The X2 Modular Evolutionary Robotics Platform

  • Kyrre Glette
  • Mats Hovin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6274)


We present a configurable modular robotic system which is suitable for prototyping of various robotic concepts and a corresponding simulator which enables evolution of both morphology and control systems. The modular design has an emphasis on industrial configurations requiring solidity and precision, rather than rapid (self-)reconfiguration and a multitude of building blocks. As an initial validation, a three-axis industrial manipulator design has been constructed. Evolutionary experiments have been conducted using the simulator, resulting in plausible locomotion behavior for two experimental configurations.


Robotic System Inner Core Core Module Evolutionary Search Modular Robot 
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.
    Yim, M., Shen, W., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., Chirikjian, G.: Modular self-reconfigurable robot systems [grand challenges of robotics]. IEEE Robotics & Automation Magazine 14(1), 43–52 (2007)CrossRefGoogle Scholar
  2. 2.
    Zykov, V., Chan, A., Lipson, H.: Molecubes: An open-source modular robotics kit. In: Proc. IROS (2007)Google Scholar
  3. 3.
    Moeckel, R., Jaquier, C., Drapel, K., Upegui, A., Ijspeert, A.: YaMoR and Bluemove – an autonomous modular robot with Bluetooth interface for exploring adaptive locomotion. In: Proceedings CLAWAR 2005, pp. 685–692 (2005)Google Scholar
  4. 4.
    Duff, D., Yim, M., Roufas, K.: Evolution of polybot: A modular reconfigurable robot. In: Proc. of the Harmonic Drive Intl. Symposium, Nagano, Japan (November 2001)Google Scholar
  5. 5.
    Kamimura, A., Kurokawa, H., Yoshida, E., Murata, S., Tomita, K., Kokaji, S.: Automatic locomotion design and experiments for a modular robotic system. IEEE/ASME Transactions on Mechatronics 10(3), 314–325 (2005)CrossRefGoogle Scholar
  6. 6.
    Sproewitz, A., Billard, A., Dillenbourg, P., Ijspeert, A.: Roombots–Mechanical Design of Self-Reconfiguring Modular Robots for Adaptive Furniture. In: Proceedings of the 2009 IEEE international conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 2735–2740 (2009)Google Scholar
  7. 7.
    Universal Robots: UR-6-85-5-A product sheet,
  8. 8.
    Hornby, G., Lipson, H., Pollack, J.: Generative representations for the automated design of modular physical robots. IEEE transactions on Robotics and Automation 19(4), 703–719 (2003)CrossRefGoogle Scholar
  9. 9.
    Marbach, D., Ijspeert, A.: Online optimization of modular robot locomotion. In: 2005 IEEE International Conference Mechatronics and Automation, vol. 1 (2005)Google Scholar
  10. 10.
    Jakobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: The use of simulation in evolutionary robotics. In: Morán, F., Merelo, J.J., Moreno, A., Chacon, P. (eds.) ECAL 1995. LNCS, vol. 929, pp. 704–720. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  11. 11.
    Garder, L.M., Hovin, M.E.: Robot gaits evolved by combining genetic algorithms and binary hill climbing. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1165–1170. ACM, New York (2006)Google Scholar
  12. 12.
    Rieffel, J., Saunders, F., Nadimpalli, S., Zhou, H., Hassoun, S., Rife, J., Trimmer, B.: Evolving soft robotic locomotion in PhysX. In: GECCO 2009: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, pp. 2499–2504. ACM, New York (2009)Google Scholar
  13. 13.
    Glette, K., Hovin, M.: Evolution of Artificial Muscle-Based Robotic Locomotion in PhysX. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (to appear, 2010)Google Scholar
  14. 14.
  15. 15.
    Wall, M.: GAlib: A C++ library of genetic algorithm components,
  16. 16.
    Goldberg, D.: Genetic Algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  17. 17.
    Hornby, G., Fujita, M., Takamura, S., Yamamoto, T., Hanagata, O.: Autonomous evolution of gaits with the Sony quadruped robot. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1297–1304 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kyrre Glette
    • 1
  • Mats Hovin
    • 1
  1. 1.Department of InformaticsUniversity of OsloOsloNorway

Personalised recommendations