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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

4.11 Nine-axis attitude sensor

11、Nine-axis attitude sensor to obtain data-ICM20948

11、Nine-axis attitude sensor to obtain data-ICM2094811.1、Experimental purpose11.2、Configuration pin information11.3、Experimental flow chart analysis11.4、Core code explanation11.5、Hardware connection11.6、Experimental effect

11.1、Experimental purpose

Use the SPI communication of STM32 to read the raw data of the nine-axis attitude sensor ICM20948 and print it out through the serial port assistant.

11.2、Configuration pin information

1.Import the ioc file from the Serial project and name it Read_ICM20948.

According to the schematic diagram, the SDA/SDI pin of the nine-axis attitude sensor is connected to PB15, the SCL/SCLK pin is connected to PB13, the AD0/SDO pin is connected to PB14, and the NSS pin is connected to PB12.

image-20230606113411298

image-20220326160924577

2.Enable the SPI2 interface, set PB13, PB14, and PB15 as SPI2 pins, and use the PB12 software control method for the NSS chip select pin. The specific parameters are shown in the figure below:

image-20230605183103284

Then set PB12 to output mode and rename it ICM20948_CS.

image-20230605183410441

11.3、Experimental flow chart analysis

image-20220326163358899

11.4、Core code explanation

1.Create new bsp_icm20948.h and bsp_icm20948.c, and add the following content in bsp_icm20948.h.

​x
void ICM20948_init();
void AK09916_init();

// read raw data.
void ICM20948_gyro_read(raw_data_t* data);
void ICM20948_accel_read(raw_data_t* data);
bool AK09916_mag_read(raw_data_t* data);

// Convert 16 bits ADC value to their unit.
void ICM20948_gyro_read_dps(axises_t* data);
void ICM20948_accel_read_g(axises_t* data);
bool AK09916_mag_read_uT(axises_t* data);

bool ICM20948_who_am_i();
bool AK09916_who_am_i();

void ICM20948_Read_Data_Handle(void);

2..Create the following content in the bsp_icm20948.c file:

Start initializing the ICM20948. First, reset and wake up the ICM20948, set the clock source, magnetometer access method and filtering configuration, then calibrate the gyroscope and accelerometer, and set the scaling ratio.

xxxxxxxxxx
void ICM20948_init()
{
    while(!ICM20948_who_am_i());

    ICM20948_device_reset();
    ICM20948_wakeup();

    ICM20948_clock_source(1);
    ICM20948_odr_align_enable();

    ICM20948_spi_slave_enable();

    ICM20948_gyro_low_pass_filter(0);
    ICM20948_accel_low_pass_filter(0);

    ICM20948_gyro_sample_rate_divider(0);
    ICM20948_accel_sample_rate_divider(0);

    ICM20948_gyro_calibration();
    ICM20948_accel_calibration();

    ICM20948_gyro_full_scale_select(_2000dps);
    ICM20948_accel_full_scale_select(_16g);
}

3.Initialize the AK09916 magnetometer.

xxxxxxxxxx
void AK09916_init()
{
    ICM20948_i2c_master_reset();
    ICM20948_i2c_master_enable();
    ICM20948_i2c_master_clk_frq(7);
    while(!AK09916_who_am_i());
    AK09916_soft_reset();
    AK09916_operation_mode_setting(continuous_measurement_100hz);
}

4.Enables and disables SPI communication of ICM20948.

xxxxxxxxxx
static void ICM20948_NoActive()
{
    HAL_GPIO_WritePin(ICM20948_CS_GPIO_Port, ICM20948_CS_Pin, SET);
}
static void ICM20948_Active()
{
    HAL_GPIO_WritePin(ICM20948_CS_GPIO_Port, ICM20948_CS_Pin, RESET);
}

5.The SPI method of reading data, read_single_reg is to read one byte of data from a register, and read_multiple_reg is to read multiple bytes of data from a register.

xxxxxxxxxx
static uint8_t read_single_reg(userbank_t ub, uint8_t reg)
{
    uint8_t read_reg = READ | reg;
    uint8_t reg_val;
    select_user_bank(ub);
    ICM20948_Active();
    HAL_SPI_Transmit(ICM20948_SPI, &read_reg, 1, 1000);
    HAL_SPI_Receive(ICM20948_SPI, &reg_val, 1, 1000);
    ICM20948_NoActive();
    return reg_val;
}
static uint8_t* read_multiple_reg(userbank_t ub, uint8_t reg, uint8_t len)
{
    uint8_t read_reg = READ | reg;
    static uint8_t reg_val[6];
    select_user_bank(ub);
    ICM20948_Active();
    HAL_SPI_Transmit(ICM20948_SPI, &read_reg, 1, 1000);
    HAL_SPI_Receive(ICM20948_SPI, reg_val, len, 1000);
    ICM20948_NoActive();
    return reg_val;
}

6.The SPI method of writing data, write_single_reg is to write one byte of data to a register, and write_multiple_reg is to write multiple bytes of data to a register.

xxxxxxxxxx
static void write_single_reg(userbank_t ub, uint8_t reg, uint8_t val)
{
    uint8_t write_reg[2];
    write_reg[0] = WRITE | reg;
    write_reg[1] = val;
    select_user_bank(ub);
    ICM20948_Active();
    HAL_SPI_Transmit(ICM20948_SPI, write_reg, 2, 1000);
    ICM20948_NoActive();
}
static void write_multiple_reg(userbank_t ub, uint8_t reg, uint8_t* val, uint8_t len)
{
    uint8_t write_reg = WRITE | reg;
    select_user_bank(ub);
    ICM20948_Active();
    HAL_SPI_Transmit(ICM20948_SPI, &write_reg, 1, 1000);
    HAL_SPI_Transmit(ICM20948_SPI, val, len, 1000);
    ICM20948_NoActive();
}

7.Read accelerometer raw data. Because the calibration function cancels out the acceleration due to gravity, adding a scaling factor to the Z-axis brings the reading back to normal.

xxxxxxxxxx
void ICM20948_accel_read(raw_data_t* data)
{
    uint8_t* temp = read_multiple_reg(ub_0, B0_ACCEL_XOUT_H, 6);
    data->x = (int16_t)(temp[0] << 8 | temp[1]);
    data->y = (int16_t)(temp[2] << 8 | temp[3]);
    // data->z = (int16_t)(temp[4] << 8 | temp[5]);
    data->z = (int16_t)(temp[4] << 8 | temp[5]) + g_scale_accel;
    // Add scale factor because calibraiton function offset gravity acceleration.
}

Combine the accelerometer raw data and the scale factor to convert the unit to g.

xxxxxxxxxx
void ICM20948_accel_read_g(axises_t* data)
{
    ICM20948_accel_read(&g_raw_accel);
    data->x = (float)(g_raw_accel.x / g_scale_accel);
    data->y = (float)(g_raw_accel.y / g_scale_accel);
    data->z = (float)(g_raw_accel.z / g_scale_accel);
}

8.Read gyroscope raw data.

xxxxxxxxxx
void ICM20948_gyro_read(raw_data_t* data)
{
    uint8_t* temp = read_multiple_reg(ub_0, B0_GYRO_XOUT_H, 6);
    data->x = (int16_t)(temp[0] << 8 | temp[1]);
    data->y = (int16_t)(temp[2] << 8 | temp[3]);
    data->z = (int16_t)(temp[4] << 8 | temp[5]);
}

Combine the gyroscope raw data and the scale factor to convert the unit to dps.

xxxxxxxxxx
void ICM20948_gyro_read_dps(axises_t* data)
{
    ICM20948_gyro_read(&g_raw_gyro);
    data->x = (float)(g_raw_gyro.x / g_scale_gyro);
    data->y = (float)(g_raw_gyro.y / g_scale_gyro);
    data->z = (float)(g_raw_gyro.z / g_scale_gyro);
}

9.Read magnetometer raw data.

xxxxxxxxxx
bool AK09916_mag_read(raw_data_t* data)
{
    uint8_t* temp;
    uint8_t drdy, hofl;
    drdy = read_single_mag_reg(MAG_ST1) & 0x01;
    if(!drdy)   return false;
    temp = read_multiple_mag_reg(MAG_HXL, 6);
    hofl = read_single_mag_reg(MAG_ST2) & 0x08;
    if(hofl)    return false;
    data->x = (int16_t)(temp[1] << 8 | temp[0]);
    data->y = (int16_t)(temp[3] << 8 | temp[2]);
    data->z = (int16_t)(temp[5] << 8 | temp[4]);
    return true;
}

Convert the magnetometer raw data into uT units.

xxxxxxxxxx
bool AK09916_mag_read_uT(axises_t* data)
{
    bool new_data = AK09916_mag_read(&g_raw_mag);
    if(!new_data)   return false;
    data->x = (float)(g_raw_mag.x * 0.15);
    data->y = (float)(g_raw_mag.y * 0.15);
    data->z = (float)(g_raw_mag.z * 0.15);
    return true;
}

10.Add content to initialize ICM20948 and AK09916 in the Bsp_Init() function.

xxxxxxxxxx
void Bsp_Init(void)
{
    USART1_Init();
    ICM20948_init();
    AK09916_init();
    Beep_On_Time(50);
}

8.Add the function of reading ICM20948 data in the Bsp_Loop() function.

xxxxxxxxxx
void Bsp_Loop(void)
{
    // Detect button down events
    if (Key1_State(KEY_MODE_ONE_TIME))
    {
        Beep_On_Time(50);
        static int press = 0;
        press++;
        printf("press:%d\n", press);
    }
    ICM20948_Read_Data_Handle();

    Bsp_Led_Show_State_Handle();
    // The buzzer automatically shuts down when times out
    Beep_Timeout_Close_Handle();
    HAL_Delay(10);
}

 

11.5、Hardware connection

The ICM20948 nine-axis attitude sensor is already soldered on the expansion board, so there is no need to manually connect the device.

image-20230606110430675

11.6、Experimental effect

After burning the program, the LED light flashes every 200 milliseconds. Open the serial port assistant (the parameters are shown in the figure below), and you can see that the serial port assistant keeps printing the data of the accelerometer accel, gyroscope gyro, and magnetometer mag of ICM20948.

image-20230606110506441

Control serial port servos
Control motor

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