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Artificial Life and Robotics

, Volume 23, Issue 4, pp 459–468 | Cite as

Phototactic supersmarticles

  • William Savoie
  • Sarah Cannon
  • Joshua J. Daymude
  • Ross Warkentin
  • Shengkai Li
  • Andréa W. Richa
  • Dana Randall
  • Daniel I. GoldmanEmail author
Original Article

Abstract

Smarticles or smart active particles are small robots equipped with only basic movement and sensing abilities that are incapable of rotating or displacing individually. We study the ensemble behavior of smarticles, i.e., the behavior a collective of these very simple computational elements can achieve, and how such behavior can be implemented using minimal programming. We show that an ensemble of smarticles constrained to remain close to one another (which we call a supersmarticle), achieves directed locomotion toward or away from a light source, a phenomenon known as phototaxing. We present experimental and theoretical models of phototactic supersmarticles that collectively move with a directed displacement in response to light. The motion of the supersmarticle is stochastic, performing approximate free diffusion, and is a result of chaotic interactions among smarticles. The system can be directed by introducing asymmetries among the individual smarticle’s behavior, in our case, by varying activity levels in response to light, resulting in supersmarticle-biased motion.

Keywords

Swarm robotics Locomotion Phototaxing Active matter Programmable matter 

Notes

Acknowledgements

S. Cannon: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. NSF DGE-1650044. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. J. J. Daymude and A. W. Richa: Supported in part by NSF CCF-1422603, CCF-1637393, and CCF-1733680. D. I. Goldman: Funding provided by NSF PoLS #0957659 and #PHY-1205878, and ARO #W911NF-13-1-0347. D. Randall: Supported in part by NSF CCF-1526900, CCF-1637031, and CCF-1733812.

References

  1. 1.
    Ramaswamy S (2010) The mechanics and statistics of active matter. Annu Rev Condens Matter Phys 1(1):323–345.  https://doi.org/10.1146/annurev-conmatphys-070909-104101 CrossRefGoogle Scholar
  2. 2.
    Derakhshandeh Z, Gmyr R, Richa AW, Scheideler C, Strothmann T (2017) Universal coating for programmable matter. Theor Comput Sci 671:56MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cannon S, Daymude JJ, Randall D, Richa AW (2016) A Markov chain algorithm for compression in self-organizing particle systems. In: Proc. of the 2016 ACM Symposium on Principles of Distributed Computing (PODC ’16), pp 279–288Google Scholar
  4. 4.
    Mlot NJ, Tovey CA, Hu DL (2011) Fire ants self-assemble into waterproof rafts to survive floods. Proc Natl Acad Sci 108(19):7669CrossRefGoogle Scholar
  5. 5.
    Cheung KC, Demaine ED, Bachrach JR, Griffith S (2011) Programmable assembly with universally foldable strings (Moteins). IEEE Trans Robot 27(4):718CrossRefGoogle Scholar
  6. 6.
    Woods D (2013) Intrinsic universality and the computational power of self-assembly. In: Proceedings of machines, computations and universality 2013 (MCU ’13), pp 16–22MathSciNetCrossRefGoogle Scholar
  7. 7.
    Angluin D, Aspnes J, Diamadi Z, Fischer MJ, Peralta R (2006) Computation in networks of passively mobile finite-state sensors. Distrib Comput 18(4):235CrossRefGoogle Scholar
  8. 8.
    Cieliebak M, Flocchini P, Prencipe G, Santoro N (2012) Distributed computing by mobile robots: gathering. SIAM J Comput 41(4):829MathSciNetCrossRefGoogle Scholar
  9. 9.
    Rubenstein M, Cornejo A, Nagpal R (2014) Programmable self-assembly in a thousand-robot swarm. Science 345(6198):795CrossRefGoogle Scholar
  10. 10.
    Chazelle B (2009) Natural algorithms. In: Proceedings of the 2009 ACM-SIAM symposium on discrete algorithms (SODA09), pp 422–431CrossRefGoogle Scholar
  11. 11.
    Yim M, Shen WM, Salemi B, Rus D, Moll M, Lipson H, Klavins E, Chirikjian GS (2007) Modular self-reconfigurable robot systems. IEEE Robot Autom Mag 14(1):43CrossRefGoogle Scholar
  12. 12.
    Woods D, Chen HL, Goodfriend S, Dabby N, Winfree E, Yin P (2013) Active self-assembly of algorithmic shapes and patterns in polylogarithmic time. In: Proceedings of the 4th Innovations in Theoretical Computer Science Conference (ITCS ’13), pp 353–354Google Scholar
  13. 13.
    Daymude JJ, Richa AW, Scheideler C (2017) The Amoebot model. The Mmoebot model. https://sops.engineering.asu.edu/sops/amoebot
  14. 14.
    Junot G, Briand G, Ledesma-Alonso R, Dauchot O (2017) Active versus passive hard disks against a membrane: mechanical pressure and instability. Phys Rev Lett 119:028002CrossRefGoogle Scholar
  15. 15.
    Solon AP, Stenhammar J, Wittkowski R, Kardar M (2015) Pressure and phase equilibria in interacting active brownian spheres. Phys Rev Lett 114(19):198301CrossRefGoogle Scholar
  16. 16.
    Andrés Arroyo M, Cannon S, Daymude JJ, Randall D, Richa AW (2017) A stochastic approach to shortcut bridging in programmable matter. In: DNA computing and molecular programming, pp 122–138zbMATHGoogle Scholar
  17. 17.
    Reid CR, Lutz MJ, Powell S, Kao AB, Couzin ID, Garnier S (2015) Army ants dynamically adjust living bridges in response to a cost-benefit trade-off. Proc Natl Acad Sci 112(49):15113CrossRefGoogle Scholar
  18. 18.
    Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087CrossRefGoogle Scholar
  19. 19.
    Hastings WK (1970) Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 57(1):97MathSciNetCrossRefGoogle Scholar
  20. 20.
    Lynch N (1996) Distributed algorithms. Morgan Kauffman, BurlingtonzbMATHGoogle Scholar
  21. 21.
    Tarantino N, Tinevez JY, Crowell E, Boisson B, Henriques R, Mhlanga M, Agou F, Israël A, Laplantine E (2014) TNF and IL-1 exhibit distinct ubiquitin requirements for inducing NEMO-IKK supramolecular structures. J Cell Biol 204(2):231CrossRefGoogle Scholar
  22. 22.
    Berg H (1983) Random walks in biology. Princeton University Press, PrincetonGoogle Scholar

Copyright information

© ISAROB 2018

Authors and Affiliations

  • William Savoie
    • 1
  • Sarah Cannon
    • 2
  • Joshua J. Daymude
    • 3
  • Ross Warkentin
    • 1
  • Shengkai Li
    • 1
  • Andréa W. Richa
    • 3
  • Dana Randall
    • 2
  • Daniel I. Goldman
    • 1
    Email author
  1. 1.School of PhysicsGeorgia Institute of TechnologyAtlantaUSA
  2. 2.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Computer Science, CIDSEArizona State UniversityTempeUSA

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