Parallel Reconfiguration Planning for Modular Self-Reconfigurable Satellites via Multi-Agent Reinforcement Learning

Modular self-reconfigurable satellites can adapt to diverse space missions by dynamically rearranging their configurations. Enabling multiple modules to move simultaneously enhances reconfiguration efficiency but remains constrained by existing planning algorithms. This article introduces the Parallel Reconfiguration Planner (PRP), a learning-based approach that ensures safe, concurrent module movements. PRP leverages multi-agent reinforcement learning to generate reconfiguration policies while incorporating safety constraints modeled using Linear Temporal Logic (LTL). To improve learning efficiency, PRP integrates a guarded exploration mechanism based on these safety specifications. Experiments on 3D reconfiguration scenarios demonstrate that PRP achieves more efficient reconfiguration policies in scenarios that have more moving modules compared to the baseline method.