Optimization-based shape design of soft-rigid hybrid fingers for adaptive parallel robotic gripper

Abstract

Parallel robotic gripper is an efficient tool for grasping and manipulating objects in industrial applications. In recent years, to enable adaptive grasping of objects with complex shapes, many parallel grippers are equipped with soft robotic fingers. However, due to the low structural stiffness of the utilized soft materials, the grasping payload of the soft fingers is usually limited. To cope with this issue, we propose a topology-optimization-based method in this article to enhance the grasping payload of soft fingers for parallel grippers. Using a multiobjective algorithm, the adaptive grasping ability of the monolithic finger and the holding stiffness of the fingertip are taken into account in the optimization procedure. The realized finger is fabricated with thermoplastic polyurethane (TPU) material. To evaluate the soft-rigid hybrid performance of the synthesized finger, stiffness tests and grasping payload tests are also conducted. Experimental results show that the optimized finger has a higher payload capacity than the conventional finray-like soft finger, while maintaining similar adaptive grasping properties. Furthermore, a series of grasping tests have also demonstrated the grasping adaptability of the synthesized soft robotic finger for objects with different materials, shapes and weights.

Keywords

Adaptive parallel robotic gripper / Soft-rigid hybrid finger / Topology optimization / Multiobjective algorithm

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