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Guerrilla Performance Analysis for Robot Swarms: Degrees of Collaboration and Chains of Interference Events

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Swarm Intelligence (ANTS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

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Abstract

Scalability is a key feature of swarm robotics. Hence, measuring performance depending on swarm size is important to check the validity of the design. Performance diagrams have generic qualities across many different application scenarios. We summarize these findings and condense them in a practical performance analysis guide for swarm robotics. We introduce three general classes of performance: linear increase, saturation, and increase/decrease. As the performance diagrams may contain rich information about underlying processes, such as the degree of collaboration and chains of interference events in crowded situations, we discuss options for quickly devising hypotheses about the underlying robot behaviors. The validity of our performance analysis guide is then made plausible in a number of simple examples based on models and simulations.

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Notes

  1. 1.

    See http://doi.org/10.5281/zenodo.3947822 for videos, screenshot, and data.

References

  1. Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: AFIPS Conference Proceedings, pp. 483–485. ACM (1967)

    Google Scholar 

  2. Bjerknes, J.D., Winfield, A., Melhuish, C.: An analysis of emergent taxis in a wireless connected swarm of mobile robots. In: Shi, Y., Dorigo, M. (eds.) IEEE Swarm Intelligence Symposium, pp. 45–52. IEEE Press, Los Alamitos (2007)

    Google Scholar 

  3. Dimidov, C., Oriolo, G., Trianni, V.: Random walks in swarm robotics: an experiment with kilobots. In: Dorigo, M., et al. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 185–196. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44427-7_16

    Chapter  Google Scholar 

  4. Dussutour, A., Fourcassié, V., Helbing, D., Deneubourg, J.L.: Optimal traffic organization in ants under crowded conditions. Nature 428, 70–73 (2004)

    Article  Google Scholar 

  5. Frederick, P., Brooks, J.: The Mythical Man-Month. Addison-Wesley, Boston (1995)

    Google Scholar 

  6. Goldberg, D., Matarić, M.J.: Interference as a tool for designing and evaluating multi-robot controllers. In: Kuipers, B.J., Webber, B. (eds.) Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI 1997), pp. 637–642. MIT Press, Cambridge (1997)

    Google Scholar 

  7. Gunther, N.J.: A simple capacity model of massively parallel transaction systems. In: CMG National Conference, pp. 1035–1044 (1993)

    Google Scholar 

  8. Gunther, N.J.: Guerrilla Capacity Planning. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-31010-5

    Book  Google Scholar 

  9. Gunther, N.J., Puglia, P., Tomasette, K.: Hadoop super-linear scalability: the perpetual motion of parallel performance. ACM Queue 13(5), 46–55 (2015)

    Article  Google Scholar 

  10. Gustafson, J.L.: Reevaluating Amdahl’s law. Commun. ACM 31(5), 532–533 (1988). https://doi.org/10.1145/42411.42415

    Article  Google Scholar 

  11. Hamann, H.: Towards swarm calculus: urn models of collective decisions and universal properties of swarm performance. Swarm Intell. 7(2–3), 145–172 (2013). https://doi.org/10.1007/s11721-013-0080-0

    Article  Google Scholar 

  12. Hamann, H.: Superlinear scalability in parallel computing and multi-robot systems: shared resources, collaboration, and network topology. In: Berekovic, M., Buchty, R., Hamann, H., Koch, D., Pionteck, T. (eds.) ARCS 2018. LNCS, vol. 10793, pp. 31–42. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77610-1_3

    Chapter  Google Scholar 

  13. Swarm Robotics: A Formal Approach. Lecture Notes in Computer Science. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74528-2_7

  14. Hamann, H., Reina, A.: Scalability in computing and robotics. arXiv, June 2020. https://arxiv.org/abs/2006.04969

  15. Hamann, H., Valentini, G., Khaluf, Y., Dorigo, M.: Derivation of a micro-macro link for collective decision-making systems. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 181–190. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_18

    Chapter  Google Scholar 

  16. Hayes, A.T.: How many robots? Group size and efficiency in collective search tasks. In: Asama, H., Arai, T., Fukuda, T., Hasegawa T. (eds.) Distributed Autonomous Robotic Systems, vol. 5, pp. 289–298. Springer, Tokyo (2002). https://doi.org/10.1007/978-4-431-65941-9_29

  17. Hill, M.D.: What is scalability? ACM SIGARCH Comput. Archit. News 18(4), 18–21 (1990)

    Article  MathSciNet  Google Scholar 

  18. Ijspeert, A.J., Martinoli, A., Billard, A., Gambardella, L.M.: Collaboration through the exploitation of local interactions in autonomous collective robotics: the stick pulling experiment. Auton. Robots 11, 149–171 (2001). https://doi.org/10.1023/A:1011227210047

    Article  MATH  Google Scholar 

  19. Jensen, K.H., Kim, W., Holbrook, N.M., Bush, J.W.M.: Optimal concentrations in transport systems. J. Roy. Soc. Interface 10(83), 20130138 (2013)

    Article  Google Scholar 

  20. Keeling, M.J., Rohani, P.: Modeling Infectious Diseases in Humans and Animals. Princeton University Press, Princeton (2011)

    Google Scholar 

  21. Laure-Anne, P., Sebastien, M., Jacques, G., Buhl, J., Audrey, D.: Experimental investigation of ant traffic under crowded conditions. eLife 8, e48945 (2019)

    Google Scholar 

  22. Lerman, K., Galstyan, A.: Mathematical model of foraging in a group of robots: effect of interference. Auton. Robots 13, 127–141 (2002)

    Article  Google Scholar 

  23. Levenspiel, O.: Chemical reaction engineering. Ind. Eng. Chem. Res. 38(11), 4140–4143 (1999)

    Article  Google Scholar 

  24. Lighthill, M.J., Whitham, G.B.: On kinematic waves. II. A theory of traffic flow on long crowded roads. Proc. Roy. Soc. London A229(1178), 317–345 (1955)

    Google Scholar 

  25. Font Llenas, A., Talamali, M.S., Xu, X., Marshall, J.A.R., Reina, A.: Quality-sensitive Foraging by a robot swarm through virtual pheromone trails. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Reina, A., Trianni, V. (eds.) ANTS 2018. LNCS, vol. 11172, pp. 135–149. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00533-7_11

    Chapter  Google Scholar 

  26. Mateo, D., Kuan, Y.K., Bouffanais, R.: Effect of correlations in swarms on collective response. Sci. Rep. 7, 10388 (2017). https://doi.org/10.1038/s41598-017-09830-w

    Article  Google Scholar 

  27. Mayya, S., Pierpaoli, P., Egerstedt, M.: Voluntary retreat for decentralized interference reduction in robot swarms. In: International Conference on Robotics and Automation (ICRA), pp. 9667–9673, May 2019. https://doi.org/10.1109/ICRA.2019.8794124

  28. Mondada, F., Gambardella, L.M., Floreano, D., Nolfi, S., Deneubourg, J.L., Dorigo, M.: The cooperation of swarm-bots: physical interactions in collective robotics. IEEE Robot. Autom. Mag. 12(2), 21–28 (2005)

    Article  Google Scholar 

  29. Neuman, B.C.: Scale in distributed systems. In: Readings in Distributed Computing Systems. IEEE Computer Society Press (1994)

    Google Scholar 

  30. O’Grady, R., Gross, R., Christensen, A.L., Mondada, F., Bonani, M., Dorigo, M.: Performance benefits of self-assembly in a swarm-bot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 2381–2387, October 2007. https://doi.org/10.1109/IROS.2007.4399424

  31. Özdemir, A., Gauci, M., Kolling, A., Hall, M.D., Groß, R.: Spatial coverage without computation. In: International Conference on Robotics and Automation (ICRA), pp. 9674–9680. IEEE (2019)

    Google Scholar 

  32. Poissonnier, L.A., Motsch, S., Gautrais, J., Buhl, J., Dussutour, A.: Experimental investigation of ant traffic under crowded conditions. eLife 8, e48945 (2019). https://doi.org/10.7554/eLife.48945

  33. Pratt, E.L.: Virtual teams in very small classes. Virtual Teamwork 91 (2010)

    Google Scholar 

  34. Rausch, I., Reina, A., Simoens, P., Khaluf, Y.: Coherent collective behaviour emerging from decentralised balancing of social feedback and noise. Swarm Intell. 321–345 (2019). https://doi.org/10.1007/s11721-019-00173-y

  35. Reina, A.: Robot teams stay safe with blockchains. Nat. Mach. Intell. 2, 240–241 (2020). https://doi.org/10.1038/s42256-020-0178-1

    Article  Google Scholar 

  36. Reina, A., Miletitch, R., Dorigo, M., Trianni, V.: A quantitative micro-macro link for collective decisions: the shortest path discovery/selection example. Swarm Intell. 9(2–3), 75–102 (2015)

    Article  Google Scholar 

  37. Riedo, F., Chevalier, M., Magnenat, S., Mondada, F.: Thymio II, a robot that grows wiser with children. In: IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO 2013), pp. 187–193. IEEE (2013)

    Google Scholar 

  38. Ringelmann, M.: Recherches sur les moteurs animés: Travail de l’homme. Annales de l’Institut National Agronomique, 2nd series 12, 1–40 (1913)

    Google Scholar 

  39. Rosenfeld, A., Kaminka, G.A., Kraus, S.: A study of scalability properties in robotic teams. In: Scerri, P., Vincent, R., Mailler, R. (eds.) Coordination of Large-Scale Multiagent Systems, pp. 27–51. Springer, Boston (2006). https://doi.org/10.1007/0-387-27972-5_2

  40. Salman, M., Ligot, A., Birattari, M.: Concurrent design of control software and configuration of hardware for robot swarms under economic constraints. PeerJ Comput. Sci. 5, e221 (2019). https://doi.org/10.7717/peerj-cs.221

    Article  Google Scholar 

  41. Sornette, D., Maillart, T., Ghezzi, G.: How much is the whole really more than the sum of its parts? 1 \(\boxplus \) 1 = 2.5: superlinear productivity in collective group actions. PLOS ONE 9(8), 1–15 (2014). https://doi.org/10.1371/journal.pone.0103023

  42. Talamali, M.S., Bose, T., Haire, M., Xu, X., Marshall, J.A.R., Reina, A.: Sophisticated collective foraging with minimalist agents: a swarm robotics test. Swarm Intell. 14(1), 25–56 (2019). https://doi.org/10.1007/s11721-019-00176-9

    Article  Google Scholar 

  43. Trianni, V., Groß, R., Labella, T.H., Şahin, E., Dorigo, M.: Evolving aggregation behaviors in a swarm of robots. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 865–874. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39432-7_93

    Chapter  Google Scholar 

  44. Valentini, G.: Achieving Consensus in Robot Swarms. SCI, vol. 706. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53609-5

    Book  MATH  Google Scholar 

  45. Wahby, M., Petzold, J., Eschke, C., Schmickl, T., Hamann, H.: Collective change detection: adaptivity to dynamic swarm densities and light conditions in robot swarms. Artif. Life Conf. Proc. 31, 642–649 (2019). https://doi.org/10.1162/isal_a_00233

    Article  Google Scholar 

  46. Webots: version r2020a by Cyberbotics Ltd. (2020). https://cyberbotics.com

  47. Zahadat, P., Hofstadler, D.N.: Toward a theory of collective resource distribution: a study of a dynamic morphogenesis controller. Swarm Intell. (1), 347–380 (2019). https://doi.org/10.1007/s11721-019-00174-x

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Authors and Affiliations

  1. Institute of Computer Engineering, University of Lübeck, Lübeck, Germany

    Heiko Hamann & Till Aust

  2. Department of Computer Science, University of Sheffield, Sheffield, UK

    Andreagiovanni Reina

Authors
  1. Heiko Hamann
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  2. Till Aust
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  3. Andreagiovanni Reina
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Corresponding author

Correspondence to Heiko Hamann .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  4. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  5. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  6. Université Libre de Bruxelles, Brussels, Belgium

    Mary Katherine Heinrich

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

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Hamann, H., Aust, T., Reina, A. (2020). Guerrilla Performance Analysis for Robot Swarms: Degrees of Collaboration and Chains of Interference Events. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-60376-2_11

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