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Distributed Camouflage for Swarm Robotics and Smart Materials

  • Yang Li
  • John Klingner
  • Nikolaus Correll
Chapter
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 6)

Abstract

We present a distributed algorithm for a swarm of active particles to camouflage in an environment. Each particle is equipped with sensing, computation, and communication, allowing the system to take color and gradient information from the environment and self-organize into an appropriate pattern. Current artificial camouflage systems are either limited to static patterns, which are adapted for specific environments, or rely on back-projection, which depend on the viewer’s point of view. Inspired by the camouflage abilities of the cuttlefish, we propose a distributed estimation and pattern formation algorithm that allows to quickly adapt to different environments. We present convergence results both in simulation as well as on a swarm of miniature robots “Droplets” for a variety of patterns.

Notes

Acknowledgements

This research has been supported by NSF grant #1150223.

References

  1. 1.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  2. 2.
    Farrow, N., Klingner, J., Reishus, D., Correll, N.: Miniature six-channel range and bearing system: algorithm, analysis and experimental validation. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 6180–6185. IEEE (2014)Google Scholar
  3. 3.
    Fekete, S.P., Fey, D., Komann, M., Kröller, A., Reichenbach, M., Schmidt, C.: Distributed vision with smart pixels. In: Proceedings of the Twenty-Fifth Annual Symposium on Computational Geometry, pp. 257–266. ACM (2009)Google Scholar
  4. 4.
    Hanlon, R.: Cephalopod dynamic camouflage. Curr. Biol. 17(11), R400–R404 (2007)CrossRefGoogle Scholar
  5. 5.
    Hanlon, R.T., Messenger, J.B.: Adaptive coloration in young cuttlefish (sepia officinalis l.): the morphology and development of body patterns and their relation to behaviour. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 320(1200), 437–487 (1988)CrossRefGoogle Scholar
  6. 6.
    Inami, M., Kawakami, N., Tachi, S.: Optical camouflage using retro-reflective projection technology. In: Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality, p. 348. IEEE Computer Society (2003)Google Scholar
  7. 7.
    Klingner, J., Kanakia, A., Farrow, N., Dustin, R., Correll, N.: A stick-slip omnidirectional drive-train for low-cost swarm robotics: Mechanism, calibration, and control. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2014)Google Scholar
  8. 8.
    Lin, H.Y., Lie, W.N., Wang, M.L.: A framework of view-dependent planar scene active camouflage. Int. J. Imaging Syst. Technol. 19(3), 167–174 (2009)CrossRefGoogle Scholar
  9. 9.
    McEvoy, M., Correll, N.: Materials that couple sensing, actuation, computation, and communication. Science 347(6228), 1261689 (2015)CrossRefGoogle Scholar
  10. 10.
    Meinhardt, H.: Models of Biological Pattern Formation, vol. 6. Academic Press, London (1982)Google Scholar
  11. 11.
    Messenger, J.B.: Evidence that octopus is colour blind. J. Exp. Biol. 70(1), 49–55 (1977)Google Scholar
  12. 12.
    Messenger, J.B.: Cephalopod chromatophores: neurobiology and natural history. Biol. Rev. 76(4), 473–528 (2001)CrossRefGoogle Scholar
  13. 13.
    Mirollo, R.E., Strogatz, S.H.: Synchronization of pulse-coupled biological oscillators. SIAM J. Appl. Math. 50(6), 1645–1662 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Morin, S.A., Shepherd, R.F., Kwok, S.W., Stokes, A.A., Nemiroski, A., Whitesides, G.M.: Camouflage and display for soft machines. Science 337(6096), 828–832 (2012)CrossRefGoogle Scholar
  15. 15.
    Ramirez, M.D., Oakley, T.H.: Eye-independent, light-activated chromatophore expansion (lace) and expression of phototransduction genes in the skin of octopus bimaculoides. J. Exp. Biol. 218(10), 1513–1520 (2015)CrossRefGoogle Scholar
  16. 16.
    Rossiter, J., Yap, B., Conn, A.: Biomimetic chromatophores for camouflage and soft active surfaces. Bioinspiration Biomimetics 7(3), 036009 (2012)CrossRefGoogle Scholar
  17. 17.
    Stevens, M., Merilaita, S.: Animal camouflage: current issues and new perspectives. Philos. Trans. R. Soc. B: Biol. Sci. 364(1516), 423–427 (2009)CrossRefGoogle Scholar
  18. 18.
    Werner-Allen, G., Tewari, G., Patel, A., Welsh, M., Nagpal, R.: Firefly-inspired sensor network synchronicity with realistic radio effects. In: Proceedings of the 3rd international conference on Embedded networked sensor systems, pp. 142–153. ACM (2005)Google Scholar
  19. 19.
    Xiao, L., Boyd, S., Lall, S.: A scheme for robust distributed sensor fusion based on average consensus. In: Fourth International Symposium on Information Processing in Sensor Networks, 2005. IPSN 2005, pp. 63–70. IEEE (2005)Google Scholar
  20. 20.
    Young, D.A.: A local activator-inhibitor model of vertebrate skin patterns. Math. Biosci. 72(1), 51–58 (1984)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yu, C., Li, Y., Zhang, X., Huang, X., Malyarchuk, V., Wang, S., Shi, Y., Gao, L., Su, Y., Zhang, Y., et al.: Adaptive optoelectronic camouflage systems with designs inspired by cephalopod skins. Proc. Natl. Acad. Sci. 111(36), 12998–13003 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Colorado BoulderBoulderUSA

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