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MicroROS V2 Robot Car📖
01.Introduction
  • 0.Unboxing Video
  • 1. Readme Learning Route
  • 2. Must Read Before Use
  • 3. Introduction to MicroROS V2 Control Board
  • 4. Introduction to LiDAR
  • 5. Introduction to ESP32 Camera Module
  • 6. FAQ
  • 7. About Charging
02.Assembly course
  • Assembly Video
03.Preparation
  • 1. Quickly Start
  • 2. Write Firmware
  • 3. Install VM and Start System
  • 4. Configure VM and Connect Robot
  • 5. Connect to MicroROS Agent
04.Quick start control course
  • 1. VM Keyboard Remote Control
  • 2. VM Handle Remote Control
  • 3. USB Handle Control
05.Robot control course
  • 1. Robot Information Release
  • 2. Robot Keyboard Control
  • 3. Robot Handle Control
  • 4. Robot State Estimation
  • 5. Linear Speed Calibration
  • 6. Angular Velocity Calibration
  • 7. Robot URDF Model
06.Lidar course
  • 1. Precautions for LiDAR Mapping and Navigation
  • 2. Lidar Avoid
  • 3. Lidar Following
  • 4. Lidar Guard
  • 5. Lidar Patrol
  • 6. Gmapping Mapping
  • 7. Cartographer Mapping
  • 8. Cartographer Quickly Repositioning Navigation
  • 9. Slam Toolx Mapping
  • 10. Navigation2 Single Point Navigation
  • 11. Navigation2 Multi Point Navigation
  • 12. Ros Robot App Mapping And Navigation
07.Road network planning
  • 1. Introduction to Road Network Planning and Quick Start
  • 2. Build a Pose Map
  • 3. Road Network Annotation
  • 4. Planning and Navigation Between Road Networks
  • 5. Road Network Navigation in Any Pose
  • 6. Path Replanning
08.Vision basic course
OpenCV Image Processing
  • 1. Introduction to OpenCV 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
09.Visual General Course (For Standard&Superior version)
4. Yolov11&26 Visual Recognition
  • 1. YOLO Environment Setup
  • 2. CLI Usage
  • 3. Object Detection
  • 4. Instance Segmentation
  • 5. Pose Estimation
  • 6. Image Classification
  • 7. Oriented Bounding Boxes Object Detection
  • 8. Model Prediction
1. ESP32 Visual Recognition Module Configuration
  • 1. Serial Port Configuration WiFi Instructions
  • 2. Use ESP32 Camera Module in Robot Car
  • 3. Servo PTZ Calibration
2. OpenCV Visual Recognition
  • 1. Human Pose Estimation
  • 2. Object Detection
  • 3. AR Vision
  • 4. QR Code
  • 5. Color Recognition
  • 6. Facial Recognition
  • 7. AprilTag Machine Code Recognition
3. Mediapipe Visual Recognition
  • 1. Hand Detection
  • 2. Pose Detection
  • 3. Holistic Detection
  • 4. Facial Detection
  • 5. Facial Recognition
  • 6. Facial Special Effects
  • 7. 3D Object Recognition
  • 8. Brush
  • 9. Gesture Recognition
  • 10. Palm Target Localization
  • 11. Fingertip Trajectory Recognition
10.Visual Advanced Course (Just for Standard version)
1. Visual Control
  • 1. QR Code Recognition Control Action
  • 2. AprilTag Control Car Action Group
  • 3. Gesture Control Car Action Group
  • 4. Visual Line Tracking
2. Visual Tracking
  • 1. Color Tracking
  • 2. Facial Tracking
  • 3. QR Code Tracking
  • 4. Object Recognition And Tracking
  • 5. Mediapipe Palm Tracking
  • 6. Mediapipe Gesture Tracking
  • 7. Mediapipe Human Pose Tracking
  • 8. KCF Object Tracking
  • 9. AprilTag Machine Code Tracking
3. Visual Following
  • 1. Color Follow
  • 2. Face Follow
  • 3. QR Code Follow
  • 4. Mediapipe Palm Follow
  • 5. Mediapipe Gesture Follow
  • 6. Mediapipe Human Posture Follow
  • 7. AprilTag Machine Code Follow
  • 8. Object Follow
11.Visual Advanced Course (Just for Superior version)
1. Visual Control
  • 1. QR Code Recognition Control Action
  • 2. AprilTag Control Car Action Group
  • 3. Gesture Control Car Action Group
  • 4. Visual Line Tracking
2. Visual Tracking
  • 1. Color Tracking
  • 2. Facial Tracking
  • 3. QR Code Tracking
  • 4. YOLO Object Recognition and Tracking
  • 5. Mediapipe Palm Tracking
  • 6. Mediapipe Gesture Tracking
  • 7. Mediapipe Human Pose Tracking
  • 8. KCF Object Tracking
  • 9. AprilTag Machine Code Tracking
3. Visual Follow
  • 1. Color Follow
  • 2. Face Follow
  • 3. QR Code Follow
  • 4. Mediapipe Palm Follow
  • 5. Mediapipe Gesture Follow
  • 6. Mediapipe Human Pose Follow
  • 7. AprilTag Machine Code Follow
  • 8. Object Follow
12.AI Large Model Basic and Applications Course
1. AI Large Model Preparation
  • 1.AI Large Model Preparation
2. AI Large Model Basic Course
  • 1. AI Large Model Types and Principles
  • 2. RAG Retrieval Augmented and Training Samples
  • 3. Embodied Intelligent System Architecture
  • 4. Embodied Intelligence Core Source Code
3. AI Large Model Development
  • 1. Register Model Service Account
  • 2. Configure API KEY
  • 3. Dify Introduction
  • 4. Dify Basic Features
  • 5. RAG Knowledge Base Deployment
  • 6. AI Application Development Chatbot
  • 7. RAG Knowledge Base and Chatbot
  • 8. AI Agent Workflow
4. AI Large Model (Text Version)
  • 1. Semantic Understanding and Instruction Following
  • 2. Multimodal Visual Understanding
  • 3. Multimodal Visual Understanding + Basic Control
  • 4. Multimodal Visual Understanding + Object Following
  • 5. Multimodal Visual Understanding + Visual Line Following
  • 6. Multimodal Visual Understanding + SLAM Navigation
  • 7. Multimodal Visual Understanding + SLAM Navigation + Visual Line Following
  • 8. Multimodal Visual Understanding + SLAM Navigation + Object Following
  • 9. Intent Inference + Multimodal Visual Understanding + SLAM Navigation
13.Openclaw Basics Course
  • 1. Openclaw Introduction
  • 2. Openclaw Core Concepts
  • 3. Openclaw Api-Key Configuration
  • 4. Openclaw Switching Models
  • 5. Openclaw System Prompt
  • 6. Openclaw Common CLI Command
14. Openclaw Interaction Course
  • 1. Openclaw Webchat Interaction
  • 2. Openclaw Tui Interaction
  • 3. Openclaw Whatsapp Interaction
15.Openclaw Development Course
  • 1. Openclaw Robot Mcp Service Integration
  • 2. Robot CLI Command Tools
  • 3. Robot-Control Plugin Tools
  • 4. Openclaw Skills Development
  • 5. Openclaw Scheduled Tasks
  • 6. Openclaw Multi Agent System
  • 7. Openclaw Proactive Interaction
  • 8. Openclaw Vector Memory
16.Openclaw Embodied Intelligence Course
  • 1. OpenClaw Natural Interaction + Basic Control
  • 2. OpenClaw Natural Interaction + Slam Navigation
  • 3. OpenClaw Natural Interaction + Multimodal Visual Understand
  • 4. OpenClaw Natural Interaction + Multimodal Visual Understand + Slam Navigation + Visual Follow
  • 5. OpenClaw Natural Interaction + Multimodal Visual Understand + Slam Navigation + Visual Track
17.Multi-machine Course
  • 1. Multi Machine Handle Control
  • 2. Multi Machine Keyboard Control
  • 3. Multi Machine Navigation
18.MicroROS-V2 Control Board Development Environment
  • 1. Set Up the ESP32-IDF Development Environment
  • 2. ESP32-IDF Configuration Tool
  • 3. ESP Flash Tool Flashing Firmware
  • 4. Install ESP32 Microros Components
  • 5. Install And Start Microros Agent
19.MicroROS-V2 Control Board ESP32 Basic Course
  • 1. Led Light Up
  • 2. Button Function
  • 3. Drive Buzzer
  • 4. Oled
  • 5. Usb Host Function
  • 6. Serial Communication
  • 7. Battery Voltage Detection
  • 8. Drive Pwm Servo
  • 9. Drive Motors
  • 10. Read Motor Encoder Data
  • 11. Pid Motor Speed Control
  • 12. Robot Kinematics Analysis Theory
  • 13. Read Imu Data
  • 14. Read Lidar Data
  • 15. Flash Data Storage
  • 16. Custom Partition Table
  • 17. Bluetooth Communication
  • 18. Wifi Connection
  • 19. ESP32 Visual Recognition Module
20.MicroROS-V2 Control Board Micro_ros Basic Course
  • 1. Publish Topics
  • 2. Subscribe To Topics
  • 3. Multi Topic Subscription And Publishing
  • 4. Subscribe To Buzzer Topic
  • 5. Subscribe To Pwm Servo Topic
  • 6. Subscribe To Speed Control Topic
  • 7. Publish Odometry Topic
  • 8. Publish Imu Data Topic
  • 9. Publish Lidar Data Topic
21. Linux System Course
  • 1.Introduction to Linux system
  • 2.Ubuntu file system
  • 3.Ubuntu common commands
  • 4.Ubuntu common editors
  • 5.Ubuntu software operation commands
  • 6.Virtual machine installation
  • 7.SSH remote control
  • 8.VNC remote control
  • 9.Transfer files remotely
  • 10.Driver library and communication
  • 11.Static IP and hotspot mode
  • 12.Bind device ID
  • 13.Capacity expansion and resource allocation
  • 14.Update system software sources
  • 15.Set root user password
  • 16.sudo free password
  • 17.Connect to WiFi network
  • 18.View system version
  • 19.Customized service management
  • 20.Back up system image
22.Docker Course
  • 1.Docker overview and docker installation
  • 2.Common commands for docker image containers
  • 3.Docker images deeply understand and publish images
23.ROS Basic Course
  • 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
Download
  • Mapping_APP(iOS_Android)
  • Code
  • Software_Tools
  • VM_System_File
  • 3D_Model_File
  • ESP32_Firmware
  • Dify LLM Base
  • Instruction_Manual
  • VMware
Download ZIP

Welcome to MicroROS V2 Robot Car repository

7. Battery Voltage Detection

Battery Voltage Detection

Experiment Objective

This section will guide you through detecting the battery voltage through the ADC interface of the MicroROS-V2 control board. Through this tutorial, you will learn how to use the ESP32-S3's ADC peripheral for single-shot sampling, configure ADC calibration to obtain accurate voltage values, and calculate the battery's actual terminal voltage using the voltage divider ratio.

Experiment Principle

1. Keywords

KeywordDescription
ADC (Analog-to-Digital Converter)A hardware peripheral that converts analog voltage signals into digital values; the ESP32-S3 defaults to 12-bit resolution with an output range of 0~4095
ADC AttenuationConfigures the ADC input voltage range; 11 dB attenuation corresponds to about 0~2.6 V, protecting the ADC from overvoltage damage
ADC resolutionThe number of quantization bits of the ADC; the higher the number of bits, the higher the precision. The ESP32-S3's ADC supports up to 12 bits
Voltage DividerReduces a high voltage proportionally through two resistors in series so that the ADC can sample safely; this design has a divider ratio of 4.03:1
ADC CalibrationUses the calibration parameters in the chip's eFuse to convert raw ADC values into accurate voltage values (mV)
Curve FittingThe ADC calibration scheme provided by Espressif, which compensates for ADC nonlinearity errors through a curve fitting algorithm to improve measurement precision

2. Technical Architecture

The program samples the battery voltage after voltage division using the ESP32-S3's ADC peripheral. GPIO3 is connected to the battery voltage divider circuit (10KΩ + 3.3KΩ). After the ADC samples the voltage at the divider point, the calibration module converts the raw ADC value into a millivolt voltage, which is then converted into the battery's actual terminal voltage according to the divider ratio (4.03).

Main component files:

  • main/main.c — main program entry, reads and prints the battery voltage every 500 ms

  • components/battery/battery.c / battery.h — battery voltage detection driver, including ADC initialization, calibration, and voltage calculation

Experiment Steps

1. Hardware Preparation

HardwareRequirement
MicroROS-V2 control board✅ Required
USB data cable✅ Required (for power supply and serial monitor)
12V lithium battery✅ Required (connected to the control board power interface)

2. Prerequisites

  1. ESP32-IDF development environment set up

  2. MicroROS-V2 control board connected to the PC via USB

  3. Insert the lithium battery into the control board's power interface (XT60 or DC jack)

3. Compile and Flash

​x

cd ~/esp/base_samples/voltage
source ~/esp/esp-idf/export.sh
idf.py build flash monitor

After the program starts, it initializes the battery ADC detection module and reads the battery voltage every 500 ms, outputting the voltage value to the serial monitor via logs.

4. How to Stop

Press Ctrl+] in the terminal to exit the serial monitor.

Experiment Result

After correctly flashing and running the program, the serial monitor outputs the following information (the voltage value changes according to the actual battery level):

image-20260811164638648

  • With a 7.4V lithium battery, the normal voltage range is about 7.4V ~ 8.4V

  • With an 11.1V lithium battery, the normal voltage range is about 9.6V ~ 12.6V

  • If no battery is connected, it may show a value close to 0V or an abnormal value

Code Explanation

Source code paths:

  • Firmware source code: ~/esp/base_samples/voltage/main/main.c

  • Battery driver: ~/esp/base_samples/voltage/components/battery/battery.h, battery.c

1. Project Structure

xxxxxxxxxx
voltage/
├── CMakeLists.txt                  # Top-level CMake project file
├── main/
│   ├── CMakeLists.txt              # main component build configuration
│   └── main.c                      # Main program entry
└── components/
    └── battery/
        ├── CMakeLists.txt          # Component configuration (depends on driver, esp_adc)
        ├── battery.c               # Battery voltage detection driver implementation
        └── battery.h               # Battery voltage detection interface declarations

2. Main Program Logic

The execution flow of app_main in main.c:

  1. Print the startup messages "hello yahboom" and "Nice to meet you!"

  2. Call Battery_Init() to initialize the ADC and start the voltage reading task

  3. Enter the main loop, calling Battery_Get_Voltage() every 500 ms to get the battery voltage

  4. Print the voltage value with ESP_LOGI (keeping two decimal places)

3. Key Components

Battery Voltage Detection Driver (battery.h / battery.c)

Pin and ADC configuration:

Parameter definition file: ~/esp/base_samples/voltage/components/battery/battery.h

xxxxxxxxxx
#define BATTERY_GPIO            3           // Battery voltage detection GPIO
#define ADC_ATTEN_BATTERY       ADC_ATTEN_DB_11  // 11 dB attenuation (range about 0~2.6V)
#define ADC_CHANNEL_BATTERY     ADC_CHANNEL_2    // ADC channel 2 (corresponds to GPIO3)

ADC initialization (Battery_Adc_Init):

  1. Create an ADC unit (adc_oneshot_new_unit), configured as ADC_UNIT_1

  2. Configure the ADC channel parameters:

    xxxxxxxxxx
    adc_oneshot_chan_cfg_t config = {
        .bitwidth = ADC_BITWIDTH_DEFAULT,   // Default bit width (12 bits)
        .atten = ADC_ATTEN_BATTERY,         // 11 dB attenuation
    };
  3. Configure the ADC channel (adc_oneshot_config_channel)

  4. Enable ADC calibration (Battery_Adc_Calibration_Init), using the curve fitting scheme

ADC calibration initialization (Battery_Adc_Calibration_Init):

xxxxxxxxxx
adc_cali_curve_fitting_config_t cali_config = {
    .unit_id = unit,                    // ADC_UNIT_1
    .chan = channel,                    // ADC_CHANNEL_2
    .atten = atten,                     // ADC_ATTEN_DB_11
    .bitwidth = ADC_BITWIDTH_DEFAULT,   // Default bit width
};

Create the calibration scheme via adc_cali_create_scheme_curve_fitting.

Voltage calculation principle:

The control board detects the battery voltage through resistive voltage division; GPIO3 samples the voltage after the division. The divider resistors are 10KΩ and 3.3KΩ, and the divider ratio is:

xxxxxxxxxx
V_battery = V_gpio × (10K + 3.3K) / 3.3K = V_gpio × 4.03

The corresponding code implementation (Battery_Update_Voltage):

xxxxxxxxxx
battery_voltage = gpio_voltage_mV / 1000.0 * 4.03;

Voltage reading task (Battery_Task):

  1. Perform sampling every 100 ms:

    • Call adc_oneshot_read() to read the raw ADC value

    • Call adc_cali_raw_to_voltage() to convert the raw value into a calibrated voltage (mV)

    • Call Battery_Update_Voltage() to calculate the battery terminal voltage based on the divider ratio

  2. The task runs on core 1, with a stack size of 3 KB and priority 2

API functions:

FunctionDescription
void Battery_Init(void)Initialize battery ADC detection and start the voltage reading task
float Battery_Get_Voltage(void)Get the current battery voltage, unit: V (volts)

Common Problems

ProblemCauseSolution
The voltage always displays 0VNo battery connected or ADC initialization failedCheck whether the battery is connected and restart the control board to retry
The voltage value is too low or too highWrong divider ratio conversion or calibration failedConfirm the battery type (7.4V / 11.1V); you can measure the battery terminal voltage with a multimeter to calibrate against it
ADC calibration fails and prints error logsThe chip's eFuse does not store calibration parametersSome ESP32-S3 chips may lack calibration data; try using ADC_ATTEN_DB_0 to reduce the attenuation
Serial Communication
Drive Pwm Servo

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