Human state digital twin architecture for physiology constrained adaptive robotic autonomy

Abstract

Time-dependent human cognitive and physical conditions should be considered in adaptive autonomy in collaborative robotics to ensure safety, performance, and operator comfort. But most of the current methods depend on heuristic levels of workload or individual physiological measures and fail to combine human-state estimation with the control structure. This paper suggests a human state digital twin (HSDT) framework of physiological-constrained adaptive robotic autonomy. This framework is a combination of multimodal physiological measures of electrocardiography-based heart-rate variability, electromyography, electrodermal activity, and task context to predict latent human states such as cognitive workload, fatigue, and stress. These estimates are included in a twin-constrained adaptive autonomy controller, which varies the control authority of the robot in real time. The simulated collaborative manipulation situation is tested in the proposed framework in different workload and fatigue conditions. It has demonstrated better tracking performance, less torque ripple, reduced operator stress indicators and less torque ripple. The paper presents a controlled and reconfigurable physiology-aware adaptive autonomy control architecture and a robotics-based digital twin control design framework of human-aware control design.