Safe learning-based control for an aerial robot with manipulator arms

Abstract

Aerial manipulation aims at combining the versatility and the agility of some aerial platforms as multirotor unmanned aerial vehicles (UAVs) with the manipulation capabilities of robotic arms. Trajectory tracking control of these vehicles is a substantial aspect for an increasing range of application domains. However, external disturbances and parts of the internal dynamics are often unknown or very time-consuming to model. To overcome this issue, we present a tracking control law using an learning-based oracle based on Gaussian process models. The presented approach guarantees a bounded tracking error with high probability where the bound is explicitly given. A numerical example highlights the effectiveness of the proposed control law.