Machine Learning Based Relative Orbit Transfer for Swarm Spacecraft Motion Planning
Alex Sabol, Kyongsik Yun, Muhammad Adil, Changrak Choi, Ramtin Madani

TL;DR
This paper introduces a machine learning framework using neural networks to efficiently plan collision-free relative orbit transfers for spacecraft swarms, aiming for real-time onboard implementation.
Contribution
It presents a neural network approach trained on centralized convex optimization data to enable scalable, real-time trajectory planning for multi-spacecraft formations.
Findings
Neural networks can generate feasible, collision-free trajectories.
The approach reduces computational demands for onboard planning.
The method generalizes across different swarm sizes.
Abstract
In this paper we describe a machine learning based framework for spacecraft swarm trajectory planning. In particular, we focus on coordinating motions of multi-spacecraft in formation flying through passive relative orbit(PRO) transfers. Accounting for spacecraft dynamics while avoiding collisions between the agents makes spacecraft swarm trajectory planning difficult. Centralized approaches can be used to solve this problem, but are computationally demanding and scale poorly with the number of agents in the swarm. As a result, centralized algorithms are ill-suited for real time trajectory planning on board small spacecraft (e.g. CubeSats) comprising the swarm. In our approach a neural network is used to approximate solutions of a centralized method. The necessary training data is generated using a centralized convex optimization framework through which several instances of the n=10…
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Taxonomy
TopicsSatellite Communication Systems · Space Satellite Systems and Control · Optimization and Search Problems
