オフライン強化学習による重機の経路計画

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2024/05/01

タイトル(英語): Path Planning for Construction Machines by Offline Reinforcement Learning

著者名: 中山　達也（芝浦工業大学），加志　東樹（芝浦工業大学），内村　裕（芝浦工業大学）

著者名(英語): Tatsuya Nakayama (Shibaura Institute of Technology), Haruki Kashi (Shibaura Institute of Technology), Yutaka Uchimura (Shibaura Institute of Technology)

キーワード: 人工知能，機械学習，深層強化学習，建設重機，土砂整地　　artificial intelligence，machine learning，deep reinforcement learning，construction machines，soil preparation

要約(英語): Controlling bulldozers at construction sites requires advanced skills. However, training skilled operators is time consuming and expensive. Additionally, the shortage of young workers is problematic. To address these issues, automation of construction machines using artificial intelligence has been studied, which has yielded positive results. Furthermore, using data collected from construction sites, reducing learning time and improving work accuracy is possible. Therefore, in this paper, we propose a path planning method for heavy equipment using offline reinforcement learning, leveraging existing datasets.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.144 No.5 （2024） 特集：2023年産業応用部門大会

本誌掲載ページ: 367-373 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/144/5/144_367/_article/-char/ja/