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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Videos and logs of the experiments: http://dx.doi.org/10.5281/zenodo.49582.
References
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)
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)
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)
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)
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)
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)
Gomes, J., Mariano, P., Christensen, A.L.: Novelty-driven cooperative coevolution. Evol. Comput. (2016, in press)
Jakobi, N.: Evolutionary robotics and the radical envelope-of-noise hypothesis. Adapt. Behav. 6(2), 325–368 (1997)
Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)
Nitschke, G.: Designing emergent cooperation: a pursuit-evasion game case study. Artif. Life Robot. 9(4), 222–233 (2005)
Nitschke, G.S., Eiben, A.E., Schut, M.C.: Evolving team behaviors with specialization. Genet. Program. Evolvable Mach. 13(4), 493–536 (2012)
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)
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)
Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. Auton. Agent. Multi-Agent Syst. 11(3), 387–434 (2005)
Panait, L., Luke, S., Wiegand, R.P.: Biasing coevolutionary search for optimal multiagent behaviors. IEEE Trans. Evol. Comput. 10(6), 629–645 (2006)
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)
Potter, M.A., De Jong, K.A.: Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8(1), 1–29 (2000)
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)
Silva, F., Duarte, M., Correia, L., Oliveira, S.M., Christensen, A.L.: Open issues in evolutionary robotics. Evol. Comput. 24(2), 205–236 (2016)
Stanley, K., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
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)
Yong, C.H., Miikkulainen, R.: Coevolution of role-based cooperation in multiagent systems. IEEE Trans. Auton. Ment. Dev. 1(3), 170–186 (2009)
Acknowledgements
This work was supported by centre grant (to BioISI, Centre Reference: UID/MULTI/04046/2013), from FCT/MCTES/PIDDAC, Portugal, and by grants SFRH/BD/89095/2012 and UID/EEA/50008/2013.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Gomes, J., Duarte, M., Mariano, P., Christensen, A.L. (2016). Cooperative Coevolution of Control for a Real Multirobot System. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-45823-6_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45822-9
Online ISBN: 978-3-319-45823-6
eBook Packages: Computer ScienceComputer Science (R0)