Product Overview
MicroROS Self-balancing Car Robot Support 4KG Load Vision Kit
The MicroROS Self-balancing Car Robot Support 4KG Load Vision Kit is an advanced Self-balancing Car designed for learning, research, and real-world robotics applications. Built on a powerful STM32 + ESP32 dual-processor architecture, it features a sturdy metal chassis, high-power DC motors, and a 6-axis IMU sensor to deliver stable and precise balancing performance. With support for up to 4 kg load, this Self-balancing two wheel Car can easily handle slopes, obstacles, and dynamic movements while maintaining excellent control and responsiveness.
Integrated with MicroROS and ROS2, the robot supports advanced functions such as SLAM mapping, navigation, and AI vision interaction. The Vision Kit includes TOF laser lidar and a ROS-WiFi camera module, enabling real-time obstacle avoidance, autonomous navigation, and visual recognition. An onboard OLED displays live data, while free mobile apps allow remote control and mapping. Ideal for students, educators, and robotics enthusiasts, this Self-balancing Car bridges hands-on hardware learning with powerful ROS-based software development in a single, expandable platform
Basic Function
The 6-axis IMU enables posture recognition, automatically starting the balance system when placed on the ground and safely shutting it down when lifted vertically.
The self-balancing car supports up to 4 kg load, thanks to its sturdy multi-layer structure and high-torque encoder motor, allowing DIY expansion and accessories.
With a powerful balance control system, the robot can smoothly climb slopes of up to 30° while maintaining stability.
Ultrasonic sensors enable intelligent obstacle avoidance and target following, with easy switching between both modes during operation.
The onboard OLED display shows real-time data such as operating mode and battery voltage for quick status monitoring.
Equipped with ESP32 + STM32 Dual Processors
The
ESP32 communication board
uses the ESP32-S3-WROOM-1U-N4R2 chip to support MicroROS wireless communication. It enables real-time transmission of data from multiple sensors—such as motors, gyroscopes, and LiDAR—to the virtual machine ROS master, ensuring fast and stable communication between the robot chassis and the ROS system.
The
STM32 control board
, powered by the STM32F103RCT6 chip, is responsible for precise motor driving, gyroscope data processing, and motor encoder feedback. This ensures stable self-balancing performance and accurate motion control. The board also provides onboard expansion interfaces, supporting a wide range of peripheral connections for future upgrades.
Hardware Features
High-performance TOF Laser LiDAR (T-mini Plus) for fast, accurate indoor and outdoor mapping with up to 12 m range and strong light resistance
ROS-WiFi camera module (AI Vision) enables real-time image streaming and AI processing with OpenCV and MediaPipe support
High-precision encoder DC motors with AB phase Hall encoders deliver stable balancing, accurate speed feedback, and smooth motion control
Durable metal self-balancing chassis with 2 mm thick construction ensures strength, stability, and reliable performance under load
Features:
Self-balancing two-wheel robot with support for up to 4 kg load
STM32 + ESP32 dual-processor architecture for stable control and wireless communication
Integrated MicroROS and ROS2 support for SLAM, navigation, and AI applications
TOF laser LiDAR and ROS-WiFi camera for obstacle avoidance and visual recognition
6-axis IMU enables accurate posture recognition and balance control
Capable of climbing slopes up to 30° with powerful DC motors
Ultrasonic obstacle avoidance and target following modes
Onboard OLED display for real-time status and voltage monitoring
Free mobile apps for remote control and mapping
Modular design with expansion interfaces for DIY learning and upgrades
Technical Specifications
| Processor | STM32F103RCT6 + ESP32-S3-WROOM-1U-N4R2 |
|---|---|
| ROS Master Control | PC Virtual Machine (Ubuntu 22.04 + ROS2 Humble) |
| Control Methods | Mobile App, Mouse & Keyboard, PC Virtual Machine, USB Wireless Handle (Optional) |
| Programming Language | C, Python |
| Program Download | Serial Port, ST-Link |
| LiDAR | TOF Laser LiDAR (T-mini PLUS) |
| Camera Module | ROS-WiFi Camera Module (AI Vision version only) |
| AI Vision Support | OpenCV Image Processing, MediaPipe Machine Learning |
| Load Capacity | Up to 4 kg |
| IMU | 6-axis IMU |
| Balance Control | Supports PID / LQR |
| Maximum Climbing Angle | Up to 30° |
| Motor Type | 520 Encoder DC Reduction Motor × 2 |
| Encoder Type | AB Phase Incremental Hall Encoder |
| Gear Reduction Ratio | 1:30 |
| Motor Speed | 333 ±10 RPM |
| Magnetic Ring Lines | 11 |
| Input Devices | LiDAR, Camera, Encoder Motors, IMU, Ultrasonic Sensor, Buttons |
| Output Devices | Motors, OLED Display, Buzzer |
| Communication | MicroROS Wireless, USART, I2C, SPI |
| Expansion Interfaces | USART, I2C, SPI, Sensor Expansion Ports |
| Display | OLED (mode & voltage display) |
| Battery Capacity | 2200mAh Battery Pack |
| Battery Protection | Over-charge, Over-current, Over-discharge, Short-circuit |
| Battery Working Time | Approx. 20 hours (self-balancing & static) |
| Charging Method | 12.6V–2A Power Adapter (DC4017 Port) |
| Power Output Port | DC5521 Elbow Discharge Port |
| Body Material | Metal + PCB + Acrylic |
| Dimensions (L × W × H) | 194 × 115.59 × 127.59 mm |
| Weight | 1060 g |
Use Cases
- Self-balancing two-wheel robot with up to 4 kg load capacity for stable performance
- STM32 + ESP32 dual processors ensure precise control and fast wireless communication
- Integrated MicroROS & ROS2 support for SLAM, navigation, and advanced robotics learning
- TOF LiDAR + AI vision camera enable real-time obstacle avoidance and smart interaction
