Skip to main content
SpringerLink
Log in
Book cover

Advances in Hybridization of Intelligent Methods pp 17–38Cite as

Analysis of Biologically Inspired Swarm Communication Models

  • Chapter
  • First Online:

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 85))

Abstract

The biological swarm literature presents communication models that attempt to capture the nature of interactions among the swarm’s individuals. The reported research derived algorithms based on the metric, topological, and visual biological swarm communication models. The evaluated hypothesis is that the choice of a biologically inspired communication model can affect the swarm’s performance for a given task. The communication models were evaluated in the context of two swarm robotics tasks: search for a goal and avoid an adversary. The general findings demonstrate that the swarm agents had the best overall performance when using the visual model for the search for a goal task and performed the best for the avoid an adversary task when using the topological model. Further analysis of the performance metrics by the various experimental parameters provided insights into specific situations in which the models will be the most or least beneficial. The importance of the reported research is that the task performance of a swarm can be amplified through the deliberate selection of a communications model.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Videos of example trials can be found at http://www.eecs.vanderbilt.edu/research/hmtl/wp/index.php/research-projects/human-swarm-interaction/emulating-swarm-communications/.

  2. 2.

    Videos of example trials can be found at http://eecs.vanderbilt.edu/research/hmtl/wp/index.php/research-projects/human-swarm-interaction/emulating-swarm-communications/.

References

  1. Abaid, N., Porfiri, M.: Fish in a ring: spatio-temporal pattern formation in one-dimensional animal groups. J. R. Soc. Interface 7(51), 1441–1453 (2010)

    Article  Google Scholar 

  2. Aoki, I.: A simulation study on the schooling mechanism in fish. Bull. Japan. Soc. Sci. Fish. 48(8), 1081–1088 (1982)

    Article  Google Scholar 

  3. Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., Zdravkovic, V.: Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. PNAS 105(4), 1232–1237 (2008)

    Article  Google Scholar 

  4. Barberis, L., Albano, E.V.: Evidence of a robust universality class in the critical behavior of self-propelled agents: metric versus topological interactions. Phys. Rev. E 89(1), 9–26 (2014)

    Article  Google Scholar 

  5. Bode, N.W.F., Frank, D.W., Wood, A.J.: Limited interactions in flocks: relating model simulations to empirical data. J. R. Soc. Interface 8, 301–304 (2011)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  7. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems, No. 1. Oxford University Press (1999)

    Google Scholar 

  8. Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press, Princeton, NJ (2003)

    MATH  Google Scholar 

  9. Cianci, C.M., Raemy, X., Pugh, J., Martinoli, A.: Communication in a swarm of miniature robots: the e-puck as an educational tool for swarm robotics. In: Proceedings of the 2nd International Conference on Swarm Robotics, pp. 103–115 (2007)

    Google Scholar 

  10. Clough, B.T.: Uav swarming? so what are those swarms, what are the implications, and how do we handle them?. Technical report, DTIC Document (2002)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Couzin, I.D., Krause, J., James, R., Ruxton, G.D., Franks, N.R.: Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218(1), 1–11 (2002)

    Article  MathSciNet  Google Scholar 

  13. Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York, NY (2010)

    Google Scholar 

  14. Ferrante, E., Turgut, A.E., Mathews, N., Birattari, M., Dorigo, M.: Flocking in stationary and non-stationary environments: a novel communication strategy for heading alignment. In: Parallel Problem Solving from Nature, pp. 331–340 (2010)

    Google Scholar 

  15. Goodrich, M., Kerman, S., Pendleton, B., Sujit, P.: What types of interactions do bio-inspired robot swarms and flocks afford a human? In: Proceedings of Robotics: Science and Systems, pp. 105–112 (2012)

    Google Scholar 

  16. Goodrich, M.A., Sujit, P., Kerman, S., Pendleton, B., Pinto, J.: Enabling human interaction with bio-inspired robot teams: topologies, leaders, predators, and stakeholders. Technical Report BYU-HCMI 2011-1, Computer Science Department, Brigham Young University, Provo, USA, Aug 2011

    Google Scholar 

  17. Huth, A., Wissel, C.: The simulation of the movement of fish schools. J. Theor. Biol. 156, 365–385 (1992)

    Article  Google Scholar 

  18. Kolling, A., Walker, P., Chakraborty, N., Sycara, K., Lewis, M.: Human interaction with robot swarms: a survey. IEEE Trans. Hum.-Mach. Syst. 46(1), 9–26 (2016)

    Article  Google Scholar 

  19. Kolpas, A., Busch, M., Li, H., Couzin, I.D., Petzold, L., Moehlis, J.: How the spatial position of individuals affects their influence on swarms: a numerical comparison of two popular swarm dynamics models. PLoS ONE 8(3), e58525 (2013)

    Article  Google Scholar 

  20. Krause, J., Ruxton, G.D.: Living in Groups. Oxford University Press, New York, NY (2002)

    Google Scholar 

  21. Maçãs, C., Cruz, P., Martins, P., Machado, P.: Swarm systems in the visualization of consumption patterns. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 2466–2472 (2015)

    Google Scholar 

  22. Mannor, S., Shamma, J.: Multi-agent learning for engineers. Artif. Intell. 171(7), 417–422 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  23. McLurkin, J., McMullen, A., Robbins, N., Habibi, G., Becker, A., Chou, A., Li, H., John, M., Okeke, N., Rykowski, J., Kim, S., Xie, W., Vaughn, T., Zhou, Y., Shen, J., Chen, N., Kaseman, Q., Langford, L., Hunt, J., Boone, A., Koch, K.: A robot system design for low-cost multi-robot manipulation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2014)

    Google Scholar 

  24. Mondada, F., Gambardella, L.M., Floreano, D., Nolfi, S., Deneubourg, J.L., Dorigo, M.: The cooperation of swarm-bots: physical interactions in collective robotics. Robot. Autom. Mag. 12(2), 21–28 (2005)

    Article  Google Scholar 

  25. Nunamaker, J.F., Applegate, L.M., Konsynski, B.R.: Computer-aided deliberation: model management and group decision support: special focus article. Oper. Res. 36(6), 826–848 (1988)

    Article  Google Scholar 

  26. Parrish, J.K., Viscido, S.V., Grünbaum, D.: Self-organized fish schools: an examination of emergent properties. Biol. Bull. 202, 296–305 (2002)

    Google Scholar 

  27. Reynolds, C.: Flocks, herds and schools: a distributed behavioral model. Comput. Graph. 21(4), 25–34 (1987)

    Article  Google Scholar 

  28. Rubenstein, M., Ahler, C., Nagpal, R.: Kilobot: A low cost scalable robot system for collective behaviors. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2012)

    Google Scholar 

  29. Shang, Y., Bouffanais, R.: Consensus reaching in swarms ruled by a hybrid metric-topological distance. Eur. Phys. J. B 87(294) (2014)

    Google Scholar 

  30. Shoham, Y., Powers, R., Grenager, T.: If multi-agent learning is the answer, what is the question? Artif. Intell. 171(7), 365–377 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  31. Strandburg-Peshkin, A., Twomey, C.R., Bode, N.W., Kao, A.B., Katz, Y., Ioannou, C.C., Rosenthal, S.B., Torney, C.J., Wu, H.S., Levin, S.A., Couzin, I.D.: Visual sensory networks and effective information transfer in animal groups. Curr. Biol. 23(17), R709–R711 (2013)

    Article  Google Scholar 

  32. Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., Shochet, O.: Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75(6), 1226–1229 (1995)

    Article  MathSciNet  Google Scholar 

  33. Yanco, H.A., Drury, J.: Classifying human-robot interaction: an updated taxonomy. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2841–2846. IEEE (2004)

    Google Scholar 

Download references

Acknowledgements

This material is based upon research supported by, or in part by, the U.S. Office of Naval Research under award #N000141210987.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA

    Musad Haque, Electa Baker, Christopher Ren, Douglas Kirkpatrick & Julie A. Adams

Authors
  1. Musad Haque
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Electa Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Douglas Kirkpatrick
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Julie A. Adams
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Musad Haque .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Computer Engineering and Informatics, University of Patras, Patras, Greece

    Ioannis Hatzilygeroudis

  2. The Faculty of Engineering, Environment and Computing, School of Computing, Coventry University, Coventry, United Kingdom

    Vasile Palade

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Haque, M., Baker, E., Ren, C., Kirkpatrick, D., Adams, J.A. (2018). Analysis of Biologically Inspired Swarm Communication Models. In: Hatzilygeroudis, I., Palade, V. (eds) Advances in Hybridization of Intelligent Methods. Smart Innovation, Systems and Technologies, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-319-66790-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-66790-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66789-8

  • Online ISBN: 978-3-319-66790-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation