Robot hand synergy mapping using multi-factor model and EMG signal

Abstract

In this paper, it is investigated how a robot hand can be controlled from a human motion and an EMG signal in a tele-operation system. The proposed method uses a tensor to represent a multi-factor model relevant to different individuals and motions in multiple dimensions. Therefore, the synergies extracted by the proposed algorithm can account for not only various grasping motions but also the different characteristics of different people. Moreover, a synergy-level controller which generates motion and force of the robot is developed with postural synergies and an EMG signal. The effectiveness of the proposed new mapping algorithm is verified through experiments, which demonstrate better representation of hand motions with synergies and greater performance on grasping tasks than those of conventional synergy-based algorithms.