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Group-Size Regulation in Self-organized Aggregation in Robot Swarms

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Swarm Intelligence (ANTS 2020)

Abstract

In swarm robotics, self-organized aggregation refers to a collective process in which robots form a single aggregate in an arbitrarily chosen aggregation site among those available in the environment, or just in an arbitrarily chosen location. Instead of focusing exclusively on the formation of a single aggregate, in this study we discuss how to design a swarm of robots capable of generating a variety of final distributions of the robots to the available aggregation sites. We focus on an environment with two possible aggregation sites, A and B. Our study is based on the following working hypothesis: robots distribute on site A and B in quantities that reflect the relative proportion of robots in the swarm that selectively avoid A with respect to those that selectively avoid B. This is with an as minimal as possible proportion of robots in the swarm that selectively avoid one or the other site. We illustrate the individual mechanisms designed to implement the above mentioned working hypothesis, and we discuss the promising results of a set of simulations that systematically consider a variety of experimental conditions.

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References

  1. Bayindir, L., Şahin, E.: Modeling self-organized aggregation in swarm robotic systems. In: IEEE Swarm Intelligence Symposium, SIS 2009, pp. 88–95. IEEE (2009)

    Google Scholar 

  2. Bonabeau, E., Dorigo, M., Marco, D.d.R.D.F., Theraulaz, G., et al.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Oxford (1999)

    Google Scholar 

  3. Bonani, M., et al.: 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 (2010)

    Google Scholar 

  4. Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)

    Article  Google Scholar 

  5. Cambier, N., Frémont, V., Trianni, V., Ferrante, E.: Embodied evolution of self-organised aggregation by cultural propagation. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Reina, A., Trianni, V. (eds.) ANTS 2018. LNCS, vol. 11172, pp. 351–359. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00533-7_29

    Chapter  Google Scholar 

  6. Campo, A., Garnier, S., Dédriche, O., Zekkri, M., Dorigo, M.: Self-organized discrimination of resources. PLoS ONE 6(5), e19888 (2010)

    Article  Google Scholar 

  7. Correll, N., Martinoli, A.: Modeling and designing self-organized aggregation in a swarm of miniature robots. Int. J. Robot. Res. 30(5), 615–626 (2011)

    Article  Google Scholar 

  8. Couzin, I., Krause, J., Franks, N., Levin, S.: Effective leadership and decision making in animal groups on the move. Nature 433, 513–516 (2005)

    Article  Google Scholar 

  9. Deneubourg, J., Lioni, A., Detrain, C.: Dynamics of aggregation and emergence of cooperation. Biol. Bull. 202(3), 262–267 (2002)

    Article  Google Scholar 

  10. Dorigo, M., et al.: Evolving self-organizing behaviors for a swarm-bot. Auton. Robots 17(2), 223–245 (2004)

    Article  Google Scholar 

  11. Ferrante, E., Turgut, A.E., Huepe, C., Stranieri, A., Pinciroli, C., Dorigo, M.: Self-organized flocking with a mobile robot swarm: a novel motion control method. Adapt. Behav. 20(6), 460–477 (2012)

    Article  Google Scholar 

  12. Ferrante, E., Turgut, A.E., Stranieri, A., Pinciroli, C., Birattari, M., Dorigo, M.: A self-adaptive communication strategy for flocking in stationary and non-stationary environments. Nat. Comput. 13(2), 225–245 (2013). https://doi.org/10.1007/s11047-013-9390-9

    Article  MathSciNet  Google Scholar 

  13. Firat, Z., Ferrante, E., Cambier, N., Tuci, E.: Self-organised aggregation in swarms of robots with informed robots. In: Fagan, D., Martín-Vide, C., O’Neill, M., Vega-Rodríguez, M.A. (eds.) TPNC 2018. LNCS, vol. 11324, pp. 49–60. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04070-3_4

    Chapter  Google Scholar 

  14. Firat, Z., Ferrante, E., Gillet, Y., Tuci, E.: On self-organised aggregation dynamics in swarms of robots with informed robots. Neural Comput. Appl. 1–17 (2020). https://doi.org/10.1007/s00521-020-04791-0

  15. Garnier, S., et al.: The embodiment of cockroach aggregation behavior in a group of micro-robots. Artif. Life 14(4), 387–408 (2008)

    Article  Google Scholar 

  16. Garnier, S., et al.: Aggregation behaviour as a source of collective decision in a group of cockroach-like-robots. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 169–178. Springer, Heidelberg (2005). https://doi.org/10.1007/11553090_18

    Chapter  Google Scholar 

  17. Garnier, S., Gautrais, J., Asadpour, M., Jost, C., Theraulaz, G.: Self-organized aggregation triggers collective decision making in a group of cockroach-like robots. Adapt. Behav. 17(2), 109–133 (2009)

    Article  Google Scholar 

  18. Gauci, M., Chen, J., Li, W., Dodd, T., Groß, R.: Self-organized aggregation without computation. Int. J. Robot. Res. 33(8), 1145–1161 (2014)

    Article  Google Scholar 

  19. Gillet, Y., Ferrante, E., Firat, Z., Tuci, E.: Guiding aggregation dynamics in a swarm of agents via informed individuals: an analytical study. In: The 2018 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE), pp. 590–597. MIT Press (2019)

    Google Scholar 

  20. Çelikkanat, H., Şahin, E.: Steering self-organized robot flocks through externally guided individuals. Neural Comput. Appl. 19(6), 849–865 (2010)

    Article  Google Scholar 

  21. Jeanson, R., et al.: Self-organized aggregation in cockroaches. Animal Behav. 69(1), 169–180 (2005)

    Article  Google Scholar 

  22. Kato, S., Jones, M.: An extended family of circular distributions related to wrapped Cauchy distributions via Brownian motion. Bernoulli 19(1), 154–171 (2013)

    Article  MathSciNet  Google Scholar 

  23. Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)

    Article  Google Scholar 

  24. Rubenstein, M., Ahler, C., Hoff, N., Cabrera, A., Nagpal, R.: Kilobot: a low cost robot with scalable operations designed for collective behaviors. Robot. Auton. Syst. 62(7), 966–975 (2014). https://doi.org/10.1016/j.robot.2013.08.006. http://dx.doi.org/10.1016/j.robot.2013.08.006

  25. Tuci, E., Alkilabi, M., Akanyety, O.: Cooperative object transport in multi-robot systems: a review of the state-of-the-art. Front. Robot. AI 5, 1–15 (2018)

    Article  Google Scholar 

  26. Valentini, G., Ferrante, E., Dorigo, M.: The best-of-n problem in robot swarms: Formalization, state of the art, and novel perspectives. Front. Robot. AI 4,  9 (2017). https://doi.org/10.3389/frobt.2017.00009. https://www.frontiersin.org/article/10.3389/frobt.2017.00009

  27. Valentini, G., Ferrante, E., Hamann, H., Dorigo, M.: Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems. Auton. Agents Multi-Agent Syst. 30(3), 553–580 (2016)

    Article  Google Scholar 

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Correspondence to Elio Tuci .

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Firat, Z., Ferrante, E., Zakir, R., Prasetyo, J., Tuci, E. (2020). Group-Size Regulation in Self-organized Aggregation in Robot Swarms. 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_26

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  • DOI: https://doi.org/10.1007/978-3-030-60376-2_26

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  • Online ISBN: 978-3-030-60376-2

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