産業時系列データにおける簡潔な区分的多項式関係の同定：マニピュレータロボットへの応用
Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots
異常検知・位置特定のための正規性評価に使える簡潔な陰的関係を導出する既存手法を拡張し、その多項式群から明示的な区分的表現を構築するアルゴリズムを提案。6軸・4軸ロボットの逆モデル同定で有効性と汎化性能を検証した。
著者: Mazen Alamir, Sacha Clavel
分類: cs.RO, cs.AI
原文アブストラクト
This paper addresses the problem of identifying parsimonious explicit piece-wise polynomial relationships that might involve a relatively large number of raw features. The algorithm leverages a recently proposed identification algorithm that yields parsimonious implicit relationships enabling to derive normality characterization in the context of anomaly detection and localization. The algorithm proposed in this paper goes a step further by deriving explicit piece-wise representations that are built using the set of polynomials involved in the implicit representations. The framework is illustrated on the problem of identifying parsimonious explicit representations of the inverse model of a 6-axis manipulator robot. Moreover, further experiments on a 4-axis robot are also shown which are designed to investigate the generalization capability of parsimonious models compared to state-of-the-art DNNs structures, when models face unseen contexts of use.