A Bio-inspired Aggregation with Robot Swarm Using Real and Simulated Mobile Robots

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10965)


This paper presents an implementation of a bio-inspired aggregation scenario using swarm robots. The aggregation scenario took inspiration from honeybee’s thermotactic behaviour in finding an optimal zone in their comb. To realisation of the aggregation scenario, real and simulated robots with different population sizes were used. Mona, which is an open-source and open-hardware platform was deployed to play the honeybee’s role in this scenario. A model of Mona was also generated in Stage for simulation of aggregation scenario with large number of robots. The results of aggregation with real- and simulated-robots showed reliable aggregations and a population dependent swarm performance. Moreover, the results demonstrated a direct correlation between the results observed from the real robot and simulation experiments.


Aggregation Swarm robotics Bio-inspired Open-source 



This work was supported by the EPSRC (Project No. EP/P01366X/1).


  1. 1.
    Hamann, H.: Swarm Robotics: A Formal Approach. Springer, Heidelberg (2018). Scholar
  2. 2.
    Schmickl, T., Thenius, R., Moeslinger, C., et al.: Get in touch: cooperative decision making based on robot-to-robot collisions. Auton. Agents Multi-Agent Syst. 18(1), 133–155 (2009)CrossRefGoogle Scholar
  3. 3.
    Turgut, A.E., Çelikkanat, H., Gökçe, F., Şahin, E.: Self-organized flocking in mobile robot swarms. Swarm Intell. 2(2), 97–120 (2008)CrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006)CrossRefGoogle Scholar
  5. 5.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W.M. (eds.) SR 2004. LNCS, vol. 3342, pp. 10–20. Springer, Heidelberg (2005). Scholar
  6. 6.
    Arvin, F., Espinosa, J., Bird, B., West, A., Watson, S., Lennox, B.: Mona: an affordable open-source mobile robot for education and research. J. Intell. Robot. Syst. (2018).
  7. 7.
    Hu, C., Arvin, F., Xiong, C., Yue, S.: Bio-inspired embedded vision system for autonomous micro-robots: the LGMD case. IEEE Trans. Cogn. Dev. Syst. 9(3), 241–254 (2017)CrossRefGoogle Scholar
  8. 8.
    Arvin, F., Krajník, T., Turgut, A.E., Yue, S.: COS\(\varPhi \): artificial pheromone system for robotic swarms research. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 407–412 (2015)Google Scholar
  9. 9.
    Arvin, F., Turgut, A.E., Krajnk, T., Yue, S.: Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm. Adapt. Behav. 24(2), 102–118 (2016)CrossRefGoogle Scholar
  10. 10.
    Arvin, F., Watson, S., Turgut, A., Espinosa, J., Krajník, T., Lennox, B.: Perpetual robot swarm: long-term autonomy of mobile robots using on-the-fly inductive charging. J. Intell. Robot. Syst. 1–18 (2017)Google Scholar
  11. 11.
    Arvin, F., Bekravi, M.: Encoderless position estimation and error correction techniques for miniature mobile robots. Turk. J. Electr. Eng. Comput. Sci. 21(6), 1631–1645 (2013)CrossRefGoogle Scholar
  12. 12.
    Arvin, F., Samsudin, K., Ramli, A.: Development of IR-based short-range communication techniques for swarm robot applications. Adv. Electr. Comput. Eng. 10(4), 61–68 (2010)CrossRefGoogle Scholar
  13. 13.
    Arvin, F., Samsudin, K., Ramli, A.R., Bekravi, M.: Imitation of honeybee aggregation with collective behavior of swarm robots. Int. J. Comput. Intell. Syst. 4(4), 739–748 (2011)Google Scholar
  14. 14.
    Arvin, F., Turgut, A.E., Bazyari, F., Arikan, K.B., Bellotto, N., Yue, S.: Cue-based aggregation with a mobile robot swarm: a novel fuzzy-based method. Adapt. Behav. 22(3), 189–206 (2014)CrossRefGoogle Scholar
  15. 15.
    Arvin, F., Turgut, A.E., Bellotto, N., Yue, S.: Comparison of different cue-based swarm aggregation strategies. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds.) ICSI 2014. LNCS, vol. 8794, pp. 1–8. Springer, Cham (2014). Scholar
  16. 16.
    Arvin, F., Murray, J., Zhang, C., Yue, S.: Colias: an autonomous micro robot for swarm robotic applications. Int. J. Adv. Rob. Syst. 11(7), 113 (2014)CrossRefGoogle Scholar
  17. 17.
    Ivaldi, S., Padois, V., Nori, F.: Tools for dynamics simulation of robots: a survey based on user feedback. arXiv:1402.7050 (2014)
  18. 18.
    Tan, Y., Zheng, Z.Y.: Research advance in swarm robotics. Def. Technol. 9(1), 18–39 (2013)CrossRefGoogle Scholar
  19. 19.
    Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2149–2154 (2004)Google Scholar
  20. 20.
    Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6, 271–295 (2012)CrossRefGoogle Scholar
  21. 21.
    Dorigo, M., et al.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20(4), 60–71 (2013)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Montes de Oca, M.A., et al.: Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intell. 5(3), 305–327 (2011)CrossRefGoogle Scholar
  23. 23.
    Ducatelle, F., Di Caro, G.A., Pinciroli, C., Gambardella, L.M.: Self-organized cooperation between robotic swarms. Swarm Intell. 5(2), 73 (2011)CrossRefGoogle Scholar
  24. 24.
    Michel, O.: Cyberbotics Ltd. webots: professional mobile robot simulation. Int. J. Adv. Rob. Syst. 1(1), 5 (2004)CrossRefGoogle Scholar
  25. 25.
    Vaughan, R.: Massively multiple robot simulations in stage. Swarm Intell. 2(1), 189–208 (2008)CrossRefGoogle Scholar
  26. 26.
    Hereford, J.: Analysis of BEECLUST swarm algorithm. In: IEEE Symposium on Swarm Intelligence, pp. 1–7 (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Robotics for Extreme Environments Lab (REEL), School of Electrical and Electronic EngineeringThe University of ManchesterManchesterUK

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