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Conference Towards Autonomous Robotic Systems

TAROS 2011: Towards Autonomous Robotic Systems pp 376–377Cite as

Adaptive Particle Filter for Fault Detection and Isolation of Mobile Robots

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6856))

Abstract

Particle filters have recently gained major attention as a powerful diagnostic tool. Their severe drawback is the computational burden closely related to the number of particles used. Therefore, it is often necessary to work out a compromise between computation time and the quality of results, especially in the case of systems with limited computational resources such as mobile robots. This work outlines the concept of a fault detection and isolation (FDI) system for a mobile robot which is based on a bank of adaptive particle filters and accounts for the aforementioned problems.

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References

  1. Chen, J., Patton, R.: Robust model-based fault diagnosis for dynamic systems. Kluwer, Boston (1999)

    Book  MATH  Google Scholar 

  2. Kadirkamanathan, V., Li, P., Jaward, M.H., Fabri, S.G.: Particle filtering-based fault detection in non-linear stochastic systems. International Journal of Systems Science 33, 259–265 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Korbicz, J., Kościelny, J., Kowalczuk, Z., Cholewa, W.: Fault Diagnosis: Models, Artificial Intelligence, Applications. Springer, Berlin (2004)

    Book  MATH  Google Scholar 

  4. Li, P., Kadirkamanathan, V.: Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews 31, 337–343 (2001)

    Article  Google Scholar 

  5. Li, P., Kadirkamanathan, V.: Fault detection and isolation in non-linear stochastic systems–a combined adaptive monte carlo filtering and likelihood ratio approach. International Journal of Control 77, 1101–1114 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  6. Plagemann, C., Stachniss, C., Burgard, W.: Efficient failure dtection for mobile robots using mixed abstraction particle filters. In: Proceedings of European Robotics Symposium (2006)

    Google Scholar 

  7. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)

    MATH  Google Scholar 

  8. Zając, M., Uciński, D.: Adaptacyjny filtr cząsteczkowy w detekcji i lokalizacji uszkodzeń robota mobilnego. Prace Naukowe Politechniki Warszawskiej. Elektronika 175, 635–644 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Michał Zając

Authors
  1. Michał Zając
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Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Roderich Groß

  2. Materials and Engineering Research Institute, Sheffield Hallam University, City Campus, Horward Street, S1 1WB, Sheffield, UK

    Lyuba Alboul  & Jacques Penders  & 

  3. Department of Computing, Engineering and Mathematical Sciences, Bristol Robotics Laboratory (BRL), University of Bristol and the West of England, Bristol Business Park, BS16 1QD, Bistol, UK

    Chris Melhuish

  4. Department of Electrical and Electronic Engineering, Imperial College London, Exhibition Road, SW7 2BT, London, UK

    Mark Witkowski

  5. Department of Psychology, The University of Sheffield, Wstern Bank, S10 2TN, Sheffield, UK

    Tony J. Prescott

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© 2011 Springer-Verlag Berlin Heidelberg

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Zając, M. (2011). Adaptive Particle Filter for Fault Detection and Isolation of Mobile Robots. In: Groß, R., Alboul, L., Melhuish, C., Witkowski, M., Prescott, T.J., Penders, J. (eds) Towards Autonomous Robotic Systems. TAROS 2011. Lecture Notes in Computer Science(), vol 6856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23232-9_35

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  • DOI: https://doi.org/10.1007/978-3-642-23232-9_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23231-2

  • Online ISBN: 978-3-642-23232-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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