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Towards manufacturing robotics accuracy degradation assessment A vision-based data-driven implementation.pdf (1.526Mb)
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Title
Towards manufacturing robotics accuracy degradation assessment: A vision-based data-driven implementation
Author
Izagirre, Unai
andonegui, imanol
Eciolaza, Luka
Zurutuza, Urko
Research Group
Análisis de datos y ciberseguridad
Robótica y automatización
Version
Published version
Rights
© 2021 Elsevier
Access
Embargoed access
URI
https://hdl.handle.net/20.500.11984/6567
Publisher’s version
https://doi.org/10.1016/j.rcim.2020.102029
Published at
Robotics and Computer-Integrated Manufacturing  2021. Vol. 67. N. Art. 102029
Publisher
Elsevier
Keywords
robot health monitoring
industrial robot
PHM
Machine learning ... [+]
robot health monitoring
industrial robot
PHM
Machine learning
augmented reality [-]
Abstract
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 comp ... [+]
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. [-]
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