Contact modelling and tactile data processing for robot skin

TL;DR

This paper analyzes computational methods for contact modeling and tactile data processing in large-scale capacitance-based robot skin, evaluating algorithm performance with real and synthetic datasets to enhance robotic tactile sensing capabilities.

Contribution

It characterizes the performance of classical contact modeling algorithms on large-scale robot skin data, providing insights into their computational efficiency and accuracy.

Findings

Boussinesq-Cerruti's solution performance analyzed

Love's approach evaluated for distributed contact problems

Algorithms tested on real and synthetic tactile datasets

Abstract

Tactile sensing is a key enabling technology to develop complex behaviours for robots interacting with humans or the environment. This paper discusses computational aspects playing a significant role when extracting information about contact events. Considering a large-scale, capacitance-based robot skin technology we developed in the past few years, we analyse the classical Boussinesq-Cerruti's solution and the Love's approach for solving a distributed inverse contact problem, both from a qualitative and a computational perspective. Our contribution is the characterisation of algorithms performance using a freely available dataset and data originating from surfaces provided with robot skin.