Autonomous Robots

, Volume 33, Issue 1–2, pp 89–102 | Cite as

Adaptive fast open-loop maneuvers for quadrocopters

Article

Abstract

We present a conceptually and computationally lightweight method for the design and iterative learning of fast maneuvers for quadrocopters. We use first-principles, reduced-order models and we do not require nor make an attempt to follow a specific state trajectory—only the initial and the final states of the vehicle are taken into account. We evaluate the adaptation scheme through experiments on quadrocopters in the ETH Flying Machine Arena that perform multi-flips and other high-performance maneuvers.

Keywords

Aerial robotics Aerobatics Learning Policy gradient 

Notes

Acknowledgements

We thank Markus Hehn and Angela Schoellig for their contributions to the Flying Machine Arena and for the fruitful discussions about all things flying and falling.

References

  1. Abbeel, P., Coates, A., & Ng, A. Y. (2010). Autonomous helicopter aerobatics through apprenticeship learning. The International Journal of Robotics Research, 29, 1608–1639. CrossRefGoogle Scholar
  2. Diebel, J. (2006). Representing attitude: Euler angles, unit quaternions, and rotation vectors. Google Scholar
  3. Gerig, M. B. (2008). Modeling, guidance, and control of aerobatic maneuvers of an autonomous helicopter. Ph.D. thesis, ETH Zurich, No. 17805. Google Scholar
  4. Gillula, J. H., Huang, H., Vitus, M. P., & Tomlin, C. J. (2009). Design and analysis of hybrid systems, with applications to robotic aerial vehicles. In International symposium of robotics research, 2009. Google Scholar
  5. Gurdan, D., Stumpf, J., Achtelik, M., Doth, K. M., Hirzinger, G., & Rus, D. (2007). Energy-efficient autonomous four-rotor flying robot controlled at 1 kHz. In IEEE international conference on robotics and automation, 2007 (pp. 361–366). CrossRefGoogle Scholar
  6. Hoffmann, G. M., Huang, H., Waslander, S. L., & Tomlin, C. J. (2011). Precision flight control for a multi-vehicle quadrotor helicopter testbed. Control Engineering Practice, 19(9), 1023–1036. CrossRefGoogle Scholar
  7. How, J., Bethke, B., Frank, A., Dale, D., & Vian, J. (2008). Real-time indoor autonomous vehicle test environment. IEEE Control Systems Magazine, 28(2), 51–64. MathSciNetCrossRefGoogle Scholar
  8. Hughes, P. C. (1986). Spacecraft attitude dynamics. New York: Wiley. ISBN 0-471-81842-9. Google Scholar
  9. Kolter, J. Z., & Ng, A. Y. (2009). Policy search via the signed derivative. In Robotics: science and systems. Google Scholar
  10. Leishman, J. G. (2006). Principles of helicopter aerodynamics (2nd edn.). Cambridge: Cambridge University Press. Google Scholar
  11. Lupashin, S., & D’Andrea, R. (2011). Adaptive open-loop aerobatic maneuvers for quadrocopters. In IFAC world congress. Google Scholar
  12. Lupashin, S., Schöllig, A., Sherback, M., & D’Andrea, R. (2010). A simple learning strategy for high-speed quadrocopter multi-flips. In IEEE international conference on robotics and automation (ICRA), 2010 (pp. 1642–1648). CrossRefGoogle Scholar
  13. Mellinger, D., Michael, N., & Kumar, V. (2010). Trajectory generation and control for precise aggressive maneuvers with quadrotors. In Int. symposium on experimental robotics. Google Scholar
  14. Michael, N., Mellinger, D., Lindsey, Q., & Kumar, V. (2010). The GRASP multiple micro-UAV testbed. IEEE Robotics & Automation Magazine, 17(3), 56–65. CrossRefGoogle Scholar
  15. Purwin, O., & D’Andrea, R. (2011). Performing and extending aggressive maneuvers using iterative learning control. Robotics and Autonomous Systems, 59, 1–11. CrossRefGoogle Scholar
  16. Ritz, R., Hehn, M., Lupashin, S., & D’Andrea, R. (2011). Quadrotor performance benchmarking using optimal control. In IEEE/RSJ international conference on intelligent robots and systems (pp. 5179–5186). Google Scholar
  17. Simon, D., & Chia, T. (2002). Kalman filtering with state equality constraints. IEEE Transactions on Aerospace and Electronic Systems, 39, 128–136. CrossRefGoogle Scholar
  18. Wolpert, D., & Flanagan, J. (2010). Motor learning. Current Biology, 20(11), R467–472. CrossRefGoogle Scholar
  19. Wolpert, D., Ghahramani, Z., & Flanagan, J. (2001). Perspectives and problems in motor learning. Trends in Cognitive Sciences, 5(11), 487–494. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Institute for Dynamic Systems and ControlETH ZurichZurichSwitzerland

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