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A Data-Driven Method to Identify Fault Mitigation Strategies in Robot Swarms

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Swarm Intelligence (ANTS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14987))

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Abstract

As robot swarms are increasingly deployed in the real-world, making them safe will be critical to improving adoption and trust. A robot swarm is composed of many individual robots each susceptible to failure at any given time, which may decrease the performance of the swarm as a whole. The ability to mitigate critical faults is therefore necessary. The difficulty with designing an effective mitigation strategy lies in the complexity of the swarm as a system, where individual interactions give rise to emergent behaviour. In this paper, we present a data-driven method to identify effective local actions available to faulty robots in the swarm. We make the assumption that robots are able to self-detect faults and that pre-coded actions are indeed available. An effective action should mitigate any negative impact of faults on overall swarm performance. We consider two intralogistics scenarios where the swarm must retrieve and deliver boxes. The first concerns single robot transport (one robot per box) and the second, collective transport (four robots per box). Our method is able to identify effective actions for particular fault types. We also consider the impact of actions across ratios of fault in the swarm. Interestingly, faults do not always benefit from mitigations, with mitigations causing overall lower system performance for certain fault types.

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Notes

  1. 1.

    https://bitbucket.org/suet_lee/swarm_mitigation_cpp/src/master/.

References

  1. Abeywickrama, D.B., et al.: Aeros: assurance of emergent behaviour in autonomous robotic swarms. arXiv preprint arXiv:2302.10292 (2023)

  2. Bai, Y., Wang, J.: Fault detection and isolation using relative information for multi-agent systems. ISA Trans. 116, 182–190 (2021)

    Article  Google Scholar 

  3. Bjerknes, J., Winfield, A.: On fault tolerance and scalability of swarm robotic systems 83, 431–444 (2013). https://doi.org/10.1007/978-3-642-32723-0_31

  4. Bossens, D.M., Tarapore, D.: Rapidly adapting robot swarms with swarm map-based bayesian optimisation. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 9848–9854 (2021). https://doi.org/10.1109/ICRA48506.2021.9560958

  5. Carrasco, R.A., Núñez, F., Cipriano, A.: Fault detection and isolation in cooperative mobile robots using multilayer architecture and dynamic observers. Robotica 29(4), 555–562 (2011)

    Article  Google Scholar 

  6. Carrillo-Zapata, D., et al.: Mutual shaping in swarm robotics: user studies in fire and rescue, storage organization, and bridge inspection. Front. Robot. AI 7, 53 (2020)

    Google Scholar 

  7. Christensen, A.L., OGrady, R., Dorigo, M.: From fireflies to fault-tolerant swarms of robots. IEEE Trans. Evol. Comput. 13(4), 754–766 (2009)

    Google Scholar 

  8. Gallehdari, Z., Meskin, N., Khorasani, K.: An h\(^\infty \) cooperative fault recovery control of multi-agent systems. Automatica 84, 101–108 (2017)

    Article  MathSciNet  Google Scholar 

  9. Ghedini, C., Ribeiro, C., Sabattini, L.: Toward fault-tolerant multi-robot networks. Networks 70(4), 388–400 (2017)

    Article  MathSciNet  Google Scholar 

  10. Guo, M., Dimarogonas, D.V., Johansson, K.H.: Distributed real-time fault detection and isolation for cooperative multi-agent systems. In: 2012 American Control Conference (ACC), pp. 5270–5275. IEEE (2012)

    Google Scholar 

  11. Hogg, E., Harvey, D., Hauert, S., Richards, A.: Social exploration in robot swarms. In: Bourgeois, J., et al. (ed.) Distributed Autonomous Robotic Systems, DARS 2022, SPAR, vol. 28, pp. 69–82. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-51497-5_6

  12. Jones, S., Hauert, S.: Frappe: fast fiducial detection on low cost hardware. J. Real-Time Image Proc. 20(6), 119 (2023)

    Article  Google Scholar 

  13. Jones, S., Milner, E., Sooriyabandara, M., Hauert, S.: Distributed situational awareness in robot swarms. Adv. Intell. Syst. 2(11), 2000110 (2020)

    Article  Google Scholar 

  14. Jones, S., Milner, E., Sooriyabandara, M., Hauert, S.: Dots: an open testbed for industrial swarm robotic solutions. arXiv preprint arXiv:2203.13809 (2022)

  15. Lau, H., Bate, I., Cairns, P., Timmis, J.: Adaptive data-driven error detection in swarm robotics with statistical classifiers. Robot. Auton. Syst. 59(12), 1021–1035 (2011)

    Article  Google Scholar 

  16. Lee, S., Milner, E., Hauert, S.: A data-driven method for metric extraction to detect faults in robot swarms. IEEE Robot. Autom. Lett. 7(4), 10746–10753 (2022). https://doi.org/10.1109/LRA.2022.3189789

    Article  Google Scholar 

  17. Minelli, M., Panerati, J., Kaufmann, M., Ghedini, C., Beltrame, G., Sabattini, L.: Self-optimization of resilient topologies for fallible multi-robots. Robot. Auton. Syst. 124, 103384 (2020)

    Article  Google Scholar 

  18. Nachar, N.: The mann-whitney u: a test for assessing whether two independent samples come from the same distribution. Tutorials Quant. Methods Psychol. 4 (2008). https://doi.org/10.20982/tqmp.04.1.p013

  19. Neupane, A., Goodrich, M.A.: Learning resilient swarm behaviors via ongoing evolution. In: Dorigo, M., et al. (ed.) Swarm Intelligence, ANTS 2022, LNCS, vol. 13491, pp. 155–170. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20176-9_13

  20. O’Keeffe, J., Tarapore, D., Millard, A.G., Timmis, J.: Fault diagnosis in robot swarms: an adaptive online behaviour characterisation approach. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2017)

    Google Scholar 

  21. Oladiran, O.: Fault recovery in swarm robotics systems using learning algorithms learning algorithms (2019)

    Google Scholar 

  22. Parker, L.: Alliance: an architecture for fault tolerant multirobot cooperation. IEEE Trans. Robot. Autom. 14(2), 220–240 (1998). https://doi.org/10.1109/70.681242

    Article  Google Scholar 

  23. Penders, J., et al.: A robot swarm assisting a human fire-fighter. Adv. Robot. 25, 93–117 (2011). https://doi.org/10.1163/016918610X538507

    Article  Google Scholar 

  24. Putter, R., Nitschke, G.: Evolving morphological robustness for collective robotics. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2017)

    Google Scholar 

  25. Şahin, E.: Swarm robotics: From sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics, pp. 10–20. Springer, Berlin, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30552-1_2

    Chapter  Google Scholar 

  26. Schranz, M., Umlauft, M., Sende, M., Elmenreich, W.: Swarm robotic behaviors and current applications. Front. Robot. AI 7, 36 (2020)

    Google Scholar 

  27. Shiliang, S., Ting, W., Chen, Y., Xiaofan, L., Hai, Z.: Distributed fault detection and isolation for flocking in a multi-robot system with imperfect communication. Int. J. Adv. Rob. Syst. 11(6), 86 (2014)

    Article  Google Scholar 

  28. Subha, N.A.M., Mahyuddin, M.N.: Distributed adaptive cooperative control with fault compensation mechanism for heterogeneous multi-robot system. IEEE Access 9, 128550–128563 (2021)

    Article  Google Scholar 

  29. Tarapore, D., Lima, P.U., Carneiro, J., Christensen, A.L.: To err is robotic, to tolerate immunological: fault detection in multirobot systems. Bioinspiration Biomimetics 10(1), 016014 (2015)

    Article  Google Scholar 

  30. Tarapore, D., Timmis, J., Christensen, A.L.: Fault detection in a swarm of physical robots based on behavioral outlier detection. IEEE Trans. Rob. 35(6), 1516–1522 (2019)

    Article  Google Scholar 

  31. Timmis, J., Ismail, A.R., Bjerknes, J., Winfield, A.: An immune-inspired swarm aggregation algorithm for self-healing swarm robotic system. Biosystems 146 (2016). https://doi.org/10.1016/j.biosystems.2016.04.001

  32. Vassev, E., Sterritt, R., Rouff, C., Hinchey, M.: Swarm technology at NASA: building resilient systems. IT Professional 14(2), 36–42 (2012)

    Article  Google Scholar 

  33. Werfel, J., Petersen, K., Nagpal, R.: Designing collective behavior in a termite-inspired robot construction team. Science (New York, N.Y.) 343, 754–758 (2014)

    Google Scholar 

  34. Winfield, A.F., Nembrini, J.: Safety in numbers: fault-tolerance in robot swarms. Int. J. Model. Ident. Control 1(1), 30–37 (2006)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the US Air Force Office of Scientific Research, European Office of Aerospace Research and Development, the UKRI Trustworthy Autonomous Systems Node in Functionality (EP/V026518/1), and the European Union under Grant Agreement 101070918 and UKRI grant number 10038942.

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Authors and Affiliations

  1. Bristol Robotics Laboratory, University of Bristol, Bristol, UK

    Suet Lee & Sabine Hauert

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  1. Suet Lee
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  2. Sabine Hauert
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Correspondence to Suet Lee .

Editor information

Editors and Affiliations

  1. University of Konstanz, Konstanz, Germany

    Heiko Hamann

  2. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  3. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Leslie Pérez Cáceres

  4. University of Konstanz, Konstanz, Germany

    Andreagiovanni Reina

  5. University of Konstanz, Konstanz, Germany

    Jonas Kuckling

  6. University of Technology Nuremberg, Nuremberg, Germany

    Tanja Katharina Kaiser

  7. University of Southampton, Southampton, UK

    Mohammad Soorati

  8. Royal Military Academy, Brussels, Belgium

    Ken Hasselmann

  9. University of Konstanz, Konstanz, Germany

    Eduard Buss

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Lee, S., Hauert, S. (2024). A Data-Driven Method to Identify Fault Mitigation Strategies in Robot Swarms. In: Hamann, H., et al. Swarm Intelligence. ANTS 2024. Lecture Notes in Computer Science, vol 14987. Springer, Cham. https://doi.org/10.1007/978-3-031-70932-6_2

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  • DOI: https://doi.org/10.1007/978-3-031-70932-6_2

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-70932-6

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