Gal A. Kaminka's 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 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.

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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OPTlocalfile = {},
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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