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NASA's renewed human spaceflight exploration efforts, starting with the Artemis Program, are accelerating the development of deep space habitats. However, there are still many unresolved challenges to achieving viable operations for deep space habitats, mainly due to communication latencies. These latencies will limit the ability of mission control on Earth to command and control these space habitats and will also prevent mission control from assisting astronauts in real time. For example, astronauts occasionally require assistance while following procedures to perform maintenance tasks. Traditionally, mission controllers provide real-time support while monitoring astronauts during the execution of these maintenance tasks in low Earth orbit habitats (e.g., International Space Station). To mitigate the loss of this type of real-time support for deep space habitats, the use of Autonomous systems powered by artificial intelligence (AI) methods has been proposed. To develop these autonomous systems, we first need to understand the current state of the art for human task support in low earth orbit. To improve our understanding, we designed and conducted a survey study with ten former astronauts who performed space habitat maintenance tasks to identify and quantify feedback options (or suggestions) to be given during task execution. Although assistance from mission control is far more complex than just simple status feedback, simplifying assistance as a limited set of feedback options may make the implementation of human-AI teams easier. In this paper, we present the design process and results of the survey study regarding the preferred feedback options as a means of assistance for maintenance tasks in space habitats.
Ulusoy et al. (Sat,) studied this question.