Adaptive Authority Allocation in Shared Control of Robots Using Bayesian Filters

Abstract

In the present paper, we propose a novel system-driven adaptive shared control framework in which the autonomous system allocates the authority among the human operator and itself. Authority allocation is based on a metric derived from a Bayesian filter, which is being adapted online according to real measurements. In this way, time-varying measurement noise characteristics are incorporated. We present the stability proof for the proposed shared control architecture with adaptive authority allocation, which includes time delay in the communication channel between the operator and the robot. Furthermore, the proposed method is validated through experiments and a user-study evaluation. The obtained results indicate significant improvements in task execution compared with pure teleoperation.