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ROSMASTER X3📖
1.About ROSMASTER X3
  • 1.1 Wiring Introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
2.Assembly steps
  • Installation Video(1-10)
  • Installation Video(11-18)
  • Installation Video(20-28)
  • Installation Video(29-30)
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
  • 12.1 Lidar basic-SLAM
  • 12.1 Lidar basic-4ROS
  • 12.2 Lidar avoiding
  • 12.3 Lidar guard
  • 12.4 Lidar follow
  • 12.5 Patrol game
  • 12.6 gmapping mapping algorithm
  • 12.7 hector mapping algorithm
  • 12.8 karto mapping algorithm
  • 12.9 cartographer mapping algorithm
  • 12.10 rrt_exploration mapping algorithm
  • 12.11 AMCL
  • 12.12 Navigation and avoiding
  • 12.13 APP mapping and navigation
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 control
  • 2. Model aircraft control
  • 3. Wireless Handle Control Tutorial
18.ROS2- Hardware course
  • 1. About expansion board_V1.0
  • 1. About expansion board_V3.0
  • 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
19.ROS2- ROSMASTER 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
20.ROS2- 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 Raspberry Pi write system
  • 9.3 Orin series write system
  • 10.Jetpack old system upgraded to SUPER systemnew
21.ROS2- Docker(Nano Pi)
  • 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. Entering the Docker Container of the Robot
  • 6. Building a Robot Development Environment in Docker
  • 7. Method for customers to update 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- Depth camera course
  • 1.Depth camera usage
  • 2. Camera internal reference 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
25.ROS2- Lidar course
  • 1. Introduction and Use of Lidar
  • 2. Lidar avoiding
  • 3. Lidar follow
  • 4. Lidar guard
  • 5. Patrol game
  • 6. Gmapping mapping algorithm
  • 7. Cartographer mapping algorithm
  • 8. Navigation and obstacle avoidance
  • 9. Rosmaster_ X3 conducts mapping through the APP
  • 10. Rosmaster_ X3 Navigation through APP
26.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、line patrol automatic driving
27.ROS2- Voice control courses
  • 0.How to use old voice module in new image
  • 1. Introduction of voice module-new
  • 2. Voice control module port binding-new
  • 1. Introduction of voice module and 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
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, ROSMASTER X3 cannot start normally
Download
  • RemoteCtrl_APP(Android/iOS)
  • Mapping_APP(iOS_Android)
  • Instruction Manual
  • SystemFile_ROS2
  • SystemFile_ROS1
  • SystemFile_ROS2_OrinNANOSUPER
  • SystemFile_ROS2_OrinNXSUPER
  • Code
  • Jetson_Boot_USB.tar.gz
  • VM software
  • Autopilot Annex
  • Other_Annex_File
  • LLM_SystemFile
  • ROS2_Basic_VM
Download ZIP
FAQ
  • 1、How to connect OLED?

Welcome to ROSMASTER X3 repository

5. Patrol game

5、Patrol function gameplay

1、Program function description

After the program starts, open the patrol route set by the dynamic parameter setter, click the "switch" of the GUI interface, the trolley moves according to the set patrol route, during operation, the radar works at the same time, and if an obstacle is detected within the detection range, it will stop. After the controller program is turned on, you can also pause/resume the trolley movement through the R2 button.

2、Program code reference path

Raspberry Pi PI5 master needs to enter the docker container first, Orin master does not need to enter,

the location of the source code of this function is located at,

x
~/yahboomcar_ros2_ws/yahboomcar_ws/src/yahboomcar_bringup/yahboomcar_bringup/patrol_a1_X3.py

3、The program starts

3.1、start the command

according to the actual model and radar model, the terminal input,

xxxxxxxxxx
#Start the trolley chassis
ros2 launch yahboomcar_bringup yahboomcar_bringup_X3_launch.py
#Start the A1 radar
ros2 launch sllidar_ros2 sllidar_launch.py
#Start the S2 radar
ros2 launch sllidar_ros2 sllidar_s2_launch.py
#Start the patrol program X3 model
ros2 run yahboomcar_bringup patrol_a1_X3
#Start the handle, if needed
ros2 run yahboomcar_ctrl yahboom_joy_X3
ros2 run joy joy_node

3.2、View the topic communication node diagram

docker terminal input,

xxxxxxxxxx
ros2 run rqt_graph rqt_graph

image-20230421161538192

Set the size of parameters via dynamic parameter adjuster, terminal input,

xxxxxxxxxx
ros2 run rqt_reconfigure rqt_reconfigure

The parameters of the dynamic parameter regulator are described as follows:

image-20230421161626269

Parameter nameParameter meaning
odom_frameOdometer coordinate system
base_frameCoordinate system
circle_adjustRotational angular velocity adjustment factor
SwitchGameplay switch
CommandPatrol routes
Set_loopSet up a loop
ResponseDistRadar obstacle avoidance response range
LaserAngleRadar scan angle
LinearLine speed
Angularangular velocity
LengthStraight-line test distance
RotationToleranceSteering error tolerance
RotationScalingCorner scale factor

After the program starts, enter any of the following routes in the Comand field in the GUI interface of the Dynamic Parameter Adjuster interface:

  • LengthTest: Straight-line test

  • Circle: Circular route patrol

  • Square: Square route patrol

  • Triangle: Triangle route patrol

After selecting the route, click the blank parameter to write, and then click the Switch button to start the patrol movement. If the loop is set, you can loop through the previous route to patrol, and if the loop is false, it will stop when the patrol is completed.

4、Core source code analysis

The implementation source code of this code is to subscribe to the TF transformation of odom and base_footprintf, so that you can know "how long you have walked" at any time, and then issue speed instructions according to the set route, take Triangle as an example, here to do the analysis,

xxxxxxxxxx
#Set the patrol route and enter the self. Triangle function
self.command_src = "Triangle"
triangle = self.Triangle()
#Take part of self. Triangle code parsing
def Triangle(self):
    if self.index == 0:
        print("Length")
        step1 = self.advancing(self.Length) #Start with a straight line and walk through one side of the triangle
        #sleep(0.5)
        if step1 == True:
            #self.distance = 0.0
            self.index = self.index + 1; 
            self.Switch  = rclpy.parameter.Parameter('Switch',rclpy.Parameter.Type.BOOL,True)
            all_new_parameters = [self.Switch]
            self.set_parameters(all_new_parameters)
    elif self.index == 1:
            print("Spin")
            step2 = self.Spin(120)#随后调换方向,转向120,三角形3*120=360
            #sleep(0.5)
            if step2 == True:
                self.index = self.index + 1;
                self.Switch  = rclpy.parameter.Parameter('Switch',rclpy.Parameter.Type.BOOL,True)
                all_new_parameters = [self.Switch]
                self.set_parameters(all_new_parameters)
#The following goes through 3 loops, that is, to complete the triangle patrol, mainly to see the self.advancing and self.Spin functions, these two functions will return True after the execution is completed,
 def advancing(self,target_distance):
    #Here's how to get the xy coordinates, calculate the coordinates with the previous moment, and calculate how far you have come
    #The way to get XY coordinates is to listen to the tf transformation of Odom and base_footprint, which can refer to the self.get_position() function
    self.position.x = self.get_position().transform.translation.x
    self.position.y = self.get_position().transform.translation.y
    move_cmd = Twist()
    self.distance = sqrt(pow((self.position.x - self.x_start), 2) +
                         pow((self.position.y - self.y_start), 2))
    self.distance *= self.LineScaling
    print("distance: ",self.distance)
    self.error = self.distance - target_distance
    move_cmd.linear.x = self.Linear
    if abs(self.error) < self.LineTolerance : 
        print("stop")
        self.distance = 0.0
        self.pub_cmdVel.publish(Twist())
        self.x_start = self.position.x;
        self.y_start = self.position.y;
        self.Switch  = rclpy.parameter.Parameter('Switch',rclpy.Parameter.Type.BOOL,False)
        all_new_parameters = [self.Switch]
        self.set_parameters(all_new_parameters)
        return True
    else:
        if self.Joy_active or self.warning > 10:
            if self.moving == True:
                self.pub_cmdVel.publish(Twist())
                self.moving = False
                print("obstacles")
             else:
                #print("Go")
                self.pub_cmdVel.publish(move_cmd)
            self.moving = True
            return False
        
def Spin(self,angle):
        self.target_angle = radians(angle)
        #The following is to obtain the pose, calculate how many degrees you turned, and get the pose can refer to the self.get_odom_angle function, which is also obtained by listening to the TF transformation of odom and base_footprint.
        self.odom_angle = self.get_odom_angle()
        self.delta_angle = self.RotationScaling * self.normalize_angle(self.odom_angle - self.last_angle)
        self.turn_angle += self.delta_angle
        print("turn_angle: ",self.turn_angle)
        self.error = self.target_angle - self.turn_angle
        print("error: ",self.error)
        self.last_angle = self.odom_angle
        move_cmd = Twist()
        if abs(self.error) < self.RotationTolerance or self.Switch==False :
            self.pub_cmdVel.publish(Twist())
            self.turn_angle = 0.0
            '''self.Switch  = rclpy.parameter.Parameter('Switch',rclpy.Parameter.Type.BOOL,False)
            all_new_parameters = [self.Switch]
            self.set_parameters(all_new_parameters)'''
            return True
        if self.Joy_active or self.warning > 10:
            if self.moving == True:
                self.pub_cmdVel.publish(Twist())
                self.moving = False
                print("obstacles")
        else:
            if self.Command == "Square" or self.Command == "Triangle": 
                #move_cmd.linear.x = 0.2
                move_cmd.angular.z = copysign(self.Angular, self.error)
            elif self.Command == "Circle":
                length = self.Linear * self.circle_adjust / self.Length
                #The circle_adjust here is the coefficient of the angle of rotation, and the larger the length can be understood, the greater the radius of the circle
                #print("length: ",length)
                move_cmd.linear.x = self.Linear
                move_cmd.angular.z = copysign(length, self.error)
                #print("angular: ",move_cmd.angular.z)
                '''move_cmd.linear.x = 0.2
                move_cmd.angular.z = copysign(2, self.error)'''
            self.pub_cmdVel.publish(move_cmd)
        self.moving = True

 

Lidar guard
Gmapping mapping algorithm

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