Motion Primitives for Robotic Flight Control

TL;DR

This paper presents a framework for learning and combining motion primitives to enable aggressive and obstacle-avoiding flight maneuvers in UAVs, inspired by biological movement analysis and extended with nonlinear contraction theory.

Contribution

It introduces a novel method for analyzing and extending movement primitives for UAV flight control, demonstrated on a Quanser Helicopter.

Findings

Successfully imitated aggressive maneuvers

Combined primitives to fly over obstacles

Validated on Quanser Helicopter

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.