Gal A. Kaminka: Publications

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Online Anomaly Detection in Unmanned Vehicles

Eliahu Khalastchi, Meir Kalech, Gal A. Kaminka, and Raz Lin . Online Anomaly Detection in Unmanned Vehicles. In Proceedings of the Tenth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-11), pp. 115–122, 2011.

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Abstract

Autonomy requires robustness. The use of unmanned (autonomous) vehicles is appealing for tasks which are dangerous or dull. However, increased reliance onautonomous robots increases reliance on their robustness. Even with validated software,physical faults can cause the controlling software to perceive the environmentincorrectly, and thus to make decisions that lead to task failure. We present anonline anomaly detection method for robots, that is light-weight, and is ableto take into account a large number of monitored sensors and internalmeasurements, with high precision. We demonstrate a specialization of thefamiliar \mahal for robot use, and also show how it can be used even with verylarge dimensions, by online selection of correlated measurements for its use. Weempirically evaluate these contributions in different domains: commercialUnmanned Aerial Vehicles (UAVs), a vacuum-cleaning robot, and a high-fidelityflight simulator. We find that the online Mahalanobis distance technique, presented here, issuperior to previous methods.

Additional Information

BibTeX

@InProceedings{aamas11eli,
author = { Eliahu Khalastchi and Meir Kalech and Gal A. Kaminka and Raz Lin },
title = {Online Anomaly Detection in Unmanned Vehicles},
booktitle = AAMAS-11,
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pages = {115--122},
year = {2011},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
OPTmonth = {},
OPTorganization = {},
OPTpublisher = {},
OPTnote = {},
OPTannote = {},
OPTurl = {},
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abstract = {Autonomy requires robustness. The use of unmanned (autonomous) vehicles is appealing for
  tasks which are dangerous or dull. However, increased reliance on
autonomous robots increases reliance on their robustness. Even with validated software,
physical faults can cause the controlling software to perceive the environment
incorrectly, and thus to make decisions that lead to task failure. We present an
online anomaly detection method for robots, that is light-weight, and is able
to take into account a large number of monitored sensors and internal
measurements, with high precision. We demonstrate a specialization of the
familiar \mahal for robot use, and also show how it can be used even with very
large dimensions, by online selection of correlated measurements for its use. We
empirically evaluate these contributions in different domains: commercial
Unmanned Aerial Vehicles (UAVs), a vacuum-cleaning robot, and a high-fidelity
flight simulator. We find that the online Mahalanobis distance technique, presented here, is
superior to previous methods.},
  wwwnote = {},
}

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