Design and Development of an Affordable ESP32-based Micro drone with Automated Landing and Kalman filtered TOF Sensing

Abstract

The need for accessible and reasonable prices drone platforms has increased due to the expansion of UAVs (Unmanned Aerial Vehicles) in fields including agriculture, disaster management and surveillance. However most of the commercial and research drones relay on expensive flight controllers and patented ecosystem, thus limiting their adoption in educational and resource constraint environment. This paper describes the design, implementation, and testing of low-cost micro drone with an ESP32-S3 microcontroller as the core flight controller. By incorporating an MPU6050 (IMU) for attitude stabilization and a VL53L0X (TOF) laser sensor for accurate altitude measurement, the firmware expands an open-source baseline. The use of a 1-D linear Kalman filter to smooth TOF altitude data caused by propeller vibrations is a significant addition of this study. This allows for dependable automatic landing, a capability lacking in drones in this price range. The drone has a custom 3D printed X-frame, 8250 coreless motors, controlled by a motor driver circuit made of AO3400 N-channel MOSFETs, SS34 Schottky diodes, and 10k? gate resistors. A mobile app was developed to serve as a wireless controller communicating through Wi-Fi network hosted by ESP32 via low-latency UDP packets.

Introduction

This project presents a low-cost ESP32-S3-based autonomous micro drone designed for research, education, and hobby applications. Unlike expensive commercial flight controllers, the proposed system provides an affordable alternative while supporting advanced features such as altitude stabilization, autonomous landing, and Wi-Fi-based control. The entire drone is built using off-the-shelf components and a 3D-printed frame at a total cost of approximately ?1,744.

The drone uses an ESP32-S3 Sense as the flight controller, interfaced with an MPU6050 IMU for attitude estimation and a VL53L0X Time-of-Flight (TOF) sensor for precise altitude measurement. To reduce vibration-induced sensor noise, a 1D Linear Kalman Filter is implemented, providing accurate altitude estimation with minimal latency. A cascade PID controller stabilizes roll, pitch, yaw, and altitude, while a discrete AO3400 MOSFET motor driver circuit controls four 8250 coreless motors. The drone is operated through an Android mobile application using Wi-Fi and UDP communication, eliminating the need for a dedicated RC transmitter.

Key contributions include:

• Development of a complete low-cost micro drone using ESP32-S3.
• Integration of TOF-based altitude sensing with Kalman filtering for accurate altitude hold.
• Design of a cost-effective MOSFET motor driver as an alternative to commercial driver ICs.
• Implementation of a Wi-Fi mobile control application for real-time operation.
• Development of an autonomous landing state machine that safely lands the drone using filtered altitude data.

Experimental results demonstrated strong performance:

• Total drone weight: 58 g
• Build cost: ~?1,744
• Flight time: ~6 minutes
• Altitude measurement variance reduced by over 91% using the Kalman filter.
• Stable altitude hold within ±1.2 cm.
• Roll and pitch stability of approximately ±2°.
• 95% autonomous landing success rate with an average landing time of 4.8 seconds.
• Average Wi-Fi control latency of 18 ms with less than 0.5% packet loss.

Conclusion

This paper presented the design and implementation of a low-cost micro drone based on ESP32-S3 sense. By extending an open-source baseline firmware, the project successfully integrated a VL53L0X TOF sensor and implemented a 1-D linear Kalman filter, solving the critical challenge of vibration induced altitude noise. This filtered data enabled a reliable automated landing state machine. Furthermore, a discrete MOSFET-based motor driver and a Wi-Fi mobile application kept the total build cost under Rs. 2,000. The drone achieves stable flight with attitude stability of ±2º, altitude hold accuracy of ±1.2 cm, and a 95% automated landing success rate. Future development will includes adding optical flow for horizontal positioning hold, integrating a camera for computer vision, and a magnetometer for yaw stability.

References

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Copyright

Copyright © 2026 Prajjwal Rawat, Samarth Saxena , Rohit Yadav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.