Abstract
Applications of robotic swarms often face limitations in sensing and motor capabilities. We aim at providing evidence that the modest equipment of the individual robots can be compensated by the interaction within the swarm. If the robots, such as the well-known Kilobots, have no sense of place or directionality, their collective behaviour can still result in meaningful spatial organisation. We show that a variety of patterns can be formed based on a reaction-diffusion system and that these patters can be used by the robots to solve spatial tasks. In this contribution, we present first results for applications of this approach based on ‘physically realistic’ Kilobot simulations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adam, J.A.: Mathematics in Nature: Modeling Patterns in the Natural World. Princeton University Press, Princeton (2006)
Correll, N., Martinoli, A.: Modeling and designing self-organized aggregation in a swarm of miniature robots. Int. J. Robot. Res. 30(5), 615–626 (2011)
Dimidov, C., Oriolo, G., Trianni, V.: Random walks in swarm robotics: an experiment with kilobots. In: Dorigo, M., Birattari, M., Li, X., López-Ibáñez, M., Ohkura, K., Pinciroli, C., Stützle, T. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 185–196. Springer, Cham (2016). doi:10.1007/978-3-319-44427-7_16
Erskine, A., Joyce, T., Herrmann, J.M.: Parameter selection in particle swarm optimisation from stochastic stability analysis. In: Dorigo, M., Birattari, M., Li, X., López-Ibáñez, M., Ohkura, K., Pinciroli, C., Stützle, T. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 161–172. Springer, Cham (2016). doi:10.1007/978-3-319-44427-7_14
Guo, H., Meng, Y., Jin, Y.: A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. BioSystems 98(3), 193–203 (2009)
Jin, Y., Meng, Y.: Morphogenetic robotics: an emerging new field in developmental robotics. IEEE Trans. Syst. Man Cybern. C 41(2), 145–160 (2011)
Li, W., Gauci, M., Groß, R.: Turing learning: a metric-free approach to inferring behavior and its application to swarms. Swarm Intell. 10(3), 211–243 (2016)
Oh, H., Shirazi, A.R., Sun, C., Jin, Y.: Bio-inspired self-organising multi-robot pattern formation: a review. Robot. Auton. Syst. 91, 83–100 (2017)
Rubenstein, M., Ahler, C., Nagpal, R.: Kilobot: a low cost scalable robot system for collective behaviors. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3293–3298 (2012)
Turing, A.M.: The chemical basis of morphogenesis. Philos. Trans. R. Soc. Lond. B 237(641), 37–72 (1952)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Imrie, C., Herrmann, J.M. (2017). Self-organisation of Spatial Behaviour in a Kilobot Swarm. In: Mangan, M., Cutkosky, M., Mura, A., Verschure, P., Prescott, T., Lepora, N. (eds) Biomimetic and Biohybrid Systems. Living Machines 2017. Lecture Notes in Computer Science(), vol 10384. Springer, Cham. https://doi.org/10.1007/978-3-319-63537-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-63537-8_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63536-1
Online ISBN: 978-3-319-63537-8
eBook Packages: Computer ScienceComputer Science (R0)