Advertisement

Evolutionary Learning of Basic Functionalities for Snake-Like Robots

  • Damaso Perez-Moneo Suarez
  • Claudio Rossi
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)

Abstract

The objective of the work presented in this paper is to investigate the optimal learning strategy for snake-like modular robots using a (1+1) Evolutionary Algorithm. We take into account three different but correlated tasks: efficient locomotion, reaching a given point and obstacle avoidance.

Starting from earlier results on locomotion, we have performed three different sets of experiments. In the first, the snake must learn to go to a goal point, and we investigate how employing different fitness functions affect the learning of this task. In the second experiment the snake must learn how to avoid obstacles. In this experiment we test two possible behaviors, called mixed and switched strategies. Finally, in the third set of experiments, we introduce the concept of incremental learning and compare it with the “all-at-once” learning schemes of the first two experiments.

The results of the simulations indicates that the modular robots are able to learn both tasks with the (1+1) Evolutionary Strategy adopted, and that the fitness function that explicitly rewards each of the tasks perform better that the fitness function that takes into account the locomotion task only implicitly, rewarding only the reaching of the target point. We also demonstrate that the obstacles avoidance configuration with only one behaviour (mixed) is better than the configuration with two behaviours (switched) and that incremental learning provide a faster evolution towards good controllers.

Keywords

Evolutionary robotics embodied evolution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Eiben, A., Kernbach, S., Haasdijk, E.: Embodied artificial evolution. Evolutionary Intelligence 5, 261–272 (2012)CrossRefGoogle Scholar
  2. 2.
    Pérez-Moneo Suárez, D., Rossi, C.: Comparison between different evolutive configurations for learning basic functionalities. In: Proceedings of the 2013 European conference on Applications of Evolutionary Computation, EvoApplications 2013 (2013)Google Scholar
  3. 3.
    Pfeifer, R., Bongard, J.C.: How the Body Shapes the Way We Think. A New View of Intelligence. MIT Press (2007)Google Scholar
  4. 4.
    Ijspeert, A.: Central pattern generators for locomotion control in animals and robots: a review. Preprint of Neural Networks 21(4), 642–653 (2008)CrossRefGoogle Scholar
  5. 5.
    Haasdijk, E., Rusu, A.A., Eiben, A.E.: HyperNEAT for locomotion control in modular robots. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds.) ICES 2010. LNCS, vol. 6274, pp. 169–180. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Clune, J., Beckmann, B.E., Ofria, C., Pennock, R.T.: Evolving coordinated quadruped gaits with the hyperNEAT generative encoding. In: Congress on Evolutionary Comutation (CEC) (May 2009)Google Scholar
  7. 7.
    Sims, K.: Evolving virtual creatures. Annual Conference Series, pp. 15–22 (July 1994)Google Scholar
  8. 8.
    Bongard, J.: Morphological change in machines accelerates the evolution of robust behavior. Proceedings of the National Academy of Sciences 108, 1234–1239 (2011)CrossRefGoogle Scholar
  9. 9.
    Becerra, J.A., Bellas, F., Duro, R.J., Lope, J.D.: Snake-like behaviors using macroevolutionary algorithms and modulation based architecturesGoogle Scholar
  10. 10.
    Weel, B., Haasdijk, E., Eiben, A.E.: The emergence of multi-cellular robot organisms through on-line on-board evolution. In: Di Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., de Vega, F.F., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Langdon, W.B., Merelo-Guervós, J.J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Togelius, J., Urquhart, N., Uyar, A.Ş., Yannakakis, G.N. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 124–134. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  11. 11.
    Lal, S., Yamada, K., Endo, S.: Evolving motion control for a modular robot. In: Ellis, R., Allen, T., Petridis, M. (eds.) Applications and Innovations in Intelligent Systems XV, pp. 245–258. Springer, London (2008)CrossRefGoogle Scholar
  12. 12.
    Stanley, K., Bryant, B., Miikkulainen, R.: Real-time neuroevolution in the nero video game. IEEE Transactions on Evolutionary Computation 9(6), 653–668 (2005)CrossRefGoogle Scholar
  13. 13.
    Eiben, A., Haasdijk, E., Bredeche, N.: Embodied, On-line, On-board Evolution for Autonomous Robotics. In: Levi, S.K.E.P. (ed.) Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution. Cognitive Systems Monographs, vol. 7, pp. 361–382. Springer (2010)Google Scholar
  14. 14.
    Bredeche, N., Haasdijk, E., Eiben, A.: On-line, on-board evolution of robot controllers. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds.) EA 2009. LNCS, vol. 5975, pp. 110–121. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Hirose, S., Morishima, A.: Design and control of a mobile robot with an articulated body. Int. J. Robot. Res. 9, 99–114 (1990)CrossRefGoogle Scholar
  16. 16.
    Colorado, J., Barrientos, A., Rossi, C., Garzón, M., Galán, M., del Cerro, J.: Efficient locomotion on non-wheeled snake-like robots. In: Filipe, J., Andrade-Cetto, J., Ferrier, J.-L. (eds.) ICINCO (2), pp. 246–251. INSTICC Press (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Centre for Automation and RoboticsUPM-CSICMadridSpain

Personalised recommendations