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ROSMASTER X3 Plus📖
1.Preparation and First Trial
  • 1.1 Wiring Introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
  • 1.4 Precautions for DIY Robotic Arm
2. Assembly steps
  • Installation Video
3.Remote control course
  • 3.1 APP remote control
  • 3.2 Model aircraft remote control
  • 3.3 Handle remote control
4.Hardware course
  • 4.1 About expansion board_V3.0
  • 4.1 About expansion board_V1.0
  • 4.2 STM32 Development
  • 4.3 Button to control the buzzer
  • 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
  • 4.10 Control serial port servos
  • 4.11 Nine-axis attitude sensor
  • 4.12 Control motor
  • 4.13 Timer captures the encoder data
  • 4.14 Kinematic Analysis
  • 4.15 PID control robot movement
5.Basic course
  • 5.1 Update the firmware
  • 5.2 Turn off the self-starting process
  • 5.3 Install the Rosmaster driver
  • 5.4 Buzzer whistle
  • 5.5 Control PWM servo
  • 5.6 RGB colorful light bar
  • 5.7 Control motor
  • 5.8 Control robot movement
  • 5.9 Control Serial Servo
6.Linux operating system
  • 6.1 Virtual machine installation
  • 6.2 Linux basics
  • 6.3 Remote control
  • 6.4 Multi-machine communication
  • 6.5 Static IP and hotspot mode
  • 6.6 Bind the device ID
  • 6.7 Web page real-time monitor
  • 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.Docker-Just for pi5
  • 7.1 Docker overview and docker installation
  • 7.2 Common commands for docker image containers
  • 7.3 Docker images deeply understand and publish images
  • 7.4 Docker hardware interaction and data processing
  • 7.5 Enter the bot's docker container
  • 7.6 Robot development environment construction in Docker
  • 7.7 How customers can update docker images in the future
8.ROS basic course
  • 8.1 ROS introduction
  • 8.2 ROS installation
  • 8.3 ROS common command tools
  • 8.4 ROS workspace
  • 8.5 ROS function package
  • 8.6 ROS node
  • 8.7 ROS topic publisher
  • 8.8 ROS topic subscribers
  • 8.9 ROS service client
  • 8.10 ROS service server
  • 8.11 ROS action client
  • 8.12 ROS action server
  • 8.13 ROS custom message reception
  • 8.14 ROS-launch file
  • 8.15 ROS-TF transformation
  • 8.16 ROS parameter service
  • 8.17 ROS-rviz use
  • 8.18 ROS-rqt tool usage
  • 8.19 Topic message recording and playback
  • 8.20 urdf model introduction
  • 8.21 gazebo introduction
  • 8.22 ROS distributed communication
9.OpenCV courses
  • 9.1 Getting started with Open Source CV
  • 9.2 Geometric transformation
  • 9.3 Image processing and draw
  • 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
  • 9.11 MediaPipe development
10.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.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
  • 11.7 RTAB-Map 3D mapping
  • 11.8 ORB_SLAM2 basics
  • 11.9 ORB_SLAM2_Octomap
12.Lidar course
  • 12.1 Lidar basic-SLAM
  • 12.1 Lidar basic-EAI
  • 12.2 Lidar avoiding
  • 12.3 Lidar guard
  • 12.4 Lidar follow
  • 12.5 Patrol game
  • 12.6 gmapping mapping algoritm
  • 12.7 hector mapping algorithm
  • 12.8 karto mapping algorithm
  • 12.9 cartographer mapping
  • 12.10 rrt_exploration mapping
  • 12.11 AMCL
  • 12.12 Navigation and avoiding
  • 12.13 APP mapping and navigation
13.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.Deep learning courses
  • 14.1 KNN recognizes handwritten
  • 14.2 Basic use of TensorFlow
  • 14.3 Basic use of PyTorch(jetson)
  • 14.4 yolov5 model training(jetson)
  • 14.5 yolov5+tensorrt acceleration
  • 14.6 yolov4-tiny
15.Voice control courses
  • 0. How to use old voice module in new image
  • 15.1 Introduction of voice control-new
  • 15.2 Voice control module port-new
  • 15.3.Introduction of voice module-old
  • 15.4.Voice control module port binding-old
  • 15.5 Voice control car movement
  • 15.6 Voice control Autopilot
  • 15.7 Voice control color recogzition
  • 15.8 Voice control color tracking
  • 15.9 Voice control multi-point nagavation
16.Robotic arm control course
  • 16.1 MoveIt configuration
  • 16.2 MoveIt control the real machine
  • 16.3 MoveIt moves randomly
  • 16.4 MoveIt kinematics design
  • 16.5 MoveIt cartesian path
  • 16.6 MoveIt avoiding
  • 16.7 MoveIt scene design
  • 16.8 MoveIt trajectory planning
  • 16.9 Mediapipe palm control car
  • 16.10 Mediapipe gesture control robotic arm
  • 16.11 Mediapipe gesture control car
  • 16.12 Mediapipe arm attitude control robotic arm
  • 16.13 Mediapipe recognizes and tracks palm control robotic arm
  • 16.14 Recognizes color block and handling
  • 16.15 Autopilot to clear obstacles
17.Voice control robotic arm course
  • 17.1 Voice control robotic arm movement
  • 17.2 Voice control robotic arm learning
  • 17.3 Voice control color block transport
  • 17.4 Voice control robotic arm grip objects
  • 17.5 Voice control robotic arm garbage sorting
18.Model training Autopilot
  • 18.0 ReadMe
  • 18.1 yolov5 to train traffic signs
  • 18.2 TensorRt acceleration identification mark
  • 18.3 Road sign indication function description
  • 18.4 Collect data
  • 18.5 Training model
  • 18.6 Model transformation and application
  • 18.7 AI Autopilot
19.For Orin-Remote control course
1. APP control
  • 1. APP control
  • 2. Model aircraft control
  • 3. Handle control
2. Model aircraft control
3. Handle control
20.Orin-Hardware course
  • 1. About expansion board_V1.0
  • 1. About expansion board_V2.0
  • 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
21.Orin-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
22.Orin-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 Jetson Xavier NX write system
  • 9.3 Jetson TX2 NX-Orin series write system
  • 9.4 Raspberry Pi write system
  • 10.Jetpack old system upgraded to SUPER system
23.Orin-Dockernew
SUPER image uses ros environment in docker
  • 1、Docker overview and docker installationnew
  • 2、Common commands for docker image containersnew
  • 3、Docker images deeply understand and publish imagesnew
  • 4、Docker hardware interaction and data processingnew
  • 5、Enter the bot's docker containernew
  • 6、Robot development environment construction in Dockernew
23.Orin-ROS basic course
  • 1.ROS introduction
  • 2.Project file structure
  • 3.Common commands and tools
  • 4.Publisher
  • 5.Subscribers
  • 6.Customize topic messages and use
  • 7.Client
  • 8.Server
  • 9.Custom service messages and usage
  • 10.TF release and monitoring
24.Orin-OpenCV courses
  • 1. Getting started with Open Source CV
  • 2. Open Source CV geometric transformation
  • 3. Open Source CV image processing and drawing text line segments
  • 4. Open Source CV image beautification
  • 5. Opencv application
  • 6. AR vision
  • 7. AR QR code
  • 8. ROS+Opencv foundation
  • 9. ROS+Opencv application
  • 10. Data transformation and point cloud
  • 11. MediaPipe development
25.Orin-Robot control course
  • 1.PID algorithm theory
  • 2.Robot PID debugging
  • 3.Robot information release
  • 4.Robot keyboard control
  • 5.Robot handle control
  • 6.Robot state estimation
  • 7.Robot calibration
  • 8.Robot URDF model
26.Orin-Depth camera course
  • 01.How to use the Astra camera
  • 02.Astra camera calibration
  • 03.Astra Color Tracking
  • 04.Astra Object Tracking
  • 05.Visual tracking autopilot
  • 06.Pure visual 2D mapping and navigation
  • 07.RTAB-Map 3D mapping navigation
  • 08.ORB_SLAM2 basics
  • 09.ORB_SLAM2_Octomap
27.Orin-Lidar course
  • 1.Lidar basic-4ROS-EAI
  • 1.Lidar basic-SLAM
  • 2.Lidar avoiding
  • 3.Lidar guard
  • 4.Lidar follow
  • 5.Patrol game
  • 6.gmapping mapping algorithm
  • 7.hector mapping algorithm
  • 8.karto mapping algorithm
  • 9.cartographer mapping algorithm
  • 10.rrt_exploration mapping algorithm
  • 11.AMCL
  • 12.Navigation and avoiding
  • 13.APP mapping and navigation
28.Orin-Multi-robot control course
  • 1.Multi-machine handle control
  • 2.Multi-machine navigation
  • 3.Multi-machine formation
  • 4.Multi-machine surround
29.Orin-Deep learning courses
  • 1.KNN recognizes handwritten digits
  • 2.Basic use of TensorFlow
  • 3.Basic use of PyTorch(jetson)
  • 4.YOLOv11Model conversion(SUPER)new
  • 5.YOLOv11Dataset annotation(SUPER)new
  • 6.YOLOv11Model conversion + tensorrt acceleration(SUPER)new
  • 7.YOLOv11Object recognition(SUPER)new
30.Orin-Voice control courses
  • 0. How to use old voice module in new image
  • 1.1 Introduction of voice module-new
  • 1.2 Introduction of voice module-old
  • 2.1 Voice control module port binding-new
  • 2.2 Voice control module 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
31.Orin-Robotic arm control course
  • 1.MoveIt configuration(VM)
  • 2.MoveIt control the real machine(VM)
  • 3.MoveIt moves randomly(VM)
  • 4.MoveIt kinematics design(VM)
  • 5.MoveIt cartesian path(VM)
  • 6.MoveIt avoiding(VM)
  • 7.MoveIt scene design(VM)
  • 8.MoveIt trajectory planning(VM)
  • 9.Mediapipe palm control car
  • 10.Mediapipe gesture control robotic arm
  • 11.Mediapipe gesture control car
  • 12.Mediapipe arm attitude control robotic arm
  • 13.Mediapipe recognizes and tracks palm control robotic arm
  • 14.Recognizes color block and handling
  • 15.Autopilot to clear obstacles
32.Orin-Voice control robotic arm course
  • 1.Voice control robotic arm movement
  • 2.Voice control robotic arm learning
  • 3.Voice control color block transport
  • 4.Voice control robotic arm grip objects
  • 5.Voice control robotic arm garbage sorting
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
  • Using your own Jetson board, car cannot start normally
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • SystemFile_OrinNANOSUPER
  • SystemFile_OrinNXSUPER
  • SystemFile_RPi_NANO
  • Instruction Manual
  • Code
  • Autopilot Annex
  • VM software
  • Jetson_Boot_USB.tar.gz
  • Other_Annex_File
Download ZIP
FAQ
  • 1、How to connect OLED?
  • 2、Precautions for DIY Robotic Arm

Welcome to ROSMASTER X3 Plus repository

2.Multi-machine navigation

2 Multi-machine navigation

2 Multi-machine navigation 2.1 Introduction 2.2 use 1.2.1 Turn on the robot 1.2.2 Enable multi-machine navigation 1.2.3 Set the communication topic 1.2.4 Set the initial pose 2.3 launch file 2.4 frame analysis

2.1 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

Open the [.bashrc] file

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 

2.2 use

Take the virtual machine as the host and the three robots as slaves as an example; a map must be available before use.

1.2.1 Turn on 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.

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.

1.2.2 Enable multi-machine navigation

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

image-20220303102707202

After the multi-machine navigation is turned on, it is densely packed, and the pose of each robot is incorrect. First, set the initial pose for each robot.

1.2.3 Set the communication topic

image-20220303103526172

Right-click on the toolbar(green box) and a dialog box(red box) will pop up, as shown in the figure above, right-click at [2D Pose Estimate], and then select [Tool Properties], as shown in the figure below, a dialog box will pop up.

image-20220303103719432

From top to bottom [2D Pose Estimate], [2D Nav Goal] and [rviz] toolbar correspond one by one from left to right. Set the topic name pointed by the red arrow, this method is quick and convenient.

What if there are 4 or more robots and the initial pose and navigation icons seem to be insufficient?

At this time, you need to open the [navigation_multi.rviz] file(whichever rviz is used, take [navigation_multi.rviz] as an example), and find the following content. These contents correspond to the icons of the rviz toolbar one by one. If you want to add which icon, copy the entire [Class] content of which icon, you must not miss anything, and the format must be correct.

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Tools : 
    -  Class :  rviz/MoveCamera 
    -  Class :  rviz/Interact 
      Hide  Inactive  Objects :  true 
    -  Class :  rviz/Select 
    -  Class : rviz/SetInitialPose 
      Theta  std  deviation :  0.2617993950843811 
      Topic :  robot1/initialpose 
      X  std  deviation :  0.5 
      Y  std  deviation :  0.5 
    -  Class :  rviz/SetGoal 
      Topic :  robot1/move_base_simple/goal 
    -  Class : rviz/SetInitialPose 
      Theta  std  deviation :  0.2617993950843811 
      Topic :  robot2/initialpose 
      X  std  deviation :  0.5 
      Y  std  deviation :  0.5 
    -  Class :  rviz/SetGoal 
      Topic :  robot2/move_base_simple/goal 
    -  Class : rviz/SetInitialPose 
      Theta  std  deviation :  0.2617993950843811 
      Topic :  robot3/initialpose 
      X  std  deviation :  0.5 
      Y  std  deviation :  0.5 
    -  Class :  rviz/SetGoal 
      Topic :  robot3/move_base_simple/goal 
    -  Class :  rviz/Measure 

1.2.4 Set the initial pose

There are too many robots on the map, and the information on the map is particularly complicated. In this case, you can tick the check mark behind the robot in the [Displays] display item list on the left, and set the pose for each robot in turn. The effect after setting is as follows. Once set up you can navigate.

image-20220303103345297

  • Use the [2D Pose Estimate] of the [rviz] tool to set the initial pose until the position of the car in the simulation is consistent with the position of the actual car.
  • Click the [2D Nav Goal] of the [rviz] tool, and then select the target point on the map where there are no obstacles, release the mouse to start the navigation, only one target point can be selected, and it will stop when it arrives.

2.3 launch file

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< launch > 
    < arg  name = "first_robot1"  default = "robot1" /> 
    < arg  name = "second_robot2"  default = "robot2" /> 
    < arg  name = "third_robot3"  default = "robot3" /> 
    <!-- Whether to open rviz || Whether to open rviz --> 
    < arg  name = "use_rviz"  default = "true" /> 
    <!-- map name|| Map name --> 
    < arg  name = "map"  default = "my_map" /> 
    <!-- 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 $(find yahboomcar_multi)/rviz/navigation_multi.rviz"  if = "$(arg use_rviz)" /> 
    <!-- Multi machine handle control|| Multi machine handle control--> 
    < include  file = "$(find yahboomcar_multi)/launch/joy_multi.launch" /> 
    <!-- Mobile app node|| Mobile app node--> 
    < include  file = "$(find yahboomcar_nav)/launch/library/app.launch" /> 
    <!-- ############################# first_robot1 ############################# --> 
    < include  file = "$(find yahboomcar_multi)/launch/library/move_base_multi.launch" > 
        < arg  name = "ns"  value = "$(arg first_robot1)" /> 
    </ include > 
    <!-- ############################# second_robot2 ############################# --> 
    < include  file = "$(find yahboomcar_multi)/launch/library/move_base_multi.launch" > 
        < arg  name = "ns"  value = "$(arg second_robot2)" /> 
    </ include > 
    <!-- ############################# third_robot3 ############################# --> 
    < include  file = "$(find yahboomcar_multi)/launch/library/move_base_multi.launch" > 
        < arg  name = "ns"  value = "$(arg third_robot3)" /> 
    </ include > 
</ launch > 

You can modify it according to your own needs. If there are 4 or more robots, you can add relevant content following the cases of the first 3 robots.

2.4 frame analysis

  • Node view
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rqt_graph 

1225

There are a lot of node graphs, which look complicated, but are actually very organized. The internal nodes are almost the same for each robot, as is the navigation for a single robot. It should be noted here that the map loading only needs to be loaded once, and every robot does not need to be loaded. At the same time, I also opened the node controlled by the handle. One handle controls multiple robots at the same time, and only needs to start the [joy_node] node once.

  • View tf tree
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rosrun rqt_tf_tree rqt_tf_tree 

1226

There are also a lot of tf coordinate systems, which need to be enlarged. There is only one [map] in the whole world, and each [amcl] locates each robot separately, and then it is the same as the navigation of a single robot.

Multi-machine handle control
Multi-machine formation

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