Modeling and analysis of peg-in-hole task based on mode segmentation

Abstract

Recently, the demand for a man-machine cooperative system is rapidly growing in the industrial fields. To meet this demand, the human model is required to design the suitable assist controller in the man-machine cooperative system. This paper presents a new human behavior model based on a piecewise ARX model which is a class of hybrid system, and apply it to a peg-in-hole task. Since the human behavior is considered to consist of several primitive motions expressed by continuous dynamics and a decision-making expressed by the discrete switch, it seems to be natural to introduce the hybrid system modeling. The measured data are classified into several modes by clustering technique based on some feature values of dynamics. Then, each primitive motion in each mode is identified based on the ARX model. Finally, the switching conditions among modes are identified by applying Support Vector Machine to the classified data. The obtained piecewise ARX model can quantitatively represent both primitive motions and decision-making in the human behavior.