. Robot state estimation
  • 5. Robot calibration
  • 6. URDF model
24.ROS2- Depth camera course
  • 1.Depth camera usage
  • 2. Camera internal reference calibration
  • 3. color tracking
  • 4. KCF object tracking
  • 5. ORB_SLAM2 basics
  • 6. ORB_ SLAM2 PCL mapping
  • 7. ORB_ SLAM2 Octomap mapping
  • 8. RTAB-Map 3D mapping navigation
25.ROS2- Lidar course
  • 1. Introduction and Use of Lidar
  • 2. Lidar avoiding
  • 3. Lidar follow
  • 4. Lidar guard
  • 5. Patrol game
  • 6. Gmapping mapping algorithm
  • 7. Cartographer mapping algorithm
  • 8. Navigation and obstacle avoidance
  • 9. Rosmaster_ X3 conducts mapping through the APP
  • 10. Rosmaster_ X3 Navigation through APP
26.ROS2- OpenCV courses
  • 1. OpenCV Basic Course
  • 2. ROS+opencv application
  • 3. QR code creation and recognition
  • 4. AR Vision
  • 5. Mediapipe fun gameplay
  • 6、line patrol automatic driving
27.ROS2- Voice control courses
  • 0.How to use old voice module in new image
  • 1. Introduction of voice module-new
  • 2. Voice control module port binding-new
  • 1. Introduction of voice module and port binding-old
  • 3. Voice control car movement
  • 4. Voice control Autopilot
  • 5. Voice control color recognition
  • 6. Voice control color tracking
  • 7. Voice control multi-point navigation
28.Deep learning courses(SUPER)
  • 1.KNN recognizes handwritten digitsnew
  • 2.Basic use of TensorFlownew
  • 3.Basic use of PyTorch(jetson)new
  • 4.YOLOv11Model conversion(SUPER)new
  • 5.YOLOv11Dataset annotation(SUPER)new
  • 6.YOLOv11Model conversion + tensorrt acceleration(SUPER)new
  • 7.YOLOv11Object recognition(SUPER)new
29.ROS2-Multi-robot control
  • 1. Multi-robot control
  • 2. Multi-robot navigation
LLM tutorial
1.Development environment
  • 0. Model usage instructions
  • 1.Environments deployment
  • 2.Dialogue platform installation
2.Jetson Nano B01
  • 1.Qwen2 model
  • 2.TinyLlama model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.Orca Mini model
  • 6.StarCoder2 model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA-Phi3
3.Jetson Orin NXNANO
  • 1.Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Codellama model
  • 7.DeepSeek-R1 model
  • 8.Orca Mini model
  • 9.StarCoder2 model
  • 10.TinyLlama model
  • 11.Expand LLaVA
  • 12.Expand LLaVA-Phi3
4.Raspberry Pi 5B
  • 1.Meta AI Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Meta AI Codellama model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA
FAQ
  • 1.Using your own Jetson board, ROSMASTER X3 cannot start normally
Download
  • RemoteCtrl_APP(Android/iOS)
  • Mapping_APP(iOS_Android)
  • Instruction Manual
  • SystemFile_ROS2
  • SystemFile_ROS1
  • SystemFile_ROS2_OrinNANOSUPER
  • SystemFile_ROS2_OrinNXSUPER
  • Code
  • Jetson_Boot_USB.tar.gz
  • VM software
  • Autopilot Annex
  • Other_Annex_File
  • LLM_SystemFile
  • ROS2_Basic_VM
Download ZIP
FAQ
  • 1、How to connect OLED?

Welcome to ROSMASTER X3 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.

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 <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.

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<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.

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 <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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