A modular DMP framework mixes discrete and rhythmic movement primitives in parallel or sequence, preserving independent scaling and rotation of each movement.
Motion Primitives for Robotic Flight Control
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abstract
We introduce a simple framework for learning aggressive maneuvers in flight control of UAVs. Having inspired from biological environment, dynamic movement primitives are analyzed and extended using nonlinear contraction theory. Accordingly, primitives of an observed movement are stably combined and concatenated. We demonstrate our results experimentally on the Quanser Helicopter, in which we first imitate aggressive maneuvers and then use them as primitives to achieve new maneuvers that can fly over an obstacle.
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Combining Movement Primitives with Contraction Theory
A modular DMP framework mixes discrete and rhythmic movement primitives in parallel or sequence, preserving independent scaling and rotation of each movement.