提出显式分段多项式模型，精准建模机械臂逆运动学关系。
Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots
- 基于隐式关系推导显式分段多项式表达
- 在6轴机械臂上实现高精度逆模型建模
- 相比DNN更具泛化能力，适合工业场景
本文针对工业时间序列中存在大量原始特征时，识别简洁显式分段多项式关系的问题。算法基于一种新提出的识别方法，该方法可获得简洁的隐式关系，进而用于异常检测与定位中的正态性表征。本文所提方法进一步将隐式关系转化为显式分段多项式形式，利用隐式表示中涉及的多项式集合构建显式模型。该框架应用于6轴机械臂逆模型的简洁显式表示识别，并对4轴机械臂进行额外实验，验证了简洁模型相较于主流深度神经网络（DNN）在面对未见使用场景时的泛化性能优势。
原文摘要 · Abstract (English)
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.
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