Gal A. Kaminka's Publications

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Detecting Anomalies in Unmanned Vehicles Using the Mahalanobis Distance

Raz Lin, Eliyahu Khalastchi, and Gal A. Kaminka. Detecting Anomalies in Unmanned Vehicles Using the Mahalanobis Distance. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA-10), 2010.
A slightly different version of this paper also appears in the European Workshop on Multi-Agent Systems (EUMAS).

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Abstract

The use of unmanned autonomous vehicles is becoming more and more significant in recent years. The fact that the vehicles are unmanned (whether autonomous or not), can lead to greater difficulties in identifying failure and anomalous states, since the operator cannot rely on its own body perceptions to identify failures. Moreover, as the autonomy of unmanned vehicles increases, it becomes more difficult for operators to monitor them closely, and this further exacerbates the difficulty of identifying anomalous states, in a timely manner. Model-based diagnosis and fault-detection systems have been proposed to recognize failures. However, these rely on the capabilities of the underlying model, which necessarily abstracts away from the physical reality of the robot. In this paper we propose a novel, model-free, approach for detecting anomalies in unmanned autonomous vehicles, based on their sensor readings (internal and external). Experiments conducted on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) demonstrate the efficacy of the approach by detecting the vehicles deviations from nominal behavior.

Additional Information

BibTeX

@InProceedings{icra10anomaly,
author = {Raz Lin and Eliyahu Khalastchi and Gal A. Kaminka},
title = {Detecting Anomalies in Unmanned Vehicles Using the Mahalanobis Distance},
booktitle = ICRA-10,
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year = {2010},
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OPTmonth = {},
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abstract = {The use of unmanned autonomous vehicles is becoming more and more 
significant in recent years. The fact that the vehicles are unmanned 
(whether autonomous or not), can lead to greater difficulties 
in identifying failure and anomalous states, since the operator cannot 
rely on its own body perceptions to identify failures. Moreover, as 
the autonomy of unmanned vehicles increases, it becomes more difficult 
for operators to monitor them closely, and this further exacerbates 
the difficulty of identifying anomalous states, in a timely manner. 
Model-based diagnosis and fault-detection systems have been proposed 
to recognize failures. However, these rely on the capabilities of 
the underlying model, which necessarily abstracts away from the physical 
reality of the robot.   In this paper we propose a novel, model-free, approach 
for detecting anomalies in unmanned autonomous 
vehicles, based on their sensor readings (internal and external). Experiments 
conducted on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) 
 demonstrate the efficacy of the approach by detecting the vehicles 
deviations from nominal behavior.},
  wwwnote = { A slightly different version of this paper also appears in the European Workshop on 
  Multi-Agent Systems (EUMAS).}, 
}

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