Adaptive fast open-loop maneuvers for quadrocopters
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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.
KeywordsAerial robotics Aerobatics Learning Policy gradient
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.
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