Adaptive Authority Allocation in Shared Control of Robots Using Bayesian Filters
Ribin Balachandran, Hrishik Mishra, Matteo Cappelli, Bernhard Weber, Cristian Secchi, Christian Ott, Alin Albu-Schaeffer
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.
BibTeX
@inproceedings{icra2020_adaptiveauthorit,
title = {Adaptive Authority Allocation in Shared Control of Robots Using Bayesian Filters},
author = {Ribin Balachandran and Hrishik Mishra and Matteo Cappelli and Bernhard Weber and Cristian Secchi and Christian Ott and Alin Albu-Schaeffer},
booktitle = {ICRA 2020},
year = {2020}
}