TL;DR
This paper introduces AEOS-Bench, a large-scale realistic benchmark suite for Earth observation satellite constellation scheduling, and proposes AEOS-Former, a Transformer-based model that effectively handles complex constraints and improves scheduling performance.
Contribution
The paper presents the first large-scale realistic benchmark suite for AEOS scheduling and a novel Transformer-based model incorporating constraint-aware attention for improved performance.
Findings
AEOS-Former outperforms baseline models in task completion.
AEOS-Former improves energy efficiency in scheduling.
The benchmark suite enables realistic evaluation of scheduling algorithms.
Abstract
Agile Earth Observation Satellites (AEOSs) constellations offer unprecedented flexibility for monitoring the Earth's surface, but their scheduling remains challenging under large-scale scenarios, dynamic environments, and stringent constraints. Existing methods often simplify these complexities, limiting their real-world performance. We address this gap with a unified framework integrating a standardized benchmark suite and a novel scheduling model. Our benchmark suite, AEOS-Bench, contains finely tuned satellite assets and scenarios. Each scenario features to satellites and to imaging tasks. These scenarios are generated via a high-fidelity simulation platform, ensuring realistic satellite behavior such as orbital dynamics and resource constraints. Ground truth scheduling annotations are provided for each scenario. To our knowledge, AEOS-Bench is…
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