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We propose a novel modular soft robotic pneumatic actuator that is developed via rapid prototyping and evaluated using image processing and machine learning models. The curled resting shape allows for simple manufacturing and achieves a greater range of bending than other actuators of its kind. A feedforward neural network provides accurate prediction of end-effector displacement upon inflation and loading to deliver precise manipulation and control.

Interventional catheter ablation treatment is a noninvasive approach for normalizing heart rhythm in patients with arrhythmia. Catheter ablation can be assisted with magnetic resonance imaging (MRI) to provide high-contrast images of the heart vasculature for diagnostic and intraprocedural purposes. Typical MRI images are captured using surface imaging coils that are external to the tissue being imaged. The image quality and the scanning time required for producing an image are directly correlated to the distance between the tissue being imaged and the imaging coil. The objective of this work is to minimize the spatial distance between the target tissue and the imaging coil by placing the imaging coil directly inside the heart using an expandable origami catheter structure. In this study, geometrical analysis is utilized to optimize the size and shape of the origami structure and MRI scans are taken to confirm the MRI compatibility of the structure. The origami expandable mechanism could also be applied to other medical device designs that require expandable structures.

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