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

TL;DR

This paper introduces a unified computational framework and a novel Autonomy Necessity Score to quantify decision-making autonomy in diverse autonomous systems, and evaluates an LLM-based onboard decision support layer demonstrating high accuracy and real-time performance.

Contribution

It develops a comprehensive metric for autonomy assessment across mission architectures and demonstrates the effectiveness of foundation models as onboard decision support in high-ANS scenarios.

Findings

Autonomy Necessity Score effectively maps system latency to autonomy levels.

LLM-based decision support achieves 80% accuracy in anomaly scenarios.

All inference calls completed within 2 seconds, suitable for onboard deployment.

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.