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A Computational Framework for Cross-Domain Mission Design and Onboard Cognitive Decision Support

J. de Curtò, Adrianne Schneider, Ricardo Yanez, María Begara, Álvaro Rodríguez, Javier López, Martina Fraga, Ignacio Gómez, Arman Akdag, Sumit Kulkarni, Siddhant Nair, Kiyan Govender, Eian Wratchford, Eli Lynskey, Seamus Dunlap, Cooper Nervick, Nicolas Tête, Rocío Fernández, Pablo González, Elena Municio, I. de Zarzà

Abstract

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.

A Computational Framework for Cross-Domain Mission Design and Onboard Cognitive Decision Support

Abstract

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.

Paper Structure

This paper contains 16 sections, 2 equations, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Computational Studies
  5. Walker Spherical-Cap Coverage
  6. Infrared Detection and Neyman--Pearson Optimality
  7. Extended Kalman Filter Hypersonic Tracking
  8. TDMA Monte Carlo Science-Yield Sensitivity
  9. Cross-Mission Link Budget Analysis
  10. Latency--Autonomy Mapping
  11. Cross-Mission Power Budget Sizing
  12. Distributed Magnetic Signature Emulation
  13. Arrhenius-Corrected Swarm Reliability
  14. LLM Autonomous Mission Decision Support
  15. Discussion
  16. ...and 1 more sections

Figures (11)

  • Figure 1: Mission portfolio on the Autonomy Necessity Score spectrum. Teal: underwater (AHMS); blue: Earth LEO (SCOPE, H.S.A.D.S.); coral: Mars (MarsNav, MOC/EDL); purple: deep space (ChipSat, Titan). Headline result per mission annotated at each node.
  • Figure 2: Walker constellation coverage. (A) Earth LEO coverage fraction vs. constellation size; (B) equatorial revisit gap vs. altitude for SCOPE and H.S.A.D.S.; (C) MarsNav latitude coverage for design and degraded configurations.
  • Figure 3: IR detection chain and Neyman--Pearson decision. (A) Target irradiance vs. slant range; (B) SNR vs. range; (C) ROC curves at GEO.
  • Figure 4: EKF hypersonic target tracking. (A) Azimuth truth and filter tracks; (B) azimuth estimation error; (C) NIS consistency test; (D) RMSE vs. process-noise tuning.
  • Figure 5: ChipSat TDMA Monte Carlo science-yield sensitivity. (A) Yield distribution; (B) duty-cycle and failure-rate heatmap; (C) yield vs. duty cycle; (D) yield vs. deployment failure rate.
  • ...and 6 more figures