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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2408.17295 |
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| _version_ | 1866929498058915840 |
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| author | Basco, Vincenzo |
| author_facet | Basco, Vincenzo |
| contents | Managing the plan of constellation of satellites for target observation requires optimal deployment and efficient operational strategies. In this paper, we introduce a new technique based on group theory tools through multi-agent constraint optimization techniques, designed for the dynamic landscapes of satellite operations. Inspired by group actions, our method models the planning problem for observing Earth targets as a cooperative game to achieve computational efficiency while simultaneously reducing computational complexity. Designed for the complex task of planning constellation of satellites, our methodology provides a feasible solution to the inherent challenges of multi-agent optimization under state constraints and subject to uncertainties. Our approach can offer avenues for improving mission efficiency and reducing costs. Through numerical simulations, we demonstrate the good performance of the approach in the presence of inter-satellite links. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_17295 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | All You Need is Group Actions: Advancing Robust Autonomous Planning Basco, Vincenzo Optimization and Control Numerical Analysis Group Theory Managing the plan of constellation of satellites for target observation requires optimal deployment and efficient operational strategies. In this paper, we introduce a new technique based on group theory tools through multi-agent constraint optimization techniques, designed for the dynamic landscapes of satellite operations. Inspired by group actions, our method models the planning problem for observing Earth targets as a cooperative game to achieve computational efficiency while simultaneously reducing computational complexity. Designed for the complex task of planning constellation of satellites, our methodology provides a feasible solution to the inherent challenges of multi-agent optimization under state constraints and subject to uncertainties. Our approach can offer avenues for improving mission efficiency and reducing costs. Through numerical simulations, we demonstrate the good performance of the approach in the presence of inter-satellite links. |
| title | All You Need is Group Actions: Advancing Robust Autonomous Planning |
| topic | Optimization and Control Numerical Analysis Group Theory |
| url | https://arxiv.org/abs/2408.17295 |