yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
ROSMASTER R2📖
1.Preparation and First Trial
  • 1.1 Accessories introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
2.Assembly steps
  • Installaction Video
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
  • 11.1 Lidar basic-SLAM
  • 11.1 Lidar basic-EAI
  • 11.2 Lidar avoiding
  • 11.3 Lidar guard
  • 11.4 Lidar follow
  • 11.5 Patrol game
  • 11.6 gmapping mapping
  • 11.7 hector mapping
  • 11.8 karto mapping
  • 11.9 cartographer mapping
  • 11.10 rrt_exploration mappin
  • 11.11 AMCL
  • 11.12 Navigation and avoiding
  • 11.13 APP mapping and navigation
  • 11.14 TEB path planning algorithm
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 remote control
  • 2. Model aircraft remote control
  • 3. Handle remote control
18.ROS2-Hardware course
  • 1. Expansion board introduction_V3.0
  • 1. Expansion board introduction_V1.0
  • 2. STM32 development environment build
  • 3. Key control buzzer sounding
  • 4. FreeRTOS applications
  • 5. Serial communication
  • 6. SBUS model airplane remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt control PWM servos
  • 10. Control serial servos
  • 11. Nine-axis attitude sensor acquires data
  • 12. Control motor forward and reverse
  • 13. Timer Capture Encoder Data
  • 14. Theory of Robot Kinematics Analysis
  • 14.1.Mecanum wheel
  • 14.2.Ackerman
  • 14.3.4WD
  • 15. PID control of robot motion
19. ROS2-Basic course
  • 1. Update the firmware of the expansion board
  • 2.Close the large self-starting program at boot
  • 3. Install Rosmaster driver library
  • 4. Buzzer sounding
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects show
  • 7. Control motor forward and reverse
  • 8. Controlling robot motion
  • 9. Control serial servos
20. ROS2-Linux operating systemnew
  • 01. Virtual Machines
  • 02. Linux Basics
  • 03. Remote control
  • 04. Multi-computer communication configuration
  • 05. Static ip and hotspot mode
  • 06. Bind device ID
  • 07.Webpage real-time monitoring
  • 08. Expansion Tutorial
  • 09-1.Jetson Nano write system file
  • 09-2. Raspberry Pi write system file
  • 09-3.Orin write system
  • 10.Jetpack old system upgraded to SUPER systemnew
21.ROS2 -Docker(Nano Pi)
  • 1. docker overview and docker installation
  • 2. docker image container common command
  • 3. docker image in-depth understanding and publishing images
  • 4. docker hardware interaction and data processing
  • 5. Enter the docker container of the robot
  • 6. docker in the robot development environment build
  • 7. Customer's method of updating 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-Lidar course
  • 1-1. Introduction and use of SLAM lidar
  • 1-2. Introduction and use of 4ROS lidar
  • 2. Lidar obstacle avoidance fun gameplay
  • 3. Lidar tracking fun gameplay
  • 4. Patrol function play
  • 5. gmapping map building algorithm
  • 6. cartographer building algorithm
  • 7. Navigation and obstacle avoidance
  • 8. Rosmaster_R2 using the app to build maps
  • 9. Rosmaster_R2 navigating with the app
25. 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. Patrol line autopilot
26. ROS2-Depth camera courses
  • 1. Depth camera use
  • 2. Camera 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
27.ROS2- Voice control courses
  • 0. How to use old voice module in new image
  • 1.1 Introduction to the module-New
  • 1.2 Voice Control Module Port Binding-New
  • 1.3 Introduction to the module-Old
  • 2. Voice control car movement
  • 3. Voice control autopilot
  • 4. Voice control color recognition
  • 5. Voice control color tracking
  • 6. 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, car cannot start normally
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • Instruction Manual
  • SystemFile_ROS1
  • SystemFile_ROS2
  • SystemFile_ROS2_OrinNANOSUPER
  • SystemFile_ROS2_OrinNXSUPER
  • VM software
  • Code
  • Jetson_Boot_USB.tar.gz
  • Autopilot Annex
  • Other_Annex_File
  • LLM_SystemFile
  • ROS2_Basic_VM
Download ZIP

Welcome to ROSMASTER R2 repository

11. Nine-axis attitude sensor acquires data

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 servos
Control motor forward and reverse

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

  • Tutorials
  • Edge AI hardware
  • Educational robots
  • Embedded intelligent hardware

WHERE TO BUY

  • Yahboom Online Store
  • Amazon
  • Alibaba
  • AliExpress
  • Mercado

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文