A Model Predictive Control Approach to Blending in Shared Control
Elio Jabbour, Margot Vulliez, Célestin Préault, Vincent Padois
AI summary
Problem
Conventional blending techniques in shared control fail to guarantee motion feasibility, respect system constraints, or ensure smooth authority transitions, often causing instability and operator fatigue.
Approach
The method formulates blending as a constrained optimal control problem solved via Model Predictive Control (MPC), prospectively computing safe trajectories that optimally merge human inputs with autonomous assistance.
Key results
- Fewer kinematic constraint violations
- Improved task performance and repeatability
- Reduced physical and cognitive operator effort
- Validated superiority over linear blending and unassisted teleoperation
Why it matters
It offers a theoretically sound, constraint-aware blending mechanism that enhances safety and usability for human-robot teleoperation in complex environments.
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.