Abstract
Human space exploration has never been more exciting than it is today. Human presence to outer worlds is becoming a reality as humans are leveraging much of our prior knowledge to the new mission of going to Mars. Exploring the solar system at greater distances from Earth than ever before will possess some unique challenges, which can be overcome thanks to the advances in modeling and simulation technologies. The National Aeronautics and Space Administration (NASA) is at the forefront of exploring our solar system. NASA’s Human Research Program (HRP) focuses on discovering the best methods and technologies that support safe and productive human space travel in the extreme and harsh space environment. HRP uses various methods and approaches to answer questions about the impact of long duration missions on the human in space including: gravity’s impact on the human body, isolation and confinement on the human, hostile environments impact on the human, space radiation, and how the distance is likely to impact the human. Predictive models are included in the HRP research portfolio as these models provide valuable insights into human-system operations. This paper will provide an overview of NASA’s HRP and will present a number of projects that have used modeling and simulation to provide insights into human-system issues (e.g. automation, habitat design, schedules) in anticipation of deep-space exploration.
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Notes
- 1.
The MIDAS-FAST project arose from the workload monitoring and modeling task of the HRP SHFE portfolio from 2008–2010 [9].
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Acknowledgments
This research was funded by the NASA HRP’s Space Human Factors and Habitability Element in 2016 and by HFBP in 2017. The author would like to express sincere appreciation to NASA Johnson Space Center, Ames Research Center, and to all reviewers for their input on this document.
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Gore, B.F. (2018). Minimizing Human Risk: Human Performance Models in the Human Factors and Behavioral Performance Elements. 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_19
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