Voronoi Features for Tactile Sensing: Direct Inference of Pressure, Shear, and Contact Locations

TL;DR

This paper introduces a novel Voronoi tessellation-based method for directly inferring tactile features like pressure, shear, and contact location from optical tactile sensor data, enhancing data interpretation and sensor versatility.

Contribution

The paper presents a new approach using Voronoi tessellation to infer tactile features without training classifiers, improving sensor data visualization and interpretation.

Findings

Inferred shear direction within ~2.3° accuracy.

Validated calibration of tip displacement and shear magnitude.

Enhanced visualization mode for tactile data interpretation.

Abstract

There are a wide range of features that tactile contact provides, each with different aspects of information that can be used for object grasping, manipulation, and perception. In this paper inference of some key tactile features, tip displacement, contact location, shear direction and magnitude, is demonstrated by introducing a novel method of transducing a third dimension to the sensor data via Voronoi tessellation. The inferred features are displayed throughout the work in a new visualisation mode derived from the Voronoi tessellation; these visualisations create easier interpretation of data from an optical tactile sensor that measures local shear from displacement of internal pins (the TacTip). The output values of tip displacement and shear magnitude are calibrated to appropriate mechanical units and validate the direction of shear inferred from the sensor. We show that these methods can infer the direction of shear to $\sim$2.3$^{\circ}$ without the need for training a classifier or regressor. The approach demonstrated here will increase the versatility and generality of the sensors and thus allow sensor to be used in more unstructured and unknown environments, as well as improve the use of these tactile sensors in more complex systems such as robot hands.