Sliding mode observer based fault identification in automatic vision system of robot

Sergiyenko, Oleg; Tyrsa, Vera; Zhirabok, Alexey; Зуев, А. В.

doi:10.1016/j.conengprac.2023.105614

Cited by 10 publications

(1 citation statement)

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“…A low likelihood score indicates that the observed sequence is unlikely under normal operating conditions and is considered an anomaly [ 55 ]. Further, employing sliding mode observers for meticulous fault identification within robotic 271 vision systems underscores the indispensable utility of precise, model-informed diagnostics 272 in safeguarding autonomous robotic missions’ fidelity and operational efficacy [ 56 ].…”

Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%

Anomaly Detection Methods in Autonomous Robotic Missions

Chirayil Nandakumar,

Mitchell,

Erden

et al. 2024

Sensors

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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.

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Section: Methods Of Anomaly Detection In Armsmentioning

confidence: 99%