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Eliahu Khalastchi. Anomaly Detection in Unmanned Vehicles. Master's Thesis, Bar Ilan University,2011.
Autonomy implies robustness. The use of unmanned (autonomous) vehicles is appealing for tasks which are dangerous or dull. However, increased reliance on autonmous 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. 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. We present two novel, model-free, domain independent approaches for detecting anomalies in unmanned autonomous vehicles, based on their sensor readings (internal and external). Both approaches use the familiar Mahalanobis Distance for the online anomaly detection. The first approach uses an offline training process. With this approach, we show the importance of a training process, which enables the Mahalanobis Distance to detect anomalies successfully. The second approach uses an online training process, in a way that is light-weight, and is able to take into account a large number of monitored sensors and internal measurements. These properties make the approach a “plug & play” anomaly detection mechanism for different robotic platforms. We demonstrate a specialization of the Mahalanobis Distance 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, a high-fidelity flight simulator, and an electrical power system. We find that the online Mahalanobis distance technique, presented here, is superior to previous methods.
@MastersThesis{eli-msc,
author = {Eliahu Khalastchi},
title = {Anomaly Detection in Unmanned Vehicles},
school = {{B}ar {I}lan {U}niversity},
year = {2011},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
OPTnote = {Available at \url{http://www.cs.biu.ac.il/~galk/Publications/b2hd-eli-msc.html}},
OPTannote = {},
wwwnote = {},
abstract = {
Autonomy implies robustness. The use of unmanned (autonomous) vehicles is
appealing for tasks which are dangerous or dull. However, increased reliance on
autonmous 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.
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.
We present two novel, model-free, domain independent
approaches for detecting anomalies in unmanned autonomous vehicles, based on
their sensor readings (internal and external). Both approaches use the familiar
Mahalanobis Distance for the online anomaly detection. The first approach uses an
\emph{offline} training process. With this approach, we show the importance of a
training process, which enables the Mahalanobis Distance to detect
anomalies successfully. The second approach uses an \emph{online} training process, in a
way that is light-weight, and is able to take into account a large number of
monitored sensors and internal measurements. These properties make the approach
a ``plug \& play'' anomaly detection mechanism for different robotic platforms.
We demonstrate a specialization of the Mahalanobis Distance 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, a high-fidelity
flight simulator, and an electrical power system. We find that the online
Mahalanobis distance technique, presented here, is superior to previous methods.
},
}
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