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.
KeywordsSwarm robotics Locomotion Phototaxing Active matter Programmable matter
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.
- 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
- 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
- 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.Daymude JJ, Richa AW, Scheideler C (2017) The Amoebot model. The Mmoebot model. https://sops.engineering.asu.edu/sops/amoebot
- 22.Berg H (1983) Random walks in biology. Princeton University Press, PrincetonGoogle Scholar