Anomaly Detection Methods in Autonomous Robotic Missions

Abstract

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

Data-Driven Prediction Model for Analysis of Sensor Data

“…The primary difficulty in identifying temporal anomalies lies in creating models or systems capable of retaining previous encounters or interpreting patterns from extended and intricate time series data. Distinguishing temporal anomalies should not be mistaken for predicting sensor noise problems, as noise fluctuations or sudden changes in signal amplitude or frequency can be effectively addressed using established digital signal processing techniques like Fast Fourier Transform (FFT) and Radon Fourier Transform (RFT) [18].…”