Implicit and Robust Evaluation Methodology for the Evolutionary Design of Feasible Robots

  • Andrés Faíña
  • Felix Orjales
  • Francisco Bellas
  • Richard J. Duro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7931)

Abstract

This paper deals with the evolutionary design of feasible and manufacturable robots. Specifically, here we address the problem of defining a methodology for the evaluation of candidate robots that guides the evolution of morphology and control towards a valid design when transferred to reality. We aim to minimize the explicit knowledge introduced by the designer in the fitness function. As a consequence of this higher flexibility, we must include elements to ensure that the obtained robots are feasible. To do it, we propose an extension of the principles proposed by classical authors from traditional evolutionary robotics to brain-body evolution. In this paper we describe this methodology and show its application in a benchmark example of evolutionary robot design. To this end, previously presented elements like the structural definition of the robotic units, the encoding of the morphology and control and the specific evolutionary algorithm applied are also briefly described.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sims, K.: Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 15–22. ACM (1994)Google Scholar
  2. 2.
    Komosinski, M.: The framsticks system: versatile simulator of 3d agents and their evolution. Kybernetes 32, 156–173 (2003)CrossRefGoogle Scholar
  3. 3.
    Yim, M., Shen, W., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., Chirikjian, G.: Modular self-reconfigurable robot systems. IEEE Robotics & Automation Magazine 14, 43–52 (2007)CrossRefGoogle Scholar
  4. 4.
    Marbach, D., Ijspeert, A.: Online optimization of modular robot locomotion. In: IEEE International Conference on Mechatronics and Automation, vol. 1, pp. 248–253. IEEE (2005)Google Scholar
  5. 5.
    Rommerman, M., Kuhn, D., Kirchner, F.: Robot design for space missions using evolutionary computation. In: IEEE Congress on Evolutionary Computation, pp. 2098–2105 (2009)Google Scholar
  6. 6.
    Farritor, S., Dubowsky, S.: On modular design of field robotic systems. Autonomous Robots 10(1), 57–65 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Jakobi, N.: Evolutionary robotics and the radical envelope-of-noise hypothesis. Adaptive Behavior 6(2), 325–368 (1997)CrossRefGoogle Scholar
  8. 8.
    Faiña, A., Orjales, F., Bellas, F., Duro, R.J.: First steps towards a heterogeneous modular robotic architecture for intelligent industrial operation. In: Workshop on Reconfigurable Modular Robotics, IROS, San Francisco (2011)Google Scholar
  9. 9.
    Faíña, A., Bellas, F., Souto, D., Duro, R.J.: Towards an evolutionary design of modular robots for industry. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2011, Part I. LNCS, vol. 6686, pp. 50–59. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2149–2154 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrés Faíña
    • 1
  • Felix Orjales
    • 1
  • Francisco Bellas
    • 1
  • Richard J. Duro
    • 1
  1. 1.Integrated Group for Engineering ResearchUniversidade da CoruñaFerrolSpain

Personalised recommendations