A Model Predictive Control Approach to Blending in Shared Control

Abstract

Shared control aims at assisting human operators using robots in physically and cognitively demanding tasks which cannot be automated as they require human expertise and deliberative abilities. Sharing control for a given task typically involves blending algorithms that combine human control inputs and (pre)planned assistance trajectories. Conventional blending techniques, such as Linear Blending, compute a combined output but neither guarantee the feasibility of the blended motion nor the optimality of the combined decision. In the context of teleoperation, this paper presents a formulation where blending is defined as a constrained optimal control problem. Model Predictive Control is used to determine a feasible blended trajectory through a receding horizon constrained optimization. The proposed method is evaluated in a 13-participant pick and place teleoperation study and compared to Linear Blending and unassisted Teleoperation. The experimental results demonstrate the superiority of the proposed shared control framework in terms of safety, performance as well as physical and cognitive comfort.