Advertisement

Autonomous Robots

, Volume 21, Issue 3, pp 243–254 | Cite as

Flying over the reality gap: From simulated to real indoor airships

  • Jean-Christophe ZuffereyEmail author
  • Alexis Guanella
  • Antoine Beyeler
  • Dario Floreano
Article

Abstract

Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approach.

Keywords

Indoor Airship Auton Robot Evolutionary Robotic Physical Robot Rate Gyro 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bermúdez i Badia, S., Pyk, P., and Verschure, P. 2005. A biologically based flight control system for a blimp-based uav. In IEEE International Conference on Robotics and Automation.Google Scholar
  2. Cliff, D. and Miller, G.F. 1996. Co-evolution of pursuit and evasion ii: Simulation methods and results. In P. Maes, M. Mataric, J.A. Meyer, J. Pollack, H. Roitblat, and S. Wilson (eds), From Animals to Animats IV: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press-Bradford Books, Cambridge, MA.Google Scholar
  3. da Silva Metelo, F.M. and Garcia Campos, L.R. 2003. Vision based control of an autonomous blimp. Technical report.Google Scholar
  4. Fossen, T.I. 1995. Guidance and Control of Ocean Vehicles. Wiley, New York.Google Scholar
  5. Iida, F. 2003. Biologically inspired visual odometer for navigation of a flying robot. Robotics and Autonomous Systems, 44:201–208.Google Scholar
  6. Jakobi, N., Husbands, P., and Harvey, I. 1995. Noise and the reality gap: The use of simulation in evolutionary robotics. Lecture Notes in Computer Science, 929:704–720.Google Scholar
  7. Khoury, G.A. and Gillet, J.D. 1999. Airship Technology. Cambridge University Press, London.Google Scholar
  8. Lamb, H. 1932. Hydrodynamics. Cambridge University Press, London.Google Scholar
  9. Melhuish, C. and Welsby, J. 2002. Gradient ascent with a group of minimalist real robots: Implementing secondary swarming. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.Google Scholar
  10. Michel, O. 2004. Webots: Professional mobile robot simulation. International Journal of Advanced Robotic Systems, 1(1):39–42.Google Scholar
  11. Munk, M.M. 1934. Fluid mechanics, second part. In W.F. Durand (ed.), Aerodynamic Theory I. Julius Springer, pp. 224–304.Google Scholar
  12. Munk, M.M. 1936. Aerodynamics of airships. In W.F. Durand (ed.), Aerodynamic Theory VI. Julius Springer, pp. 32–48.Google Scholar
  13. Nicoud, J.D. and Zufferey, J.-C. 2002. Toward indoor flying robots. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 787–792.Google Scholar
  14. Nolfi, S. and Floreano, D. 2000. Evolutionary Robotics. The Biology, Intelligence, and Technology of Self-organizing Machines. MIT Press, Cambridge, MA.Google Scholar
  15. Planta, C., Conradt, J., Jencik, A., and Verschure, P. 2002. A neural model of the fly visual system applied to navigational tasks. In Proceedings of the International Conference on Artificial Neural Networks (ICANN).Google Scholar
  16. Sagatun, S.I. and Fossen, T. 1991. Lagrangian formulation of underwater vehicles’ dynamics. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp 1029–1034.Google Scholar
  17. Schlichting, H. and Truckenbrodt, E. 2001. Aerodynamik des Flugzeuges. Springer, Berlin.Google Scholar
  18. Urzelai, J. and Floreano, D. 2001. Evolution of adaptive synapses: robots with fast adaptive behavior in new environments. Evolutionary Computation, 9:495–524.Google Scholar
  19. van der Zwaan, S., Bernardino, A., and Santos-Victor, J. 2002. Visual station keeping for floating robots in unstructured environments. Robotics and Autonomous Systems, 39:145–155.Google Scholar
  20. Zhang, H. and Ostrowski, J. 1998. Visual servoing with dynamics: Control of an unmanned blimp. Technical report.Google Scholar
  21. Zufferey, J.-C. 2005. Bio-inspired Vision-based Flying Robots. PhD thesis, Swiss Federal Institute of Technology in Lausanne (EPFL).Google Scholar
  22. Zufferey, J.-C. and Floreano, D. 2006. Fly-inspired visual steering of an ultralight indoor aircraft. IEEE Transactions on Robotics, 22(1):137–146.Google Scholar
  23. Zufferey, J.-C., Floreano, D., van Leeuwen, M., and Merenda, T. 2002. Evolving vision-based flying robots. In Blthoff, Lee, Poggio, and Wallraven (eds.), Biologically Motivated Computer Vision: Second International Workshop, BMCV 2002, Tbingen, Germany. Lecture Notes in Computer Science, Vol. 2525. Springer-Verlag, pp. 592–600.Google Scholar
  24. Zufferey, J.-C., Beyeler, A., and Floreano, D. 2003. Vision-based navigation from wheels to wings. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2968–2973.Google Scholar
  25. Zufferey, J.-C., Klaptocz, A., Beyeler, A., Nicoud, J.D., and Floreano, D. 2006. A 10-gram microflyer for vision-based indoor navigation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • Jean-Christophe Zufferey
    • 1
    Email author
  • Alexis Guanella
    • 1
  • Antoine Beyeler
    • 1
  • Dario Floreano
    • 1
  1. 1.Laboratory of Intelligent SystemsEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland

Personalised recommendations