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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

12.5 Patrol game

5.Patrol game

5. Patrol gameplay

5. Patrol gameplay5.1. How to use5.1.1. Start5.1.2. Parameter modification5.1.3. Patrol function5.2. Source code analysis

5.1. How to use

Note: [R2] on the remote control handle has the [pause/start] function for this gameplay. This section takes the X3 Mai Lun car as an example.

Depending on the environment, the parameters will be different; this function requires patient debugging to achieve good results.

5.1.1. Start

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#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

One-click startup (robot side)

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roslaunch yahboomcar_bringup patrol.launch

Started successfully, print log

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# Log of successful patrol function startup
Bring up rqt_reconfigure to control the Robot.
# Log of successful startup of the underlying driver
First IMU message received.
# Log of successful lidar startup
RPLIDAR S/N: 6A97EDF9C7E29BD1A7E39EF2FA44431B
[ INFO] [1631677752.206795121]: Firmware Ver: 1.29
[ INFO] [1631677752.208026726]: Hardware Rev: 7
[ INFO] [1631677752.210976099]: RPLidar health status : 0
[ INFO] [1631677752.808115075]: current scan mode: Sensitivity, max_distance: 12.0 m, Point number: 7.9K, angle_compensate: 2

At this time, after the above three parts are started successfully, the dynamic parameter debugging tool is started on the virtual machine side.

<PI5 needs to open another terminal to enter the same docker container

image-20240408144126098

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rosrun rqt_reconfigure rqt_reconfigure

image-20220224102543673

Parameter analysis:

ParametersRangeParse
[SetLoop][False, True]Loop patrol, default False.
[Linear][0.0, 1.0]The running linear speed of the car
[Angular][0.0, 5.0]The running angular speed of the car
[Length][0.0, 2.0]The straight-line running distance of the car
[Angle][0.0, 360.0]Rotation angle of the car
[LineScaling][0.0, 2.0]Straight line distance scaling ratio, default 0.9
[RotationScaling][0.0, 2.0]Rotation angle scaling, default 1.0
[LineTolerance][0.0, 1.0]Allowable straight line distance error
[RotationTolerance][0.0, 5.0]Allowed rotation angle error
[ResponseDist][0.0, 8.0]If there are obstacles within the response distance, the car will stop moving;
Remove the obstacles and the car will continue to complete the patrol task.
[LaserAngle][10, 180]Lidar detection angle (left and right angles)
[Command]Default [Square]Patrol mode: [LengthTest, AngleTest, Triangle, Square, Parallelogram, Circle]
[Switch][False, True]Patrol function [Start/Pause]

When debugging parameters, mainly debug [RotationScaling]. It can be debugged to [1.0, 1.1, 1.2, 1.3, 1.4], etc. according to the actual situation. Observe the effect. The circle is generally 0.9.

1), [LengthTest]: Run the test command straight, adjust the [LineScaling] and [LineTolerance] parameters so that the actual running distance of the car is close to the value [Length].

[LineScaling] The smaller the parameter, the greater the straight distance. The smaller the [LineTolerance] parameter is, the greater the front and rear vibration will be. Just debug multiple times and find the best data. Errors will always exist.

2), [AngleTest]: Rotation test command, adjust the [RotationScaling] and [RotationTolerance] parameters so that the true rotation distance of the car is close to the value [Angle].

[RotationScaling] The smaller the parameter, the larger the rotation angle. The smaller the [RotationTolerance] parameter is, the greater the left and right vibration will be. Just debug multiple times and find the best data. Errors will always exist.

After debugging [1)] and [2)], [LineScaling] and [LineTolerance], [RotationScaling] and [RotationTolerance] generally do not need to be adjusted.

5.1.2. Parameter modification

When the parameters are adjusted to the optimal state, the corresponding parameters are modified into the file, and no adjustment is required when using again.

According to the optimal parameters of the [rqt_reconfigure] debugging tool, enter the [scripts] folder of the [yahboomcar_bringup] function package and modify the parameters corresponding to the [patrol.py] file, as shown below

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class YahboomCarPatrol():
     def __init__(self):
         ... ...
         self.SetLoop = False
         self.Linear = 0.5
         self.Angular = 1.0
         self.Length = 1.0
         self.Angle = 360.0
         self.LineScaling = 1.1
         self.RotationScaling = 0.75
         self.LineTolerance = 0.1
         self.RotationTolerance = 0.3
         self.ResponseDist = 0.6
         self.LaserAngle = 20
         self.Command = "finish"
         self.circle_adjust = rospy.get_param('~circle_adjust', 2.0)

[rqt_reconfigure] Modification of the initial value of the debugging tool

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gen.add("SetLoop", bool_t, 0, "SetLoop", False)
gen.add("Linear", double_t, 0, "Linear in robot", 0.5, 0, 1.0)
gen.add("Angular", double_t, 0, "Angular in robot", 1.0, 0, 5.0)
gen.add("Length", double_t, 0, "Length in limit", 1.0, 0, 2.0)
gen.add("Angle", double_t, 0, "Angle in limit", 360.0, 0, 360.0)
gen.add("LineScaling", double_t, 0, "Line Scaling", 1.1, 0, 2.0)
gen.add("RotationScaling", double_t, 0, "Rotation Scaling", 0.75, 0, 2.0)
gen.add("LineTolerance", double_t, 0, "LineTolerance", 0.1, 0, 3.0)
gen.add("RotationTolerance", double_t, 0, "RotationTolerance", 0.3, 0, 5.0)
gen.add("ResponseDist", double_t, 0, "ResponseDist in limit", 0.6, 0.0, 8.0)
gen.add("LaserAngle", int_t, 0, "LaserAngle in limit", 30, 10, 180)

Enter the [cfg] folder of the [yahboomcar_bringup] function package and modify the initial value of the corresponding parameter in the [PatrolParam.cfg] file.

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gen.add("Linear", double_t, 0, "Linear in robot", 0.5, 0, 1.0)

Take the above article as an example to analyze

ParametersAnalysisCorresponding parameters
nameThe name of the parameter"Linear"
typeparameter data typedouble_t
levela bitmask passed to the callback0
descriptionA description parameter"Linear in robot"
defaultInitial value for node startup0.5
minparameter minimum value0
maxparameter maximum value1.0

Note: After modification, you must recompile and update the environment to be effective.

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cd ~/yahboomcar_ws
catkin_make
source devel/setup.bash

5.1.3. Patrol function

  • After successful startup, select the trajectory [Triangle, Square, Parallelogram, Circle] to be executed for patrol in [Command].

  • The parameter [Length] can be adjusted according to needs. For example, the default value is 1.0, the command is Square, and the trajectory of the car is a square with a side length of 1.0.

  • When adjusting the parameter [Linear], please note that the greater the speed, the greater the inertia, and the smaller the accuracy.

  • Parameter [LaserAngle]: For example: the angle is 30°. At this time, the system only analyzes 30° on the left and right of the car (0° in front of the car)

  • Parameter [Switch]: After setting, click the box behind [Switch] to start patrolling. By default, it is executed once. After the execution is completed, the check mark in the box disappears automatically.

  • If you need to patrol in a loop, click the box behind [SetLoop] to continue patrolling, and the error will accumulate to become larger and larger.

Node view

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rqt_graph

image-20210915122503006

5.2. Source code analysis

launch file

  • patrol.launch

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Lidar follow
gmapping mapping algoritm

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