Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning

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 preven...

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Bibliographic Details
Published in:IEEE internet of things journal 2018-08, Vol.5 (4), p.2315-2322
Main Authors: Huimin Lu, Yujie Li, Shenglin Mu, Dong Wang, Kim, Hyoungseop, Serikawa, Seiichi
Format: Article
Language:English
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
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Summary: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.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2017.2737479