A Goal-Oriented Reinforcement Learning-Based Path Planning Algorithm for Modular Self-Reconfigurable Satellites Article Swipe

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Bojian Liu , Dong Ye , Z. P. Yao , Zhaowei Sun ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2505.01966

Modular self-reconfigurable satellites refer to satellite clusters composed of individual modular units capable of altering their configurations. The configuration changes enable the execution of diverse tasks and mission objectives. Existing path planning algorithms for reconfiguration often suffer from high computational complexity, poor generalization capability, and limited support for diverse target configurations. To address these challenges, this paper proposes a goal-oriented reinforcement learning-based path planning algorithm. This algorithm is the first to address the challenge that previous reinforcement learning methods failed to overcome, namely handling multiple target configurations. Moreover, techniques such as Hindsight Experience Replay and Invalid Action Masking are incorporated to overcome the significant obstacles posed by sparse rewards and invalid actions. Based on these designs, our model achieves a 95% and 73% success rate in reaching arbitrary target configurations in a modular satellite cluster composed of four and six units, respectively.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2505.01966
PDF: https://arxiv.org/pdf/2505.01966
OA Status: green
OpenAlex ID: https://openalex.org/W4415029156

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DOI: https://doi.org/10.48550/arxiv.2505.01966

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Title: A Goal-Oriented Reinforcement Learning-Based Path Planning Algorithm for Modular Self-Reconfigurable Satellites

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-05-04

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Authors: Bojian Liu, Dong Ye, Z. P. Yao, Zhaowei Sun

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Landing page: https://arxiv.org/abs/2505.01966

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PDF URL: https://arxiv.org/pdf/2505.01966

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