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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

12. Robot Kinematics Analysis Theory

Robot Kinematics Analysis Theory

I. Experiment Objective

Understand the kinematics model of the differential-drive robot chassis, and master the forward/inverse kinematics solving methods from the left and right wheel speeds to the overall linear velocity and angular velocity of the robot. By reading the source code, understand how to implement robot motion control functions on the ESP32-S3, and how to control the car's motion state through interfaces such as Motion_Ctrl() and Motion_Ctrl_State().

II. Experiment Principle

1. Keywords

KeywordExplanation
Differential driveA chassis structure with two independently driven wheels that steers by the speed difference between the left and right wheels. When the left wheel speed < the right wheel speed, it turns left; when the left wheel speed > the right wheel speed, it turns right; when the speeds are equal in magnitude and opposite in direction, it rotates in place.
Forward kinematicsGiven the actual speeds of the left and right wheels, deduce the overall linear velocity Vx and angular velocity Wz of the chassis. Formulas: Vx = (V_left + V_right) / 2, Wz = -(V_left - V_right) / wheel track.
Inverse kinematicsGiven the target linear velocity Vx and angular velocity Wz, calculate the target speeds of the left and right wheels. Formulas: V_left = Vx - Wz * half wheel track, V_right = Vx + Wz * half wheel track.
OdometryA basic localization method that accumulates encoder pulses to calculate the robot's traveled distance and heading change. Kinematics solving is the core mathematical basis of odometry.
Turning radiusThe radius of the circular arc trajectory when the robot turns. In this experiment, the turning radius is fixed at 0.30m, configured through the ROBOT_TURN_RADIUS parameter, and the turning scale coefficient is 1/0.30 ≈ 3.333.

2. Technical Architecture

Building on PID motor control (Section 16.11), the program adds the car_motion kinematics solving layer, which encapsulates the forward/inverse kinematics calculation of the differential chassis and provides high-level motion control interfaces such as Motion_Ctrl, Motion_Get_Speed, and Motion_Ctrl_State.

main/main.c
    └── Motion_Ctrl(Vx, Vy, Wz)  /  Motion_Ctrl_State(state, speed)
         └── components/car_motion/       Kinematics solving layer (inverse → wheel speeds / forward → chassis velocity)
              ├── components/motor/        Reuse PID motor control (Section 11)
              ├── components/encoder/      Reuse the encoder driver
              └── components/pwm_motor/    Reuse the PWM motor driver
       

Main control flow: call Motion_Ctrl(1.0, 0, 0) to set the linear velocity → inverse kinematics calculates the target speeds of the left and right wheels → call Motor_Set_Speed to drive the motors → PID closed-loop adjustment → encoder feedback → forward kinematics calculates the actual chassis velocity.

III. Experiment Steps

1. Hardware Preparation

HardwareRequirement
MicroROS-V2 control board✅ Required
USB data cable (Type-C)✅ Required
Motor (with encoder)✅ Required (built into the chassis)
Battery (12V)✅ Required (chassis power supply)

2. Prerequisites

  1. Set up the ESP32-IDF development environment (see Chapter 15).

  2. Complete Section 16.11, PID Motor Speed Control, and understand the basic flow of motor closed-loop control

  3. Connect the MicroROS-V2 control board to the computer via USB

  4. The motor power supply is normal

3. Compile and Flash

​x

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

To exit the monitor: press Ctrl + ].

4. Stopping Method

In the terminal, press Ctrl+] to exit the serial monitor.

IV. Experiment Result

After flashing and running, you can see the following output in the serial monitor terminal:

xxxxxxxxxx
hello yahboom
I (1012) MAIN: Nice to meet you!
I (1022) MOTOR: Start Motor_Task with core:1
I (1022) MAIN: Read Motion:0.28, 0.00, 0.02
I (1122) MAIN: Read Motion:0.52, 0.00, -0.01
I (1222) MAIN: Read Motion:0.73, 0.00, 0.01
I (1322) MAIN: Read Motion:0.89, 0.00, -0.01
I (1422) MAIN: Read Motion:0.96, 0.00, 0.00
I (1522) MAIN: Read Motion:0.99, 0.00, 0.00
  • Vx gradually accelerates from 0 to near the set 1.0 m/s

  • Vy is always 0 (the differential chassis has no Y-direction velocity)

  • Wz is near 0 (straight-line motion, theoretically no rotation)

  • About 20 seconds later, the motor coasts to a stop

V. Program Analysis

Source code paths:

  • Firmware source: ~/esp/base_samples/car_motion/main/main.c

  • Kinematics solving: ~/esp/base_samples/car_motion/components/car_motion/car_motion.h, car_motion.c

  • Motor PID: ~/esp/base_samples/car_motion/components/motor/motor.h, motor.c

1. Directory Structure

xxxxxxxxxx
car_motion/
├── CMakeLists.txt
├── main/
│   ├── CMakeLists.txt
│   └── main.c
└── components/
    ├── car_motion/           # Kinematics solving layer
    │   ├── car_motion.c
    │   └── car_motion.h
    ├── motor/                # Reuses the motor control from Section 11
    ├── encoder/              # Reuses the encoder from Section 11
    └── pwm_motor/            # Reuses the PWM motor driver from Section 11

The current main program only demonstrates motion control.

2. Main Program Logic

main/main.c implements a straight-line motion test:

  1. Delay 1 second to wait for the system to stabilize

  2. Call Motor_Init() to initialize the motor system

  3. Call Motion_Ctrl(1.0, 0, 0.0) to set the linear velocity to 1.0 m/s and the angular velocity to 0 rad/s, i.e., moving straight forward

  4. Loop to read and print the car's current linear velocity and angular velocity

  5. After running 200 cycles (about 20 seconds), call Motion_Stop(STOP_COAST) to stop

xxxxxxxxxx
Motor_Init();
Motion_Ctrl(1.0, 0, 0.0);   // Linear velocity 1.0 m/s, angular velocity 0 rad/s

while (1) {
    Motion_Get_Speed(&micro_car);
    ESP_LOGI(TAG, "Read Motion:%.2f, %.2f, %.2f",
             micro_car.Vx, micro_car.Vy, micro_car.Wz);
    vTaskDelay(pdMS_TO_TICKS(100));
    count++;
    if(count >= 200) {
        Motion_Stop(STOP_COAST);
    }
}

3. Key Components

car_motion layer (kinematics model)

Files: components/car_motion/car_motion.h, components/car_motion/car_motion.c

Physical parameters:

Parameter definition file: ~/esp/base_samples/car_motion/components/car_motion/car_motion.h

MacroValueDescription
ROBOT_WIDTH0.1714fDistance between the centers of the left and right wheels (171.40mm)
ROBOT_HALF_WIDTH0.0857fHalf wheel track (85.70mm)
ROBOT_MAX_LINEAR_SPEED1.0fMaximum linear velocity (m/s)
ROBOT_MAX_ANGULAR_SPEED6.0fMaximum angular velocity (rad/s)
ROBOT_TURN_RADIUS0.30fTurning radius (m)
ROBOT_TURN_SCALE3.333fTurning scale coefficient (= 1/0.30)
ROBOT_SPIN_MAX_WZ3.0fMaximum in-place spin angular velocity (rad/s)

Motion state enum:

xxxxxxxxxx
typedef enum {
    MOTION_STOP,       // Stop (coast)
    MOTION_RUN,        // Straight forward
    MOTION_BACK,       // Straight backward
    MOTION_LEFT,       // Turn left while moving forward
    MOTION_RIGHT,      // Turn right while moving forward
    MOTION_SPIN_LEFT,  // Spin left in place
    MOTION_SPIN_RIGHT, // Spin right in place
    MOTION_BRAKE,      // Brake
} motion_state_t;

Data structure:

xxxxxxxxxx
typedef struct {
    float Vx;  // Linear velocity in the X direction (m/s), positive means forward
    float Vy;  // Linear velocity in the Y direction (m/s), always 0 for a differential chassis
    float Wz;  // Angular velocity around the Z axis (rad/s), positive means counterclockwise
} car_motion_t;

Detailed Kinematics Model

The car is a two-wheel differential-drive chassis. The left and right wheels are driven independently, and steering is achieved by the speed difference between the left and right wheels.

Inverse kinematics (target velocity -> wheel speeds):

Given the target linear velocity Vx and angular velocity Wz, calculate the target speeds of the left and right wheels:

xxxxxxxxxx
V_left  = Vx - Wz * (ROBOT_WIDTH / 2)
V_right = Vx + Wz * (ROBOT_WIDTH / 2)

where ROBOT_WIDTH / 2 = 0.0857m is the half wheel track.

Code implementation (the Motion_Ctrl function):

xxxxxxxxxx
speed_L_setup = line_v - angular_v * ROBOT_HALF_WIDTH;
speed_R_setup = line_v + angular_v * ROBOT_HALF_WIDTH;
Motor_Set_Speed(speed_L_setup, speed_R_setup);
  • When Vz = 0, the left and right wheel speeds are equal, and the car moves straight forward/backward

  • When Vz > 0, the left wheel speed < the right wheel speed, and the car turns left

  • When Vx = 0 and Vz ≠ 0, the left and right wheel speeds are equal in magnitude and opposite in direction, and the car rotates in place

The speed values are automatically limited to ±1.0 m/s.

Forward kinematics (wheel speeds -> actual velocity):

Given the actual linear velocities speed_m1 (left wheel) and speed_m2 (right wheel), calculate the actual linear and angular velocities of the chassis:

xxxxxxxxxx
Vx = (V_left + V_right) / 2
Wz = -(V_left - V_right) / ROBOT_WIDTH

Note the negative sign in the angular velocity formula. This is because the positive direction of the angular velocity is defined as counterclockwise, while the encoder direction sign configuration makes an increase in the right wheel speed correspond to clockwise rotation.

Code implementation (the Motion_Get_Speed function):

xxxxxxxxxx
car->Vx = (speed_m1 + speed_m2) / 2.0f;
car->Vy = 0;
car->Wz = -(speed_m1 - speed_m2) / (ROBOT_WIDTH);

Motion state control:

The Motion_Ctrl_State function encapsulates common motion control modes. You only need to pass the state enum and the speed magnitude:

ModeActual CallEffect
MOTION_RUNMotion_Ctrl(speed, 0, 0)Straight forward
MOTION_BACKMotion_Ctrl(-speed, 0, 0)Straight backward
MOTION_LEFTMotion_Ctrl(speed, 0, speed * 3.333)Turn left while moving forward with a turning radius of 0.3m
MOTION_RIGHTMotion_Ctrl(speed, 0, -speed * 3.333)Turn right while moving forward with a turning radius of 0.3m
MOTION_SPIN_LEFTMotion_Ctrl(0, 0, speed * 3.0)Spin left in place (max 3 rad/s)
MOTION_SPIN_RIGHTMotion_Ctrl(0, 0, -speed * 3.0)Spin right in place (max 3 rad/s)
MOTION_BRAKEMotor_Stop(STOP_BRAKE)Brake (emergency stop)
MOTION_STOPMotor_Stop(STOP_COAST)Stop (coast)

Core functions:

  • Motion_Ctrl(float V_x, float V_y, float V_z) — Controls the car's motion. The V_y parameter is invalid in the current differential chassis model and is kept only for interface extension

  • Motion_Get_Speed(car_motion_t *car) — Gets the current linear velocity and angular velocity of the car

  • Motion_Ctrl_State(uint8_t state, float speed) — Controls motion through predefined states for quick invocation

  • Motion_Stop(uint8_t brake) — Stops the car's motion

GPIO pins:

  • S1: GPIO8

  • S2: GPIO16

PWM parameters:

  • Resolution: 1 MHz (1us/tick)

  • Period: 20000 ticks (20ms, i.e., 50Hz)

  • Pulse width range: 500 ~ 2500us (corresponding to angle -90° ~ +90°)

  • S1 default angle: 0°, range: -90° ~ 90°

  • S2 default angle: -45° (camera facing straight ahead), range: -90° ~ 20°

VI. Common Problems

ProblemCauseSolution
The car does not drive straightThe encoder direction signs of the left and right wheels are inconsistent, or the wheel diameter has deviationAdjust MOTOR_ENCODER_SIGN_M1/M2 or check whether the wheels are slipping
The car turns in the opposite directionThe sign definition of the angular velocity does not match expectationsAdjust the sign in the Wz calculation formula in Motion_Get_Speed()
Vy is always 0The differential chassis model has no Y-direction degree of freedom; this is normalNo action needed
Inaccurate speed readingThe encoder parameters (pulse count/wheel diameter) do not match the actual hardwareCheck the MOTOR_ENCODER_CIRCLE and WHEEL_DIAMETER configurations
Pid Motor Speed Control
Read Imu Data

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