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Hybrid Control of Swarms for Resource Selection

  • Marco TrabattoniEmail author
  • Gabriele Valentini
  • Marco Dorigo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11172)

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.

Notes

Acknowledgements

Gabriele Valentini acknowledges support from the NSF grant No. PHY-1505048. Marco Dorigo acknowledges support from the Belgian F.R.S.-FNRS, of which he is a Research Director. The work presented in this paper was partially supported by the FLAG-ERA project RoboCom++ and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 681872).

References

  1. 1.
    Antonelli, G., Chiaverini, S.: Kinematic control of platoons of autonomous vehicles. IEEE Trans. Robot. 22(6), 1285–1292 (2006)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  4. 4.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Dorigo, M., Birattari, M., Brambilla, M.: Swarm robotics. Scholarpedia 9(1), 1463 (2014)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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_38CrossRefGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Lambiotte, R., Saramäki, J., Blondel, V.D.: Dynamics of latent voters. Phys. Rev. E 79, 046107 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lindsey, Q., Mellinger, D., Kumar, V.: Construction with quadrotor teams. Auton. Robot. 33(3), 323–336 (2012)CrossRefGoogle Scholar
  18. 18.
    Mathews, N., Christensen, A.L., O’Grady, R., Mondada, F., Dorigo, M.: Mergeable nervous systems for robots. Nat. Commun. 8(1), 439 (2017)CrossRefGoogle Scholar
  19. 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)CrossRefGoogle Scholar
  20. 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_22CrossRefzbMATHGoogle Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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_11CrossRefGoogle Scholar
  23. 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.2008.2011746CrossRefGoogle Scholar
  24. 24.
    Nowak, M.A.: Five rules for the evolution of cooperation. Science 314(5805), 1560–1563 (2006)CrossRefGoogle Scholar
  25. 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)CrossRefGoogle Scholar
  26. 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)Google Scholar
  27. 27.
    Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012)CrossRefGoogle Scholar
  28. 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)Google Scholar
  29. 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_17CrossRefGoogle Scholar
  30. 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)CrossRefGoogle Scholar
  31. 31.
    Rubenstein, M., Cornejo, A., Nagpal, R.: Programmable self-assembly in a thousand-robot swarm. Science 345(6198), 795–799 (2014)CrossRefGoogle Scholar
  32. 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)Google Scholar
  33. 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)CrossRefGoogle Scholar
  34. 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_93CrossRefGoogle Scholar
  35. 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/10.1007/978-3-319-53609-5CrossRefzbMATHGoogle Scholar
  36. 36.
    Valentini, G., et al.: Kilogrid: a novel experimental environment for the kilobot robot. Swarm Intell. 12(3), 245–266 (2018)CrossRefGoogle Scholar
  37. 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_6CrossRefGoogle Scholar
  38. 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)CrossRefGoogle Scholar
  39. 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)CrossRefGoogle Scholar
  40. 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)CrossRefGoogle Scholar
  41. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.IRIDIAUniversité Libre de BruxellesBrusselsBelgium
  2. 2.School of Earth and Space ExplorationArizona State UniversityTempeUSA

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