ArticleNPJ microgravity2023
Reinforcement learned adversarial agent (ReLAA) for active fault detection and prediction in space habitats.
Article in NPJ microgravity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Flexible robotic platforms for surgical applications in microgravity environments: a comprehensive systematic review of minimally invasive mechatronic systems and the impact of artificial intelligence on behalf of the Center for Space Systems (C-SET) & TROGSS-The Robotic Global Surgical Society.Journal of robotic surgery · 2025Pooled it
- Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach.Journal of imaging informatics in medicine · 2025Article
- Article
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Authors and funding
5 authors.
Funding
Abstract
With growing interest for human space tourism in the twenty-first century, much attention has been directed to the robust engineering of Environmental Control and Life Support Systems in space habitats. The stable, reliable operation of such a habitat is partly achieved with an ability to recognize and predict faults. For these two purposes, a reinforcement learning adversarial agent (ReLAA) is utilized in this work. A ReLAA is trained with experimental data to actively recognize and predict faults. These capabilities are achieved by proposing actions that activate known faults in a system. Instead of issuing these harmful actions to the actual hardware, a digital twin of the mock space habitat is simulated to discover vulnerabilities that would lead to faulted operation. The methods developed in this work will allow for the discovery of damaging latent behavior, and the reduction of false positive and negative fault identification.
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Registered trials
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