Online anomaly detection in unmanned vehicles

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 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, and a high-fidelity flight simulator. We find that the online Mahalanobis distance technique, presented here, is superior to previous methods.

• Publication year

  2011

• Venue

  Adaptive Agents and Multi-Agent Systems

• Publication date

  2011-05-02

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.65109/sfhb7322
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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