Applying machine learning techniques to localize quadcopter sensor failures

The paper describes an algorithm for localization and classification of quadcopter sensor (accelerometer and gyroscope) failures. Based on the obtained attributes; the conclusion is made about the serviceability of a quadcopter-type unmanned aerial vehicle (UAV). Two machine learning methods are used: logistic regression and random forest method. Their performance evaluated and compared using the data obtained by simulating the physical model of a quadcopter. A computer simulation of position sensor failure detection is presented. The proposed approach has shown its effectiveness and, therefore, it can be used to build a fault-tolerant adaptive motion control subsystem for UAVs.