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2018 | OriginalPaper | Buchkapitel

Hybrid Control of Swarms for Resource Selection

verfasst von : Marco Trabattoni, Gabriele Valentini, Marco Dorigo

Erschienen in: Swarm Intelligence

Verlag: Springer International Publishing

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Abstract

The design and control of swarm robotics systems generally relies on either a fully self-organizing approach or a completely centralized one. Self-organization is leveraged to obtain systems that are scalable, flexible and fault-tolerant at the cost of reduced controllability and performance. Centralized systems, instead, are easier to design and generally perform better than self-organizing ones but come with the risks associated with a single point of failure. We investigate a hybrid approach to the control of robot swarms in which a part of the swarm acts as a control entity, estimating global information, to influence the remaining robots in the swarm and increase performance. We investigate this concept by implementing a consensus achievement system tasked with choosing the best of two resource locations. We show (i) how estimating and leveraging global information impacts the decision-making process and (ii) how the proposed hybrid approach improves performance over a fully self-organizing approach.

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Vorheriges Kapitel Guidance of Swarms with Agents Having Bearing Only and Limited Visibility Sensors

Nächstes Kapitel Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning

In the paper we use the terms ‘robot opinion’ and ‘robot preference’ interchangeably.

A swarm is connected if a path of communicating robots can be found between any two robots in the swarm.

Antonelli, G., Chiaverini, S.: Kinematic control of platoons of autonomous vehicles. IEEE Trans. Robot. 22(6), 1285–1292 (2006)CrossRef

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)CrossRef

Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)MATH

Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRef

Brutschy, A., Scheidler, A., Ferrante, E., Dorigo, M., Birattari, M.: “Can ants inspire robots?” Self-organized decision making in robotic swarms. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4272–4273. IEEE Press (2012)

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)CrossRef

Campo, A., Gutiérrez, Á., Nouyan, S., Pinciroli, C., Longchamp, V., Garnier, S., Dorigo, M.: Artificial pheromone for path selection by a foraging swarm of robots. Biol. Cybern. 103(5), 339–352 (2010)CrossRef

De La Cruz, C., Carelli, R.: Dynamic modeling and centralized formation control of mobile robots. In: IECON 2006–32nd Annual Conference on IEEE Industrial Electronics, pp. 3880–3885. IEEE (2006)

Dorigo, M., Birattari, M., Brambilla, M.: Swarm robotics. Scholarpedia 9(1), 1463 (2014)CrossRef

10.

Ferrante, E., Turgut, A.E., Huepe, C., Stranieri, A., Pinciroli, C., Dorigo, M.: Self-organized flocking with a mobile robot swarm: a novel motion control method. Adapt. Behav. 20(6), 460–477 (2012)CrossRef

11.

Francesca, G., Brambilla, M., Brutschy, A., Trianni, V., Birattari, M.: AutoMoDe: a novel approach to the automatic design of control software for robot swarms. Swarm Intell. 8(2), 89–112 (2014)CrossRef

12.

Francesca, G., Brambilla, M., Trianni, V., Dorigo, M., Birattari, M.: Analysing an evolved robotic behaviour using a biological model of collegial decision making. In: Ziemke, T., Balkenius, C., Hallam, J. (eds.) SAB 2012. LNCS (LNAI), vol. 7426, pp. 381–390. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33093-3_38CrossRef

13.

Gutiérrez, Á., Campo, A., Monasterio-Huelin, F., Magdalena, L., Dorigo, M.: Collective decision-making based on social odometry. Neural Comput. Appl. 19(6), 807–823 (2010)CrossRef

14.

Hausman, K., Müller, J., Hariharan, A., Ayanian, N., Sukhatme, G.S.: Cooperative multi-robot control for target tracking with onboard sensing. Int. J. Robot. Res. 34(13), 1660–1677 (2015)CrossRef

15.

King, J., Pretty, R.K., Gosine, R.G.: Coordinated execution of tasks in a multiagent environment. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 33(5), 615–619 (2003)CrossRef

16.

Lambiotte, R., Saramäki, J., Blondel, V.D.: Dynamics of latent voters. Phys. Rev. E 79, 046107 (2009)MathSciNetCrossRef

17.

Lindsey, Q., Mellinger, D., Kumar, V.: Construction with quadrotor teams. Auton. Robot. 33(3), 323–336 (2012)CrossRef

18.

Mathews, N., Christensen, A.L., O’Grady, R., Mondada, F., Dorigo, M.: Mergeable nervous systems for robots. Nat. Commun. 8(1), 439 (2017)CrossRef

19.

Montes de Oca, M.A., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., Dorigo, M.: Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making. Swarm Intell. 5(3–4), 305–327 (2011)CrossRef

20.

Nagpal, R., Shrobe, H., Bachrach, J.: Organizing a global coordinate system from local information on an ad hoc sensor network. In: Zhao, F., Guibas, L. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 333–348. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36978-3_22CrossRefMATH

21.

Nouyan, S., Campo, A., Dorigo, M.: Path formation in a robot swarm: self-organized strategies to find your way home. Swarm Intell. 2(1), 1–23 (2008)CrossRef

22.

Nouyan, S., Dorigo, M.: Chain based path formation in swarms of robots. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 120–131. Springer, Heidelberg (2006). https://doi.org/10.1007/11839088_11CrossRef

23.

Nouyan, S., Groß, R., Bonani, M., Mondada, F., Dorigo, M.: Teamwork in self-organized robot colonies. IEEE Trans. Evol. Comput. 13(4), 695–711 (2009). https://doi.org/10.1109/TEVC.2008.2011746CrossRef

24.

Nowak, M.A.: Five rules for the evolution of cooperation. Science 314(5805), 1560–1563 (2006)CrossRef

25.

Parker, C.A.C., Zhang, H.: Cooperative decision-making in decentralized multiple-robot systems: the best-of-N problem. IEEE/ASME Trans. Mechatron. 14(2), 240–251 (2009)CrossRef

26.

Pinciroli, C., Talamali, M.S., Reina, A., Marshall, J.A.R., Trianni, V.: Simulating Kilobots within ARGoS: models and experimental validation. In: Dorigo, M. (ed.) ANTS 2018. LNCS, vol. 11172, pp. 176–187. Springer, Heidelberg (2018)

27.

Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)CrossRef

28.

Preiss, J.A., Honig, W., Sukhatme, G.S., Ayanian, N.: Crazyswarm: a large nano-quadcopter swarm. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3299–3304. IEEE (2017)

29.

Reina, A., Dorigo, M., Trianni, V.: Towards a cognitive design pattern for collective decision-making. In: Dorigo, M., et al. (eds.) ANTS 2014. LNCS, vol. 8667, pp. 194–205. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09952-1_17CrossRef

30.

Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., Trianni, V.: A design pattern for decentralised decision making. PLoS One 10(10), e0140950 (2015)CrossRef

31.

Rubenstein, M., Cornejo, A., Nagpal, R.: Programmable self-assembly in a thousand-robot swarm. Science 345(6198), 795–799 (2014)CrossRef

32.

Şahin, E., et al.: SWARM-BOT: pattern formation in a swarm of self-assembling mobile robots. In: 2002 IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 1–6. IEEE Press, Piscataway (2002)

33.

Saska, M., Vonásek, V., Chudoba, J., Thomas, J., Loianno, G., Kumar, V.: Swarm distribution and deployment for cooperative surveillance by micro-aerial vehicles. J. Intell. Robot. Syst. 84(1–4), 469–492 (2016)CrossRef

34.

Trianni, V., Groß, R., Labella, T.H., Şahin, E., Dorigo, M.: Evolving aggregation behaviors in a swarm of robots. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 865–874. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39432-7_93CrossRef

35.

Valentini, G.: Achieving Consensus in Robot Swarms: Design and Analysis of Strategies for the Best-of-N Problem. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-53609-5CrossRefMATH

36.

Valentini, G., et al.: Kilogrid: a novel experimental environment for the kilobot robot. Swarm Intell. 12(3), 245–266 (2018)CrossRef

37.

Valentini, G., Brambilla, D., Hamann, H., Dorigo, M.: Collective perception of environmental features in a robot swarm. In: Dorigo, M., et al. (eds.) ANTS 2016. LNCS, vol. 9882, pp. 65–76. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44427-7_6CrossRef

38.

Valentini, G., Ferrante, E., Dorigo, M.: The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives. Front. Robot. AI 4, 9 (2017)CrossRef

39.

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 (2016)CrossRef

40.

Weigel, T., Gutmann, J.S., Dietl, M., Kleiner, A., Nebel, B.: CS Freiburg: coordinating robots for successful soccer playing. IEEE Trans. Robot. Autom. 18(5), 685–699 (2002)CrossRef

41.

Winfield, A.F., Holland, O.: The application of wireless local area network technology to the control of mobile robots. Microprocess. Microsyst. 23(10), 597–607 (2000)CrossRef

Titel: Hybrid Control of Swarms for Resource Selection
verfasst von: Marco Trabattoni
Gabriele Valentini
Marco Dorigo
Verlag: Springer International Publishing
Buch: Swarm Intelligence
Print ISBN: 978-3-030-00532-0

Electronic ISBN: 978-3-030-00533-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00533-7_5

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