Online Acquisition of Close-Range Proximity Sensor Models for Precise Object Grasping and Verification

Abstract

This study presents an approach for acquiring model parameters of close-range approximate proximity sensors on a robot hand using long-range distance sensors while that hand is grasping an object. The acquired models are used to generate a precise close-range distance output. We aim herein to acquire proximity sensors that have little dependence on object properties and that can sense a wide range (i.e., both close and long ranges). Simple close-range sensors strongly depend on object properties such as reflectance, material, volume, and/or conductivity, whereas long-range sensors cannot precisely measure the close range. To accomplish our goal, we fused close- and long-range sensors. Simple fusion remains object dependent at the close range. Hence, we acquired an object-dependent parameter in the close-range sensor model using the distance output of the long-range sensor at the overlap of the two sensor types. Through real robot experiments, we evaluated the precision of the generated distance output at the close range and found it useful to the precise grasping of compliant objects. We also confirmed that the acquired object-dependent parameter can verify ultra-thin object grasping.