カテゴリ: 論文誌(論文単位)グループ名: 【D】産業応用部門（英文）発行日: 2024/03/01タイトル(英語): Deep Neural Network based Estimation of Missing Measurement Data for Construction Path Optimization著者名: Yuki Yoshino (Shibaura Institute of Technology), Chieko Hoshino (Shibaura Institute of Technology), Yutaka Uchimura (Shibaura Institute of Technology)著者名(英語): Yuki Yoshino (Shibaura Institute of Technology), Chieko Hoshino (Shibaura Institute of Technology), Yutaka Uchimura (Shibaura Institute of Technology)キーワード: deep neural network，digital evaluation model (DEM)，construction automation，construction path optimization要約(英語): With a rapidly aging population, declining birthrate, and decreasing number of skilled workers, automation of construction machinery is expected. On construction sites, automated earth moving work by a bulldozer requires the measurement of the soil pile. However, measuring entire pile data using sensors mounted on the bulldozer is difficult, since the back side of the soil pile becomes blind spot. To solve this problem, it is required to generate the path of the pile spread only using the image of the part visible from the bulldozer. This paper proposes a method to generate digital evaluation model (DEM) of the soil pile from occluded measurement pile data using a convolutional neural network. Since deep learning-based methods require a large amount of training data, we generated pile data and captured images via simulations. Considering the sensing device, three image patterns and their estimation accuracy were evaluated. By using the trained network model, construction path optimization for earth moving tasks was performed using the estimated DEM data. The results of DEM estimation and filling performance of soil moving tasks are also shown.本誌: IEEJ Journal of Industry Applications Vol.13 No.2 （2024） Special Issue on “Motion Control and its Related Technologies”本誌掲載ページ: 171-177 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/13/2/13_23004875/_article/-char/ja/
