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swarmFSTaxis: Borrowing a Swarm Communication Mechanism from Fireflies and Slime Mold

  • Joshua Cherian VarugheseEmail author
  • Daniel Moser
  • Ronald Thenius
  • Franz Wotawa
  • Thomas Schmickl
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

One main motivation for studying swarm intelligence comes from observing the resilience of living systems in nature. Swarm intelligence has provided important inspirations for the engineering of technical systems. The swarmtaxis algorithm and the FSTaxis algorithm are swarm intelligent algorithms that aim to move a group of agents from a starting point to a predefined goal. The swarmtaxis algorithm bases its state transition on a voting like mechanism in which the agents count the number of “pings” they get from their surroundings. In contrast, the FSTaxis algorithm uses a scroll wave based communication mechanism inspired by slime mold and fireflies. The scroll wave based communication is expected to be more resilient than the voting like mechanism of the swarmtaxis algorithm. In this paper, we borrow the communication mechanism used in FSTaxis algorithm to improve the swarmtaxis algorithm. We will also discuss how this modified algorithm performs in comparison to the parent algorithm.

Keywords

Swarm intelligence Swarm robotics Bio-inspiration Signaling Taxis 

Notes

Acknowledgements

This work was supported by EU-H2020 Project no. 640967, subCULTron, funded by the European Unions Horizon 2020 research and innovation programmer under grant agreement No 640967.

References

  1. 1.
    Bjerknes, J.D., Winfield, A., Melhuish, C.: An analysis of emergent taxis in a wireless connected swarm of mobile robots. In: IEEE Swarm Intelligence Symposium. pp. 45–52. IEEE Press, Los Alamitos, CA (2007)Google Scholar
  2. 2.
    Hoff, N.R., Sagoff, A., Wood, R.J., Nagpal, R.: Two foraging algorithms for robot swarms using only local communication. In: 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 123–130. IEEE (2010)Google Scholar
  3. 3.
    Moeslinger, C., Schmickl, T., Crailsheim, K.: Emergent flocking with low-end swarm robots. In: Dorigo, M., Birattari, M., Di Caro, G., Doursat, R., Engelbrecht, A., Floreano, D., Gambardella, L., Gro, R., Sahin, E., Sayama, H., Sttzle, T. (eds.) Swarm Intelligence. Lecture Notes in Computer Science, pp. 424–431. Springer, Berlin, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15461-4_40Google Scholar
  4. 4.
    Schmickl, T., Crailsheim, K.: Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Auton. Robot. 25(1–2), 171–188 (2008).  https://doi.org/10.1007/s10514-007-9073-4CrossRefGoogle Scholar
  5. 5.
    Siegert, F., Weijer, C.J.: Three-dimensional scroll waves organize Dictyostelium slugs. PNAS 89(14), 6433–6437 (1992)CrossRefGoogle Scholar
  6. 6.
    subCULTron: submarine cultures perform long-term robotic exploration of unconventional environmental niches (2015). http://www.subcultron.eu/
  7. 7.
    Thenius, R., Moser, D., Cherian Varughese, J., Kernbach, S., Kuksin, I., Kernbach, O., Kuksina, E., Miškovi, N., Bogdan, S., Petrovi, T., Babi, A., Boyer, F., Lebastard, V., Bazeille, S., William Ferrari, G., Donati, E., Pelliccia, R., Romano, D., Jansen Van Vuuren, G., Stefanini, C., Morgantin, M., Campo, A., Schmickl, T.: subCULTron—Cultural Development as a Tool in Underwater Robotics Consortium for coordination of research activities concerning the Venice lagoon system. In: Artificial Life and Intelligent Agents. Springer (2016) in printGoogle Scholar
  8. 8.
    Varughese, J.C., Thenius, R., Wotawa, F., Schmickl, T.: Fstaxis algorithm: bio-inspired emergent gradient taxis. In: Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems. MIT Press (2016)Google Scholar
  9. 9.
    Werger, B.B., Mataric, M.J.: Robotic “food” chains: externalization of state and program for minimal-agent foraging. In: Proceedings, From Animals to Animats 4, Fourth International Conference on Simulation of Adaptive Behavior (SAB-96), pp. 625–634. MIT Press (1996)Google Scholar
  10. 10.
    Winfield, A.F., Nembrini, J.: Emergent swarm morphology control of wireless networked mobile robots. In: Morphogenetic Engineering, pp. 239–271. Springer (2012)Google Scholar
  11. 11.
    Zahadat, P., Schmickl, T.: Division of labor in a swarm of autonomous underwater robots by improved partitioning social inhibition. Adapt. Behav. 24(2), 87–101 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Joshua Cherian Varughese
    • 1
    • 2
    Email author
  • Daniel Moser
    • 1
  • Ronald Thenius
    • 1
  • Franz Wotawa
    • 2
  • Thomas Schmickl
    • 1
  1. 1.Institut für ZoologieKarl Franzens Universität GrazGrazAustria
  2. 2.Institut für Software TechnologieTechnische Universität GrazGrazAustria

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