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| Autori principali: | , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.28926 |
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| _version_ | 1866915900876128256 |
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| author | de Curtò, J. Schneider, Adrianne Yanez, Ricardo Begara, María Rodríguez, Álvaro López, Javier Fraga, Martina Gómez, Ignacio Akdag, Arman Kulkarni, Sumit Nair, Siddhant Govender, Kiyan Wratchford, Eian Lynskey, Eli Dunlap, Seamus Nervick, Cooper Tête, Nicolas Fernández, Rocío González, Pablo Municio, Elena de Zarzà, I. |
| author_facet | de Curtò, J. Schneider, Adrianne Yanez, Ricardo Begara, María Rodríguez, Álvaro López, Javier Fraga, Martina Gómez, Ignacio Akdag, Arman Kulkarni, Sumit Nair, Siddhant Govender, Kiyan Wratchford, Eian Lynskey, Eli Dunlap, Seamus Nervick, Cooper Tête, Nicolas Fernández, Rocío González, Pablo Municio, Elena de Zarzà, I. |
| contents | The design of distributed autonomous systems for operation beyond reliable ground contact presents a fundamental tension: as round-trip communication latency grows, the set of decisions delegable to ground operators shrinks. This paper establishes a unified computational methodology for quantifying and comparing this constraint across seven heterogeneous mission architectures, spanning Earth low-orbit surveillance constellations, Mars orbital navigation systems, autonomous underwater mine-clearing swarms, deep-space inter-satellite link networks, and outer-planet in-situ buoy platforms. We introduce the Autonomy Necessity Score, a log-domain latency metric mapping each system continuously from the ground-dependent to the fully-autonomous regime, grounded in nine independently validated computational studies covering Walker spherical-cap coverage mechanics, infrared Neyman-Pearson detection, Extended Kalman Filter hypersonic tracking, cross-mission RF and acoustic link budgets spanning seven orders of magnitude in range, Monte Carlo science-yield sensitivity for TDMA inter-satellite protocols, cross-architecture power budget sizing, distributed magnetic-signature formation emulation, and Arrhenius-corrected cryogenic swarm reliability. Building on this foundation, we evaluate an LLM-based Autonomous Mission Decision Support layer in which three foundation models (Llama-3.3-70B, DeepSeek-V3, and Qwen3-A22B) are queried live via the Nebius AI Studio API across ten structured anomaly scenarios derived directly from the preceding analyses. The best-performing model achieves 80% decision accuracy against physics-grounded ground truth, with all 180 inference calls completing within a 2 s latency budget consistent with radiation-hardened edge deployment, establishing the viability of foundation models as an onboard cognitive layer for high-ANS missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_28926 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Computational Framework for Cross-Domain Mission Design and Onboard Cognitive Decision Support de Curtò, J. Schneider, Adrianne Yanez, Ricardo Begara, María Rodríguez, Álvaro López, Javier Fraga, Martina Gómez, Ignacio Akdag, Arman Kulkarni, Sumit Nair, Siddhant Govender, Kiyan Wratchford, Eian Lynskey, Eli Dunlap, Seamus Nervick, Cooper Tête, Nicolas Fernández, Rocío González, Pablo Municio, Elena de Zarzà, I. Systems and Control The design of distributed autonomous systems for operation beyond reliable ground contact presents a fundamental tension: as round-trip communication latency grows, the set of decisions delegable to ground operators shrinks. This paper establishes a unified computational methodology for quantifying and comparing this constraint across seven heterogeneous mission architectures, spanning Earth low-orbit surveillance constellations, Mars orbital navigation systems, autonomous underwater mine-clearing swarms, deep-space inter-satellite link networks, and outer-planet in-situ buoy platforms. We introduce the Autonomy Necessity Score, a log-domain latency metric mapping each system continuously from the ground-dependent to the fully-autonomous regime, grounded in nine independently validated computational studies covering Walker spherical-cap coverage mechanics, infrared Neyman-Pearson detection, Extended Kalman Filter hypersonic tracking, cross-mission RF and acoustic link budgets spanning seven orders of magnitude in range, Monte Carlo science-yield sensitivity for TDMA inter-satellite protocols, cross-architecture power budget sizing, distributed magnetic-signature formation emulation, and Arrhenius-corrected cryogenic swarm reliability. Building on this foundation, we evaluate an LLM-based Autonomous Mission Decision Support layer in which three foundation models (Llama-3.3-70B, DeepSeek-V3, and Qwen3-A22B) are queried live via the Nebius AI Studio API across ten structured anomaly scenarios derived directly from the preceding analyses. The best-performing model achieves 80% decision accuracy against physics-grounded ground truth, with all 180 inference calls completing within a 2 s latency budget consistent with radiation-hardened edge deployment, establishing the viability of foundation models as an onboard cognitive layer for high-ANS missions. |
| title | A Computational Framework for Cross-Domain Mission Design and Onboard Cognitive Decision Support |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.28926 |