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Toward Autonomous Cooperation in Heterogeneous Nanosatellite Constellations Using Dynamic Graph Neural Networks

2024-03-01 17:26:02
Guillem Casadesus-Vila, Joan-Adria Ruiz-de-Azua, Eduard Alarcon

Abstract

The upcoming landscape of Earth Observation missions will defined by networked heterogeneous nanosatellite constellations required to meet strict mission requirements, such as revisit times and spatial resolution. However, scheduling satellite communications in these satellite networks through efficiently creating a global satellite Contact Plan (CP) is a complex task, with current solutions requiring ground-based coordination or being limited by onboard computational resources. The paper proposes a novel approach to overcome these challenges by modeling the constellations and CP as dynamic networks and employing graph-based techniques. The proposed method utilizes a state-of-the-art dynamic graph neural network to evaluate the performance of a given CP and update it using a heuristic algorithm based on simulated annealing. The trained neural network can predict the network delay with a mean absolute error of 3.6 minutes. Simulation results show that the proposed method can successfully design a contact plan for large satellite networks, improving the delay by 29.1%, similar to a traditional approach, while performing the objective evaluations 20x faster.

Abstract (translated)

即将到来的地球观测任务的地形将受到网络异质纳米卫星星座的定义,以满足严格的任务要求,例如再访时间和空间分辨率。然而,通过有效地创建一个全球卫星接触计划(CP)来安排卫星通信,这是一个复杂的任务,现有的解决方案需要地面协调或受到船上计算能力的限制。本文提出了一种新方法,通过将星座和CP建模为动态网络,并采用基于图的技术来克服这些挑战。所提出的方法利用最先进的动态图神经网络来评估给定CP的性能,并使用基于模拟退火的外行算法对其进行更新。训练后的神经网络可以预测每个CP的平均绝对误差为3.6分钟。模拟结果表明,与传统方法相比,所提出的方法可以成功地设计大型卫星网络的接触计划,将延迟降低29.1%,类似于传统方法,同时将目标评估速度提高了20倍。

URL

https://arxiv.org/abs/2403.00692

PDF

https://arxiv.org/pdf/2403.00692.pdf


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