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Balancing robot swarm cost and interference effects by varying robot quantity and size

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Abstract

Designing a robot swarm requires a swarm designer to understand the trade-offs unique to a swarm. The most basic design decisions are how many robots there should be in the swarm and the individual robot size. These choices in turn impact swarm cost and robot interference, and therefore swarm performance. The underlying physical reasons for why the number of robots and the individual robot size affect interference are explained in this work. A swarm interference function was developed and used to build an analytical basis for swarm performance. A swarm cost model was also developed and used with the analytical basis for swarm performance to generate performance cost curves for swarms with different numbers of robots and different robot sizes. The swarm designer can use this analytical basis, cost model, and these curves to weigh how the number of robots in the swarm and the individual robot size can be selected to minimize swarm cost and maximize swarm performance. This work is motivated by the desire to engineer a swarm to collect harmful algae from water. In this foraging application, the robots are not required to deposit algae in a central location. Stepping through the design process for this application has exposed several of the knowledge gaps addressed herein.

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Notes

  1. MATLAB script available at https://www.mathworks.com/matlabcentral/fileexchange/65598-collision-detection or under Zenodo https://doi.org/10.5281/zenodo.1323875.

References

  • Bjerknes, J. D., & Winfield, A. F. T. (2013). On fault tolerance and scalability of swarms. In A. Martinoli, et al. (Eds.), Distributed autonomous robotic systems, Springer tracts in advanced robotics (Vol. 83, pp. 431–444). Berlin: Springer.

    Chapter  Google Scholar 

  • Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013). Swarm robotics: A review from the swarm engineering perspective. Swarm Intelligence, 7(1), 1–41.

    Article  Google Scholar 

  • Christensen, A. L., O’Grady, R., Birattari, M., & Dorigo, M. (2007). Exogenous fault detection in a collective robotic task. In F. Almeida e Costa, L. M. Rocha, E. Costa, I. Harvey, & A. Coutinho (Eds.), Advances in artificial life. ECAL 2007. LNCS 4648 (pp. 555–564). Berlin: Springer.

    Google Scholar 

  • Christensen, A. L., O’Grady, R., & Dorigo, M. (2009). From fireflies to fault-tolerant swarms of robots. IEEE Transactions on Evolutionary Computation, 13(4), 754–766.

    Article  Google Scholar 

  • Dawson, S., Wellman, B. L., & Anderson, M. (2011). Categorizing interference in real robot experiments. In Proceedings of the 2011 IEEE international conference on systems, man, and cybernetics, IEEE (pp. 3561–3565).

  • Dimidov, C., Oriolo, G., & Trianni, V. (2016). Random walks in swarm robotics: An experiment with Kilobots. In M. Dorigo, et al. (Eds.), 10th International conference on swarm intelligence, ANTS 2016, LNCS 9882 (pp. 185–196). Springer.

  • Duarte, M., Costa, V., Gomes, J. C., Rodrigues, T., Silva, F., Oliveira, S. M., et al. (2016). Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE, 11(3), e0151834.

    Article  Google Scholar 

  • Guerrero, J., Oliver, G., & Valero, O. (2017). Multi-robot coalitions formation with deadlines: Complexity analysis and solutions. PLoS ONE, 12(1), 1–27.

    Google Scholar 

  • Gunther, N. J. (1993). A simple capacity model of massively parallel transaction systems. In CMG national conference (pp. 1035–1044).

  • Hamann, H. (2012). Towards swarm calculus: Universal properties. In M. Dorigo, et al. (Eds.), 8th International conference on swarm intelligence, ANTS 2012, LNCS 7461 (pp. 168–179). Springer.

  • Hamann, H. (2018a). Superlinear scalability in parallel computing and multi-robot systems: Shared resources, collaboration, and network topology. In M. Berekovic, R. Buchty, H. Hamann, D. Koch, & T. Pionteck (Eds.), Architecture of computing systems ARCS 2018. ARCS 2018. Lecture notes in computer science, LNCS (Vol. 10793 , pp. 31–42). Cham: Springer.

  • Hamann, H. (2018b). Swarm robotics: A formal approach. Cham: Springer.

    Book  Google Scholar 

  • Hecker, J. P., & Moses, M. E. (2015). Beyond pheromones: Evolving error-tolerant, flexible, and scalable ant-inspired robot swarms. Swarm Intelligence, 9(1), 43–70.

    Google Scholar 

  • Lerman, K., & Galstyan, A. (2001). Mathematical model of foraging in a group of robots: Effect of interference. Autonomous Robots, 13(2), 127–141.

    Article  MATH  Google Scholar 

  • Mataric, M. J. (1992). Controlling a mobile robot herd: Theory and practice. Technical report, Applications of artificial intelligence to real-world autonomous mobile robots. Papers from the 1992 Fall Symposium, Technical Report FS-92.

  • McLurkin, J., Lynch, A. J., Rixner, S., Barr, T. W., Chou, A., Foster, K., et al. (2012). A low-cost multi-robot system for research, teaching, and outreach. In A. Martinoli, et al. (Eds.), Distributed autonomous robotic systems, DARS2010, Springer tracts in advanced robotics (Vol. 83, pp. 597–609). Berlin: Springer.

  • Nurzaman, S. G., Matsumoto, Y., Nakamura, Y., Koizumi, S., & Ishiguro, H. (2010). Biologically inspired adaptive mobile robot search with and without gradient sensing. In IROS 2009. Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, 2009, IEEE (pp. 142–147).

  • Pavone, M., Arsie, A., Frazzoli, E., & Bullo, F. (2011). Equitable partitioning policies for mobile robotic networks. IEEE Transactions of Automatic Control, 56(8), 1834–1848.

    Article  MathSciNet  MATH  Google Scholar 

  • Rosenfeld, A., Kaminka, G. A., & Kraus, S. (2006). A study of scalability properties in robotic teams. In P. Scerri, R. Vincent, & R. Mailler (Eds.), Coordination of large-scale multiagent systems (pp. 27–51). Berlin: Springer.

    Chapter  Google Scholar 

  • Rubenstein, M., Cabrera, A., Werfel, J., Habibi, G., McLurkin, J., & Nagpal, R. (2013). Collective transport of complex objects by simple robots. In Proceedings of the 2013 international conference on autonomous agents and multi-agent systems (pp. 47–54).

  • Rubenstein, M., Ahler, C., Hoff, N., Cabrera, A., & Nagpal, R. (2014). Kilobot: A low cost robot with scalable operations designed for collective behaviors. Robotics and Autonomous Systems, 62(7), 966–975.

    Article  Google Scholar 

  • Scharf, I., Filin, I., & Ovadia, O. (2008). An experimental design and a statistical analysis separating interference from exploitation competition. Population Ecology, 50(3), 319–324.

    Article  Google Scholar 

  • Scheutz, M. (2006). A scalable, robust, ultra-low complexity agent swarm for area coverage and interception tasks. In Proceedings of the IEEE international symposium on intelligent control, 2006, IEEE (pp. 1258–1263).

  • Schroeder, A., Subramanian, R., Kumar, M., & Trease, B. (2017). Efficient spatial coverage by a robot swarm based on an Ant foraging model and the Lévy distribution. Swarm Intelligence, 11(1), 1–31.

    Article  Google Scholar 

  • Soriano Marcolino, L., Tavares dos Passos, Y., Fonseca de Souza, Á. A., dos Santos Rodrigues, A., & Chaimowicz, L. (2017). Avoiding target congestion on the navigation of robotic swarms. Autonomous Robots, 41(6), 1297–1320.

    Article  Google Scholar 

  • Sutantyo, D. K., Kernbach, S., Nepomnyashchikh, V. A., & Levi, P. (2010). Multi-robot searching algorithm using Lévy flight and artificial potential field. In Proceedings of the IEEE international workshop on safety, security, and rescue robotics, IEEE (pp. 1–6).

  • Tarapore, D., Christensen, A. L., & Timmis, J. (2017). Generic, scalable and decentralized fault detection for robot swarms. PLoS ONE, 12(8), 1–30.

    Article  Google Scholar 

  • Tribe, M. A., & Alpine, R. L. (1986). Scale economies and the “0.6 rule”. Engineering Costs and Production Economics, 10(1), 271–278.

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Toledo, Toledo, USA

    Adam Schroeder, Brian Trease & Alessandro Arsie

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  1. Adam Schroeder
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  2. Brian Trease
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  3. Alessandro Arsie
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Correspondence to Adam Schroeder.

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Schroeder, A., Trease, B. & Arsie, A. Balancing robot swarm cost and interference effects by varying robot quantity and size. Swarm Intell 13, 1–19 (2019). https://doi.org/10.1007/s11721-018-0161-1

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  • DOI: https://doi.org/10.1007/s11721-018-0161-1

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