Bistable Stopper Design and Force Prediction for Precision and Power Grasps of Soft Robotic Fingers for Industrial Manipulation

Abstract

This article aims at presenting a detailed and practical comparison between three designs of robotic soft fingers for industrial grippers. While the soft finger based on the fin ray effect (FRE) shown here has been proposed long ago, few works in the literature have studied its reliance on the presence of the crossbeams or its precision grasp performance compared to its power grasps. Aiming at addressing these gaps, two novel designs are proposed and compared to FRE fingers in this article. First, the three designs are presented. One of these fingers, named PacomeFlex, embeds changeable grasping modes by relying on two sets of kinematic structures with a bistable stopper design. Then, finite element analyses (FEA) are conducted to simulate power and precision grasps of the three fingers followed by the estimation of the grasp forces produced. These FEA are then used to train neural networks capable of predicting these grasp forces. Finally, the grasp strength and pullout resistance of the fingers are experimentally measured, and experimental results are shown to be in good accordance with the FEA and neural network models. As will also be shown, the PacomeFlex finger introduced in this work provides a noticeably higher performance level than all other fingers with respect to typical metrics in soft grasping.