Online anomaly detection in unmanned vehicles

Eliahu Khalastchi, Gal A. Kaminka, Meir Kalech, Raz Lin

Research output: Contribution to conferencePaperpeer-review

37 Scopus citations


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 Maha- lanobis 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. Categories and Subject Descriptors 1.2.9 [Artificial Intelligence]: Robotics General Terms Experimentation.

Original languageEnglish
Number of pages8
StatePublished - 1 Jan 2011
Event10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 - Taipei, Taiwan, Province of China
Duration: 2 May 20116 May 2011


Conference10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011
Country/TerritoryTaiwan, Province of China


  • Anomaly detection
  • Machine learning
  • Mahalanobis distance
  • Robotics
  • Uncertainty

ASJC Scopus subject areas

  • Artificial Intelligence


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