Human-Inspired Control of Dual-Arm Exoskeleton Robots With Force and Impedance Adaptation

Humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. Traditional robots perform tasks independently without considering their interactions with the external environment, which leads to poor flexibility and adaptability. Comparatively, humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. In order to solve the problems of human-robot security and adaptability to unknown environment, a human-inspired control with force and impedance adaptation is proposed to interact with unknown environments and exhibit this biological behavior on the developed dual-arm exoskeleton robots. First, we propose a computationally model utilizing the sampled surface electromyogram (sEMG) signals to calculate the human arm endpoint stiffness and define a co-contraction index to describe the dynamic behaviors of the muscular activities in the tasks. Then, the obtained human limb impedance stiffness parameters and the sampling position information are transferred to the slave arm of the exoskeleton as the input variables of the controller in real-time. In addition, a variable stiffness observer is used here to compensate for the errors of the calculated stiffness by sEMG signals. The experimental studies of human impedance transfer control have been conducted to show the effectiveness of the developed approach. Results of the experimental suggest that the proposed controller can achieve human motor adaptation and enable the subjects to execute a skill transfer control by a dual-arm exoskeleton robot.