Skip to main content

On-Line, On-Board Evolution of Robot Controllers

  • Conference paper
Artifical Evolution (EA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5975))

Abstract

This paper reports on a feasibility study into the evolution of robot controllers during the actual operation of robots (on-line), using only the computational resources within the robots themselves (on-board). We identify the main challenges that these restrictions imply and propose mechanisms to handle them. The resulting algorithm is evaluated in a hybrid system, using the actual robots’ processors interfaced with a simulator that represents the environment. The results show that the proposed algorithm is indeed feasible and the particular problems we encountered during this study give hints for further research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beyer, H.G.: Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering 186(2-4), 239–267 (2000)

    Article  MATH  Google Scholar 

  2. Brooks, R.A.: Intelligence without reason. In: Proceedings of the 12th International Joint Conference on Artificial Intelligence (IJCAI-91), Sydney, Australia, pp. 569–595. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  3. Elfwing, S., Uchibe, E., Doya, K., Christensen, H.: Biologically inspired embodied evolution of survival. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation IEEE Congress on Evolutionary Computation, Edinburgh, UK, September 2-5, vol. 3, pp. 2210–2216. IEEE Press, Los Alamitos (2005)

    Chapter  Google Scholar 

  4. Floreano, D., Husbands, P., Nolfi, S.: Evolutionary robotics. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, pp. 1423–1451. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Montanier, J.M., Bredeche, N.: Embedde evolutionary robotics: The (1+1)-restart-online adaptation algorithm. In: IEEE IROS Workshop on Exploring new horizons in Evolutionary Design of Robots, Evoderob09 (2009)

    Google Scholar 

  6. Nehmzow, U.: Physically embedded genetic algorithm learning in multi-robot scenarios: The pega algorithm. In: Proceedings of The Second International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, Edinburgh, UK, August 2002. Lund University Cognitive Studies, no. 94, LUCS (2002)

    Google Scholar 

  7. Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press/Bradford Books, Cambridge (2000)

    Google Scholar 

  8. Nolfi, S., Parisi, D.: Auto-teaching: networks that develop their own teaching input. In: Free University of Brussels. MIT Press, Cambridge (1993)

    Google Scholar 

  9. Nolfi, S., Parisi, D., Elman, J.L.: Learning and evolution in neural networks. Adapt. Behav. 3(1), 5–28 (1994)

    Article  Google Scholar 

  10. Nordin, P., Banzhaf, W.: An on-line method to evolve behavior and to control a miniature robot in real time with genetic programming. Adaptive Behavior 5(2), 107–140 (1997)

    Article  Google Scholar 

  11. Perez, A.L.F., Bittencourt, G., Roisenberg, M.: Embodied evolution with a new genetic programming variation algorithm. ICAS 0, 118–123 (2008)

    Google Scholar 

  12. Rechenberg, I.: Evolutionstrategie: Optimierung Technisher Systeme nach Prinzipien des Biologischen Evolution. Fromman-Hozlboog Verlag, Stuttgart (1973)

    Google Scholar 

  13. Schwefel, H.P.: Numerical Optimisation of Computer Models. Wiley, New York (1981)

    Google Scholar 

  14. Nolfi, S., Parisi, D., Elman, J.L.: Learning and evolution in neural networks. Adapt. Behav. 3(1), 5–28 (1994)

    Article  Google Scholar 

  15. Usui, Y., Arita, T.: Situated and embodied evolution in collective evolutionary robotics. In: Proceedings of the 8th International Symposium on Artificial Life and Robotics, pp. 212–215 (2003)

    Google Scholar 

  16. Walker, J.H., Garrett, S.M., Wilson, M.S.: The balance between initial training and lifelong adaptation in evolving robot controllers. IEEE Transactions on Systems, Man, and Cybernetics, Part B 36(2), 423–432 (2006)

    Article  Google Scholar 

  17. Watson, R.A., Ficici, S.G., Pollack, J.B.: Embodied evolution: Distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems 39(1), 1–18 (2002)

    Article  Google Scholar 

  18. Wischmann, S., Stamm, K., Wörgötter, F.: Embodied evolution and learning: The neglected timing of maturation. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) ECAL 2007. LNCS (LNAI), vol. 4648, pp. 284–293. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bredeche, N., Haasdijk, E., Eiben, A.E. (2010). On-Line, On-Board Evolution of Robot Controllers. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds) Artifical Evolution. EA 2009. Lecture Notes in Computer Science, vol 5975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14156-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14156-0_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14155-3

  • Online ISBN: 978-3-642-14156-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics