Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning
Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning
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DOI：
10.1109/jiot.2017.2737479
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发表时间：
2018-08
影响因子：
10.6
通讯作者：
Huimin Lu-;Yujie Li;Shenglin Mu;D. Wang;Hyoungseop Kim;S. Serikawa
中科院分区：
文献类型：
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作者：
Huimin Lu-;Yujie Li;Shenglin Mu;D. Wang;Hyoungseop Kim;S. Serikawa
无人机被广泛应用于气象观测、农业生产、基础设施检查、灾区监测等领域。然而，目前可用的无人机很容易坠毁。本文的目标是开发一种防止无人机电机在异常温度下运行的异常检测系统。在该异常检测系统中，电机的温度是使用DS18B20传感器记录的。然后，利用强化学习，由树莓PI处理单元判断电机运行异常。专门构建的用户界面允许在平板电脑上跟踪Raspberry PI的活动，以便进行观察。拟议的系统提供了当发动机温度超过自动生成的阈值时降落无人机的能力。实验结果证实，所提出的系统可以利用安装在电机上的温度传感器获得的信息来安全地控制无人机。
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.