Emergent Swarm Morphology Control of Wireless Networked Mobile Robots

Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

We describe a new class of decentralised control algorithms that link local wireless connectivity to low-level robot motion control in order to maintain both swarm aggregation and connectivity, which we term “coherence”, in unbounded space. We investigate the potential of first-order and second-order connectivity information to maintain swarm coherence. For the second-order algorithm we show that a single \(\beta \) parameter—the number of shared neighbours that each robot tries to maintain—acts as an “adhesion” parameter. Control of \(\beta \) alone affects the global area coverage of the swarm. We then add a simple beacon sensor to each robot and show that, by creating a \(\beta \) differential between illuminated and occluded robots, the swarm displays emergent global taxis towards the beacon; it also displays interesting global obstacle avoidance properties. The chapter then extends the idea of \(\beta \) heterogeneity within the swarm to demonstrate variants of the algorithm that exhibit emergent concentric or linear segregation of subgroups within the swarm, or—in the presence of an external beacon—the formation of horizontal or vertical axial configurations. This emergent swarm morphology control is remarkable because apparently simple variations generate very different global properties. These emergent properties are interesting both because they appear to have parallels in biology, and because they could have value to a wide range of future applications in swarm robotics.

References

  1. 1.
    Agassounon, W., Martinoli, A., Easton, K.: Macroscopic modeling of aggregation experiments using embodied agents in teams of constant and time-varying sizes. Auton. Robots 17(2–3), 163–192 (2004)CrossRefGoogle Scholar
  2. 2.
    Artaud, G., Plancke, P., Magness, R., Durrant, D., Plummer, C.: IEEE 802.15.4: Wireless transducer networks. In: Datasystems in Aerospace, DASIA’04, Nice (2004)Google Scholar
  3. 3.
    Balch, T., Arkin, R.: Communication in reactive multiagent robotic systems. Auton. Robots 1, 1–25 (1994)CrossRefGoogle Scholar
  4. 4.
    Balch, T., Arkin, R.: Behaviour-based formation control for multi-robot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)CrossRefGoogle Scholar
  5. 5.
    Balch, T., Hybinette, M.: Social potentials for scalable multi-robot formations. In: Proceedings of the International Conference on Robotics and Automation ICRA’00, vol. 1, pp. 73–80 (2000)Google Scholar
  6. 6.
    Beckers, R., Holland, O., Deneubourg, J.L.: From local actions to global tasks: stigmergy and collective robotics. In: Press, M. (ed.) Artificial Life IV, pp. 181–189. MIT Press, Cambridge (1994)Google Scholar
  7. 7.
    Billard, A., Ijspeert, A., Martinoli, A.: Adaptive exploration of a frequently changing environment by a group of communicating robots. In: Floreano, D. et al. (eds.) Advances in Artfificial, Life, ECAL’99, vol. 1674, pp. 596–605. Springer Verlag, Berlin (1999)Google Scholar
  8. 8.
    Bjerknes, J., Winfield, A.: On fault-tolerance and scalability of swarm robotic systems. In: Proceedings of the 10th International Symposium on Distributed Autonomous Robotic (DARS 2010), Springer Tracts in Advanced Robotics, vol. 83, pp. 431–444 (2013)Google Scholar
  9. 9.
    Bonabeau, E., Dorigo, M., Théraulaz, G.: Swarm Intelligence—From Natural to Artificial Systems. Oxford University Press, New York (1999)MATHGoogle Scholar
  10. 10.
    Braitenberg, V.: Vehicles—Experiments in Synthetic Psychology. MIT Press, Cambridge (1984)Google Scholar
  11. 11.
    Brooks, R.: A robust layered control system for a mobile robot. J. Robot. Autom. 2, 14–23 (1986)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Şahin, E.: Swarm robotics: from sources of inspiration to domains of application. In: Şahin, E., Spears, W. (eds.) Swarm Robotics Workshop: State-of-the-art Survey. Lecture Notes in Computer Science, vol. 3342, pp. 10–20. Springer, Berlin (2005)Google Scholar
  13. 13.
    Dorigo, M., Tuci, E., Groß, T., Trianni, V., Labella, T., Nouyan, S., Ampatzis, C.: The SWARM-BOTS project. In: Şahin, E., Spears, W. (eds.) Swarm Robotics Workshop: State-of-the-art Survey. Lecture Notes in Computer Science, vol. 3342, pp. 31–44. Springer, Berlin (2005)Google Scholar
  14. 14.
    Hayes, A., Martinoli, A., Goodman, R.: Comparing distributed exploration strategies with simulated and real robots. In: Distributed Autonomous Robotic Systems, vol. IV, pp. 261–270 (2000)Google Scholar
  15. 15.
    Hemelrijk, C.K., Kunz, H.: Density distribution and size sorting in fish schools: an individual-based model. Behav. Ecol. 16(1), 178–187 (2005)CrossRefGoogle Scholar
  16. 16.
    Hogeweg, P.: Computing an organism: on the interface between informatic and dynamic processes. BioSystems 64, 97–109 (2002)CrossRefGoogle Scholar
  17. 17.
    Kotay, K., Rus, D.: Locomotion versatility through self-reconfiguration. J. Robot. Auton. Syst. 26, 217–232 (1999)CrossRefGoogle Scholar
  18. 18.
    Krieger, M., Billeter, J.B.: The call of duty: self-organised task allocation in a population of up to twelve mobile robots. J. Robot. Auton. Syst. 30, 65–84 (2000)CrossRefGoogle Scholar
  19. 19.
    Mataric, M.: Designing emergent behaviours: from local interactions to collective intelligence. In: From Animals To Animats, pp. 432–441 (1992)Google Scholar
  20. 20.
    Melhuish, C., Holland, O., Hoddell, S.: Collective sorting and segregation in robots with minimal sensing. In: From Animals to Animat, vol. 5, pp. 465–470. MIT Press, Cambridge (1998)Google Scholar
  21. 21.
    Mondada, F., Bonani, M., Magnenat, S., Guignard, A., Floreano, D.: Physical connections and cooperation in swarm robotics. In: Groen, P. et al. (eds.) Proceedings of the International Conference on Intelligent and Autonomous Systems. IOS Press, Amsterdam (2004)Google Scholar
  22. 22.
    Nembrini, J.: Minimalist coherent swarming of wireless networked autonomous mobile robots. Ph.D. Thesis, University of the West of England, Bristol, UK, (2005). http://swis.epfl.ch/people/julien
  23. 23.
    Nishimura, S., Sasai, M.: Inertia of ameobic cell locomation as an emergent collective property of the cellular dynamics. Phys. Rev. E 71, 010902 (2005)Google Scholar
  24. 24.
    Nusslein-Volhard, C.: Gradients that organise embryo-development. Scient. Am. 275(2), 54–61 (1996)Google Scholar
  25. 25.
    Poduri, S., Sukhatme, G.: Constrained coverage for mobile sensor networks. In: IEEE International Conference on Robotics and Automation, pp. 165–172 (2004)Google Scholar
  26. 26.
    Reynolds, C.: Flocks, herds and schools: a distributed behavioral model. Comput. Graph. 21, 25–34 (1987)CrossRefGoogle Scholar
  27. 27.
    Savill, N., Hogeweg, P.: Modelling morphogenesis: from single cells to crawling slugs. J. Theor. Biol. 184, 229–235 (1997)CrossRefGoogle Scholar
  28. 28.
    Shimizu, M., Ishiguro, A., Kawakatsu, T.: Slimebot: a modular robot that exploits emergent phenomena. In: IEEE International Conference on Robotics and Automation, pp. 2982–2987, Barcelona, Spain (2005)Google Scholar
  29. 29.
    Støy, K.: Developing a solution to the foraging task using multiple robots and local comunication. In: IEEE CIRA2001 (2001). http://www.mip.sdu.dk/kaspers/publications.html
  30. 30.
    Støy, K.: Using situated communication in distributed autonomous mobile robotics. In: 7th Scandinavian Conference on AI, pp. 44–52 (2001). http://citeseer.nj.nec.com/425017.html
  31. 31.
    Støy, K.: Controlling self-reconfiguration using cellular automata and gradients. In: Groen, P. et al. (eds.) Proceedings of the International Conference on Intelligent and Autonomous Systems, IAS-8, pp. 693–702. IOS Press, Amsterdam (2004)Google Scholar
  32. 32.
    Takahashi, N., Yu, W., Yokoi, H., Kakazu, Y.: Amoeba like multi-cell robot control system. In: Groen, P. et al. (eds.) Proceedings of the International Conference on Intelligent and Autonomous Systems, IAS-8. IOS Press, Amsterdam (2004)Google Scholar
  33. 33.
    Weßnitzer, J., Adamatzky, A., Melhuish, C.: Towards self-organising structure formations: a decentralised approach. In: Proceedings of ECAL 2001, pp. 573–581. Springer, London (2001)Google Scholar
  34. 34.
    Winfield, A.: Distributed sensing and data collection via broken ad hoc wireless connected networks of mobile robots. In: Distributed Autonomous Robotic Systems, vol. IV, pp. 273–282 (2000)Google Scholar
  35. 35.
    Winfield, A., Harper, C., Nembrini, J.: Towards dependable swarms and a new discipline of swarm engineering. In: Şahin, E., Spears, W. (eds.) Swarm Robotics Workshop: State-of-the-art Survey, vol. 3342, pp. 126–142. Springer, Berlin (2005)Google Scholar
  36. 36.
    Winfield, A., Holland, O.: The application of wireless local area network technology to the control of mobile robots. Microprocess. Microsyst. 23, 597–607 (2000)CrossRefGoogle Scholar
  37. 37.
    Winfield, A., Liu, W., Nembrini, J., Martinoli, A.: Modelling a wireless connected swarm of mobile robots. Swarm Intell. 2(2–4), 241–266 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Bristol Robotics Laboratory (BRL)University of the West of EnglandBristolUK
  2. 2.Media and Design LaboratoryEPFLLausanneSwitzerland

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