マスタ・スレーブシステムにおける自律アシスト制御のための操作者の手の姿勢予測

抄録

In recent years, the demand for robots capable of performing various works in dangerous environments is increasing. In such robots, master-slave control that allows an operator to make a situation determination is suitable. However for some tasks, only master-slave control is not enough, appropriate autonomous assist control is needed for this situation. The propose of this study is to develop a control algorithm to assist task operation on a master slave system. In this paper, we propose a method for predict the hand motion of operator during master-slave control. We first clustering the operator’s hand motion into 4 most used pre-shaped grasp pattern, each pattern is performed 10 times to obtain the grasp data, analysis with nonlinear dimensionality reduction techniques. Then EM algorithm is performed to fit a Gaussian mixed model on the latent space extracted from grasp data. As results, the fitted GMM model is used to predict the hand motion of operator in real-time, the proposed method is verified by grasp experiment.