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subCULTron - Cultural Development as a Tool in Underwater Robotics

  • Ronald Thenius
  • Daniel Moser
  • Joshua Cherian Varughese
  • Serge Kernbach
  • Igor Kuksin
  • Olga Kernbach
  • Elena Kuksina
  • Nikola Mišković
  • Stjepan Bogdan
  • Tamara Petrović
  • Anja Babić
  • Frédéric Boyer
  • Vincent Lebastard
  • Stéphane Bazeille
  • Graziano William Ferrari
  • Elisa Donati
  • Riccardo Pelliccia
  • Donato Romano
  • Godfried Jansen Van Vuuren
  • Cesare Stefanini
  • Matteo Morgantin
  • Alexandre Campo
  • Thomas Schmickl
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 732)

Abstract

This paper presents the research done in the field of robotic cultural evolution in challenging real world environments. We hereby present these efforts, as part of project subCULTron, where we will create an artificial society of three cooperating sub-cultures of robotic agents operating in a challenging real-world habitat. We introduce the novel concept of “cultural learning”, which will allow a swarm of agents to locally adapt to a complex environment and exchange the information about this adaptation with other subgroups of agents. Main task of the presented robotic system is autonomous environmental monitoring including self organised task allocation and organisation of swarm movement processes. One main focus of the project is on the development and implementation of bio-inspired controllers, as well as novel bio-inspired sensor systems, communication principles, energy harvesting and morphological designs. The main scientific objective is to enable and study the emergence of a collective long-term autonomous cognitive system in which information survives the operational lifetime of individuals, allowing cross-generation learning of the society by self-optimising.

Notes

Acknowledgments

This work was supported by the European Union, by funding the Project: EU H2020 FET-Proactive project ‘subCULTron’, no. 640967.

References

  1. 1.
    Auerbach, J., Bongard, J.: Dynamic resolution in the co-evolution of morphology and control. In: Artificial Life XII: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living System, pp. 451–458 (2010)Google Scholar
  2. 2.
    Babić, A., Lončar, I., Mišković, N.: Energy-efficient environmentally adaptive consensus-based formation control with collision avoidance for multi-vehicle systems. In: Proceedings of 10th IFAC Conference on Control Applications in Marine Systems (2016)CrossRefGoogle Scholar
  3. 3.
    Bowles, S., Gintis, H.: A Cooperative Species: Human Reciprocity and Its Evolution. Princeton University Press, Princeton (2011)Google Scholar
  4. 4.
    Boyd, R., Richerson, P.: The Origin and Evolution of Cultures. Oxford University Press, New York (2005)Google Scholar
  5. 5.
    Boyer, F., Gossiaux, P., Jawad, B., Lebastard, V., Porez, M.: Model for a sensor inspired by electric fish. IEEE Trans. Robot. 28(2), 492–505 (2012)CrossRefGoogle Scholar
  6. 6.
    Boyer, F., Lebastard, V., Chevallereau, C., Servagent, N.: Underwater reflex navigation in confined environment based on electric sense. IEEE Trans. Robot. 29(4), 945–956 (2013)CrossRefGoogle Scholar
  7. 7.
    Boyer, F., Lebastard, V., Chevallereau, C., Mintchev, S., Stefanini, C.: Underwater navigation based on passive electric sense: new perspectives for underwater docking. Int. J. Robot. Res. 34(9), 1228–1250 (2015)CrossRefGoogle Scholar
  8. 8.
    Bredeche, N., Haasdijk, E., Eiben, A.E.: On-line, on-board evolution of robot controllers. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds.) EA 2009. LNCS, vol. 5975, pp. 110–121. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14156-0_10CrossRefGoogle Scholar
  9. 9.
    Cybertronica Research, Research Center of Advanced Robotics and Environmental Science. http://cybertronica.de.com/projects/subCULTron
  10. 10.
    Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (2016)Google Scholar
  11. 11.
    Fleiß, J., Palan, S.: Of coordinators and dictators: a public goods experiment. Games 4(4), 584–607 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Germán, B., Cervantes-Astorga, C.: Performance evaluation of a low-cost microbial fuel cell using municipal wastewater. Water Air Soil Pollut. 224(3), 1–8 (2013)Google Scholar
  13. 13.
    Hougen, D., Carmer, J., Woehrer, M.: Memetic learning: a novel learning method for multi-robot systems. In: International Workshop on Multi-robot Systems (2003)Google Scholar
  14. 14.
    Institute of Marine Science, Venice, Italy. http://www.ismar.cnr.it/
  15. 15.
    Kernbach, S.: Handbook of Collective Robotics. Fundamentals and Challenges. CRC Press, Boca Raton (2013)CrossRefGoogle Scholar
  16. 16.
    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_13CrossRefGoogle Scholar
  17. 17.
    Logan, B., Korneel, R.: Conversion of wastes into bioelectricity and chemicals by using microbial electrochemical technologies. Science 337(6095), 686–690 (2012)CrossRefGoogle Scholar
  18. 18.
    Marocco, D., Nolfi, S.: Origins of communication in evolving robots. In: International Conference on Simulation of Adaptive Behavior, pp. 789–803 (2006)Google Scholar
  19. 19.
    Martin, A.: Five rules for the evolution of cooperation. Science 314(5805), 1560–1563 (2006)CrossRefGoogle Scholar
  20. 20.
    Mazdin, P., Arbanas, B., Haus, T., Bogdan, S., Petrovic, T.: Trust consensus protocol for heterogeneous underwater robotic systems. In: Proceedings of 10th IFAC Conference on Control Applications in Marine Systems (2016)CrossRefGoogle Scholar
  21. 21.
    Meuth, R., Lim, M., Ong, Y., Wunsch, D.: A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comput. 1(2), 85–100 (2009)CrossRefGoogle Scholar
  22. 22.
    Mintchev, S., Donati, E., Marrazza, S., Stefanini, C.: Mechatronic design of a miniature underwater robot for swarm operations. In: IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, pp. 2938–2943 (2014)Google Scholar
  23. 23.
    Mišković, N., Naj, D., Vasilijević, A., Vukić, Z.: Dynamic positioning of a diver tracking surface platform. In: Proceedings of the 19th World Congress of the International Federation of Automatic Control, International Federation of Automatic Control, pp. 4228–4233 (2014)CrossRefGoogle Scholar
  24. 24.
    Mišković, N., Pascoal, A., Bibuli, M., Caccia, M., Neasham, J.A., Birk, A., Egi, M., Grammer, K., Marroni, A., Vasilijevic, A., Vukić, Z.: Overview of the FP7 project “CADDY - Cognitive Autonomous Diving Buddy”. In: Proceedings of MTS/IEEE OCEANS 2015 Conference, pp. 1–5 (2015)Google Scholar
  25. 25.
    Nakagaki, T.: Smart behavior of true slime mold in a labyrinth. Res. Microbiol. 152, 767–770 (2001)CrossRefGoogle Scholar
  26. 26.
    Reynolds, C.: Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 21(4), 25–34 (1987)CrossRefGoogle Scholar
  27. 27.
    Proakis, J., Sozer, E., Rice, J., Stojanovic, M.: Shallow water acoustic networks. IEEE Commun. Mag. 39(11), 114–119 (2001)CrossRefGoogle Scholar
  28. 28.
    Rabaey, K., Verstraete, W.: Microbial fuel cells: novel biotechnology for energy generation. TRENDS Biotechnol. 23(6), 291–298 (2005)CrossRefGoogle Scholar
  29. 29.
    Schmickl, T., Crailsheim, K.: TaskSelSim: a model of the self-organization of the division of labour in honeybees. Math. Comput. Model. Dyn. Syst. 14(2), 101–125 (2008)CrossRefGoogle Scholar
  30. 30.
    Schmickl, T., Thenius, R., Möslinger, C., Timmis, J., Tyrrell, A., Read, M., Hilder, J., Halloy, J., Campo, A., Stefanini, C., Manfredi, L., Orofino, S., Kernbach, S., Dipper, T., Sutantyo, D.: CoCoRo - the self-aware underwater swarm. In: Fifth IEEE Conference on Self-adaptive and Self-organizing Systems Workshops (SASOW), Ann Arbor, MI, 3–7 October 2011, pp. 120–126 (2011).  https://doi.org/10.1109/SASOW.2011.11
  31. 31.
    Servagent, N., Jawad, B., Bouvier, S., Boyer, F., Girin, A., Gomez, F., Lebastard, V., Stefanini, C., Gossiaux, P.-B.: Electrolocation sensors in conducting water bio-inspired by electric fish. IEEE Sensor J. 13, 1865–1882 (2013)CrossRefGoogle Scholar
  32. 32.
    Stradner, J., Hamann, H., Zahadat, P., Schmickl, T., Crailsheim, K.: On-line, on-board evolution of reaction-diffusion control for self-adaptation. Alife 13, 597–598 (2012)Google Scholar
  33. 33.
    Stradner, J., Thenius, R., Zahadat, P., Hamann, H., Crailsheim, K., Schmickl, T.: Algorithmic requirements for swarm intelligence in differently coupled collective systems. Chaos Solitons Fractals 50, 100–114 (2013)CrossRefGoogle Scholar
  34. 34.
    Szopek, M., Schmickl, T., Thenius, R., Radspieler, G., Crailsheim, K.: Dynamics of collective decision making of honeybees in complex temperature fields. PLoS One 10(8), e76250 (2013)CrossRefGoogle Scholar
  35. 35.
    Taylor, G., Burns, J., Kammann, S., Powers, W., Welsh, T.: The energy harvesting Eel: a small subsurface ocean/river power generator. IEEE J. Oceanic Eng. 26, 539–547 (2001)CrossRefGoogle Scholar
  36. 36.
    Thenius, R., Bodi, M., Schmickl, T., Crailsheim, K.: Novel method of virtual embryogenesis for structuring Artificial Neural Network controllers. Math. Comput. Model. Dyn. Syst. 19(4), 375–387 (2013)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Thenius, R., Schmickl, T., Crailsheim, K.: Economic optimisation in honeybees: adaptive behaviour of a superorganism. In: Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J.C.T., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D. (eds.) SAB 2006. LNCS (LNAI), vol. 4095, pp. 725–737. Springer, Heidelberg (2006).  https://doi.org/10.1007/11840541_60CrossRefGoogle Scholar
  38. 38.
    Thenius, R., Zahadat, P., Schmickl, T.: EMANN - a model of emotions in an artificial neural network. In: 12th European Conference on Artificial Life, pp. 830–837 (2013)Google Scholar
  39. 39.
    Turing, A.: The chemical basis of morphogenesis. Bull. Math. Biol. 52(1–2), 153–197 (1990)CrossRefGoogle Scholar
  40. 40.
    Varughese, J., Thenius, R., Wotawa, F., Schmickl, T.: FSTaxis algorithm: bio-inspired emergent gradient Taxis. In: Proceedings of the Artificial Life Conference, pp. 330–337 (2016)Google Scholar
  41. 41.
    Zahadat, P., Hahshold, S., Thenius, R., Crailsheim, K., Schmickl, T.: From honeybees to robots and back: division of labor based on partitioning social inhibition. Bioinspir. Biomim. 10(6), 066005 (2015)CrossRefGoogle Scholar
  42. 42.
    Zahadat, P., Schmickl, T., Crailsheim, K.: Social inhibition manages division of labour in artificial swarm systems. In: ECAL 2013, pp. 609–616 (2013)Google Scholar
  43. 43.
    Zhuwei, D., Haoran, L., Tingyue, G.: A state of the art review on microbial fuel cells: a promising technology for wastewater treatment and bioenergy. Biotechnol. Adv. 25(5), 464–482 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ronald Thenius
    • 1
  • Daniel Moser
    • 1
  • Joshua Cherian Varughese
    • 1
  • Serge Kernbach
    • 2
  • Igor Kuksin
    • 2
  • Olga Kernbach
    • 2
  • Elena Kuksina
    • 2
  • Nikola Mišković
    • 3
  • Stjepan Bogdan
    • 4
  • Tamara Petrović
    • 4
  • Anja Babić
    • 3
  • Frédéric Boyer
    • 5
  • Vincent Lebastard
    • 5
  • Stéphane Bazeille
    • 5
  • Graziano William Ferrari
    • 6
  • Elisa Donati
    • 6
  • Riccardo Pelliccia
    • 6
  • Donato Romano
    • 6
  • Godfried Jansen Van Vuuren
    • 6
  • Cesare Stefanini
    • 6
  • Matteo Morgantin
    • 7
  • Alexandre Campo
    • 8
  • Thomas Schmickl
    • 1
  1. 1.Institute for ZoologyUniversity of GrazGrazAustria
  2. 2.Cybertronica ResearchResearch Center of Advanced Robotics and Environmental ScienceStuttgartGermany
  3. 3.Laboratory for Underwater Systems and Technologies (LABUST), Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
  4. 4.Laboratory for Robotics and Intelligent Control Systems (LARICS), Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia
  5. 5.IRCCyN - Ecole des Mines de NantesNantesFrance
  6. 6.BioRobotics InstituteScuola Superiore Sant’AnnaPontederaItaly
  7. 7.CORILA, Consortium for Coordination of Research Activities Concerning the Venice Lagoon SystemVeniceItaly
  8. 8.Unit of Social EcologyUniversité Libre de BruxellesBruxellesBelgium

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