Skip to main content

Controlling Robot Swarm Aggregation Through a Minority of Informed Robots

  • Conference paper
  • First Online:
Swarm Intelligence (ANTS 2022)

Abstract

Self-organized aggregation is a well studied behavior in swarm robotics as it is the pre-condition for the development of more advanced group-level responses. In this paper, we investigate the design of decentralized algorithms for a swarm of heterogeneous robots that self-aggregate over distinct target sites. A previous study has shown that including as part of the swarm a number of informed robots can steer the dynamic of the aggregation process to a desirable distribution of the swarm between the available aggregation sites. We have replicated the results of the previous study using a simplified approach: we removed constraints related to the communication protocol of the robots and simplified the control mechanisms regulating the transitions between states of the probabilistic controller. The results show that the performances obtained with the previous, more complex, controller can be replicated with our simplified approach which offers clear advantages in terms of portability to the physical robots and in terms of flexibility. That is, our simplified approach can generate self-organized aggregation responses in a larger set of operating conditions than what can be achieved with the complex controller.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    The parameters \(\alpha \) and \(\beta \) have been fine-tuned to achieve a symmetry-breaking behavior in a homogeneous swarm of \(N=100\) non-informed robots using the same arena setup illustrated in [13].

References

  1. Bonani, M., et al.: The MarXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4187–4193. IEEE (2010)

    Google Scholar 

  2. Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013). https://doi.org/10.1007/s11721-012-0075-2

    Article  Google Scholar 

  3. Calvo Martín, M., Eeckhout, M., Deneubourg, J.L., Nicolis, S.C.: Consensus driven by a minority in heterogenous groups of the cockroach periplaneta americana. iScience 24(7) (2021). https://doi.org/10.1016/j.isci.2021.102723

  4. Cambier, N., Albani, D., Frémont, V., Trianni, V., Ferrante, E.: Cultural evolution of probabilistic aggregation in synthetic swarms. Appl. Soft Comput. 113, 108010 (2021). https://doi.org/10.1016/j.asoc.2021.108010

    Article  Google Scholar 

  5. Çelikkanat, H., Şahin, E.: Steering self-organized robot flocks through externally guided individuals. Neural Comput. Appl. 19(6), 849–865 (2010). https://doi.org/10.1007/s00521-010-0355-y

    Article  Google Scholar 

  6. Couzin, I.D., Krause, J., Franks, N.R., Levin, S.A.: Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005). https://doi.org/10.1038/nature03236

    Article  Google Scholar 

  7. Masi, G.D., Prasetyo, J., Zakir, R., Mankovskii, N., Ferrante, E., Tuci, E.: Robot swarm democracy: the importance of informed individuals against zealots. Swarm Intell. 15(4), 315–338 (2021). https://doi.org/10.1007/s11721-021-00197-3

    Article  Google Scholar 

  8. Dorigo, M., et al.: Evolving self-organizing behaviors for a swarm-bot. Auton. Robot. 17(2), 223–245 (2004). https://doi.org/10.1023/B:AURO.0000033973.24945.f3

    Article  Google Scholar 

  9. 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). https://doi.org/10.1177/1059712312462248

    Article  Google Scholar 

  10. 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 

  11. 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 

  12. Firat, Z., Ferrante, E., Gillet, Y., Tuci, E.: On self-organised aggregation dynamics in swarms of robots with informed robots. Neural Comput. Appl. 32(17), 13825–13841 (2020). https://doi.org/10.1007/s00521-020-04791-0

    Article  Google Scholar 

  13. Firat, Z., Ferrante, E., Zakir, R., Prasetyo, J., Tuci, E.: Group-size regulation in self-organized aggregation in robot swarms. In: Dorigo, M., et al. (eds.) ANTS 2020. LNCS, vol. 12421, pp. 315–323. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60376-2_26

    Chapter  Google Scholar 

  14. Francesca, G., Brambilla, M., Brutschy, A., Trianni, V., Birattari, M.: AutoMoDe: a novel approach to the automatic design of control software for robot swarms. Swarm Intell. 8(2), 89–112 (2014). https://doi.org/10.1007/s11721-014-0092-4

    Article  Google Scholar 

  15. Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., Birattari, M.: Analysing an evolved robotic behaviour using a biological model of collegial decision making. In: Ziemke, T., Balkenius, C., Hallam, J. (eds.) SAB 2012. LNCS (LNAI), vol. 7426, pp. 381–390. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33093-3_38

    Chapter  Google Scholar 

  16. Gauci, M., Chen, J., Li, W., Dodd, T.J., Groß, R.: Self-organized aggregation without computation. Int. J. Robot. Res. 33(8), 1145–1161 (2014). https://doi.org/10.1177/0278364914525244

    Article  Google Scholar 

  17. Gillet, Y., Ferrante, E., Firat, Z., Tuci, E.: Guiding aggregation dynamics in a swarm of agents via informed individuals: an analytical study. In: The 2019 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). https://doi.org/10.1162/isal_a_00225

  18. Jeanson, R., et al.: Self-organized aggregation in cockroaches. Anim. Behav. 69(1), 169–180 (2005). https://doi.org/10.1016/j.anbehav.2004.02.009

    Article  Google Scholar 

  19. Kato, S., Jones, M.: An extended family of circular distributions related to wrapped Cauchy distributions via Brownian motion. Bernoulli 19(1), 154–171 (2013). http://www.jstor.org/stable/23525635

  20. Kengyel, D., Hamann, H., Zahadat, P., Radspieler, G., Wotawa, F., Schmickl, T.: Potential of heterogeneity in collective behaviors: a case study on heterogeneous swarms. In: Chen, Q., Torroni, P., Villata, S., Hsu, J., Omicini, A. (eds.) PRIMA 2015. LNCS (LNAI), vol. 9387, pp. 201–217. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25524-8_13

    Chapter  Google Scholar 

  21. Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012). https://doi.org/10.1007/s11721-012-0072-5

    Article  Google Scholar 

  22. Pitonakova, L., Giuliani, M., Pipe, A., Winfield, A.: Feature and performance comparison of the V-REP, gazebo and ARGoS robot simulators. In: Giuliani, M., Assaf, T., Giannaccini, M.E. (eds.) TAROS 2018. LNCS (LNAI), vol. 10965, pp. 357–368. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96728-8_30

    Chapter  Google Scholar 

  23. Prasetyo, J., De Masi, G., Ferrante, E.: Collective decision making in dynamic environments. Swarm Intell. 13(3), 217–243 (2019). https://doi.org/10.1007/s11721-019-00169-8

    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

    Article  Google Scholar 

  25. Şahin, E., Girgin, S., Bayindir, L., Turgut, A.E.: Swarm robotics. In: Blum, C., Merkle, D. (eds.) Swarm Intelligence. Natural Computing Series, pp. 87–100. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-74089-6_3

    Chapter  Google Scholar 

  26. Schranz, M., Umlauft, M., Sende, M., Elmenreich, W.: Swarm robotic behaviors and current applications. Front. Robot. AI 7, 36 (2020). https://doi.org/10.3389/frobt.2020.00036

    Article  Google Scholar 

  27. Sion, A., Reina, A., Birattari, M., Tuci, E.: Impact of the update time on the aggregation of robotic swarms through informed robots (2022). Accepted to the SAB 2022 Conference

    Google Scholar 

  28. Soysal, O., Şahin, E.: Probabilistic aggregation strategies in swarm robotic systems. In: Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, pp. 325–332 (2005). https://doi.org/10.1109/SIS.2005.1501639

  29. Szopek, M., Schmickl, T., Thenius, R., Radspieler, G., Crailsheim, K.: Dynamics of collective decision making of honeybees in complex temperature fields. PLoS ONE 8(10), 1–11 (2013). https://doi.org/10.1371/journal.pone.0076250

    Article  Google Scholar 

  30. Valentini, G., et al.: Kilogrid: a novel experimental environment for the Kilobot robot. Swarm Intell. 12(3), 245–266 (2018). https://doi.org/10.1007/s11721-018-0155-z

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Service Public de Wallonie Recherche under grant n\(^{\circ }\) 2010235 - ARIAC by DIGITALWALLONIA4.AI; by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 681872); and by Belgium’s Wallonia-Brussels Federation through the ARC Advanced Project GbO (Guaranteed by Optimization). A. Reina and M. Birattari acknowledge the financial support from the Belgian F.R.S.-FNRS, of which they are Chargé de Recherches and Directeur de Recherches, respectively.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Elio Tuci .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sion, A., Reina, A., Birattari, M., Tuci, E. (2022). Controlling Robot Swarm Aggregation Through a Minority of Informed Robots. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20176-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20175-2

  • Online ISBN: 978-3-031-20176-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics