Model-Free Unsupervised Anomaly Detection of a General Robotic System Using a Stacked LSTM and Its Application to a Fixed-Wing Unmanned Aerial Vehicle
Jae-Hyeon Park,Soham Shanbhag,Dong Eui Chang,Jae-Hyeon Park,Soham Shanbhag,Dong Eui Chang
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...
