PT Journal
AU Cristhian A. Aguilera-Carrasco
   Luis Felipe Gonzalez-Böhme
   Francisco Valdes
   Francisco Javier Quitral Zapata
   Bogdan Raducanu
TI A Hand-Drawn Language for Human–Robot Collaboration in Wood Stereotomy
SO IEEE Access
JI ACCESS
PY 2023
BP 100975
EP 100985
VL 11
DI 10.1109/ACCESS.2023.3314337
AB 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’ 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’s efficacy, with the successful identification of a specific set of symbols on various wood species’ 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.
ER