Soft Robot Optimal Control Via Reduced Order Finite Element Models
This work addresses computational efficiency issues in soft robot control, which is incremental as it applies existing reduction techniques to a specific domain.
The paper tackled the challenge of real-time control for soft robots using high-dimensional finite element models by proposing a model order reduction approach combined with sequential convex programming, achieving a demonstration with a 9768-dimensional model for constrained trajectory tracking.
Finite element methods have been successfully used to develop physics-based models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing controllers for complex dynamic tasks such as trajectory optimization and trajectory tracking. However, finite element models are also typically very high-dimensional, which makes real-time control challenging. In this work we propose an approach for finite element model-based control of soft robots that leverages model order reduction techniques to significantly increase computational efficiency. In particular, a constrained optimal control problem is formulated based on a nonlinear reduced order finite element model and is solved via sequential convex programming. This approach is demonstrated through simulation of a cable-driven soft robot for a constrained trajectory tracking task, where a 9768-dimensional finite element model is used for controller design.