Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning

TLDR

UAVs are widely used for weather observation, farming, infrastructure inspection, and disaster monitoring, yet they are prone to crashes. This study aims to develop an anomaly detection system that prevents drone motors from operating at abnormal temperatures. The system records motor temperature with DS18B20 sensors, applies reinforcement learning on a Raspberry Pi to detect abnormal operation, and displays the status on a tablet interface. Experimental results show the system can safely land the drone when motor temperature exceeds a threshold, confirming its effectiveness in controlling the UAV.

Abstract

Unmanned aerial vehicles (UAVs) are used in many fields including weather observation, farming, infrastructure inspection, and monitoring of disaster areas. However, the currently available UAVs are prone to crashing. The goal of this paper is the development of an anomaly detection system to prevent the motor of the drone from operating at abnormal temperatures. In this anomaly detection system, the temperature of the motor is recorded using DS18B20 sensors. Then, using reinforcement learning, the motor is judged to be operating abnormally by a Raspberry Pi processing unit. A specially built user interface allows the activity of the Raspberry Pi to be tracked on a Tablet for observation purposes. The proposed system provides the ability to land a drone when the motor temperature exceeds an automatically generated threshold. The experimental results confirm that the proposed system can safely control the drone using information obtained from temperature sensors attached to the motor.