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ROSMASTER R2📖
1.Preparation and First Trial
  • 1.1 Accessories introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
2.Assembly steps
  • Installaction Video
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
  • 11.1 Lidar basic-SLAM
  • 11.1 Lidar basic-EAI
  • 11.2 Lidar avoiding
  • 11.3 Lidar guard
  • 11.4 Lidar follow
  • 11.5 Patrol game
  • 11.6 gmapping mapping
  • 11.7 hector mapping
  • 11.8 karto mapping
  • 11.9 cartographer mapping
  • 11.10 rrt_exploration mappin
  • 11.11 AMCL
  • 11.12 Navigation and avoiding
  • 11.13 APP mapping and navigation
  • 11.14 TEB path planning algorithm
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 remote control
  • 2. Model aircraft remote control
  • 3. Handle remote control
18.ROS2-Hardware course
  • 1. Expansion board introduction_V3.0
  • 1. Expansion board introduction_V1.0
  • 2. STM32 development environment build
  • 3. Key control buzzer sounding
  • 4. FreeRTOS applications
  • 5. Serial communication
  • 6. SBUS model airplane remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt control PWM servos
  • 10. Control serial servos
  • 11. Nine-axis attitude sensor acquires data
  • 12. Control motor forward and reverse
  • 13. Timer Capture Encoder Data
  • 14. Theory of Robot Kinematics Analysis
  • 14.1.Mecanum wheel
  • 14.2.Ackerman
  • 14.3.4WD
  • 15. PID control of robot motion
19. ROS2-Basic course
  • 1. Update the firmware of the expansion board
  • 2.Close the large self-starting program at boot
  • 3. Install Rosmaster driver library
  • 4. Buzzer sounding
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects show
  • 7. Control motor forward and reverse
  • 8. Controlling robot motion
  • 9. Control serial servos
20. ROS2-Linux operating systemnew
  • 01. Virtual Machines
  • 02. Linux Basics
  • 03. Remote control
  • 04. Multi-computer communication configuration
  • 05. Static ip and hotspot mode
  • 06. Bind device ID
  • 07.Webpage real-time monitoring
  • 08. Expansion Tutorial
  • 09-1.Jetson Nano write system file
  • 09-2. Raspberry Pi write system file
  • 09-3.Orin write system
  • 10.Jetpack old system upgraded to SUPER systemnew
21.ROS2 -Docker(Nano Pi)
  • 1. docker overview and docker installation
  • 2. docker image container common command
  • 3. docker image in-depth understanding and publishing images
  • 4. docker hardware interaction and data processing
  • 5. Enter the docker container of the robot
  • 6. docker in the robot development environment build
  • 7. Customer's method of updating 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-Lidar course
  • 1-1. Introduction and use of SLAM lidar
  • 1-2. Introduction and use of 4ROS lidar
  • 2. Lidar obstacle avoidance fun gameplay
  • 3. Lidar tracking fun gameplay
  • 4. Patrol function play
  • 5. gmapping map building algorithm
  • 6. cartographer building algorithm
  • 7. Navigation and obstacle avoidance
  • 8. Rosmaster_R2 using the app to build maps
  • 9. Rosmaster_R2 navigating with the app
25. 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. Patrol line autopilot
26. ROS2-Depth camera courses
  • 1. Depth camera use
  • 2. Camera 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
27.ROS2- Voice control courses
  • 0. How to use old voice module in new image
  • 1.1 Introduction to the module-New
  • 1.2 Voice Control Module Port Binding-New
  • 1.3 Introduction to the module-Old
  • 2. Voice control car movement
  • 3. Voice control autopilot
  • 4. Voice control color recognition
  • 5. Voice control color tracking
  • 6. 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, car cannot start normally
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • Instruction Manual
  • SystemFile_ROS1
  • SystemFile_ROS2
  • SystemFile_ROS2_OrinNANOSUPER
  • SystemFile_ROS2_OrinNXSUPER
  • VM software
  • Code
  • Jetson_Boot_USB.tar.gz
  • Autopilot Annex
  • Other_Annex_File
  • LLM_SystemFile
  • ROS2_Basic_VM
Download ZIP

Welcome to ROSMASTER R2 repository

13.4 Multi-machine surround

4. Multi-machine surround

4. Multi-machine surround 4.1 Introduction 4.2 Use 4.2.1 Start the robot 4.2.2 Turn on multi-machine surround 4.2.3 Formation control 4.3 launch file 4.4 frame analysis

4.1 Introduction

For the problem of how to configure multi-machine communication and synchronization time, please refer to the lesson [Multi-machine handle control] for details; if there is a network, the network system time can be directly synchronized without setting.

When using multi-machine handle control, it is first necessary to ensure that the robot is under the same local area network and configured with the same [ROS_MASTER_URI]; for multiple robots to control motion, there can only be one host. The example in this section sets the virtual machine as the host, and other robots as the slaves. There are several slaves. Of course, you can also set one robot as the master and others as the slaves.

According to different models, you only need to set the purchased model in [.bashrc], X1(ordinary four-wheel drive) X3(Mike wheel) X3plus(Mike wheel mechanical arm) R2(Ackerman differential) and so on. Section takes X3 as an example

#Raspberry Pi 5 master needs to enter docker first, please perform this step
#If running the script into docker fails, please refer to ROS/07, Docker tutorial
~/run_docker.sh

Open the [.bashrc] file

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sudo vim .bashrc 

Find the [ROBOT_TYPE] parameter and modify the corresponding model

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export  ROBOT_TYPE=X3    # ROBOT_TYPE: X1 X3 X3plus R2 X7 

4.2 Use

Take the virtual machine as the host and the three robots as slaves as an example; a map must be available before use. The two slaves are [robot1] and [robot2] respectively, and [robot1] is set as the leader, and [robot2] is set as the follower. Make sure the field is large enough to avoid collisions when playing this feature. And no obstacle avoidance function. This routine does not support the Raspberry PI 5 board driver

4.2.1 Start the robot

virtual machine side

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roscore 

Start the command(robot1 side), for the convenience of operation, this section takes [mono + laser + yahboomcar] as an example.

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roslaunch  yahboomcar_multi  laser_bringup_multi.launch  ns := robot1             # laser + yahboomcar 
roslaunch  yahboomcar_multi  laser_usb_bringup_multi.launch  ns := robot1         # mono + laser + yahboomcar 
roslaunch  yahboomcar_multi  laser_astrapro_bringup_multi.launch  ns := robot1    # Astra + laser + yahboomcar 

Start command(robot2 side), for the convenience of operation, this section takes [mono + laser + yahboomcar] as an example.

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roslaunch  yahboomcar_multi  laser_bringup_multi.launch  ns := robot2             # laser + yahboomcar 
roslaunch  yahboomcar_multi  laser_usb_bringup_multi.launch  ns := robot2         # mono + laser + yahboomcar 
roslaunch  yahboomcar_multi  laser_astrapro_bringup_multi.launch  ns := robot2    # Astra + laser + yahboomcar 

More bots and so on.

4.2.2 Turn on multi-machine surround

For the process of opening the handle control, please refer to the lesson [Multi-machine handle control].

virtual machine side

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roslaunch yahboomcar_multi tf_roundbroad.launch use_rviz:=true map:=my_map 
  • [use_rviz] parameter: whether to open rviz.
  • [map] Parameters: map name, the map to be loaded.

After startup, you need to initialize the pose setting of the robot. For the specific setting method, please refer to [Multi-machine Navigation This Lesson]. After setting, the following figure is shown.

image-20220303122042470

4.2.3 Formation control

Open the dynamic parameter adjustment tool

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rosrun rqt_reconfigure rqt_reconfigure 

image-20220303123936059

The tool can be set individually for each robot.

parameter parsing

[lin_Kp], [lin_Ki], [lin_Kd]: PID debugging of trolley linear speed.

[ang_Kp], [ang_Ki], [ang_Kd]: PID debugging of car angular velocity.

parameterscopeParse
[teams]Default[vertical]This parameter is invalid under this function
[robot_model]Default [omni]Model: [omni, diff]
[navigate][False,True]whether to run in navigation mode
[Switch][False,True]Function switch [Start/Pause]
[dist][0.5, 2.0]wrap radius

After the [Switch] function switch is turned on, the robot automatically obtains its own position and the position launched by the pilot robot. When it reaches the closest point for the second time, it will follow the robot to automatically circle. As shown below

image-20220303123854168

4.3 launch file

tf_roundbroad.launch

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< launch > 
    < arg  name = "first_robot1"  default = "robot1" /> 
    < arg  name = "second_robot2"  default = "robot2" /> 
    <!--  rviz  ||  Whether  to  open  rviz  --> 
    < arg  name = "use_rviz"  default = "true" /> 
    <!--  Map name  ||  Map  name  --> 
    < arg  name = "map"  default = "my_apm" /> 
    <!--  Load map  ||  Load  map  --> 
    < node  name = "map_server"  pkg = "map_server"  type = "map_server"  args = "$(find yahboomcar_nav)/maps/$(arg map).yaml" /> 
    < node  pkg = "rviz"  type = "rviz"  name = "rviz"  required = "true"  args = "-d $(find yahboomcar_multi)/rviz/tf_multi.rviz"  if = "$(arg use_rviz)" /> 
    <!--  ############################# first_robot1 ############################# --> 
    < node  pkg = "yahboomcar_multi"  type = "broad_round.py"  name = "RoundBroad"  output = "screen"  args = "$(arg first_robot1)" /> 
    < include  file = "$(find yahboomcar_multi)/launch/library/move_base_multi.launch" > 
        < arg  name = "ns"  value = "$(arg first_robot1)" /> 
    </ include > 
    <!--  ############################# second_robot2 ############################# --> 
    < node  pkg = "yahboomcar_multi"  type = "listener.py"  name = "RobotListener"  output = "screen" 
          args = "$(arg second_robot2) point1"  ns = "$(arg second_robot2)/" > 
        < rosparam  param = "linPIDparam" > [ 1.0, 0, 1.0 ] </ rosparam > 
        < rosparam  param = "angPIDparam" > [ 0.8, 0, 1.0 ] </ rosparam > 
    </ node > 
    < include  file = "$(find yahboomcar_multi)/launch/library/move_base_multi.launch" > 
        < arg  name = "ns"  value = "$(arg second_robot2)" /> 
    </ include > 
</ launch > 

4.4 frame analysis

Node view

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rqt_graph 

rosgraph

View tf tree

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rosrun rqt_tf_tree rqt_tf_tree 

frames

It can be seen from the above figure that [robot1] will send out the [point1] coordinate system, and [robot2] monitors the relationship between itself and the coordinate system in real time, and makes its own coordinate system coincide with the coordinate system. In the [tf_roundbroad.launch] file, we can see that [robot2] follows the [point1] coordinate system.

Multi-machine formation
KNN recognizes handwritten digits

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EN/中文