Theory of Geometric Super-resolution for Haptic Sensor Design

Haptic feedback is important to make robots more dexterous and effective. High-resolution haptic sensors are still not widely available, and their application is often bound by robustness issues. A route towards high-resolution and robust sensors is to embed a few sensor units (taxels) into a flexible surface material and use signal processing to achieve sensing with super-resolution accuracy. We propose a theory for geometric super-resolution to guide the development of haptic sensors of this kind. This theory is based on sensor isolines and allows us to predict force sensitivity and accuracy in force magnitude and contact position as a spatial quantity. We evaluate the influence of different factors, such as the elastic properties of the material, using finite element simulations. We compare three representative real sensor unit types, empirically determine their isolines, and validate the theory in a custom-built sensor. Using machine learning techniques, we obtain an average super-resolution factor of 300. As we illustrate, our theory can guide future haptic sensor designs and inform various design choices.