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.