← Back to all news
arXiv Machine Learning By Mohammed Abdel Razek

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

Read the original on arXiv Machine Learning →

The paper introduces the Universal Observatory Graph (UOG), an AI‑driven framework that models autonomous observatories at L2 Lagrange points as nodes in a weighted graph, with edges defined by interplanetary distance, latency, transmission power, and reliability. Using a six‑observatory Solar System configuration (Earth, Mars, Jupiter, Saturn, Uranus, Neptune), the authors evaluate instantaneous sky coverage with three methods, all showing complete network union coverage and modest overlap. They formulate communication routing as a finite‑horizon Markov decision process solved via tabular Q‑learning, identifying the Earth‑Saturn‑Uranus‑Neptune path as the highest‑return route among 41 feasible simple paths under a four‑hop constraint.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Oct 26

Heterogeneous Space Data Centers: A Key Paradigm for Future Non-Terrestrial and In-Orbit Services

The paper introduces heterogeneous space data centers (SDCs) that combine large and small nodes to deliver AI‑centric computing and communication services for ground, aerial, and in‑orbit users. It outlines use cases, node sizing, placement strategies, and interconnection topologies, and focuses on dawn‑dusk low‑Earth orbit configurations to analyze solar power, visibility windows, and latency‑aware coverage. The authors evaluate latency, service reliability, and energy requirements for various SDC roles and deployment scales, highlighting trade‑offs among node size, orbit selection, and service portfolio, and offer design guidance and future research directions for AI infrastructure in space.

By Peng Hu, Mustafa Alhassan, Amjad Iqbal
More like this →
arXiv Machine Learning
5d ago

RAMA: Retrieval-Augmented Multi-Agent Routing with Episodic Memory for LEO Satellite Networks

RAMA is a retrieval‑augmented multi‑agent deep reinforcement learning framework for routing in Low‑Earth Orbit satellite networks. It combines each satellite’s local observation, recent routing history, neighboring state information, and retrieved entries from a per‑satellite episodic memory, integrating them via a Transformer‑based decision network trained with Approximate Information State objectives. In a 140‑satellite Kepler constellation, RAMA cuts mean end‑to‑end latency by 13.4% and 99th‑percentile latency by 66.7% compared to a state‑of‑the‑art DDQN, and on a 1,584‑satellite Starlink shell 1 it achieves a 93.2% mean latency reduction, with further gains from online updates.

By Xin Ming Li, Peng Hu
llmsragagentsreinforcement-learning
More like this →
arXiv AI
Oct 27

Satellite Intelligence: Distributed Training, Collaborative Inference,and LLM Adaptation in LEO Constellation

arXiv:2610.35282v1 Announce Type: cross Abstract: The proliferation of orbital sensing data and the real-time demands of emerging global services are pushing low Earth orbit (LEO) satellite networks...

By Zheng Lin, Zhanwei Wang, Yuxin Zhang, Yafei Wang, Ons Aouedi, Zihan Fang, Jinfeng Xu, Jing Yang, Vu Nguyen Ha, Yuanwei Liu, Mohamed-Slim Alouini, Symeon Chatzinotas
llms
More like this →
arXiv AI
Sep 2

FractalNet-Based Heterogeneous Federated Learning for Orbital Edge Intelligence in Satellite Mega-Constellations: A Wildfire Case Study

The paper introduces a heterogeneous federated learning approach using the FractalNet architecture tailored for satellite mega‑constellations. It formalizes contact‑window‑constrained, depth‑heterogeneous optimization and proposes a distributed path scheduler that assigns model depth based on satellite SWAP‑C constraints, predicted contacts, and training statistics. The framework includes periodic update pooling and a three‑tier agentic control plane, and is validated through a wildfire detection case study across LEO, MEO, and GEO/HEO shells, demonstrating improvements in convergence, communication efficiency, energy adaptation, and robustness.

By Sai Puppala, Koushik Sinha
agents
More like this →