Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning
By Yujie Li, Shenglin Mu, Dong Wang, Hyoungseop Kim, Seiichi Serikawa. 2018
444 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering
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Citations in the corpus
- NAS-Unet: Neural Architecture Search for Medical Image Segmentation (2019, background): “Many works have designed network structures for specific tasks [15], [16].”
- Narrow Band Internet of Things (2017, background): “In 2017, Lu et al [96] firstly proposed a NB-IoT platform to monitor the unmanned aerial vehicles in smart cities, which prevents the UAVs’ accidentally falling effectively.”