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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

1.Lidar basic-4ROS-EAI

1、Lidar basic-4ROS-EAI

1、Lidar basic-4ROS-EAI1.1、Overview1.2、4ROS Lidar1.2.1、Overview1.2.2、Product Features1.2.3、Application scenarios1.2.4、Polar coordinatesystem definition1.3、Single-line lidar principle1.3.1、Trigonometric ranging1、Direct shot2、Oblique shot1.3.2、TOF (Time-of-flight) ranging1.4、Using 4ROS lidar1.7、launch parsing

  • For different models of lidar Before using LiDAR, it is necessary to declare the 【RPLIDAR_TYPE】 variable in advance in the 【. bashrc]】file based on different radar models. Open the 【. bashrc】 file.
sudo vim ~/.bashrc

If the following sentence is not present in 【. bashrc】, it needs to be manually added according to the purchased radar model. If this sentence is present, modify the radar model directly.

For example: 4ROS Lidar

xxxxxxxxxx
export RPLIDAR_TYPE=4ROS   # a1, a2, a3, s1, s2,4ROS,X3

Note: For small cars equipped with X3 or X3Pro radar, the startup mode of the two radars should be the same, and both radars should be changed to X3.

After the modifications are completed, refresh the environment variables.

xxxxxxxxxx
source ~/.bashrc

 

1.1、Overview

Single-line LiDAR refers to the single-line radar that the beam emitted by the laser source is divided into triangular ranging and TOF LiDAR, and is mainly used in the field of robotics. It has fast scanning speed, strong resolution and high reliability. Compared with multi-line lidar, single-line lidar has faster response in angular frequency and sensitivity, so it is more accurate in ranging distance and accuracy of obstacles.

1.2、4ROS Lidar

1.2.1、Overview

YDLIDAR 4ROS lidar is a 360° 2D lidar (hereinafter referred to as: 4ROS). This product is based on the principle of pulsed ToF ranging, and is equipped with related optical, electrical, and algorithm designs to achieve high-frequency and high-precision distance measurement. At the same time, the mechanical structure rotates 360 degrees to continuously obtain angle information, thus realizing 360-degree scanning ranging , which outputs the point cloud data of the scanned environment.

1.2.2、Product Features

  • Dust and waterproof, meet IP65
  • 360 degree omnidirectional scanning, 5-12Hz adjustable scanning frequency
  • High-speed ranging, ranging frequency 20000Hz
  • Small ranging error, good ranging stability
  • Strong anti-interference ability of ambient light
  • Class I eye safety

1.2.3、Application scenarios

  • Robot navigation and obstacle avoidance
  • automated industry
  • Area security
  • Smart Transportation
  • Environment scanning and 3D reconstruction
  • Digital multimedia interaction
  • Robot ROS teaching

1.2.4、Polar coordinatesystem definition

In order to facilitate secondary development, 4ROS internally defines a polar coordinate system. The system polar coordinates take the center of the 4ROS rotating core as the pole, the specified angle is clockwise as positive (top view), and the zero angle is located in the direction of the outlet of the 4ROS interface line, as shown below.

1662125811388

1.3、Single-line lidar principle

Working principle of the single-line lidar, as shown below.

20200512204828561

1.3.1、Trigonometric ranging

According to the angle relationship between the incident beam and the surface normal of the measured object, the laser triangulation method can be divided into two types: oblique type and direct type.

1、Direct shot

image-20210914120518557

As shown below, when the laser beam is perpendicular to the surface of the object to be measured, that is, when the incident light beam is collinear with the normal to the surface of the object to be measured, it is a direct laser triangulation method.

2、Oblique shot

When the angle between the incident laser beam and the normal to the surface of the object to be measured in the optical path system is less than 90°, the incident method is oblique. The optical path diagram shown in FIG. 2 is an oblique light path diagram of the laser triangulation method.

image-20210914120552057

 

1.3.2、TOF (Time-of-flight) ranging

TOF lidar is based on measuring the time of flight of light to obtain the distance of the target. Its working principle is mainly as follows: a beam of modulated laser signal is sent out through the laser transmitter, the modulated light is reflected by the measured object and received by the laser detector, and the distance to the target can be calculated by measuring the phase difference between the emitted laser and the received laser. .

image-20210914120619928

 

1.4、Using 4ROS lidar

Run following command in terminal.

#You need to enter docker first, perform this step more
#If running the script to enter docker fails, please refer to 07.Docker-orin/05, Enter the robot's docker container
~/run_docker.sh
roslaunch ydlidar_ros_driver TG.launch

Run following command in terminal. (We can print the topic data through the terminal to check whether the radar starts normally)

<Open another terminal and enter the same docker container

image-20240408144126098

xxxxxxxxxx
rostopic echo /scan

If you want to view the scan results in RVIZ, type in the terminal.

(Just enter the following commands, the above two commands do not require input)

xxxxxxxxxx
roslaunch ydlidar_ros_driver lidar_view.launch

image-20210914120619928

1.7、launch parsing

Code path:~/software/library_ws/src/ydlidar_ros_driver-master/launch

TG.launch file

​x
<launch>
  <arg name="frame_id" default="laser"/>
  <node name="ydlidar_lidar_publisher"  pkg="ydlidar_ros_driver"  type="ydlidar_ros_driver_node" output="screen" respawn="false" >
    <!-- string property -->
    <param name="port"         type="string" value="/dev/ydlidar"/>  
    <param name="frame_id" type="string" value="$(arg frame_id)"/>
    <!--param name="ignore_array"     type="string" value="-90,90"/-->
    <param name="ignore_array"     type="string" value=""/>
    <!--remap from="scan" to="scan_raw"/-->
​
    <!-- int property -->
    <param name="baudrate"         type="int" value="512000"/>  
    <!-- 0:TYPE_TOF, 1:TYPE_TRIANGLE, 2:TYPE_TOF_NET -->
    <param name="lidar_type"       type="int" value="0"/>  
    <!-- 0:YDLIDAR_TYPE_SERIAL, 1:YDLIDAR_TYPE_TCP -->
    <param name="device_type"         type="int" value="0"/>  
    <param name="sample_rate"         type="int" value="20"/>  
    <param name="abnormal_check_count"         type="int" value="4"/>  
​
    <!-- bool property -->
    <param name="resolution_fixed"    type="bool"   value="true"/>
    <param name="auto_reconnect"    type="bool"   value="true"/>
    <param name="reversion"    type="bool"   value="true"/>
    <param name="inverted"    type="bool"   value="true"/>
    <param name="isSingleChannel"    type="bool"   value="false"/>
    <param name="intensity"    type="bool"   value="false"/>
    <param name="support_motor_dtr"    type="bool"   value="false"/>
    <param name="invalid_range_is_inf"    type="bool"   value="true"/>
    <param name="point_cloud_preservative"    type="bool"   value="false"/>
​
    <!-- float property -->
    <param name="angle_min"    type="double" value="-90" />
    <param name="angle_max"    type="double" value="90" />
    <param name="range_min"    type="double" value="0.01" />
    <param name="range_max"    type="double" value="50.0" />
    <param name="frequency"    type="double" value="10.0"/>
  </node>
  <!--node pkg="tf" type="static_transform_publisher" name="base_link_to_laser4"
    args="0.0 0.0 0.2 3.14 0.0 0.0 /base_footprint /laser 40" /-->
</launch>

Main debugging parameters:

  • angle_min parameter: the left angle of the radar
  • angle_max parameter: the right angle of the radar

For other parameters, please refer to the official documentation,

ydlidar_ros_driver/README.md at master · YDLIDAR/ydlidar_ros_driver · GitHub

ORB_SLAM2_Octomap
Lidar basic-SLAM

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