AI-Powered Augmented Reality for Satellite Assembly, Integration and Test

• URL: http://arxiv.org/abs/2409.18101v1
• Date: Thu, 26 Sep 2024 17:44:52 GMT
• Title: AI-Powered Augmented Reality for Satellite Assembly, Integration and Test
• Authors: Alvaro Patricio, Joao Valente, Atabak Dehban, Ines Cadilha, Daniel Reis, Rodrigo Ventura,
• Abstract summary: This paper presents a technical description of the European Space Agency's (ESA) project "AI for AR in Satellite AIT" The project combines real-time computer vision and AR systems to assist technicians during satellite assembly. All AI models demonstrated over 70% accuracy, with the detection model exceeding 95% accuracy, indicating a high level of performance and reliability.
• Score: 2.5069344340760713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of Artificial Intelligence (AI) and Augmented Reality (AR) is set to transform satellite Assembly, Integration, and Testing (AIT) processes by enhancing precision, minimizing human error, and improving operational efficiency in cleanroom environments. This paper presents a technical description of the European Space Agency's (ESA) project "AI for AR in Satellite AIT," which combines real-time computer vision and AR systems to assist technicians during satellite assembly. Leveraging Microsoft HoloLens 2 as the AR interface, the system delivers context-aware instructions and real-time feedback, tackling the complexities of object recognition and 6D pose estimation in AIT workflows. All AI models demonstrated over 70% accuracy, with the detection model exceeding 95% accuracy, indicating a high level of performance and reliability. A key contribution of this work lies in the effective use of synthetic data for training AI models in AR applications, addressing the significant challenges of obtaining real-world datasets in highly dynamic satellite environments, as well as the creation of the Segmented Anything Model for Automatic Labelling (SAMAL), which facilitates the automatic annotation of real data, achieving speeds up to 20 times faster than manual human annotation. The findings demonstrate the efficacy of AI-driven AR systems in automating critical satellite assembly tasks, setting a foundation for future innovations in the space industry.

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