Cooperative Coevolution of Control for a Real Multirobot System

  • Jorge Gomes
  • Miguel Duarte
  • Pedro Mariano
  • Anders Lyhne Christensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)

Abstract

The potential of cooperative coevolutionary algorithms (CCEAs) as a tool for evolving control for heterogeneous multirobot teams has been shown in several previous works. The vast majority of these works have, however, been confined to simulation-based experiments. In this paper, we present one of the first demonstrations of a real multirobot system, operating outside laboratory conditions, with controllers synthesised by CCEAs. We evolve control for an aquatic multirobot system that has to perform a cooperative predator-prey pursuit task. The evolved controllers are transferred to real hardware, and their performance is assessed in a non-controlled outdoor environment. Two approaches are used to evolve control: a standard fitness-driven CCEA, and novelty-driven coevolution. We find that both approaches are able to evolve teams that transfer successfully to the real robots. Novelty-driven coevolution is able to evolve a broad range of successful team behaviours, which we test on the real multirobot system.

Keywords

Cooperative coevolution Evolutionary robotics Novelty search Reality gap Heterogeneous multirobot systems 

References

  1. 1.
    Costa, V., Duarte, M., Rodrigues, T., Oliveira, S.M., Christensen, A.L.: Design and development of an inexpensive aquatic swarm robotics system. In: OCEANS 2016-Shanghai, pp. 1–7. IEEE Press (2016)Google Scholar
  2. 2.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  3. 3.
    Duarte, M., Costa, V., Gomes, J., Rodrigues, T., Silva, F., Oliveira, S.M., Christensen, A.L.: Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE 11(3), e0151834 (2016)CrossRefGoogle Scholar
  4. 4.
    Gomes, J., Mariano, P., Christensen, A.L.: Avoiding convergence in cooperative coevolution with novelty search. In: International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1149–1156. IFAAMAS (2014)Google Scholar
  5. 5.
    Gomes, J., Mariano, P., Christensen, A.L.: Cooperative coevolution of morphologically heterogeneous robots. In: European Conference on Artificial Life, pp. 312–319. MIT Press (2015)Google Scholar
  6. 6.
    Gomes, J., Mariano, P., Christensen, A.L.: Devising effective novelty search algorithms: a comprehensive empirical study. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 943–950. ACM Press (2015)Google Scholar
  7. 7.
    Gomes, J., Mariano, P., Christensen, A.L.: Novelty-driven cooperative coevolution. Evol. Comput. (2016, in press)Google Scholar
  8. 8.
    Jakobi, N.: Evolutionary robotics and the radical envelope-of-noise hypothesis. Adapt. Behav. 6(2), 325–368 (1997)CrossRefGoogle Scholar
  9. 9.
    Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)CrossRefGoogle Scholar
  10. 10.
    Nitschke, G.: Designing emergent cooperation: a pursuit-evasion game case study. Artif. Life Robot. 9(4), 222–233 (2005)CrossRefGoogle Scholar
  11. 11.
    Nitschke, G.S., Eiben, A.E., Schut, M.C.: Evolving team behaviors with specialization. Genet. Program. Evolvable Mach. 13(4), 493–536 (2012)CrossRefGoogle Scholar
  12. 12.
    Nitschke, G.S., Schut, M.C., Eiben, A.E.: Collective neuro-evolution for evolving specialized sensor resolutions in a multi-rover task. Evol. Intell. 3(1), 13–29 (2010)CrossRefGoogle Scholar
  13. 13.
    Nitschke, G.S., Schut, M.C., Eiben, A.E.: Evolving behavioral specialization in robot teams to solve a collective construction task. Swarm Evol. Comput. 2, 25–38 (2012)CrossRefGoogle Scholar
  14. 14.
    Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. Auton. Agent. Multi-Agent Syst. 11(3), 387–434 (2005)CrossRefGoogle Scholar
  15. 15.
    Panait, L., Luke, S., Wiegand, R.P.: Biasing coevolutionary search for optimal multiagent behaviors. IEEE Trans. Evol. Comput. 10(6), 629–645 (2006)CrossRefGoogle Scholar
  16. 16.
    Popovici, E., Bucci, A., Wiegand, R.P., De Jong, E.D.: Coevolutionary principles. In: Rozenberg, G., Back, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 987–1033. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)CrossRefGoogle Scholar
  18. 18.
    Potter, M.A., Meeden, L.A., Schultz, A.C.: Heterogeneity in the coevolved behaviors of mobile robots: the emergence of specialists. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 1337–1343. Morgan Kaufmann (2001)Google Scholar
  19. 19.
    Silva, F., Duarte, M., Correia, L., Oliveira, S.M., Christensen, A.L.: Open issues in evolutionary robotics. Evol. Comput. 24(2), 205–236 (2016)CrossRefGoogle Scholar
  20. 20.
    Stanley, K., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)CrossRefGoogle Scholar
  21. 21.
    Wiegand, R.P., Liles, W.C., De Jong, K.A.: Analyzing cooperative coevolution with evolutionary game theory. In: Congress on Evolutionary Computation (CEC), vol. 2, pp. 1600–1605. IEEE Press (2002)Google Scholar
  22. 22.
    Yong, C.H., Miikkulainen, R.: Coevolution of role-based cooperation in multiagent systems. IEEE Trans. Auton. Ment. Dev. 1(3), 170–186 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jorge Gomes
    • 1
    • 2
    • 3
  • Miguel Duarte
    • 1
    • 2
    • 4
  • Pedro Mariano
    • 3
  • Anders Lyhne Christensen
    • 1
    • 2
    • 4
  1. 1.BioMachines LabLisbonPortugal
  2. 2.Instituto de TelecomunicaçõesLisbonPortugal
  3. 3.BioISIFaculdade de Ciências da Universidade de LisboaLisbonPortugal
  4. 4.Instituto Universitário de Lisboa (ISCTE-IUL)LisbonPortugal

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