Model-Free Unsupervised Anomaly Detection of a General Robotic System Using a Stacked LSTM and Its Application to a Fixed-Wing Unmanned Aerial Vehicle

Abstract

With the growing application of various robots in real life, the need for an automatic anomaly detection system for robots is necessary for safety. In this paper, we develop an anomaly detection method using a stacked LSTM that can be applied to any robot controlled by a feedback control. Our method does not need installation of additional sensors. Our method is model-free and unsupervised because it does not require the analytical model of the system and the training data does not require faulty operation conditions. We validate our method on real fixed-wing unmanned aerial vehicle flight data containing control surface failure scenarios. We demonstrate the superiority of the proposed algorithm over existing anomaly detection methods in the literature. Our code is available at https://github.com/superhumangod/Model-free-unsupervised-anomaly-detection.