Soft Robot Optimal Control Via Reduced Order Finite Element Models

TL;DR

This paper introduces a reduced order finite element modeling approach for soft robot control, enabling real-time trajectory tracking by significantly decreasing computational complexity while maintaining high fidelity.

Contribution

It presents a novel model order reduction technique for finite element models, facilitating efficient optimal control of soft robots in complex dynamic tasks.

Findings

Successful simulation of a cable-driven soft robot using a 9768-dimensional model.

Effective trajectory tracking achieved with reduced computational effort.

Demonstrated feasibility of real-time control with high-fidelity models.

Abstract

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.