Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 5, Issue 4, Pages 2315-2322Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2017.2737479
Keywords
Anomaly detection; reinforcement learning; temperature sensor; unmanned aerial vehicle (UAV)
Categories
Funding
- Leading Initiative for Excellent Young Researcher of Ministry of Education, Culture, Sports, Science, and Technology, Japan [16809746]
- JSPS [17K14694]
- Research Fund of State Key Laboratory of Marine Geology in Tongji University [MGK1608]
- Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University [1510]
- Research Fund of The Telecommunications Advancement Foundation
- Fundamental Research Developing Association for Shipbuilding and Offshore
- Fundamental Research Developing Association for Strengthening Research Support Project of Kyushu Institute of Technology
- Grants-in-Aid for Scientific Research [17K14694] Funding Source: KAKEN
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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.
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