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Behavioral specialization emerges from the embodiment of a robotic swarm

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Abstract

This paper focuses on the effect of the embodiment of robots on collective behavior in robotic swarms. The research field of swarm robotics emphasizes the importance of the embodiment of robots; however, only a few studies have discussed how it influences the collective behavior of a robotic swarm. In this paper, a path-formation task is performed by robotic swarms in computer simulations with and without considering collisions among robots to discuss the effect of the robot embodiment. Additionally, the experiments were performed with varying the size of robots. The robot controllers were obtained by an evolutionary robotics approach. The results show that the robot collisions would affect not only the performance of the robotic swarm but also the emergent behavior to accomplish the task. The robot collisions seem to provide feedback on robotic swarms to emerge the division of labor among robots to manage congestion.

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Notes

  1. The experiments are conducted with the Box2D physics engine (available at http://box2d.org).

References

  1. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford

    MATH  Google Scholar 

  2. Blum C, Groß R (2015) Swarm intelligence in optimization and robotics. Springer handbook of computational intelligence. Springer, Berlin, pp 1291–1309

    Chapter  Google Scholar 

  3. Şahin E (2005) Swarm robotics: from sources of inspiration to domains of application. Swarm robotics, lecture notes in computer science, vol 3342. Springer, Berlin, pp 10–20

    Google Scholar 

  4. Dorigo M, Birattari M, Brambilla M (2014) Swarm robotics. Scholarpedia 9(1):1463

    Article  Google Scholar 

  5. Lerman K, Galstyan A (2002) Mathematical model of foraging in a group of robots: effect of interference. Auton Robots 13(2):127–141

    Article  Google Scholar 

  6. Hiraga M, Ohkura K (2019) Effects of congestion on swarm performance and autonomous specialization in robotic swarms. J Robot Mechatron 31(4):526–534

    Article  Google Scholar 

  7. Hamann H (2013) Towards swarm calculus: urn models of collective decisions and universal properties of swarm performance. Swarm Intell 7(2–3):145–172

    Article  Google Scholar 

  8. Hamann H (2018) Swarm robotics: a formal approach. Springer, Berlin

    Book  Google Scholar 

  9. 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 

  10. Bayındır L (2016) A review of swarm robotics tasks. Neurocomputing 172:292–321

    Article  Google Scholar 

  11. Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press, Cambridge

    Google Scholar 

  12. 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 

  13. Hiraga M, Yasuda T, Ohkura K (2018a) Evolutionary acquisition of autonomous specialization in a path-formation task of a robotic swarm. J Adv Comput Intell Intell Inform 22(5):621–628

    Article  Google Scholar 

  14. Hiraga M, Wei Y, Yasuda T, Ohkura K (2018b) Evolving autonomous specialization in congested path formation task of robotic swarms. Artif Life Robot 23(4):547–554

    Article  Google Scholar 

  15. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423–1447

    Article  Google Scholar 

  16. Floreano D, Dürr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intel 1(1):47–62

    Article  Google Scholar 

  17. Beyer HG, Schwefel HP (2002) Evolution strategies: a comprehensive introduction. Nat Comput 1(1):3–52

    Article  MathSciNet  Google Scholar 

  18. Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Springer, Berlin

    Book  Google Scholar 

  19. Dussutour A, Beshers S, Deneubourg JL, Fourcassié V (2009) Priority rules govern the organization of traffic on foraging trails under crowding conditions in the leaf-cutting ant Atta colombica. J Exp Biol 212(4):499–505

    Article  Google Scholar 

  20. Fourcassié V, Dussutour A, Deneubourg JL (2010) Ant traffic rules. J Exp Biol 213(14):2357–2363

    Article  Google Scholar 

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Authors and Affiliations

  1. Graduate School of Engineering, Hiroshima University, Hiroshima, Japan

    Motoaki Hiraga & Kazuhiro Ohkura

  2. Department of Computer Science, School of Computing, Tokyo Institute of Technology, Tokyo, Japan

    Yasumasa Tamura

Authors
  1. Motoaki Hiraga
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  2. Yasumasa Tamura
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  3. Kazuhiro Ohkura
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Corresponding author

Correspondence to Kazuhiro Ohkura.

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This work was presented in part at the 3rd International Symposium on Swarm Behavior and Bio-Inspired Robotics (Okinawa, Japan, November 20–22, 2019).

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Hiraga, M., Tamura, Y. & Ohkura, K. Behavioral specialization emerges from the embodiment of a robotic swarm. Artif Life Robotics 25, 495–502 (2020). https://doi.org/10.1007/s10015-020-00641-3

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  • DOI: https://doi.org/10.1007/s10015-020-00641-3

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