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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ji-Yeon Kim & Ki-Hwan Kim & Young-Jin Kang & Young Seok Ock & Seok Chan Jeong, 2025. "Study on a LightGBM-Based Model for Detecting Anomaly Operation of Delta Robot," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-77975-6_35
    DOI: 10.1007/978-3-031-77975-6_35
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