Sensorimotor Developments in PCC-Based Soft Pneumatic Actuators

Abstract

Soft pneumatic actuators (SPAs) offer notable advantages in various applications due to their compliance and ease of manufacturing. However, accurately predicting their response remains challenging due to their non-linearity and viscoelastic behaviour.

This study, focusing on the piecewise constant curvature (PCC) model, addresses three interconnected challenges in SPA sensorimotor functionality: inverse kinematics (IK), twist motion modelling, and EGaIn-based soft sensor modelling. By tackling these limitations, the study enhances SPA adaptability, leading to improved manipulation, sensing, and control accuracy.

To achieve these objectives, IK simulations were conducted, a length model based on geodesics and covering spaces was proposed and validated, and a regression-based numerical model was developed for soft sensor behaviour. Custom experimental tests generated datasets for training and validation.

Key findings include a quadratic IK solver outperforming the linear solver and resolving singularity issues in the PCC model, a length model achieving 1.5% accuracy under twist, and a highly accurate soft sensor model demonstrating sub-millimetre precision despite hysteresis.

This research advances soft robotics by addressing key limitations, contributing to the development of more capable and adaptable machines for diverse applications.