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On self-organised aggregation dynamics in swarms of robots with informed robots

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Abstract

Aggregation is a process observed in natural systems whereby individuals gather together to form large cluster. Recent studies with cockroaches and robots have shown that relatively simple individual mechanisms can account for how individuals manage to gather on a single shelter when two or more are available in the environment. In this paper, we use simulated swarms of robots to further explore the aggregation dynamics generated by these simple individual mechanisms. Our objective is to study the introduction of “informed robots”, and to study how many of these are needed to direct the aggregation process towards a pre-defined site among those available in the environment. Informed robots are members of a group that selectively avoid the site/s where no aggregate should emerge and stop only on the experimenter pre-defined site/s for aggregation. We study the aggregation process with informed robots in three different scenarios: two that are morphologically symmetric, whereby the different types of aggregation site are equally represented in the environment; and an asymmetric scenario, whereby the target site has an area that is half the area of the sites that should be avoided. We first show what happens when no robot in the swarm is informed: in symmetric environments, the swarm is able to break the symmetry and aggregates on one of the two types of site at random, not necessarily on the target site, while in the asymmetric environment, the swarm tends to aggregate on the sites that are most represented in terms of area. The original contribution of this study is to demonstrate the effect of the introduction of a small proportion of informed robots in both environments: In symmetric environments, they selectively direct the aggregation process towards the experimenter chosen site; in the asymmetric environment, informed robots can invert the spontaneous preference for the most represented site and induce the swarm to aggregate on the least represented type of site. Moreover, for each scenario, we analyse how the dynamics of the aggregation process depends on the proportion of informed robots. As a further valuable contribution of this study, we provide analytical results by studying a system of ordinary differential equations that is an extension of a well-known model. Using this model, we show how, for certain values of the parameters, the model can predict the dynamics observed with simulated robots in one of the two symmetric scenarios.

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Notes

  1. Informed individuals are also referred to as implicit leaders in [12]. The term “implicit” signifies that these individuals do not have the right, due to social status or kinship, to lead the group. Thus, they are not recognised as leaders by the group mates. Nevertheless, informed individuals behave as they were “leaders” by trying to influence the behaviour of the group by locally interacting with the group mates.

References

  1. Alkilabi M, Narayan A, Tuci E (2017) Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies. Swarm Intell 11(3–4):185–209

    Article  Google Scholar 

  2. Amé J, Rivault C, Deneubourg J (2004) Cockroach aggregation based on strain odour recognition. Anim Behav 68:793–801

    Article  Google Scholar 

  3. Amé J, Halloy J, Rivault C, Detrain C, Deneubourg J (2006) Collegial decision making based on social amplification leads to optimal group formation. Proc Natl Acad Sci 103:5835–5840

    Article  Google Scholar 

  4. Bayindir L, Şahin E (2009) Modeling self-organized aggregation in swarm robotic systems. In: IEEE swarm intelligence symposium SIS’09. IEEE, pp 88–95

  5. Bonani M, Longchamp V, Magnenat S, Rétornaz P, Burnier D, Roulet G, Vaussard F, Bleuler H, Mondada F (2010) 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

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

    Article  Google Scholar 

  7. Camazine S (2003) Self-organization in biological systems. Princeton University Press, Princeton

    MATH  Google Scholar 

  8. Cambier N, Fremont V, Trianni V, Ferrante E (2018) Embodied evolution of self-organised aggregation by cultural propagation. In: Dorigo M, Birattari M, Blum C, Christensen A, Reina A, Trianni V (eds) Proceedings of the 11th international conference on swarm intelligence. Springer, LNCS, pp 351–359

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Dimidov C, Oriolo G, Trianni V (2016) Random walks in swarm robotics: an experiment with kilobots. In: Dorigo M, Birattari M, Li X, Nez MLI, Ohkura K, Pinciroli C, Stützle T (eds) Proceedings of the \(10^{th}\) international conference on swarm intelligence (ANTS 2016), Lecture Notes in Computer Sciences, vol 9882. Springer Verlag, Berlin, Germany, pp 185–196

  15. Dorigo M, Trianni V, Şahin E, Groß R, Labella T, Baldassarre G, Nolfi S, Deneubourg J, Mondada F, Floreano D et al (2004) Evolving self-organizing behaviors for a swarm-bot. Autono Robots 17(2):223–245

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Ferrante E, Turgut A, Stranieri A, Pinciroli C, Birattari M, Dorigo M (2014) A self-adaptive communication strategy for flocking in stationary and non-stationary environments. Nat Comput 13(2):225–245

    Article  MathSciNet  Google Scholar 

  18. Garnier S, Jost C, Jeanson R, Gautrais J, Asadpour M, Caprari G, Theraulaz G (2005) Aggregation behaviour as a source of collective decision in a group of cockroach-like-robots. In: European conference on artificial life. Springer, pp 169–178

  19. Garnier S, Jost C, Gautrais J, Asadpour M, Caprari G, Jeanson R, Grimal A, Theraulaz G (2008) The embodiment of cockroach aggregation behavior in a group of micro-robots. Artif Life 14(4):387–408

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Hauert S, Winkler L, Zufferey J, Floreano D (2008) Ant-based swarming with positionless micro air vehicles for communication relay. Swarm Intell 20(2–4):167–188

    Article  Google Scholar 

  23. Jeanson R, Rivault C, Deneubourg J, Blanco S, Fournier R, Jost C, Theraulaz G (2005) Self-organized aggregation in cockroaches. Anim Behav 69(1):169–180

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  25. Kolling A, Walker P, Chakraborty N, Sycara K, Lewis M (2016) Human interaction with robot swarms: a survey. IEEE Trans Hum–Mach Syst 46(1):9–26

    Article  Google Scholar 

  26. Montes de Oca M, Ferrante E, Scheidler A, Pinciroli C, Birattari M, Dorigo M (2011) Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intell 5(3–4):305–327

    Article  Google Scholar 

  27. Pinciroli C, Trianni V, O’Grady R, Pini G, Brutschy A, Brambilla M, Mathews N, Ferrante E, Di Caro G, Ducatelle F, Birattari M, Gambardella L, Dorigo M (2012) ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell 6(4):271–295

    Article  Google Scholar 

  28. Pini G, Brutschy A, Frison M, Roli A, Dorigo M, Birattari M (2011) Task partitioning in swarms of robots: an adaptive method for strategy selection. Swarm Intell 5(3–4):283–304

    Article  Google Scholar 

  29. Şahin E (2004) Swarm robotics: from sources of inspiration to domains of application. In: International workshop on swarm robotics. Springer, pp 10–20

  30. Sperati V, Trianni V, Nolfi S (2011) Self-organised path formation in a swarm of robots. Swarm Intell 5(2):97–119

    Article  Google Scholar 

  31. Stehlík M (2016) On convergence of topological aggregation functions. Fuzzy Sets Syst 287:46–56

    Article  MathSciNet  Google Scholar 

  32. Tuci E, Rabérin A (2015) On the design of generalist strategies for swarms of simulated robots engaged in a task-allocation scenario. Swarm Intell 9(4):267–290

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Valentini G, Ferrante E, Dorigo M (2017) The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives. Front Robot AI 4:9

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science, University of Namur, Namur, Belgium

    Ziya Firat, Yannick Gillet & Elio Tuci

  2. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Eliseo Ferrante

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  1. Ziya Firat
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  2. Eliseo Ferrante
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  3. Yannick Gillet
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  4. Elio Tuci
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Correspondence to Elio Tuci.

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Firat, Z., Ferrante, E., Gillet, Y. et al. On self-organised aggregation dynamics in swarms of robots with informed robots. Neural Comput & Applic 32, 13825–13841 (2020). https://doi.org/10.1007/s00521-020-04791-0

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