Abstract
NASA’s Resource Prospector mission is an unmanned lunar exploration mission currently in the concept development stage. Early tests have shown that the unique mission characteristics may combine to create the potential for very high workload for operators. This paper applies a taxonomy of workload drivers to identify the contextual variables expected to contribute to operator workload during the mission. Specifically, workload drivers attributed to the environment, task demands, equipment, and operator characteristics are reviewed. This research is intended to support the development of predictive models of operator workload to support the design and evaluation of workload countermeasures for the mission.
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Acknowledgments
We would like to thank the members of the Resource Prospector mission team who have shared their insights and experiences including Mark Allan, Matt Deans, Michael Furlong, Mark Shirley, and Vinh To. Also, we would like to thank Mark Micire, Eric Krotkov, and John Paschkewitz for motivating and encouraging this study as part of an effort to model and predict workload in human-machine systems.
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Hooey, B.L., Toy, J.J.N.T., Carvalho, R.E., Fong, T., Gore, B.F. (2018). Modeling Operator Workload for the Resource Prospector Lunar Rover Mission. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2017. Advances in Intelligent Systems and Computing, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-60591-3_17
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DOI: https://doi.org/10.1007/978-3-319-60591-3_17
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