Work

Design Optimization and Data‐Driven Static Modeling for Active Fiber‐Reinforced Elastomeric Enclosures

Year: 2026

Type: article

Abstract: Fiber‐reinforced elastomeric enclosures are widely used due to their lightweight, low‐cost, and compliant structure. However, these actuators produce only a single motion when pressurized, based on their fixed, embedded fibers. In contrast, active fiber‐reinforced elastomeric enclosures (AFREEs) use active fiber‐constraint layers to create different motions, enabling versatility. In this paper, we introduced and verified a static fiber model to optimize AFREEs. Next, we created AFREEs that use both embedded and active constraints and measured their twist. We implemented two machine learning (ML) models, a support vector machine (SVM) and a fully connected neural network (FCNN), to create a forward and inverse static response model. The static fiber model yielded an average error of 2.5%. Our AFREE twisted up to 110°. There were strong fits for the forward static response model (SVM: R 2 = 0.9917; FCNN: R 2 = 0.9901) and moderate fits for the inverse static response model (SVM: R 2 = 0.6985; FCNN: R 2 = 0.7656). Our results show the strength of data‐driven AFREE modeling, while laying the groundwork for customizable AFREE units.

Source: Journal of Robotics

DOI: 10.1155/joro/8827476

Authors: Kyle T. Yoshida, Justin Pilgrim, Justin Allen, Christopher Ninatanta, Ryan Dorosh, Ming Luo

Institutions: University of California, Los Angeles, Washington State University

Language: English

Topic: Soft Robotics and Applications

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,100