意思決定を加速する軽量産業シミュレータのための迅速モデリングアーキテクチャ
Rapid Modeling Architecture for Lightweight Simulator to Accelerate and Improve Decision Making for Industrial Systems
産業システム設計の初期段階での迅速な意思決定を支援するため、モデリング負担を軽減しつつ本質的な詳細を保つ軽量シミュレータ用の迅速モデリングアーキテクチャを提案し、実工場レイアウト設計に適用して従来比78.3%のモデリング時間削減を達成した。
著者: Takumi Kato, Zhi Li Hu
分類: eess.SY, cs.MA, cs.RO, cs.SY
原文アブストラクト
Designing industrial systems, such as building, improving, and automating distribution centers and manufacturing plants, involves critical decision-making with limited information in the early phases. The lack of information leads to less accurate designs of the systems, which are often difficult to resolve later. It is effective to use simulators to model the designed system and find out the issues early. However, the modeling time required by conventional simulators is too long to allow for rapid model creation to meet decision-making demands. In this paper, we propose a Rapid Modeling Architecture (RMA) for a lightweight industrial simulator that mitigates the modeling burden while maintaining the essential details in order to accelerate and improve decision-making. We have prototyped a simulator based on the RMA and applied it to the actual factory layout design problem. We also compared the modeling time of our simulator to that of an existing simulator, and as a result, our simulator achieved a 78.3% reduction in modeling time compared to conventional simulators.
関連論文
- チャンク型VLAマニピュレーションポリシーの学習と実機展開のためのSim-to-Real統合パイプラインsim2real
- 運動学を超えて:筋駆動模倣学習のためのシミュレーション忠実度ベンチマークsim2real
- CRISP: 多様な形状と接触ソルバを備えた接触リッチロボットシミュレーション基盤sim2real
- 同じ世界、異なる知識:孤立評価が世界モデルの修復を誤判定するときsim2real
- DEXTERA: 単一画像から実機展開可能な巧みなマニピュレーションへ向けたReal-to-Sim-to-Realsim2real
- 単一スキャンからのガウシアンスプラッティングによる実演合成と視覚運動ポリシー学習sim2real