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Intelligent Autonomous Systems 13 pp 43–56Cite as

Adaptive Model-Based Monitoring for Robots

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

Continuous and comprehensive monitoring is a key requirement for reliable failure detection. However, the overhead of the observation process conflicts with the limited resources of a robot platform. Therefore, robot monitoring faces high efficiency requirements. This defines a trade-off between comprehensive observation and monitoring resource overhead. In this paper, we propose an adaptive, model-based monitoring approach that addresses this trade-off. We specify an individual monitoring configuration in an abstract system model to focus the observation on expressive state aspects. Moreover, we introduce adaptivity to further improve the efficiency of the monitoring process. To evaluate this efficiency, we compare our approach with a reference monitoring system. Due to our results, we are confident that the proposed approach significantly reduces the resource overhead.

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Author information

Authors and Affiliations

  1. Distributed Systems Group, University of Kassel, 34121, Kassel, Germany

    Dominik Kirchner & Kurt Geihs

Authors
  1. Dominik Kirchner
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  2. Kurt Geihs
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Corresponding author

Correspondence to Dominik Kirchner .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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© 2016 Springer International Publishing Switzerland

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Kirchner, D., Geihs, K. (2016). Adaptive Model-Based Monitoring for Robots. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

  • eBook Packages: EngineeringEngineering (R0)

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