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Raz Lin, Eliyahu Khalastchi, and Gal A. Kaminka. Detecting Anomalies in
Unmanned Vehicles Using the Mahalanobis Distance. In European Workshop on Multi-Agent Systems (EUMAS-09), 2009.
A slightly different version of this paper also appears in Proceedings of the IEEE International Conference on Robotics
and Automation (ICRA-2010)
(unavailable)
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.
@InProceedings{eumas09,
author = {Raz Lin and Eliyahu Khalastchi and Gal A. Kaminka},
title = {Detecting Anomalies in Unmanned Vehicles Using the Mahalanobis Distance},
booktitle = {European Workshop on Multi-Agent Systems ({EUMAS}-09)},
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year = {2009},
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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 Proceedings of the IEEE International Conference on Robotics and Automation (ICRA-2010)},
}
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