Tactile Sensing via Sparse Pressure Measurements in a Suspension

Tactile sensors with few transducers can resolve rich dynamics in soft robots, haptic interfaces, and wearable devices with minimal added complexity. Using only four transducers, we design a sensor to infer position, depth, and duration of a touch input through the dissipative properties of a granular suspension. Though many tactile sensors rely on learned features to infer input characteristics, we show that models trained on simple time and pressure features perform comparatively to a convolutional neural network baseline. We extract pressure amplitude, arrival time delay, and pulse width from pressure sensor time series to estimate touch characteristics. Then, we fit interpolation functions for press position and press time as a function of pressure amplitude, relative peak time, and press width. With this approach, we directly obtain the press location and time from time series measurements. On average, the proposed sensor estimates press position within 10% with a standard deviation of 7% and press time within 5.9% with a standard deviation of 11%. Additionally, this sensor provides reasonable prediction performance with as few as two pressure transducers at various concentrations, probe sizes, and for different boundary conditions. To showcase the simplicity of this sensing and estimation scheme, we demonstrate its implementation on different geometries and embedded hardware in real-time. The rich dynamics of this sensor may further enable gesture and shape detection, ultimately enabling closed-loop feedback for granular haptic interfaces and soft robots.