yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
JetCobot📖
1.Introduction to the Jetcobot
  • 0.Unboxing
  • 1 Version Configuration Instructions
  • 2. Power adapter usage specifications
  • 3. Common Problems and Solutions
  • 4. First-time use instructions
  • 5. Get started
  • 6. Open and close the virtual desktop
2.Assembly and assembly tutorial
  • Installation Video
3.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
4.Jupyter Lab Basic Course
  • 1. Read the document before operation
  • 2. JupyterLab environment construction tutorial
  • 3. JupyterLab Basic Usage Tutorial
  • 4. JupyterWidgets Configuration Tutorial
  • 5. Getting Started Tutorial for JupyterWidgets Controls
5.Basic courses of main control board
  • 1. Introduction to the main control board
  • 2. Burn the system image
  • 3. Re-read the burned image
  • 4. System startup
  • 5. System and desktop introduction
  • 6. System expansion
  • 7. Network Configuration
  • 8. SSH remote login and file transfer
  • 9. VNC Remote Login
  • 10. System backup
  • 11. Increased swap space
  • 12. Use of Jtop
6.GPIO hardware control course
  • 1.GPIO library API usage
  • 2. Hardware library configuration
  • 3. Pin reading function
  • 4. Pin level output control
  • 5. Control LED
  • 6. External device serial communication
  • 7.I2C communication
7.Jetson-inference vision course
  • 1. Install Jetcam (optional)
  • 2. Install TensorFlow (optional)
  • 3. Jetson-inference environment construction (optional)
  • 4. Hello AI World
  • 5. Image Classification Reasoning
  • 6. Object Detection Reasoning
  • 7. Semantic Segmentation
8.Opencv basic course
  • 1. Introduction to Open Source CV
  • 2. Image reading and display
  • 3. Image writing
  • 4. Image quality
  • 5. Pixel Operation
  • 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
9.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
10.ROS+OpenCV course
  • 1.QR code
  • 2.Human pose estimation
  • 3.Target Detection
  • 4.AR vision
  • 5.AR QR code
  • 6.ROS+Opencv basics
  • 7.Face recognition
  • 8.harris corner detection
  • 9.Target tracking algorithm
  • 10.Contour moment
  • 11.Polygon outline
  • 12.Discrete fourier transform algorithm
  • 13.Edge detection algorithm
  • 14.Face detection algorithm
  • 15.Optical flow detection algorithm
  • 16.Contour detection
  • 17.General contour detection
  • 18.Feature point tracking
  • 19.HLS color filtering
  • 20.Hough circle detection
  • 21.Hough linear detection
  • 22.HSV color filtering
  • 23.LK optical flow algorithm
  • 24.Detection algorithm
  • 25.Phase dependent displacement detection
  • 26.Image pyramid sampling algorithm
  • 27.RGB color filtering
  • 28.Clear background detection algorithm
  • 29.Simplified optical flow algorithm
  • 30.Simple filter
  • 31.Threshold image processing
  • 32.Watershed segmentation algorithm
11.Robotic arm control course
  • 1.Send data
  • 2.Read data
  • 3.Control robotic arm joint angle
  • 4.Control coordinates of robotic arm end
  • 5.Control robotic arm posture
  • 6.Handle control robotic arm
  • 7.Calibrate robotic arm joint
  • 8.Calibrate robotic arm gripper
12.AI visual basic ourse
  • 1.Camera driver
  • 2.Block color calibration
  • 3.Color recognition
  • 4.Face detection
  • 5.Gesture recognition
  • 6.Model training
  • 7.Garbage recognition
13.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
14.AI visual tracking course
  • 1.PID algorithm basics
  • 2.Color block positioning
  • 3.Color block tracking
  • 4.Face positioning
  • 5.Face tracking
  • 6.Fingertip trajectory recognition
  • 7.Mediapipe gesture control robotic arm action group
  • 8.Mediapipe arm posture control robotic arm
  • 9.Mediapipe palm arm target positioning
  • 10.Mediapipe palm recognition and tracking control robotic arm
  • 11.Apriltag tag code recognition
  • 12.Apriltag tag code tracking
  • 13.KCF object tracking
15.AI visual grabbing course
  • 1.Gesture recognition stacking blocks
  • 2.Color recognition grab blocks
  • 3.Apriltag recognition grabbing blocks
  • 4.Catch game
  • 5.Color sorting
  • 6.Color stacking
  • 7.apriltag sorting
  • 8.apriltag stacking
  • 9.Garbage sorting
  • 10.Garbage stacking
  • 11.Color interaction
16.URDF Model course
  • 1.URDF model of the robotic arm
  • 2.URDF model controls the real robotic arm
17.Kinesiology advanced course
  • 1.Movelt configuration
  • 2.MoveIt control the real machine
  • 3.Moves randomly
  • 4.Movelt kinematic design
  • 5.Movelt cartesian path
  • 6.Movelt collision detection
  • 7.Movelt scene design
18.Jetson Orin SUPER -Introduction to the Jetcobotnew
  • 1. Version Configuration Instructionsnew
  • 2. Power adapter usage specificationsnew
  • 3. Common Problems and Solutionsnew
  • 4. First-time use instructionsnew
  • 5. Get startednew
  • 6. Open and close the virtual desktopnew
19.Jetson Orin SUPER -Assembly and assembly tutorial
  • Installation Videonew
20.Jetson Orin SUPER -Linux operating systemnew
  • 1.Introduction to Linux systemnew
  • 2.Ubuntu file systemnew
  • 3.Ubuntu common commandsnew
  • 4.Ubuntu common editorsnew
  • 5.Ubuntu software operation commandsnew
  • 6.Virtual machine installationnew
  • 7.SSH remote controlnew
  • 8.VNC remote controlnew
  • 9.Transfer files remotelynew
  • 10.Driver library and communicationnew
  • 11.Static IP and hotspot modenew
  • 12.Bind device IDnew
  • 13.Capacity expansion and resource allocationnew
  • 14.Update system software sourcesnew
  • 15.Set root user passwordnew
  • 16.sudo free passwordnew
  • 17.Connect to WiFi networknew
  • 18.View system versionnew
  • 19.Customized service managementnew
  • 20.Back up system imagenew
21.Jetson Orin SUPER -Jupyter Lab Basic Course
  • 1. Read the document before operationnew
  • 2. JupyterLab environment construction tutorialnew
  • 3. JupyterLab Basic Usage Tutorialnew
  • 4. JupyterWidgets Configuration Tutorialnew
  • 5. Getting Started Tutorial for JupyterWidgets Controlsnew
22.Jetson Orin SUPER -Basic motherboard usage course
  • 1. Motherboard Introductionnew
  • 2. Restore the factory image systemnew
  • 3. System startupnew
  • 4. System and desktop introductionnew
  • 5.VNC Remote Controlnew
  • 6. System expansionnew
  • 7. Network Configurationnew
  • 8.SSH remote loginnew
  • 9. Remote file transfernew
  • 10. Chinese input methodnew
  • 11. Use of Jtop toolnew
  • 12. Swap space expansionnew
  • 13.Backup SSD systemnew
  • 14.Jetpack old system upgraded to SUPER system
23.Jetson Orin SUPER -GPIO hardware control course
  • 1.GPIO library API usagenew
  • 2.Hardware library configurationnew
  • 3.Pin reading functionnew
  • 4.Pin level output controlnew
  • 5.Control LEDnew
  • 6.External device serial communicationnew
  • 7.I2C communicationnew
24.Jetson Orin SUPER -YOLO11 development
  • 01.YOLO environment constructionnew
  • 02.CLI usenew
  • 03.Object detection
  • 04.Instance segmentationnew
  • 05.Pose estimationnew
  • 06.Image classificationnew
  • 07.Oriented object detectionnew
  • 08.Model conversionnew
  • 09.Dataset annotationnew
  • 10.Model training conversionnew
  • 11.Model predictionnew
25.Jetson Orin SUPER -OpenCV course
  • 1. Introduction to Open Source CVnew
  • 2. Image reading and displaynew
  • 3. Image writingnew
  • 4. Image qualitynew
  • 5. Pixel Operationnew
  • 6. Image zoomnew
  • 7. Picture cutnew
  • 8. Picture pannew
  • 9. Picture mirroringnew
  • 10. Affine transformationnew
  • 11. Picture rotationnew
  • 12. Perspective transformationnew
  • 13. Grayscale processingnew
  • 14. Binary imagenew
  • 15. Edge green detectionnew
  • 16. Line segment drawingnew
  • 17. Rectangular circle drawingnew
  • 18. Text and picture drawingnew
26.Jetson Orin SUPER -ROS2 basic course
  • 1. Introduction to ROS2new
  • 2.ROS2 install Humblenew
  • 3.ROS2 development environmentnew
  • 4.ROS2 workspacenew
  • 5.ROS2 function packagenew
  • 6.ROS2 nodenew
  • 7.ROS2 topic communicationnew
  • 8.ROS2 service communicationnew
  • 9.ROS2 action communicationnew
  • 10.ROS2 custom interface messagenew
  • 11.ROS2 parameter service casenew
  • 12.ROS2 meta-function packagenew
  • 13.ROS2 distributed communicationnew
  • 14.ROS2 DDSnew
  • 15.ROS2 time related APInew
  • 16.ROS2 common command toolsnew
  • 17.ROS2 rviz2 usenew
  • 18.ROS2 rqt toolboxnew
  • 19.ROS2 Launch startup file configurationnew
  • 20.ROS2 recording and playback toolnew
  • 21.ROS2 URDF modelnew
  • 22.ROS2 Gazebo simulation platformnew
  • 23.ROS2 TF2 coordinate transformationnew
27.Jetson Orin SUPER -Advanced vision course
  • 1.QR codenew
  • 2.Human pose estimationnew
  • 3.Target Detectionnew
  • 4.AR visionnew
  • 5.ROS+Opencv Basicsnew
28.Jetson Orin SUPER -Robotic Arm Control Course
  • 1. Sending Datanew
  • 2. Read datanew
  • 3. Control the joint angle of the robotic armnew
  • 4. Control the coordinates of the end of the robot armnew
  • 5. Control the posture of the robotic armnew
  • 6.Handle remote control robotic armnew
  • 7. Calibrate the robot arm jointsnew
  • 8. Calibrate the robot grippernew
29.Jetson Orin SUPER -AI Vision Basic Course
  • 1. Camera drivernew
  • 2. Color threshold adjustment and color block calibrationnew
  • 3. Color recognitionnew
  • 4. Face Detectionnew
  • 5. Gesture Recognitionnew
  • 6. Garbage Identificationnew
30.Jetson Orin SUPER -Mediapipe Development Course
  • 1. Hand detectionnew
  • 2. Posture detectionnew
  • 3. Overall detectionnew
  • 4.FaceMeshnew
  • 5. Face Detectionnew
  • 6. Face effectsnew
  • 7. 3D Object Recognitionnew
  • 8. Brushnew
  • 9. Finger controlnew
  • 10. Gesture Recognitionnew
31.Jetson Orin SUPER -AI visual tracking course
  • 1.PID algorithm basicsnew
  • 2. Color block positioning experimentnew
  • 3. Color block tracking experimentnew
  • 4. Face location experimentnew
  • 5. Face tracking experimentnew
  • 6. Fingertip trajectory recognitionnew
  • 7.Mediapipe gesture control robotic arm action groupnew
  • 8.Mediapipe arm posture control robotic armnew
  • 9. Mediapipe palm arm target positioningnew
  • 10. Mediapipe identifies and tracks your palm to control the robotic armnew
  • 11. Apriltag label code recognitionnew
  • 12. Apriltag label code trackingnew
  • 13.KCF Object Trackingnew
32.Jetson Orin SUPER -AI Visual Capture Course
  • 1. Gesture recognition stacking blocksnew
  • 2. Color recognition grab blocksnew
  • 3. Apriltag recognition and grabbing building blocksnew
  • 4.Catch gamenew
  • 5. Color sortingnew
  • 6. Color stackingnew
  • 7. Apriltag sortingnew
  • 8. Apriltag stackingnew
  • 9. Garbage sortingnew
  • 10. Garbage dumpnew
  • 11.Color interactionnew
33.Jetson Orin SUPER -Robotic Arm URDF Model Course
  • 1. Robotic arm URDF modelnew
  • 2. URDF model controls the real robot armnew
34.Jetson Orin SUPER -Moveit advanced kinematics course
  • 1.Movelt Configurationnew
  • 2. Use of MoveItnew
  • 3. MoveIt drives the real machinenew
  • 4. MoveIt Random Movenew
  • 5.Movelt positive kinematics designnew
  • 6.Movelt inverse kinematics designnew
  • 7.Movelt Cartesian pathnew
  • 8.Movelt scene designnew
  • 9.Movelt Collision Detectionnew
35.FAQnew
  • 1.Using your own Jetson board, JetCobot cannot start normally
Download
  • Instruction_Manual
  • SystemFile_for_NANO4GB
  • SystemFile_For_OrinNANO
  • SystemFile_For_OrinNX
  • VM_File
  • Code
  • 3D Model File
  • Jetson_Boot_USB.tar.gz
  • ROS2_Basic_VM
Download ZIP

Welcome to JetCobot 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,

xxxxxxxxxx
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.

xxxxxxxxxx
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.

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

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

xxxxxxxxxx
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,

xxxxxxxxxx
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:

xxxxxxxxxx
<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

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