Optimizing out-of-plane stiffness for soft grippers

TL;DR

This paper introduces a data-driven optimization framework for enhancing out-of-plane stiffness in soft grippers, enabling precise control over mechanical properties like twist resistance and bendability, demonstrated through a soft pneumatic bending actuator.

Contribution

It develops a novel objective function and employs a data-driven approach with FEA and gradient-based optimization to improve out-of-plane stiffness in soft grippers.

Findings

Enhanced out-of-plane stiffness achieved in soft grippers

Validated design improvements through physical experiments

Framework applicable to various soft robotic designs

Abstract

In this paper, we presented a data-driven framework to optimize the out-of-plane stiffness for soft grippers to achieve mechanical properties as hard-to-twist and easy-to-bend. The effectiveness of this method is demonstrated in the design of a soft pneumatic bending actuator (SPBA). First, a new objective function is defined to quantitatively evaluate the out-of-plane stiffness as well as the bending performance. Then, sensitivity analysis is conducted on the parametric model of an SPBA design to determine the optimized design parameters with the help of Finite Element Analysis (FEA). To enable the computation of numerical optimization, a data-driven approach is employed to learn a cost function that directly represents the out-of-plane stiffness as a differentiable function of the design variables. A gradient-based method is used to maximize the out-of-plane stiffness of the SPBA while ensuring specific bending performance. The effectiveness of our method has been demonstrated in physical experiments taken on 3D-printed grippers.