Abstract
Transferring versatile skills of human behavior to teleoperate manipulators to execute tasks with large uncertainties is challenging in robotics. This paper proposes a hybrid mapping method with position and stiffness for manipulator teleoperation through the exoskeleton device combining with the surface electromyography (sEMG) sensors. Firstly, according to the redefinition of robot workspace, the fixed scale mapping in free space and virtual impedance mapping in fine space are presented for position teleoperation. Secondly, the stiffness at the human arm endpoint is predicted and classified into three levels based on the K nearest neighbor (KNN) and XGBoost, and the stiffness mapping method is utilized to regulate the stiffness behavior of manipulator. Finally, the proposed method is demonstrated in three complementary experiments, namely the trajectory tracking in free space, the obstacle avoidance in fine space and the human robot interaction in contact space, which illustrate the effectiveness of the method.
Highlights
Over the past decades, the bilateral teleoperation systems based on feedback sensory data have been widely used in various applications such as space robots [1,2], underwater robots [3], medical robots [4], emergency response robots [5], and etc
Electromyography (EMG) signals which describe how well the muscles respond to those signals activated by central nervous system (CNS), are considered as the best candidates to provide an insight into the overall biomechanical behavior of human arms, and have been the choice of input in many rehabilitative and human robot functionality coordinations [9,10]
It has been studied that the surface electromyography signals are highly correlated with human arm joint stiffness and the corresponding muscle tensions [11]
Summary
The bilateral teleoperation systems based on feedback sensory data have been widely used in various applications such as space robots [1,2], underwater robots [3], medical robots [4], emergency response robots [5], and etc In these systems, an operator uses a human robot interface (master) at the local site to control a robot (slave) in the remote environment to execute a task. The humans have the ability to actively adjust the impedance at their arms endpoint [8] to demonstrate a versatile and stable behavior when they perform tasks or interact with environments with large dynamic uncertainties. Transferring the impedance regulation of human arm endpoint to control the impedance of remote manipulators to execute tasks with large uncertainties is challenging and has been studied in recent years.
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