Operation State Identification Method for Unmanned Construction: Extended Search and Registration System of Novel Operation State Based on LSTM and DDTW

Ziwei Qiao, Yuichi Mizukoshi, Takumi Moteki, Hiroyasu Iwata

研究成果: Conference contribution

抄録

Unmanned construction technology is being developed to aid in disaster recovery by remotely operating construction machinery, thereby reducing the possibility of secondary disasters. However, the work efficiency of unmanned operation is only about half that of the normal operation. Thus, various support methods based on operation states have been developed to improve the work efficiency. It is important to automatically determine the operation state of the construction machinery to apply these support methods. In this paper, we propose an extended operation state identification model that can identify various operation states. The proposed model consists of two parts: operation state classification system using the classification model of Long Short-Term Memory (LSTM), and novel state search and registration system using the regression model of LSTM. Simulation of unmanned construction was conducted to verify the potential of the proposed model.

本文言語English
ホスト出版物のタイトル2022 IEEE/SICE International Symposium on System Integration, SII 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ13-18
ページ数6
ISBN(電子版)9781665445405
DOI
出版ステータスPublished - 2022
イベント2022 IEEE/SICE International Symposium on System Integration, SII 2022 - Virtual, Narvik, Norway
継続期間: 2022 1月 92022 1月 12

出版物シリーズ

名前2022 IEEE/SICE International Symposium on System Integration, SII 2022

Conference

Conference2022 IEEE/SICE International Symposium on System Integration, SII 2022
国/地域Norway
CityVirtual, Narvik
Period22/1/922/1/12

ASJC Scopus subject areas

  • 人工知能
  • ハードウェアとアーキテクチャ
  • 生体医工学
  • 制御およびシステム工学
  • 機械工学
  • 制御と最適化

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