Abstract
Designing embodied distributed systems, such as multi-robot systems, is challenging especially if the individual components have limited capabilities due to hardware restrictions. In self-organizing systems each component has only limited information and a global, organized system behavior (macro-level) has to emerge from local interactions only (micro-level). A general, structured design approach to self-organizing distributed systems is still lacking. We develop a general approach based on behaviorally heterogeneous systems. Inspired by the concept of population coding from neuroscience, we show in two case studies how designing an embodied distributed system is reduced to picking the right components from a predefined set of controller types. In this way, the design challenge is reduced to an optimization problem that can be solved by a variety of optimization techniques. Our approach is applicable to scenarios that allow for representing the component behavior as (probabilistic) finite state machine. We anticipate the paradigm of population coding to be applicable to a wide range of distributed systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
We use an implementation for the R project “NMOF: Numerical Methods and Optimization in Finance” (‘NMOF’) by Enrico Schumann, see http://cran.r-project.org/web/packages/NMOF/.
References
Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight, T., Nagpal, R., Rauch, E., Sussman, G., Weiss, R.: Amorphous computing. Commun. ACM 43(5), 74–82 (2000)
Beal, J., Dulman, S., Usbeck, K., Viroli, M., Correll, N.: Organizing the aggregate: languages for spatial computing. In: Formal and Practical Aspects of Domain-Specific Languages, pp. 436–501. Information Science Reference (2012)
Beckers, R., Holland, O.E., Deneubourg, J.L.: From local actions to global tasks: stigmergy and collective robotics. Artif. Life 4, 181–189 (1994)
Berman, S., Halasz, A., Hsieh, M., Kumar, V.: Optimized stochastic policies for task allocation in swarms of robots. IEEE Trans. Robot. 25(4), 927–937 (2009)
Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., Dorigo, M.: Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Auton. Agents Multi-Agent Syst. 28(1), 101–125 (2014)
Dorigo, M., Birattari, M., Brambilla, M.: Swarm robotics. Scholarpedia 9(1), 1463 (2014)
Dorigo, M., et al.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20, 60–71 (2013)
Dressler, F.: Self-organization in Sensor and Actor Networks. Wiley, New York (2008)
Ferrante, E., Turgut, A.E., Duez-Guzmn, E., Dorigo, M., Wenseleers, T.: Evolution of self-organized task specialization in robot swarms. PLoS Comput. Biol. 11(8), 1–21 (2015)
Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E.: Neuronal population coding of movement direction. Science 233(4771), 1416–1419 (1986)
Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)
Hamann, H.: Towards swarm calculus: Urn models of collective decisions and universal properties of swarm performance. Swarm Intell. 7(2–3), 145–172 (2013)
Hamann, H., Valentini, G., Khaluf, Y., Dorigo, M.: Derivation of a micro-macro link for collective decision-making systems. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 181–190. Springer, Heidelberg (2014)
Hamann, H., Wörn, H.: A framework of space-time continuous models for algorithm design in swarm robotics. Swarm Intell. 2(2–4), 209–239 (2008)
Hogg, T.: Coordinating microscopic robots in viscous fluids. Auton. Agents Multi-Agent Syst. 14(3), 271–305 (2006)
Kengyel, D., Hamann, H., Zahadat, P., Radspieler, G., Wotawa, F., Schmickl, T.: Potential of heterogeneity in collective behaviors: a case study on heterogeneous swarms. In: Chen, Q., Torroni, P., Villata, S., Hsu, J. (eds.) PRIMA 2015. LNCS, vol. 9387, pp. 201–217. Springer, Heidelberg (2015)
Lenaghan, S., Wang, Y., Xi, N., Fukuda, T., Tarn, T., Hamel, W., Zhang, M.: Grand challenges in bioengineered nanorobotics for cancer therapy. IEEE Trans. Biomed. Eng. 60(3), 667–673 (2013)
Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nat. Rev. Neurosci. 1, 125–132 (2000)
Prorok, A., Hsieh, M.A., Kumar, V.: Fast redistribution of a swarm of heterogeneous robots. In: International Conference on Bio-inspired Information and Communications Technologies (BICT) (2015)
Valentini, G., Ferrante, E., Hamann, H., Dorigo, M.: Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems. Auton. Agents Multi-Agent Syst. 30(3), 553–580 (2015)
Valentini, G., Hamann, H., Dorigo, M.: Global-to-local design for self-organized task allocation in swarms. Technical report TR/IRIDIA/2016-002, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, March 2016
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Hamann, H., Valentini, G., Dorigo, M. (2016). Population Coding: A New Design Paradigm for Embodied Distributed Systems. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science(), vol 9882. Springer, Cham. https://doi.org/10.1007/978-3-319-44427-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-44427-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44426-0
Online ISBN: 978-3-319-44427-7
eBook Packages: Computer ScienceComputer Science (R0)