Passive and Active Acoustic Sensing for Soft Pneumatic Actuators - Code and Data

This data and code accompanies the paper "Passive and Active Acoustic Sensing for Soft Pneumatic Actuators" [1]. Abstract: We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure relevant actuator states. The physical state of the actuator influences the modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the current state from sound recordings. We demonstrate the acoustic sensor on a PneuFlex actuator and use it to measure the contact location, contact force, object material and actuator inflation. We show that the sensor is reliable (the classification rate for six contact locations is 93%), precise (spatial resolution below 4mm), and robust against common disturbances like background noise. Finally, we compare different sounds and learning methods and achieve best results with 20ms of white noise and a support vector classifier as the sensor model. [1] Vincent Wall, Gabriel Zöller, and Oliver Brock. "Passive and Active Acoustic Sensing for Soft Pneumatic Actuators." (in preparation)