生産拠点を結ぶデータ・トゥ・ナレッジパイプラインの実証
Demonstrating Data-to-Knowledge Pipelines for Connecting Production Sites in the World Wide Lab
複数の組織のロボット軌道データをデジタルシャドウで意味付けし、データレイクハウスを介してロボット制御用の逆動力学基盤モデルを訓練するパイプラインを実証した。
著者: Leon Gorißen, Jan-Niklas Schneider, Mohamed Behery, Philipp Brauner, Moritz Lennartz, David Kötter, Thomas Kaster, Oliver Petrovic, Christian Hinke, Thomas Gries, Gerhard Lakemeyer, Martina Ziefle, Christian Brecher, Constantin Häfner
分類: cs.RO, cs.LG
原文アブストラクト
The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook.
