Towards Fault Diagnosis in Robot Swarms: An Online Behaviour Characterisation Approach

  • James O’KeeffeEmail author
  • Danesh Tarapore
  • Alan G. Millard
  • Jon Timmis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10454)


Although robustness has been cited as an inherent advantage of swarm robotics systems, it has been shown that this is not always the case. Fault diagnosis will be critical for future swarm robotics systems if they are to retain their advantages (robustness, flexibility and scalability). In this paper, existing work on fault detection is used as a foundation to propose a novel approach for fault diagnosis in swarms based on a behavioural feature vector approach. Initial results show that behavioural feature vectors can be used to reliably diagnose common electro-mechanical fault types in most cases tested.


Fault diagnosis Feature vector Behaviour characterisation Swarm robotics 


  1. 1.
    Bi, R.: Immune-inspired fault diagnosis for a robotic system. Ph.D. thesis, University of York (2012)Google Scholar
  2. 2.
    Bi, R., Timmis, J., Tyrrell, A.: The diagnostic dendritic cell algorithm for robotic systems. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)Google Scholar
  3. 3.
    Bjerknes, J.D., Winfield, A.F.T.: On fault tolerance and scalability of swarm robotic systems. In: Martinoli, A., Mondada, F., Correll, N., Mermoud, G., Egerstedt, M., Hsieh, M.A., Parker, L.E., Støy, K. (eds.) Distributed Autonomous Robotic Systems. Springer Tracts in Advanced Robotics, vol. 83, pp. 431–444. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-32723-0_31 CrossRefGoogle Scholar
  4. 4.
    Bonani, M., Longchamp, V., Magnenat, S., Rétornaz, P., Burnier, D., Roulet, G., Vaussard, F., Bleuler, H., Mondada, F.: The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4187–4193. IEEE (2010)Google Scholar
  5. 5.
    Carlson, J.: Analysis of how mobile robots fail in the field. Ph.D. thesis, University of South Florida (2004)Google Scholar
  6. 6.
    Christensen, A.L., O’Grady, R., Birattari, M., Dorigo, M.: Fault detection in autonomous robots based on fault injection and learning. Autonom. Robots 24(1), 49–67 (2008)CrossRefGoogle Scholar
  7. 7.
    O’Grady, R., Dorigo, M.: From fireflies to fault-tolerant swarms of robots. IEEE Trans. Evol. Comput. 13(4), 754–766 (2009)CrossRefGoogle Scholar
  8. 8.
    Cohen, I.R.: Tending Adam’s Garden: Evolving the Cognitive Immune Self. Academic Press, London (2000)Google Scholar
  9. 9.
    De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, London (2002)zbMATHGoogle Scholar
  10. 10.
    Khadidos, A., Crowder, R.M., Chappell, P.H.: Exogenous fault detection and recovery for swarm robotics. IFAC-PapersOnLine 48(3), 2405–2410 (2015)CrossRefGoogle Scholar
  11. 11.
    Millard, A.G.: Exogenous fault detection in swarm robotic systems. Ph.D. thesis, University of York (2016)Google Scholar
  12. 12.
    Millard, A.G., Timmis, J., Winfield, A.F.T.: Run-time detection of faults in autonomous mobile robots based on the comparison of simulated and real robot behaviour. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3720–3725. IEEE (2014)Google Scholar
  13. 13.
    Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., Stirling, T., Gutíerrez, A., Gambardella, L.M., Dorigo, M.: ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5027–5034. IEEE (2011)Google Scholar
  14. 14.
    Quinlan, R.J.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  15. 15.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). doi: 10.1007/978-3-540-30552-1_2 CrossRefGoogle Scholar
  16. 16.
    Tarapore, D., Lima, P.U., Carneiro, J., Christensen, A.L.: To err is robotic, to tolerate immunological: fault detection in multirobot systems. Bioinspiration Biomim. 10(1), 16014 (2015)CrossRefGoogle Scholar
  17. 17.
    Timmis, J., Andrews, P., Hart, E.: On artificial immune systems and swarm intelligence. Swarm Intell. 4(4), 247–273 (2010)CrossRefGoogle Scholar
  18. 18.
    Winfield, A.F.T., Nembrini, J.: Safety in numbers: fault-tolerance in robot swarms. Int. J. Model. Ident. Control 1(1), 30–37 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • James O’Keeffe
    • 1
    Email author
  • Danesh Tarapore
    • 2
  • Alan G. Millard
    • 1
  • Jon Timmis
    • 1
  1. 1.Department of Electronic EngineeringUniversity of YorkYorkUK
  2. 2.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK

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