Study on a LightGBM-Based Model for Detecting Anomaly Operation of Delta Robot

Ji-Yeon Kim (), Ki-Hwan Kim (), Young-Jin Kang (), Young Seok Ock () and Seok Chan Jeong ()
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Ji-Yeon Kim: Dong-Eui University
Ki-Hwan Kim: Dong-Eui University
Young-Jin Kang: Dong-Eui University
Young Seok Ock: Pukyong National University
Seok Chan Jeong: Dong-Eui University

A chapter in XR and Metaverse, 2025, pp 469-476 from Springer

Abstract: Abstract With advances in sensors, AI, and robotics, robots are being used in various fields, including industry, healthcare, and the home. These robots provide innovative changes and improved services in each field through several advantages such as productivity improvement, precise work performance, labor reduction, and stability enhancement. Accordingly, the maintenance of robots is becoming increasingly important. In this paper, a model for detecting anomalies in robot operations using LightGBM was built using vacuum pad data of a delta robot performing picking and packing operations in a factory. Data were preprocessed and generated to improve the abnormal motion detection rate, and through this, 82.22% accuracy was obtained.

Keywords: Anomaly detection; Robot; LightGBM (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-77975-6_35

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DOI: 10.1007/978-3-031-77975-6_35

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