Measuring Human Workload in a Collaborative Human-Robot Team

Caroline E. Harriott, Tao Zhang, Glenna L. Buford, Julie A. Adams

Abstract


Successful human-robot (H-R) teams working with direct interaction and close coupling will have relationships that vary, depending on whether or not collaboration is prioritized and how collaboration changes the human mental workload and the team’s task performance. Modeling of representative functions can provide predictions of changes in mental workload and the potential impact on team performance. The presented research focuses on modeling and quantifying mental workload for H-R teams in which team members have some individually assigned responsibilities but must also make joint decisions with a teammate. IMPRINT Pro, a discrete event simulation modeling tool created by the U.S. Army Research Laboratory, was used to model human-human (H-H) and H-R teams completing a reconnaissance task in a building. This research evaluated H-H and H-R teams completing the same reconnaissance tasks. Predictions of mental-workload levels from the model and the evaluation results showed that mental workload was lower for the H-R teams. The results for the closely coupled teams were compared to results from a prior evaluation with a master-slave relationship; similar results were found for both evaluations. Mental workload was lower in H-R teams than in H-H teams, but task performance did not differ between the two.

Keywords


Mental workload, task performance, peer-based human-robot teams, human performance modeling, physiological metrics, subjective metrics

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DOI: https://doi.org/10.5898/JHRI.4.2.Harriott

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