Operability Analysis of Bilateral Control Based on Variable Impedance Control

This paper proposes a control method for master-slave system that considers the operating state of the human. The goal is to enable the manipulation of the robot in same manner as moving one’s own arm. Since interacting with environments with different stiffness generate different level of arm tension, this paper focuses on utilizing electromyography signals from the operator’s arm to estimate the arm tension with a recurrent neural network. Force control based on two types of mechanical impedance characteristics with different stiffness is constructed on the slave robot. The estimated arm tension by the recurrent neural network is used to continuously adjust the parameters of the impedance model according to the operator’s intention. Experiment results show that the mechanical impedance characteristic changes according to the arm tension. When the mechanical impedance has a soft characteristic, the stiffness of the environment can be transmitted to the operator more easily. On the contrary, when the mechanical impedance has a hard characteristic, a large force can be applied to the environment. Overall, the proposed method allows the operator to arbitrarily adjust the mechanical impedance characteristics according to his/her intentions to suit the task.