Abstract
Helicopters have highly stochastic, nonlinear, dynamics, and autonomous helicopter flight is widely regarded to be a challenging control problem. As helicopters are highly unstable at low speeds, it is particularly difficult to design controllers for low speed aerobatic maneuvers. In this paper, we describe a successful application of reinforcement learning to designing a controller for sustained inverted flight on an autonomous helicopter. Using data collected from the helicopter in flight, we began by learning a stochastic, nonlinear model of the helicopter’s dynamics. Then, a reinforcement learning algorithm was applied to automatically learn a controller for autonomous inverted hovering. Finally, the resulting controller was successfully tested on our autonomous helicopter platform.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ng, A.Y. et al. (2006). Autonomous Inverted Helicopter Flight via Reinforcement Learning. In: Ang, M.H., Khatib, O. (eds) Experimental Robotics IX. Springer Tracts in Advanced Robotics, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552246_35
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DOI: https://doi.org/10.1007/11552246_35
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28816-9
Online ISBN: 978-3-540-33014-1
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