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Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

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Anomaly Detection Methods in Autonomous Robotic Missions.

Shivoh Chirayil Nandakumar¹, Daniel Mitchell², Mustafa Suphi Erden¹

¹School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh EH14 4AS, UK.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

This summary is machine-generated.

This review synthesizes anomaly detection in Autonomous Robotic Missions (ARMs), proposing a unified understanding and classification. This work aims to advance robust and reliable autonomous robot systems.

Keywords:

anomaly autonomous missions autonomous robots

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

Growing utilization of autonomous robots necessitates enhanced robustness and reliability.
Significant increase in research on anomaly detection in robotic systems since 2015.
Existing literature presents diverse perspectives on anomalies and their distinction from fault detection.

Purpose of the Study:

To review and synthesize the literature on anomaly detection in Autonomous Robotic Missions (ARMs).
To establish a unified understanding of anomalies within ARMs.
To propose a novel classification of anomalies based on fundamental features.

Main Methods:

Comprehensive literature review of anomaly detection in ARMs.
Comparative analysis of different anomaly definitions and their relation to fault detection.
Development of a unified anomaly definition and a classification framework (spatial, temporal, spatiotemporal).

Main Results:

Identified diverse perspectives on anomalies in ARMs, highlighting the need for a consensus.
Proposed a unified understanding of anomalies, encompassing their varied characteristics.
Introduced a classification of anomalies into spatial, temporal, and spatiotemporal categories.

Conclusions:

The proposed unified understanding and classification provide a foundation for anomaly detection in ARMs.
Further research into the specific terminology and detection methods for anomalies is crucial.
This work aims to accelerate the development of universal anomaly detection systems for ARMs.