In this paper, we focus on the problem of maximizing system performance for future space exploration missions involving both human and robot agents. One of the main challenges in human-robot interaction scenarios is determining which tasks are best done with either human, robotic systems, or in collaboration with each. Such partitioning of the task space must acknowledge the capabilities of both agents, as well as incorporate the effect of repetitive workload, or stress, on the human operator. Our methodology for role allocation, which typically consists of either the human or the machine executing a single task, is based on predicting system performance of a given scenario by incorporating the concept of task switching. Task switching is defined as the process of alternating or switching attention between tasks when responding to a sequence of stimulus presentations. Using this concept, system performance can be predicted and used to determine an optimal allocation of tasks to be divided between human controlled and autonomous robotic systems to minimize mental workload while maximizing task performance. We provide details of the approach in this paper and present our results as applied to a simulated rendezvous/docking mission scenario
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