Bayesian Exploration for Intelligent Identification of Textures
Identifieur interne : 001E41 ( Pmc/Corpus ); précédent : 001E40; suivant : 001E42Bayesian Exploration for Intelligent Identification of Textures
Auteurs : Jeremy A. Fishel ; Gerald E. LoebSource :
- Frontiers in Neurorobotics [ 1662-5218 ] ; 2012.
Abstract
In order to endow robots with human-like abilities to characterize and identify objects, they must be provided with tactile sensors and intelligent algorithms to select, control, and interpret data from useful exploratory movements. Humans make informed decisions on the sequence of exploratory movements that would yield the most information for the task, depending on what the object may be and prior knowledge of what to expect from possible exploratory movements. This study is focused on texture discrimination, a subset of a much larger group of exploratory movements and percepts that humans use to discriminate, characterize, and identify objects. Using a testbed equipped with a biologically inspired tactile sensor (the BioTac), we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reaction forces when exploring textures were used to extract measures of textural properties inspired from psychophysical literature (traction, roughness, and fineness). Different combinations of normal force and velocity were identified to be useful for each of these three properties. A total of 117 textures were explored with these three movements to create a database of
Url:
DOI: 10.3389/fnbot.2012.00004
PubMed: 22783186
PubMed Central: 3389458
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