@Article{CristhianA.Aguilera-Carrasco2023,
author="Cristhian A. Aguilera-Carrasco
and Luis Felipe Gonzalez-B{\"o}hme
and Francisco Valdes
and Francisco Javier Quitral Zapata
and Bogdan Raducanu",
title="A Hand-Drawn Language for Human--Robot Collaboration in Wood Stereotomy",
journal="IEEE Access",
year="2023",
volume="11",
pages="100975--100985",
abstract="This study introduces a novel, hand-drawn language designed to foster human-robot collaboration in wood stereotomy, central to carpentry and joinery professions. Based on skilled carpenters{\textquoteright} line and symbol etchings on timber, this language signifies the location, geometry of woodworking joints, and timber placement within a framework. A proof-of-concept prototype has been developed, integrating object detectors, keypoint regression, and traditional computer vision techniques to interpret this language and enable an extensive repertoire of actions. Empirical data attests to the language{\textquoteright}s efficacy, with the successful identification of a specific set of symbols on various wood species{\textquoteright} sawn surfaces, achieving a mean average precision (mAP) exceeding 90\%. Concurrently, the system can accurately pinpoint critical positions that facilitate robotic comprehension of carpenter-indicated woodworking joint geometry. The positioning error, approximately 3 pixels, meets industry standards.",
optnote="LAMP",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3969), last updated on Fri, 26 Jan 2024 14:53:23 +0100",
doi="10.1109/ACCESS.2023.3314337"
}