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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.

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

  • 会议论文

  • 发表日期：

    2025年

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  • Ji-Yeon Kim,
  • Ki-Hwan Kim,
  • Young-Jin Kang,
  • Young Seok Ock,
  • Seok Chan Jeong

摘要

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.