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Universal Observatory Graphs for Distributed Sky Coverage and Artificial Intelligence Based Interplanetary Routing

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Computer Science > Machine Learning

arXiv:2609.22244 (cs)
[Submitted on 5 Sep 2026]

Title:Universal Observatory Graphs for Distributed Sky Coverage and Artificial Intelligence Based Interplanetary Routing

View a PDF of the paper titled Universal Observatory Graphs for Distributed Sky Coverage and Artificial Intelligence Based Interplanetary Routing, by Mohammed Abdel Razek
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Abstract:This research proposes the Universal Observatory Graph (UOG), an AI-driven framework for distributed astronomical observation across the Solar System. The proposed architecture models autonomous observatories located at the Sun planet L2 Lagrange points as nodes in a weighted graph, while communication links are represented as graph edges characterized by multi-objective physical and operational metrics, including interplanetary distance, communication latency, transmission power, and link reliability. The resulting graph provides a unified mathematical representation of a cooperative interplanetary observatory network. This proposal examines a six-observatory Solar System configuration comprising Earth, Mars, Jupiter, Saturn, Uranus and Neptune. Instantaneous sky coverage is evaluated independently using a 200,000 direction Fibonacci sphere, a 2,000,000 direction fixed seed Monte Carlo calculation and deterministic spherical integration. All three methods yield complete network union coverage, approximately 0.43% complete six observatory intersection and approximately 24.96% mean pairwise Jaccard similarity under the adopted pointing model. Communication routing is subsequently formulated as a finite horizon Markov decision process and solved using tabular Q-learning. The reward balances node participation and a distance dependent reliability proxy against distance, light time latency and a distance squared transmission power proxy. The learned Earth-Saturn-Uranus-Neptune route is also the highest discounted return route among all 41 feasible simple paths under the four hop constraint. The framework provides a reproducible baseline for sequential coverage assessment and multi objective routing; time dependent ephemerides, mission specific visibility, calibrated link budgets and scalable graph policies remain future work.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.22244 [cs.LG]
  (or arXiv:2609.22244v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.22244
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mohammed Abdel Razek [view email]
[v1] Sat, 5 Sep 2026 09:23:40 UTC (968 KB)
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