Journal of Drive and Control (드라이브 ㆍ 컨트롤)

The Korean Society for Fluid Power and Construction Equipment (유공압건설기계학회)
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A Study of Weighing System to Apply into Hydraulic Excavator with CNN

CNN기반 굴삭기용 부하 측정 시스템 구현을 위한 연구

  • Hwang Hun Jeong (Intelligent Control Lab., Korea Construction Equipment Technology Institute) ;
  • Young Il Shin (Intelligent Control Lab., Korea Construction Equipment Technology Institute) ;
  • Jin Ho Lee (Intelligent Control Lab., Korea Construction Equipment Technology Institute) ;
  • Ki Yong Cho (Intelligent Control Lab., Korea Construction Equipment Technology Institute)
  • 정황훈 ;
  • 신영일 ;
  • 이진호 ;
  • 조기용
  • Received : 2023.11.15
  • Accepted : 2023.11.28
  • Published : 2023.12.01
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Abstract

A weighing system calculates the bucket's excavation amount of an excavator. Usually, the excavation amount is computed by the excavator's motion equations with sensing data. But these motion equations have computing errors that are induced by assumptions to the linear systems and identification of the equation's parameters. To reduce computing errors, some commercial weighing system incorporates particular motion into the excavation process. This study introduces a linear regression model on an artificial neural network that has fewer predicted errors and doesn't need a particular pose during an excavation. Time serial data were gathered from a 30tons excavator's loading test. Then these data were preprocessed to be adjusted by MPL (Multi Layer Perceptron) or CNN (Convolutional Neural Network) based linear regression models. Each model was trained by changing hyperparameter such as layer or node numbers, drop-out rate, and kernel size. Finally ID-CNN-based linear regression model was selected.

Keywords

Acknowledgement

이 연구는 국토교통부/국토교통과학기술진흥원이 시행하고 한국도로공사가 총괄하는 "스마트건설기술개발 국가R&D사업(연구개발과제번호 21SMIP-A157130-2)"의 지원으로 수행하였습니다.

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