Traditional soft robotic grippers rely on silicone or polymer structures that are expensive, slow to fabricate, and prone to durability challenges, limiting their scalability and adoption in real‐world applications. Textile‐based soft robots offer a promising alternative to silicone and polymer designs, providing lightweight, low‐cost, and easily manufacturable solutions for real‐world use. In this article, we introduce modular soft robotic grippers made from textile materials, aimed at improving the safe and gentle handling of agricultural produce. We propose modular grippers with inflatable textile‐based fingers that bend when pressurized with a compact control system, offering a lightweight and portable solution that can be quickly reconfigured with three or four fingers depending on the task. We propose different fabric combinations and finger designs and cutting patterns to achieve various bending motions and contact forces. The textile fingers achieve bending angles of more than 220°, and contact forces higher than 10 N. Our prototypes can successfully grasp a wide range of items, from delicate fruits like lemons and tangerines to larger objects like cabbage and grapefruit. We developed a nonlinear geometric model to predict actuator curvature and force exertion and employed data‐driven regression approaches to characterize how design parameters and internal pressure influence actuator performance. Material testing and characterization also showed practical design trade‐offs, with nylon‐spandex fingers exhibiting larger bending angles and contact forces than the polyester‐spandex design in most cases. This work demonstrates the potential of textiles as a scalable and accessible pathway for developing soft robotic grippers in food handling.
Building similarity graph...
Analyzing shared references across papers
Loading...
Zeyu Hou
Eran Beeri Bamani
Joao Buzzatto
ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)
Massachusetts Institute of Technology
University of Auckland
Glasgow School of Art
Building similarity graph...
Analyzing shared references across papers
Loading...
Hou et al. (Tue,) studied this question.
www.synapsesocial.com/papers/69a75b3ac6e9836116a222cb — DOI: https://doi.org/10.1002/adrr.202500182