深層強化学習を利用した油圧ショベルの積込み動作向けモデル予測制御のパラメータ調整

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2024/06/01

タイトル(英語): Tuning of Model Predictive Control for Loading Motion of Automated Excavator Using Deep Reinforcement Learning

著者名: 石原　新士（（株）日立製作所 研究開発グループ），大塚　敏之（京都大学 大学院情報学研究科 情報学専攻）

著者名(英語): Shinji Ishihara (Hitachi Ltd.,Research & Development Group), Toshiyuki Ohtsuka (Kyoto University, Graduate School of Informatics, Department of Informatics)

キーワード: モデル予測制御，深層強化学習，パラメータ調整，自動化油圧ショベル　　model predictive control，deep reinforcement learning，parameter tuning，automated excavator

要約(英語): This study deals with the control problems for automating the operation of an excavator loading soil onto the back of a dump truck. In the loading operation, the bucket should not touch the dump truck and should spill as little soil in the bucket as possible. We have been studying how to apply Model Predictive Control (MPC) to this problem to achieve ideal loading operation. When trying to achieve the desired operation using MPC, it is extremely important to tune the weights of the objective function appropriately. However, since this control problem may depend on the situations, that is, initial posture of the excavator and the position of the truck, optimization for specific conditions would not be desirable. Therefore, we constructed a method to generate suitable weight parameters according to the loading situation using reinforcement learning. The effectiveness of the proposed method was verified by numerical simulations.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.144 No.6 （2024）

本誌掲載ページ: 552-559 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/ieejeiss/144/6/144_552/_article/-char/ja/