A vision-based virtual fixture with robot learning for teleoperation

Abstract

Teleoperation plays a key role for semi-automated tasks with high complexity in remote working environment. By integrating the interaction information and control strategy, the control performance can be guaranteed by the skilled operator manipulation in terms of stability and precision. However, due to a lack of prolonged specialized training, the manipulation characteristics, such as operation habits and tremor for green hands, the control performance of teleoperation cannot be guaranteed, especially for complicated and refined tasks. To this end, a vision-based virtual fixture with robot learning approach is proposed for teleoperation. In the proposed method, a dynamic movement primitives method is utilized to learn the human tutor or skilled operator manipulation skill and then generates the expert trajectories for training of green hands. Additionally, considering the instantaneity of manipulation, a vision-based virtual fixture is utilized to generate a force selector based on position error and provides a force guidance to the green hands in order to enhance the precision of control with expert level and reduce the operation pressure. Comparative experimental results demonstrated the performance of the developed approach for teleoperation.