Embedded Object Detection and Mapping in Soft Materials Using Optical Tactile Sensing

TL;DR

This paper introduces a probabilistic tactile exploration method using optical sensors to detect and map embedded objects within soft materials, demonstrated on bead configurations beneath foam.

Contribution

It presents a novel Bayesian-based exploration and mapping approach for embedded object detection using optical tactile sensing in soft materials.

Findings

Successfully detected embedded beads in various configurations

Outperformed random sampling policy in accuracy

Reconstructed underlying topography effectively

Abstract

In this paper, we present a methodology that uses an optical tactile sensor for efficient tactile exploration of embedded objects within soft materials. The methodology consists of an exploration phase, where a probabilistic estimate of the location of the embedded objects is built using a Bayesian approach. The exploration phase is then followed by a mapping phase which exploits the probabilistic map to reconstruct the underlying topography of the workspace by sampling in more detail regions where there is expected to be embedded objects. To demonstrate the effectiveness of the method, we tested our approach on an experimental setup that consists of a series of quartz beads located underneath a polyethylene foam that prevents direct observation of the configuration and requires the use of tactile exploration to recover the location of the beads. We show the performance of our methodology using ten different configurations of the beads where the proposed approach is able to approximate the underlying configuration. We benchmark our results against a random sampling policy.