2026-04-21T23:23:26Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/65672024-07-03T06:15:35Zcom_20.500.11984_473col_20.500.11984_478

Izagirre, Unai andonegui, imanol Eciolaza, Luka Zurutuza, Urko 2024-07-02T09:28:30Z 2024-07-02T09:28:30Z 2021 0736-5845 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=159488 https://hdl.handle.net/20.500.11984/6567 In this manuscript we report on a vision-based data-driven methodology for industrial robot health assessment. We provide an experimental evidence of the usefulness of our methodology on a system comprised of a 6-axis industrial robot, two monocular cameras and five binary squared fiducial markers. The fiducial marker system permits to accurately track the deviation of the end-effector along a fixed non-trivial trajectory. Moreover, we monitor the trajectory deflection using three gradually increasing weights attached to the end-effector. When the robot is loaded with the maximum allowed payload, a deviation of 0.77mm is identified in the Z-coordinate of the end-effector. Tracing trajectory information, we train five supervised learning regression models. Such models are afterwards used to predict the deviation of the end-effector, using the pose estimation provided by the visual tracking system. As a result of this study, we show that this procedure is a stable, robust, rigorous and reliable tool for robot trajectory deviation estimation and it even allows to identify the mechanical element producing non-kinematic errors. eng © 2021 Elsevier robot health monitoring industrial robot PHM Machine learning augmented reality Towards manufacturing robotics accuracy degradation assessment: A vision-based data-driven implementation http://purl.org/coar/resource_type/c_6501