A Model Predictive Control Approach To Blending In Shared Control

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.