2025-06-30 テキサスA&M大学
A new virtual assistant developed at Texas A&M is designed to support astronauts during deep space missions by diagnosing problems and guiding solutions in real time. Credit: Rachel Barton/Texas A&M Engineering
＜関連情報＞
- https://stories.tamu.edu/news/2025/06/30/hey-siri-fix-my-spacecraft/
- https://arc.aiaa.org/doi/abs/10.2514/1.I011449?journalCode=jais
宇宙船の異常解決のためのバーチャルアシスタント： 人間のパフォーマンス指標への影響 Virtual Assistant for Spacecraft Anomaly Resolution: Effects on Human Performance Metrics
Poonampreet Kaur Josan, Prachi Dutta, Renee Abbott, Antoni Viros Martin, Bonnie J. Dunbar, Raymond K.W. Wong, Daniel Selva and Ana Diaz-Artiles
The Journal of Aerospace Information Systems Published:3 Jan 2025
DOI:https://doi.org/10.2514/1.I011449
Abstract
Virtual assistants (VAs) are known to improve performance, reduce mental workload (MWL), and improve situational awareness (SA) in complex cognitive tasks. However, there is a lack of studies focusing on anomaly resolution tasks in time- and safety-critical environments such as long-duration exploration missions. This paper aims to investigate the effects of using a VA, namely Daphne-AT, on crew performance, SA, and MWL in the context of spacecraft anomaly treatment. Participants (=12<?XML:NAMESPACE PREFIX = “[default] http://www.w3.org/1998/Math/MathML” NS = “http://www.w3.org/1998/Math/MathML” />n=12) were tasked to detect, diagnose, and resolve anomalies in two different but equivalent experimental sessions (five anomalies with the VA and five anomalies without the VA). In each condition, performance was quantified based on the number of anomalies correctly resolved and the total time needed to resolve the anomalies. SA and MWL were quantified using the Situational Awareness Rating Technique and the NASA Task Load index. Results indicate that participants resolved more anomalies in less time during sessions with VA. MWL reduced significantly during sessions with VA. Although total SA did not significantly change between VA conditions, the subscales attentional demand and understanding improved with VA. These findings suggest that VAs are promising decision support tools for anomaly resolution in domains such as spaceflight, aviation, or emergency response.
