Using Virtual Embryogenesis in Multi-robot Organisms

  • Markus Dauschan
  • Ronald Thenius
  • Thomas Schmickl
  • Karl Crailsheim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6943)


We introduce a novel method to apply a pluripotent process of virtual embryogenesis (VE) on modular robotics. The VE software is able to perform simulations on recent computer hardware and can be used to control robotic hardware. Each robot controlled by our VE-software mimics a cell within a virtual embryogenesis process and is able to signal other robots to dock, thus initiating or advancing the build process of a multi-robot organism. In addition to that, our system can also be used to perform primitive locomotion e.g. wall avoidance behaviour in single robots.


Single Robot Modular Robot Morphogen Gradient Template Pattern Framework Programme Project 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    REPLICATOR: Robotic Evolutionary Self-Programming and Self-Assembling Organisms, 7th Framework Programme Project No FP7-ICT-2007.2.1. European Communities (2008-2012)Google Scholar
  2. 2.
    SYMBRION: Symbiotic Evolutionary Robot Organisms, 7th Framework Programme Project No FP7-ICT-2007.8.2. European Communities (2008-2012)Google Scholar
  3. 3.
    Carroll, S.B.: Endless forms: the evolution of gene regulation and morphological diversity. Cell 101(6), 577–580 (2000),
  4. 4.
    Carroll, S.B.: Endless Forms Most Beautiful: The New Science of Evo Devo. W. W. Norton (April 2006)Google Scholar
  5. 5.
    Crick, F.: Diffusion in embryogenesis. Nature 225(5231), 420–422 (1970), CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Tuci, E., Groß, R., Trianni, V., Labella, T.H., Nouyan, S., Ampatzis, C., Deneubourg, J.L., Baldassarre, G., Nolfi, S., Mondada, F., Floreano, D., Gambardella, L.M.: The SWARM-BOTS project, pp. 31–44 (2005),
  7. 7.
    Ephrussi, A., Johnston, D.S.: Seeing is believing - the bicoid morphogen gradient matures. Cell 116(2), 143–152 (2004)CrossRefGoogle Scholar
  8. 8.
    GNU Project: GCC, the GNU compiler collection, version 4.4.3,
  9. 9.
    Gurdon, J.B., Bourillot, P.Y.: Morphogen gradient interpretation. Nature 413(6858), 797–803 (2001), CrossRefGoogle Scholar
  10. 10.
    Intel Corporation: Intel c++ compiler, version 12.0.2,
  11. 11.
    Jin, Y., Schramm, L., Sendhoff, B.: A gene regulatory model for the development of primitive nervous systems. In: INNS-NNN Symposia on Modeling the Brain and Nervous Systems. Springer, Heidelberg (2008)Google Scholar
  12. 12.
    Kernbach, S., Schmickl, T., Hamann, H., Stradner, J., Schwarzer, C., Schlachter, F., Winfield, A., Matthias, R.: Adaptive action selection mechanisms for evolutionary multimodular robotics. In: Proc. of the 12th International Conference on the Synthesis and Simulation of Living Systems (AlifeXII). MIT Press, Denmark (2010)Google Scholar
  13. 13.
    Kernbach, S., Scholz, O., Harada, K., Popesku, S., Liedke, J., Raja, H., Liu, W., Caparrelli, F., Jemai, J., Havlik, J., Meister, E., Levi, P.: Multi-robot organisms: State of the art. In: ICRA 2010, Workshop on Modular Robots: State of the Art, Anchorage (2010)Google Scholar
  14. 14.
    Knuth, D.E.: The art of computer programming, sorting and searching, 2nd edn. Addison Wesley Longman Publishing Co., Inc., USA (1998)zbMATHGoogle Scholar
  15. 15.
    Kornienko, S., Kornienko, O., Nagarathinam, A., Levi, P.: From real robot swarm to evolutionary multi-robot organism. In: 2007 IEEE Congress on Evolutionary Computation, pp. 1483–1490 (2007)Google Scholar
  16. 16.
    Levi, P., Kernbach, S. (eds.): Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  17. 17.
    Müller, G.B.: Evo-devo: Extending the evolutionary synthesis. Nature Reviews Genetics 8, 943–949 (2007)CrossRefGoogle Scholar
  18. 18.
    Roggen, D., Federici, D., Floreano, D.: Evolutionary morphogenesis for multi-cellular systems. Genetic Programming and Evolvable Machines 8, 61–96 (2007)CrossRefGoogle Scholar
  19. 19.
    Rubenstein, M., Shen, W.M.: Scalable self-assembly and self-repair in a collective of robots. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, Missouri, USA (October 2009)Google Scholar
  20. 20.
    Rubenstein, M., Shen, W.M.: Automatic scalable size selection for the shape of a distributed robotic collective. In: IROS (October 2010)Google Scholar
  21. 21.
    Rumbaugh, J., Blaha, M., Premerlani, W., Eddy, F., Lorenson, W.: Object-Oriented Modeling and Design, 1st edn. Prentice-Hall, Englewood Cliffs (1991)zbMATHGoogle Scholar
  22. 22.
    Schramm, L., Jin, Y., Sendhoff, B.: Evolutionary synthesis and analysis of a gene regulatory network for dynamically stable growth and regeneration. BioSystems (2010) (submitted)Google Scholar
  23. 23.
    Shvartsman, S.Y., Coppey, M., Berezhkovskii, A.M.: Dynamics of maternal morphogen gradients in drosophila. Current Opinion in Genetics & Development 18(4), 342–347 (2008)CrossRefGoogle Scholar
  24. 24.
    Stroustrup, B.: The C++ Programming Language, 3rd edn. Addison-Wesley Longman Publishing Co., Inc., USA (2000)zbMATHGoogle Scholar
  25. 25.
    Thenius, R., Bodi, M., Schmickl, T., Crailsheim, K.: Growth of structured artificial neural networks by virtual embryogenesis. In: Kampis, G., Karsai, I., Szathmáry, E. (eds.) ECAL 2009, Part II. LNCS, vol. 5778, pp. 118–125. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  26. 26.
    Thenius, R., Bodi, M., Schmickl, T., Crailsheim, K.: Using virtual embryogenesis for structuring controllers. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds.) ICARIS 2010. LNCS, vol. 6209, pp. 312–313. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  27. 27.
    Thenius, R., Dauschan, M., Schmickl, T., Crailsheim, K.: Regenerative abilities in modular robots using virtual embryogenesis. In: Bouchachia, A. (ed.) ICAIS 2011. LNCS (LNAI), vol. 6943, pp. 238–247. Springer, Heidelberg (2011)Google Scholar
  28. 28.
    Thenius, R., Schmickl, T., Crailsheim, K.: Novel concept of modelling embryology for structuring an artificial neural network. In: Troch, I., Breitenecker, F. (eds.) Proc. of the MATHMOD (2009)Google Scholar
  29. 29.
    Turing, A.M.: The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 237(641), 37–72 (1952)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Ungerer, G., Dionne, J., Durant, M.: uClinux: Embedded Linux/Microcontroller Project (September 2008),
  31. 31.
    Weatherbee, S.D., Carroll, S.B.: Selector genes and limb identity in arthropods and vertebrates. Cell 97(3), 283–286 (1999)CrossRefGoogle Scholar
  32. 32.
    Wilensky, U.: Netlogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University Evanston, IL (1999),
  33. 33.
    Wolpert, L.: Positional information and the spatial pattern of cellular differentiation. Journal of Theoretical Biology 25(1), 1–47 (1969)CrossRefGoogle Scholar
  34. 34.
    Wolpert, L.: Positional information revisited. Development 107, 3–12 (1989)Google Scholar
  35. 35.
    Wolpert, L.: One hundred years of positional information. Trends Genet 12(9), 359–364 (1996),

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Markus Dauschan
    • 1
  • Ronald Thenius
    • 1
  • Thomas Schmickl
    • 1
  • Karl Crailsheim
    • 1
  1. 1.Artificial Life Laboratory of the Department of ZoologyKarl-Franzens University GrazGrazAustria

Personalised recommendations