Personalized Variable Gain Control With Tremor Attenuation for Robot Teleoperation

Teleoperated robot systems are able to support humans to accomplish their tasks in many applications. However, the performance of teleoperation largely depends on motor functionality and human operator's skill, especially when a human operator is short of skill training. In order to adapt to various unstructured environments for the robot system and the human operator, in this paper, a teleoperation scheme using integrated tremor attenuation with a variable gain control algorithm involving surface electromyogram is proposed to achieve personalized control performance and to reduce reliance on operator's skill. For attenuating tremor, a filter based on support vector machine is developed to guarantee normal operation. This filter depends on the machine learning scheme and does not rely on a priori filter parameters. Semiphysical experiments have been performed to demonstrate the effectiveness of the proposed methods.