Operability Evaluation of Human-Adaptive Impedance Control for Human-collaborative Robots

摘要

This paper discusses an operability of a human-adaptive impedance control to achieve human-robot co-manipulation of an object, in which a recurrent neural network (RNN) estimates a human state to be used in improving the contact stability of impedance control. The human sate, which is estimated from rectified-and-integrated electromyogram (iEMG) signals, is defined in this paper as what indicates that the human-arm becomes stiffer and causes an instability of the impedance control while the person and the robot are cooperatively manipulating the object. According to the degree of the estimated human state, the proposed method changes the impedance parameters to be heavier online so as to make the system more stable and then returns the mechanical parameters to be lighter once the stability is restored. In this paper, an operability of the proposed human-adaptive impedance control was evaluated quantitatively based on the crossover model in comparison with the case of fixed impedance control. In the case that the impedance characteristic was fixed lightly, the 0dB-gain-crossover frequency of the open-loop transfer function of the human-in-the-loop system (HILS), was at 0.21 Hz, achieving light-good operability. An undesirable oscillation, however, occurred depending on the situation between the human and the cooperative robot. In the case of fixed heavy impedance characteristics, stable operation was guaranteed, but the gain crossover frequency was reduced to 0.05 Hz, and the light handling was impaired even when the human exerted more force. On the other hand, the proposed method, which has a crossover frequency of 0.18 Hz, achieved stable operability while keeping light handling. Thus, those experimental results showed that the proposed method has both the reduced human effort and the good stability and can provide the human operator with an easy-to-work environment.