Soft Acoustic Curvature Sensor: Design and Development

TL;DR

This paper presents a novel soft acoustic curvature sensor that uses acoustic wave propagation and machine learning to accurately measure curvature in flexible structures, with potential applications in soft robotics and wearable devices.

Contribution

The study introduces a new SAC sensor design with integrated audio components and ML-based curvature estimation, enabling large deformation detection and high accuracy.

Findings

Curvature measurement errors within 3.5 m-1 for 0-60 m-1 range.

Effective use of ML models for complex mapping between deformation and sound modulation.

Validated sensor performance through experimental testing with soft materials and 3D printing.

Abstract

This paper introduces a novel Soft Acoustic Curvature (SAC) sensor. SAC incorporates integrated audio components and features an acoustic channel within a flexible structure. A reference acoustic wave, generated by a speaker at one end of the channel, propagates and is received by a microphone at the other channel's end. Our previous study revealed that acoustic wave energy dissipation varies with acoustic channel deformation, leading us to design a novel channel capable of large deformation due to bending. We then use Machine Learning (ML) models to establish a complex mapping between channel deformations and sound modulation. Various sound frequencies and ML models were evaluated to enhance curvature detection accuracy. The sensor, constructed using soft material and 3D printing, was validated experimentally, with curvature measurement errors remaining within 3.5 m-1 for a range of 0 to 60 m-1 curvatures. These results demonstrate the effectiveness of the proposed method for estimating curvatures. With its flexible structure, the SAC sensor holds potential for applications in soft robotics, including shape measurement for continuum manipulators, soft grippers, and wearable devices.