Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning

• Published in: IEEE internet of things journal Vol. 5; no. 4; pp. 2315 - 2322
• Main Authors: Huimin Lu, Yujie Li, Shenglin Mu, Dong Wang, Kim, Hyoungseop, Serikawa, Seiichi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Anomaly detection; DC motors; Drones; Inspection; Learning (artificial intelligence); Military technology; Monitoring; Motors; reinforcement learning; Temperature; Temperature measurement; temperature sensor; Temperature sensors; unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2017.2737479

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.